Pete Robinson, Salesforce & Shannon Champion, Dell Technologies | Dell Tech World 2022
>>The cube presents, Dell technologies world brought to you by Dell. >>Welcome back to the cube. Lisa Martin and Dave Vale are live in Las Vegas. We are covering our third day of covering Dell technologies world 2022. The first live in-person event since 2019. It's been great to be here. We've had a lot of great conversations about all the announcements that Dell has made in the last couple of days. And we're gonna unpack a little bit more of that. Now. One of our alumni is back with us. Shannon champion joins us again, vice president product marketing at Dell technologies, and she's a company by Pete Robinson, the director of infrastructure engineering at Salesforce. Welcome. Thank >>You. >>So Shannon, you had a big announcement yesterday. I run a lot of new software innovations. Did >>You hear about that? I heard a little something >>About that. Unpack that for us. >>Yeah. Awesome. Yeah, it's so exciting to be here in person and have such a big moment across our storage portfolio, to see that on the big stage, the boom to announce major updates across power store, PowerMax and power flex all together, just a ton of innovation across the storage portfolio. And you probably also heard a ton of focus on our software driven innovation across those products, because our goal is really to deliver a continuously modern storage experience. That's what our customers are asking us for that cloud experience. Let's take the most Val get the most value from data no matter where it lives. That's on premises in the public clouds or at the edge. And that's what we, uh, unveil. That's what we're releasing. And that's what we're excited to talk about. >>Now, Pete, you, Salesforce is a long time Dell customer, but you're also its largest PowerMax customer. The biggest in the world. Tell us a little bit about what you guys are doing with PowerMax and your experience. >>Yeah, so, um, for Salesforce, trust is our number one value and that carries over into the infrastructure that we develop, we test and, and we roll out and Parex has been a key part of that. Um, we really like the, um, the technology in terms of availability, reliability, um, performance. And it, it has allowed us to, you know, continue to grow our customers, uh, continue needs for more and more data. >>So what was kind of eye popping to me was the emphasis on security. Not that you've not always emphasized security, but maybe Shannon, you could do a rundown of, yeah. Maybe not all the features, but give us the high level. And at Pete, I, I wonder how I, if you could comment on how, how you think about that as a practitioner, but please give us that. >>Sure. Yeah. So, you know, PowerMax has been leading for, uh, a long time in its space and we're continuing to lean into that and continue to lead in that space. And we're proud to say PowerMax is the world's most secure mission, critical storage platform. And the reason we can say that is because it really is designed for comprehensive cyber resiliency. It's designed with a zero trust security architecture. And in this particular release, there's 19 different security features really embedded in there. So I'm not gonna unpack all 19, but a couple, um, examples, right? So multifactor authentication also continuous ransomware anomaly detection, a leveraging cloud IQ, which is, uh, huge. Um, and last but not least, um, we have the industry's most granular cyber recovery at scale PowerMax can do up to 65 million imutable snapshots per array. So just, uh, and that's 30 times more than our next nearest competitor. So, you know, really when you're talking about recovery point objectives, power max can't be beat. >>So what does that mean to you, Pete? >>Uh, well, it's it's same thing that I was mentioning earlier about that's a trust factor. Uh, security is a big, a big part of that. You know, Salesforce invests heavily into the securing our customer data because it really is the, the core foundation of our success and our customers trust us with their data. And if we, if we were to fail at that, you know, we would lose that trust. And that's simply not, it's not an option. >>Let's talk about that trust for a minute. We know we've heard a lot about trust this week from Michael Dell. Talk to us about trust, your trust, Salesforce's trust and Dell technologies. You've been using them a long time, but cultural alignment yeah. Seems to be pretty spot on. >>I, I would agree. Um, you know, both companies have a customer first mentality, uh, you know, we, we succeed if the customer succeeds and we see that going back and forth in that partnership. So Dell is successful when Salesforce is successful and vice versa. So, um, when we've it's and it goes beyond just the initial, you know, the initial purchase of, of hardware or software, you know, how you operate it, how you manage it, um, how you continue to develop together. You know, our, you know, we work closely with the Dell engineering teams and we've, we've worked closely in development of the new, new PowerMax lines to where it's actually able to help us build our, our business. And, and again, you know, continue to help Dell in the process. So you've >>Got visibility on the new, a lot of these new features you're playing around with them. What I, I, I obviously started with security cuz that's on top of everybody's mind, but what are the things are important to you as a customer? And how do these features the new features kind of map into that? Maybe you could talk about your experience with the, I think you're in beta, maybe with these features. Maybe you could talk about that. >>Yeah. Um, probably the, the biggest thing that we're seeing right now, other than OB the obvious enhancements in hardware, which, which we love, uh, you know, better performance, better scalability, better, and a better density. Um, but also the, some of the software functionality that Dells starting to roll out, you know, we've, we've, we've uh, implemented cloud IQ for all of our PowerMax systems and it's the same thing. We continue to, um, find features that we would like. And we've actually, you know, worked closely with the cloud IQ team. And within a matter of weeks or months, those features are popping up in cloud IQ that we can then continue to, to develop and, and use. >>Yeah. I think trust goes both ways in our partnership, right? So, you know, Salesforce can trust Dell to deliver the, you know, the products they need to deliver their business outcomes, but we also have a relationship to where we can trust that Salesforce is gonna really help us develop the next generation product that's gonna, you know, really deliver the most value. Yeah. >>Can you share some business outcomes that you've achieved so far leveraging power max and how it's really enabled, maybe it's your organization's productivity perspective, but what are some of those outcomes that you've achieved so far? >>Um, there there's so many to, to, to choose from, but I would say the, probably the biggest thing that we've seen is a as we roll out new infrastructure, we have various generations that we deploy. Um, when we went to the new PowerMax, um, initially we were concerned about whether our storage infrastructure could keep up with the new compute, uh, systems that we were rolling out. And when we went through and began testing it, we came to realize that the, the performance improvements alone, that we were seeing were able to keep up with the compute demand, making that transition from the older VMAX platforms to the PMAX practically seamless and able to just deploy the new SKUs as, as they came out. >>Talk about the portfolio that you apply to PowerMax. I mean, it's the highest of the highest end mission critical the toughest workloads in the planet. Salesforce has made a lot of acquisitions. Yeah. Um, do you throw everything at PowerMax? Are you, are you selective? What's your strategy there? So >>It's, it's selective. In other words that there's no square peg that meets every need, um, you know, acquisitions take some time to, to ingest, um, you know, some run into cloud, some run in first, in, in first party. Um, but so we, we try to take a very, very intentional approach to where we deploy that technology. >>So 10 years ago, someone in your position, or maybe someone who works for you was probably do spent a lot of time managing lawns and tuning performance. And how has that changed? >>We don't do that. <laugh> we? >>We can, right. So what do you do with right. Talk, talk more double click on that. So how talk about how that transition occurred from really non-productive activities, managing storage boxes. Yeah. And, and where you are today, what are you doing with those resources? >>It, it, it all comes outta automation. Like, you know, the, you know, hardware is hardware to a point, um, but you reach a point where the, the manageability scale just goes exponential and, and we're way, well past that. And the only way we've been able to meet that, meet that need is to, to automate and really develop our operations, to be able to not just manage at a lung level or even at the system level, but manage at the data center level at the geographical, you know, location level and then being able to, to manage from there. >>Okay. Really stupid question. But I'm gonna ask it cause I wanna hear your answer. True. Why can't you just take a software defined storage platform and just run everything on that? Why do you need all these different platforms and why do you gotta spend all this money on PowerMax? Why, why can't you just do >>That? That's the million dollar question. Uh, I, I ask that all the time. <laugh>, um, I think software defined is it's on its way. Um, it's come a long way just in the last decade. Yeah. Um, but in terms of supporting what I consider mission critical, large scale, uh, applications, it's, it's not, it's just simply not on par just yet with what we do with PowerMax, for example. >>And that's exactly how we position it in our portfolio. Right? So PowerMax runs on 95% of the fortune 100 companies, top 20 healthcare companies, top 10 financial services companies in the world. So it's really mission critical high end has all of the enterprise level features and capabilities to really have that availability. That's so important to a lot of companies like Salesforce and, and Pete's right, you know, software define is on its way and it provides a lot of agility there. But at the end of the day for mission critical storage, it's all about PowerMax. >>I wonder if we're ever gonna get to, I mean, you, you, you, it was interesting answer cuz you kind of, I inferred from your that you're hopeful and even optimistic that someday will get to parody. But I wonder because you can't be just close enough. It's almost, you have to be. >>I think, I think the key answer to that is it's it's the software flying gets you halfway there. The other side of the coin is the application ecosystem has to change to be able to solve that other, other side of it. Cuz if you simply simply take an application that runs on a PowerMax and try to run it, just forklift it over to a software defined. You're not gonna have very much luck. >>Recovery has to be moved up to stack >>Operations recovery, the whole, whole whole works. >>Jenny, can you comment on how customers like Salesforce? Like what's your process for involving them in testing in roadmap and in that direction, strategic direction that you guys are going? Great >>Question. Sure. Yeah. So, you know, customer feedback is huge. You've heard it. I'm sure this is not new right product development and engineering. We love to hear from our customers. And there's multiple ways you heard about beta testing, which we're really fortunate that Salesforce can help us provide that feedback for our new releases. But we have user groups, we have forums. We, we hear directly from our sales teams, our, you know, our customers, aren't shy, they're willing to give us their feedback. And at the end of the day, we take that feedback and make sure that we're prioritizing the right things in our product management and engineering teams so that we're delivering the things that matter. Most first, >>We've heard a lot of that this week. So I would agree guys, thank you so much for joining Dave and me talking about Salesforce. What you doing with PowerMax? All the stuff that you announced yesterday, alone. Hopefully you get to go home and get a little bit of rest. >>Yes. >>I'm sure that there's, there's never a dull moment. Never. Can't wait guys. Great to have you. >>Thank you. You guys, >>For our guests on Dave Volante, I'm Lisa Martin and you're watching the queue. We are live day three of our coverage of Dell technologies world 2022, Dave and I will be right back with our final guest of the show.
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about all the announcements that Dell has made in the last couple of days. So Shannon, you had a big announcement yesterday. Unpack that for us. And you probably also heard a ton Tell us a little bit about what you guys are doing with it has allowed us to, you know, continue to grow our customers, uh, I, I wonder how I, if you could comment on how, how you think about that as a practitioner, So, you know, really when you're talking about recovery point objectives, power max can't be beat. And if we, if we were to fail at that, you know, we would lose that trust. Talk to us about trust, your trust, Salesforce's trust and Dell technologies. um, when we've it's and it goes beyond just the initial, you know, the initial purchase of, Maybe you could talk about your experience with the, I think you're in beta, maybe with these features. starting to roll out, you know, we've, we've, we've uh, implemented cloud IQ for all of our PowerMax systems Salesforce can trust Dell to deliver the, you know, the products they need to to keep up with the compute demand, making that transition from the older VMAX platforms Talk about the portfolio that you apply to PowerMax. um, you know, acquisitions take some time to, to ingest, um, you know, And how has that changed? We don't do that. So what do you do with right. but manage at the data center level at the geographical, you know, location level and then Why do you need all these different platforms and why do you gotta spend all this money on PowerMax? Uh, I, I ask that all the time. and, and Pete's right, you know, software define is on its way and it provides a lot of agility there. But I wonder because you can't be just close enough. I think, I think the key answer to that is it's it's the software flying gets you halfway there. our, you know, our customers, aren't shy, they're willing to give us their feedback. All the stuff that you announced yesterday, alone. Great to have you. You guys, of our coverage of Dell technologies world 2022, Dave and I will be right back with our final guest of the
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Adilson Jardim, Salesforce | AWS re:Invent 2021
>>And welcome back to the cubes coverage of alias reinvent 2021. I'm Jon furrier, your host of the cube we're onsite we're hybrid. It's a hybrid event. We've got Odile, Shara Dean vice president of north America solution engineering at Salesforce. We deal SIM thank you for coming on the cube >>You John, excited to be >>Here. So w you know, Salesforce obviously, um, being in Palo Alto in the bay area, they've got the Salesforce tower, great business cloud before cloud great innovation. A lot of growth has been very successful at SAS and platform. So you take that to the government, uh, an area public sector where public sector and other areas around this have been exploding with the pandemic with new use cases and just kind of a refactoring and replatforming of L all aspects of digital. It's been a big digital transformation surge, and rightfully so you guys are in the mix here. Um, talk about the Salesforce is positioned as you guys innovate and scale your platform and rethink this architecture with AWS in the public sector. >>Yeah. Thank you, John. So you're spot on Salesforce defines SAS as a delivery of services to customers, and that's really the precursor to where we are with cloud here. So let's talk about public sector and what that means. I'm very proud to work in and around public sector for many years. And I'll Salesforce, public sector group supports any number of use cases, different missions, anywhere from state and local, all the way through to federal use cases on, on a global scale. But what that means, and I mean, right back to your question is how do we deliver those in the cloud in a scalable, responsive way? You mentioned the pandemic and throughout the pandemic, we were instrumental in trying to deliver these services and getting states and localities towns, countries up and running to deliver the critical things that we all learned about in a hurry contact pricing. >>COVID testing all these ideas around vaccine management, what it takes to get vaccines to populations, but many of our customers, many of our governments just weren't well positioned to do that. So what they were relying on was a secure, scalable, flexible environment that allowed them to define their workflows or their business models in a very, very rapid pace as we were dealing with the surge and the constantly changing landscape of the pandemic. So from our perspective, we've spent years investing in public sector to make sure that we need the compliance requirements, whether that's FedRAMP or, or CMMC, or protected being Canada, how do we do that reliably quickly so that our government customers can rely on us for situations like the pandemic to be able to respond? >>Yeah, one of the things we've been doing a lot of reporting around is the idea that the pandemic has kind of forced, and it was a forcing function around digital transformation. Uh, so I have to ask you knowing the history of Salesforce and the greatness of the company that you guys have had over the years, uh, when you get into the public sector, I'm sure you get all kinds of questions. We don't have sales forces, and we don't have sales managers. Um, we don't need a CRM. Um, and we have industry regulations. We're not a commercial thing. How do you answer those? Because you guys have infrastructure, you are a hyperscale, uh, what's your take on that and how do you answer those direct questions when they come up? >>All great questions and yes, we get them all the time. Uh, so how do we answer them? Well, first and foremost, the idea of a CRM is around putting your customer at the center of your view of them. So that customer relationship management means you, you have a view into the services your customer needs and how they're engaging with you, digitally engagement, in-person engagement, et cetera. I would intend that that's no different for a government entity than it is for a consumer. Very sensitive government entity wants to treat their constituents around the services they need and getting that full 360 view of what, what are the services available to them? How do they access them, et cetera, actually fits really well into that CRM model, but it does take some explaining and reinvisioning it, but it plays really well into the digital transformation imperatives that these agencies have, because what you want to do in a digital transformation is also re-imagined all these old systems and legacy systems, how you're going to make them more accessible. >>But also to your point, how do you bring them to this level of expectation that our consumers have? I'm now accustomed to having mobile apps and on-demand, uh, applications and websites for ordering products for ordering needs, et cetera, for booking a restaurant reservation, I've developed the exact same requirements and expectations of my government services and our government customers are clearly aware of this. So they want to bring this capability to the fore and offer their constituents a better experience as well. When you asked about government regulations, this is absolutely critical to how we think about delivering that service, the value of the cloud. Isn't just, you can go get access to a service and not have to worry about that service. It's also, how do we unencumber agencies from these compliance requirements from audits, from privacy checks and needs in a constantly evolving landscape. There's always a legislative imperative to change something, add more constraints, more privacy requirements, compliance requirements, et cetera. So what we want to do is free our customers up from having to worry about that. That's what we undertake. We provide them that level of assurance, and they focus once again, on that higher value of the business flows, the mission, the constituency context, and how to make that constituent experience better. >>I have to ask you, I had a chance to sit down one-on-one with Adam. Slupski the new CEO of AWS recently prior to re-invent. And he said something to me. I want to get your reaction to, he said with scale, you can get visibility on some new use cases. So this applies to Salesforce. You guys are a hyperscaler, you have this new architecture named hyper force. What is this all about? And how does that tie into celebrities comment? Okay. >>Yeah. Uh, excellent question. And we'll talk a little about that history that brings us to two hype before. So just like many of our customers, we realize that having the ability to scale across the globe and be able to offer our services in different regions, different compliance requirements meant that our investments in first party data centers needed to be reconstructed a little bit. And that posed a bit of a rearchitecture for us as well. But that's what gave us the flexibility then to essentially decouple our architecture from the physical infrastructure layer, but it afforded us then the ability to deploy very quickly and very scaleably on AWS in regions that we previously weren't operating in. So it allows us to move along quicker, allows us to bring that flexibility and that scale to the customer where they are. And then we can meet once again, coming back to compliance and regulations. >>We can meet requirements around data residency and data privacy requirements in different regions that we were somewhat constrained in doing earlier. And that also then gives us the ability, I think, to what Adam might've been alluding to now that we're able to bring that service to the customer, they can say, well, actually here's another use case that I would like Salesforce to deliver on. And it gives us that flexibility. We do a lot in terms of expanding across use cases. And if I can point to the pandemic again, just as a great frame of reference that we're all thrust into. Initially, if you cast your mind back to may of last year, we were all worried about contact tracing, right? No side effects scenes, yet we didn't even have pelvic testing. Well, shortly thereafter, COVID testing became available and states were offering those well that from contact tracing to COVID testing is a massive shift. If you think about the use case for technology. So we enable our customers to move very quickly from contact tracing, to COVID, testing them to vaccine management. They're actually entirely different use cases, even though they all apply to solving for the pandemic where we had so many others, digital outreach, helping with loans and grants and management through the PPP programs, through unemployment programs, all different use cases that we helped our customers extend to, which you can't do that if you're not flexible enough to move quickly and scale effectively to support those. >>I think that value proposition and that notion of having that regional global support is going to really come into the whole data programmability trend. I call data as dev ops kind of vibe where data as code becomes more, more agile, right? You're going to see that. I think that's going to be, that's a big theme at a reinvent this year. So, so I have to ask you now, now we're sitting in this global scale, you've got geopolitics, you got public sector. How does Salesforce government cloud plus, and hyper force help your help governments and their partners because their ecosystems too, right? So it's not a commercial. Now it's looking a lot like a commercial lines between commercial and government looking the same. How do you guys help governments and their partners? >>So having been in this, this, uh, area for so long, I, I like to position this tonight. I use this actually as a good selling point, even in selling the value propositions for investment internally, I think of the government regulations and requirements around privacy compliance as a minimum barrier of entry. So I'll, uh, you mentioned our government cloud, plus that's really more in the U S and it's a FedRAMP, uh, tested at a federal and PI level. We've got privacy of lays. We've got our DOD out for, uh, PA in there we've got HIPAA and PCI compliance bank 10. Those are efforts that if a company or a government customer were to go run through individually, it's going to take them a lot of time, effort, and investment to support those. And you end up creating an operations business that just does that for 24 7. >>That's the only reason for them to exist is to manage those. But then we have the government adjacent industries that you're referring to. What about the parkers that service government, they have their own set of regulations. more recently CMMC coming out, et cetera. We provide all of those as a baseline for our government cloud plus. So that level of assurance is assumed by customers and consumers of the service. And again, they're worried about what type of beta and what type of business workflows they're gonna enable and not, can they meet the basic regulations to stand up the service? >>Yeah, I think that highly of the workflows piece is critical because workflows is the new integration layer, right? So these seeing a lot of that, and again, that's a big theme at a re-invent this year. I'll see the performance is key graviton to all the processor stuff and, and, you know, it's lambed and old serverless, but as you move up to the stack where there's actual agility and modern applications that need to be built, whatever they are, you need to have this programmable cloud scale, but the customization on workflows and machine learning and AI. So this is all beautiful for everyone to think about, but now they have to implement it. So how might your customers and prospects consider expanding their offerings with Salesforce in the cloud? Is there, is there a certain playbook that you see, is there a situational awareness that's needed? How would you advise your customers will want to consider expanding, uh, their portfolio in their, their apps and workflows with Salesforce? >>Yeah, that's a fantastic question. So, John, and I'm going to start with, again, going back a little bit to what is Salesforce and who are we as a company? So in as much as we started talking about Salesforce as the number one CRM platform was SAS, we've also acquired some companies and invested in a lot of different, uh, elements of businesses, uh, Tableau NeoSoft and velocity more recently, the slack acquisition, and they're all slightly outside of our platform in terms of capabilities and what we intend for those to deliver. So our customers have a lot more options in terms of what it means to partner with and invest with Salesforce. Uh, slack is a great example of where that becomes a communications mesh and infrastructure that allows them to integrate, uh, technologies, applications, workflows, et cetera. So you want to rethink almost what is Salesforce and what does it mean in your enterprise? >>And then coming back to, to the core of what we do, a lot of how we enable our customers is here's an environment. We enable these very quickly a customer's access to the environment right away. They can set up testing environments, sandboxes, start playing with workflows and really reimagine what that environment is going to look like for their internal users and their engagement with these applications. So yes, we have runbooks we have playbooks, but we've also got enablers in the form of applications. We have a huge application market, if you will, where customers can download different accelerators and try those. We've got a huge network of partners that have delivered rich value added applications. So in most cases, our customers are going to find someone's already created the use case or the application or the workflow they needed. And maybe it's a case of just announcing that a little bit or updating it a little bit, or creating the integration to an in-house system already. So it makes it very exciting, but also makes it a very quick start to solve a problem. >>Oh, Nielsen, you guys have a great opportunity with the cloud and cloud scale. Obviously, companies successful Salesforce is well-known, but as data and governance has to be more agile, more secure often, that sounds counter-intuitive, but this is the big deal that's happening right now, where you need the leverage, the scale you need to have it secure, which you'd think needs to be protective, but making it more permissive is agility. This is the core theme, your, your reaction to wrap up, >>Uh, all great points and yes, to be the data isn't useful if it's entirely locked up. So at Todd, you bring the user to the data they have access to, and that data to provide them value. But especially in a, we'll put a government lens on this. On the government side, the data is ultimately what our government entities are stewarding. So yes services, but that data is imperative. So our customers understand the value of that data and then also how to not just extract value from it, but how to shepherd and steward the security of that data very well. So for us, it's the ability to get that data to the right users, allow them to construct their business omission flow on that data. But the data has to persist has to add value, has to be available for analytics and so on >>Nielsen. Jardeen vice president of north America solutions engineering at Salesforce. Thanks for coming on the cube and, and sharing your story and congratulate a big opportunity ahead for you guys. Congratulations. >>Absolutely. John, thank you so much. Enjoy the rest of the week. Okay. >>It was coverage of eight of us reinvent 2021. Um, John for a, your host. Thanks for watching.
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We deal SIM thank you for coming on the cube Um, talk about the Salesforce is positioned as you guys innovate and scale your delivery of services to customers, and that's really the precursor to where we are with cloud here. that allowed them to define their workflows or their business models in a very, and the greatness of the company that you guys have had over the years, uh, when you get into the public sector, you have a view into the services your customer needs and how they're engaging with you, business flows, the mission, the constituency context, and how to make that constituent experience So this applies to Salesforce. the flexibility then to essentially decouple our architecture from bring that service to the customer, they can say, well, actually here's another use case that I would like Salesforce So, so I have to ask you now, now we're sitting in this global scale, So I'll, uh, you mentioned our government cloud, That's the only reason for them to exist is to manage those. modern applications that need to be built, whatever they are, you need to have this programmable So our customers have a lot more options in terms of what it means to partner with and our customers are going to find someone's already created the use case or the application or the where you need the leverage, the scale you need to have it secure, which you'd think needs to be protective, But the data has to persist has to add value, has to be available Thanks for coming on the cube and, John, thank you so much. Um, John for a, your host.
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Bill Patterson, Salesforce | IBM Think 2021
>> Announcer: From around the globe it's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> And welcome back here on theCUBE. John Walls, your host with you as we continue our IBM Think 2021 initiative. Been talking a lot about IBM's assistance in terms of what it's doing for its client-base. We're going to talk about partnerships today, a little bit with Bill Patterson who is the EVP and General Manager of CRM Applications at Salesforce who has a really good partnership in great practice right now, with IBM. And Bill, thanks for the time today. Lookin' forward to spending some time with you, here. >> Yeah, thank you John, thanks for having me today. >> You bet. Well, let's just jump right in. First off, let's share with the viewers about your core responsibilities at Salesforce. We talked about CRM, what your engagement is there, but if you would just kind of of give us an idea of the kind of things that you're handling on a day-to-day basis. >> Well, I am responsible for our CRM applications here, at Salesforce, which are our sales cloud technologies to help organizations get back to growth, our service cloud technologies which are really helping organizations to take care of their customers, you know, through all moments of the digital lifecycle, our small business solutions, so to help growing organizations thrive, and our Work.com and vaccine management solutions which are helping the economy safely reopen through the crisis modes that we've all been living in. So broad range responsibilities and my day-to-day is nothing like it was a year ago. >> Yeah and I could only imagine, especially when you throw that last component in, COVID, which hopefully, we'll have time to talk about just because, I think, people are so are taken to the subject now and obviously it's impacting business on so many different levels. But let's talk, first off, about IBM and your partnership with them, kind of the genesis of that, how that came about, and maybe how you're working together. How are you integrated these days with IBM? >> Well, you know, one of the things at Salesforce that are key value as an organization is is to establish trust around the transformation of organizations across the world. And when you think about brands that you can trust to drive transformations with, IBM and Salesforce really stand apart. So IBM is an incredible partner for us on the technology side, on a service delivery side, and in an innovation side for us to create new solutions to help our clients really go in this from-to state of how their businesses used to operate to how they need to operate in the future. I loved working with the IBM team. We have a lot of great values that are shared across our two organizations. But most fundamentally, those values are deeply rooted in customer success. And I think that that is one of the things that really draws me too, working with such a great partner here. >> Go into the process a little bit, if you will. So if I'm a prospective client of yours and I come to you with some cloud needs, you know, again, whether it's storage or whether it's applications or whether it's Edge, whatever it is I'm coming to you for, how do you then translate that to IBM and how does IBM come into play, where do the boundaries kind of start and stop or do they? Or is it a complete mesh? >> Yeah, well I think one of the things that's sort of unique about today's climate is people aren't just looking to solve technology problems, they're looking to solve business problems. And what we really do at Salesforce is lead with the business transformation opportunity and deeply partner with IBM on a number of fronts to really go help those opportunities become realized. The first is in the services line. IBM has great partnerships with Salesforce around the transformation about core business processes, configuration, integration services. That's one of the dimensions that we work together on. We also work together on areas of artificial intelligence and how we help businesses become smart in their operations every day to empower their workforce to really achieve more. And finally, you know that you mentioned about core technology, you know, oftentimes, the business requirements translate into great technology transformation. And that's what we do deeply with the IBM team is really outlining a blueprint and a roadmap for modernizing the technical infrastructure to help organizations move fast, increase their operational agility, and run at such scale and safely in today in the modern world that we all operate in on. So through all those facets of the lifecycle, IBM continues to be one of our leading partners, globally, to help clients, you know, not just here, in the United States, but around the world to think about how they need to maximize their transformational abilities. >> Yeah, and you touched on this at the outset of the interview. We were talking about IBM and the impact and obviously, the great association relationship that you have with them and the value in that. I'd like you to amplify on that a little bit more in terms of, specifically, what are you getting out of it you think, from a Salesforce perspective to have kind of the power and the weight and the bench, basically, that IBM provides. >> Well you think about transformation and you know, you read a lot about digital transformation online, that means so many different things to so many different businesses. Businesses, not just, like I said, here in one country, but globally, the transformational needs really need to come with incredible bench and domain expertise by industry, by geography, even by some micro-regions in those geographies given what we've been experiencing here, in the public sector in the United States with this COVID response activity we've been doing with the IBM team. And so when you talk about the deep bench, what I love about working with IBM on is, again, commanding just great industry insights and knowledge of where industries are heading and also cross-industry insights so that you can really bring great best practices from say, one industry to another. Second is that real understanding of the global nature of business today. And I don't think the one thing that's fascinating about digital, it is not a sovereign identity, today. Digital really means that you need to understand how to operate in every country, every region, every location, you know, safely. And so IBM has incredible depth in bench of experiences to help our clients truly transform those areas. Maybe another area that I really have appreciated working with IBM on is that deep technical understanding and deep technical domain of excellence maybe in the area of artificial intelligence. And our partnership is quite unique between Salesforce and IBM. Not only do we work together for external clients but inside of IBM, IBM is using Salesforce today to run a lot of your core operations. And so the partnership we work with, not only IBM as a kind of delivery excellence, but internally as a customer, is really helping IBM transform its operations from service to sales to marketing all around the world. So I think this partnership is one that is deeply rooted in working together and really, like I mentioned before, finding the right path to drive the outcomes of tomorrow. >> You know, you mentioned COVID and so we'd like to touch on that. But I assume that's a big part of your current relationship, if you will, in terms of the partnership goes. What, specifically, are you doing with IBM in that space and what have you done, and then what are you continuing to do as we go through now, the vaccination process and the variant identification processes and all these things? So maybe you can share with our viewers a little bit about the kinds of things that you have been working on together and the kind of progress that you've been making. >> Well, back a year ago, you know, when the world was really at a standstill, Salesforce created a solution called Work.com which was to engineer new technologies to help businesses kind of deal with the reality of a hard shutdown to business in the, say, private sector and then in the public sector, to really create new innovation around key solutions like contact tracing that you might have needed to track, you know, kind of outbreak and the rate of progression of the virus. And what we did with the IBM team, working with clients around the world first was work together to deploy those technologies rapidly into the hands of our customers. Through those moments of opportunity and realization, you know, working with our clients, we also started to hear of, you know, kind of about where we find ourselves today, this mass vaccination wave of where our citizens and societies are kind of on the recovery journey. And the work that we did with IBM was to start to plan out the next wave of recovery options around vaccine managements, Salesforce creating a core vaccine scheduling, distribution, and administration management services and IBM focusing on more of that credentialing and vaccination state of how someone has gone from receiving a shot in arm to now having a trusted profile of which vaccines, when did you receive them, are they still accurate and valid around those solutions. So where we're working with the IBM team most acutely on COVID now is in the vaccine credential management side through Watson Health. >> Well, can you give us an idea now, let's see if we can dig in a little deeper on some of those other things you talked about to about core technologies, you talked about, I mentioned Edge, you know, and that's people tryin' to figure out how they integrate these Edge technologies into their primary systems, now. So can you give us some examples, some specific examples of some things that you're actually collaborating on today in those areas or maybe another that comes to mind? >> Yeah, Edge computing is probably one of the other more exciting things that we're doing with the IBM team and I think you find that really working with our field service business and IBM cloud services, you know, globally speaking. On the Edge, as devices become smarter and more digital, they have a lot of signals that organizations can now tap into, not only for real-time intelligence but also fault intelligence when a device is starting to need repair or preventative maintenance around the solutions that kind of need to be administered. And the work that we're doing to really broker this connected, not just enterprise, but connected sort of experiences with IBM, super powerful here, because the IBM Edge services are now helping us get into anomaly detection. Those anomaly detections are automatically routing to workers who use the Salesforce field service capabilities, and now we can help organizations stay running safely and with continuity which is really all our customers are asking us for. So the ability for us to be creative and understand, you know, our parts of the picture together are really the things that I think are most exciting for what we're doing for clients around the world. >> Yeah, you mentioned continuity, kind of a cousin to that, I think, is security in a way because you're-- >> Absolutely. >> So what are you hearing from your customer-base these days with regard to security? You know, a lot of high profile instances certainly from bad state actors, as we well know. But what are you hearing in terms of security that you're looking at and maybe cooperating or collaborating with IBM on to make sure that those concerns are being addressed? >> Yeah, you know, I think, well, first off, security is on the top of minds for all decision-makers, executives, today. It's the number one threat that a lot of companies are really needed to respond to given what we've seen in the geo-political world that we're in. And security isn't just about securing your servers, it's also about securing every operational touchpoint that you might have with, you know, your every end-user or even every customer that's inter-operating with your services that you project as an organization. And what I love about working with the IBM team is, as we mentioned, you know, such great insights across all parts of technology infrastructure to really help understand both the threat level, how to contain that threat level, and more importantly, how to engineer with, you know, great solutions all the way into the hands of customers so they become safe and easy for all actors in your environment to really operate with. And that's where, again, you know, you think about a solution like mobile sales professionals, they're out traveling around the world on mobile devices, sometimes, their AG even brought their own personal devices into the enterprise. And so IBM is a great partner for ours just to help us understand the overall threat level of every device every moment that an employee might have within their organizational data, and really help create great solutions to help keep organizations running safely. >> Yeah, I think it's interesting you tell about people bringing their own devices on, back when, I remember that acronym, BYOB was like a huge thing, right? (chuckling) And this major problem or conundrum and now it's almost like an afterthought, you've got it solved, you've got it well taken care of. >> Well you think about, again, devices in the enterprise and how much we've been able to achieve with the BYOB becoming commonplace and norm, even today, the workman place from home kind of environment that we're in. I mean, who would have thought a year ago that most of our operations can be conducted safely from our home offices, not just our regional or corporate offices? And again, that's the kind of thing that working with IBM has been such a great value for our clients because no one could have forecasted that the contact center would've had to moved to your kitchen last year. And yet, we had to really go achieve that in this time and working with great partners like IBM, it became not just a conversation but real practice. >> By the way, I think I said BYOB. I meant BYOD, so you know where my mind's at, right? (chuckling) >> I wasn't going to correct you. >> Hey thanks, Bill, I appreciate that. It just kind of hit me. I think that that just, that was a Freudian slip, certainly. Hey Bill, thanks for the time. I certainly do appreciate and thanks for shining a light on this really good partnership between Salesforce and IBM. And we wish you continued success down the road with that, as well. >> Yeah, thanks again. And again, love being your partner and love the impact we're having together. >> Great, thank you very much. Bill Patterson joining us, the EVP work in CRM at Salesforce talking about IBM and that relationship that they're putting into practice for their client-base. John Walls reporting here, on theCUBE. Thanks for joining us with more on IBM Think. (soft music) ♪ Dah de dah ♪ ♪ Dah ♪
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Brought to you by IBM. And Bill, thanks for the time today. Yeah, thank you John, of the kind of things that you're handling of the digital lifecycle, kind of the genesis of of organizations across the world. and I come to you with to help clients, you know, not just here, Yeah, and you touched on this And so the partnership we in that space and what have you done, needed to track, you know, on some of those other things you talked and I think you find that really working So what are you hearing from to engineer with, you know, interesting you tell about people And again, that's the kind of I meant BYOD, so you know And we wish you continued success and love the impact we're having together. Great, thank you very much.
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Maureen Lonergan, AWS & Alyene Schneidewind, Salesforce | AWS re:Invent 2020
>>from around the >>globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the Cubes Coverage Cube Virtual coverage of AWS reinvent 2020 which is also virtual. We're not in person this year. We're doing the remote interviews. But of course, getting all the stories, of course, reinvented, full of partnerships full of news. And we've got a great segment here with Salesforce and AWS. Eileen Schneider Win, who is the senior vice president of strategic partnerships, and Maureen Lundergan, director of worldwide training and certification address. Maureen Eileen. Great to see you. Thanks for coming on. And nice keynote. What's up with the partnership? Give us a quick over your lien. What's what's the Salesforce? A day was partnership. Take a minute to explain it. >>Sure, thank you. I think I'll start out by talking about how sales were thinks about strategic partnerships. So for us, it's really it starts with the customer and being where they want us to be. And we've been so fortunate to be in this relationship with AWS for over five years now. It really started out as an infrastructure based partnership as we were seeing customers start their digital transformation journeys and moved to the cloud. But what has been really exciting as we've spent more time working together and working with our customers, we have now started to move into emotion of really bringing some differentiated solutions between the number one CRM and the most broadly adopted cloud platform to market for customers, uh, in areas like productivity, security and training and certification which will talk more about in a bit Onda. Specifically, some of those solutions are service Cloud Voice Product, which we launched this summer, announced last fall, a dream force as well as our private connect product which creates great security between the AWS platform and Salesforce. >>What? Some of the impact area is actually the two clouds you mentioned CRM and Amazon. We're seeing data obviously being a part of the equation ai machine learning. Um, what's been the impact I lean to your customer specifically >>Yeah, so specifically I'd call out to areas what one is really that foundation of security. Specifically, as government regulations and data security has become more critical, we've really been able to partner together there and and that's been crucial for certain customers in certain regions as well a certain industries like government. Uh, in addition, I would call out again that service cloud voice partnership, a zoo. We see the world moving more digital. This really allows customers to go quickly and, uh, turn on. There are solutions from anywhere at any time. >>You know, I love that any time, anywhere kind of philosophy. Now more than ever. With the pandemic collaborations required more than ever, and some people are used to it. You know, I've seen more technical developers have used to working at home, but not everyone else. The workforce still needs to get the job done. So this idea of collaboration, what is the impact in for your customers and how are you guys helping them? Because I think this is a big theme of this year That's gonna not only carry over, even when the pandemics over this idea of anywhere is all about collaboration. >>Yeah, I totally agree. I mean, the exciting thing about the partnership is we've been talking digital transformation with customers for years, but I think what we saw at the beginning of this year, as we were all thrown home and forced Thio, you know, fire up our jobs from our bedrooms or our garages. It really came down to our ability to work quickly and turn on our solutions. It's and these unprecedented times, while we're going through this now, everything we're building really is the future. So it's not just the tools and technology, it's also the processes and how work is getting done that's really come into play. But again, I'll anchor back to that service blood voice solution. So for us, call centers were completely disrupted. You think of call centers and you know, pre 2020 everyone sitting in a room together, agent side by side managers, having the ability to pop over and assist with a call or managing escalation. Now that's been completely disrupted. And it's been very exciting for us to work with our customers, to reimagine what that looks like again both from a technology perspective but also from a process perspective. And along with that, you had to reimagine how employees are learning these solutions and being trained. So we're very grateful for the partnership with AWS, and we're doing some really amazing things together. >>You know this is one of my favorite things about the enablement of Cloud. But in Salesforce has been a pioneer. As you pointed out, this connectedness feature has always been there. Now more than ever, it's highlighted with call centers, not the call center more. It's the connected center. People are connecting. And I think, Maureen, I think last time you're in the Cube. A few years ago, we were talking about virtual training online, and that was pre pet pandemic. Now you're seeing surge of online training not only because people's jobs are changing and being displaced or even shut down. New roles are emerging, right? So the virtual space Virtual world digital world, there's everyone's getting more digital faster now. How has the cove in 19 changed the landscape for training and skills demand? From your perspective, I >>mean at AWS, we've been working on our virtual capabilities for a while, so we had a digital platform out. We had a great partnership, have a great partnership with Salesforce and putting content on trailhead. We had to pivot very rapidly to virtual instructor led training and also our certifications right. We were lucky that our vendors partnered with us rapidly to pivot certification toe proctor environment. And this actually has helped to expand our ability to deliver the both training and certification in locations that we may not have been able to do before. And we have seen while it slowed. Initially, we have seen such an uptake and training over the last, um, 6 to 8 months. It's been incredible. We've been working with our customers. We've been working with our partnerships like Salesforce. We've been pushing more content out. I think customers and partners air really looking for how toe upscale their employees, uh, in a in a way, that is easy for them. And so it's actually been a great surprise to see the adoption of all of our curriculum over the last couple months. >>Well, congratulations knows a lot more work to do. It's gonna get more engaging, more virtual, more rich media. But this idea of connecting lean I wanna get back to the your your thoughts earlier, um, mentioned trailhead. Maury mentioned trailhead. You guys were doing some work with the virtual training there. What? Can you tell us more about that? And how that's going so far? >>Sounds great. So trailhead is our free online learning platform. And it really started because we have a commitment to democratizing anyone's ability to enter our industry s so you could go there and both online or with our trail head go app and experience what we call trails, which our paths for learning again on different areas of knowledge and skills and technology. And late last year, we announced an incredible partnership with AWS, where we're bringing the AWS learning content and certification to trailhead. And this is really again driven by our customers to are asking us to do our part in bringing mawr of these skilled resource is into the ecosystem. But something I also wanna highlight is I feel like this moment that we're in right now has also forced everyone to reimagine how they're doing learning even businesses, how they're training their employees and again having this free platform. And the partnership with AWS has really helped us go very quickly and create a lot of impact with customers. >>I just want to say I love the trailhead metaphor because, you know, learnings nonlinear. It's asynchronous. You've got digital. So you want to take a shortcut? You gotta know the maps And I think that's, you know, people wanna learn versus the linear, you know, tracks on. And I think that's how people have been learning online. And AWS has got a data driven strategy. Marine, I want to get your take on this because as you bring content on the trailhead, can you talk about how that works? And how you working with Railhead? >>Yeah. I mean, we started conversations a couple of years ago, and I think the interesting thing is that Salesforce and AWS have a very similar philosophy about bringing education to anybody who wants it. You'll hear me talk a lot about that in my leadership talk at reinvent, but, um, we really believe that we wanna provide content where learners learn and salesforce and trailhead have this amazing captured audience. And, um, you know, we're really looking at exploring. How do we bring education to people that might not otherwise have access to it? On DSO, we started with really foundational level content, a ws Cloud, Practitioner Essentials and AWS Cloud for technical professionals. And the interesting thing is, both of those courses have been consumed. ITT's not enough to just put it out there you want people to complete the trails and we've seen such an amazing uptake on the courses with, like 85% completion rate on one of the trails and 95% completion rate on the other one. And to keep customers engage is really a credit toe. How trailhead is designed. >>You know, it's interesting. The certification people don't lose sight of the fact that that's kind of the in the end state. Then you start a new trail. I mean, this >>is >>the this is really what it's all about. Can you just share some observations that you've seen for people that are coming into this now to say, Hey, okay, what do I expect? And what are some of the outcomes? >>Yeah, I mean, first, what we're seeing is our customers are being very clear that they need more of these skills. So we're also seeing the need for Salesforce administrators out in our ecosystem. And I think with everything going on this year, it's also an opportunity for people who are looking to pivot. Their careers were moving to tech and again, this free learning platform and the content that we're bringing has been really powerful and again for us. The need for salesforce administrators and cloud practitioners out in our ecosystem are in more demand than ever. >>Maureen. From your perspective on AWS, you see a lot of the new new jobs cybersecurity, Brazilian openings. Where do you see the most needs on for training and certification? Can you highlight some of the areas that are emerging and trending, if you will? >>I would say it's interesting because what we're seeing is is both ends of the spectrum. People that are really trying to just really understand who cloud is, whether it's, ah, business leader within an organization, a finance person, a marketing person. So cloud practitioner, you know, we're seeing huge adoption and consumption on both our platform in on Salesforce. But also some other areas are security and machine learning machine learning. We have five learning paths on our digital platform. We've also extended that content out to other platforms and the consumption rate is significant. And so, you know, I think we're seeing, uh, customers consume that. But the other thing that we're doing is we're really focused on looking at who doesn't have access to education and making sure that's available. So I think the large adoption of Cloud Practitioner in Practitioner is is largely due to the other things that we're doing with programs like Restart our academic programs >>to close it out, Alina want to get your thoughts and final thoughts on the relationship and how people can find more information about this partnership and what it means. Take, take it home. >>Thank you for asking. So just like everything else we've been talking about today, we've had to reimagine how we're showing up at this event together and very exciting thing that my team has created is the AWS Virtual Park. And anyone can access that at salesforce dot com slash aws. So please go check it out. You can experience our products here from our experts and experience its innovation on your own. >>Great insight. Thanks for coming on and participating. Really appreciate Salesforce and AWS two big winning leading clouds working together Trail had great great offering. Thanks for coming on sharing the news. Appreciate >>it. Thank you. >>It's the Cube virtual covering. It was reinvent virtual. Of course. Check out all the information here All three weeks. Walter Wall coverage. I'm John Fury with the Cube. Thanks for watching
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Casey Coleman, Salesforce | AWS Public Sector Online 2020
>>from around the globe. It's the queue with digital coverage of AWS Public sector online brought to you by Amazon Web services. >>Hi, I'm stupid man. And this is the Cube's coverage of AWS Public Sectors Summit Online. We've done this show for many years. Of course, this this time it's online rather than in person in the District of Washington D. C Happy to welcome to the program First time guest. Very good partner of Aws is from Salesforce is Casey Coleman. She is the senior vice president of Global Government Solutions, together with sales work. Casey, thanks so much for joining us. >>Thank you. Glad to be here. >>All right. So first of all, maybe if you could give us a little bit of level set your role at Salesforce and obviously, you know, a long partnership with Amazon. Tell us a little bit about that. >>Yes. My role at Salesforce is to work with our customers in the public sector globally and really help them map out their digital transformation. You know, it's an ongoing journey and we help them understand how to how to break that down into actionable steps and really transformed what they're doing to serve their constituents and citizens better. >>Excellent. So it of course said that the public sector show a lot about the leverage of govcloud and the other services. All the compliance that goes into that ahead of this event you had Ah, new update at Salesforce in partnership with AWS. Talk to us about it's the government cloud plus s o. You know what's entailed there? Uh, and, uh, tell us how AWS and Salesforce work together to launch this solution. >>Yeah, thanks Do. We are so excited to announce the launch of govcloud Plus, which is sales force is a customer 3 60 crm platform that runs on Amazon Web services in the govcloud in their govcloud environment. And we've just received a provisional 80 0 provisional authority to operate from the FEDRAMP program office at the high security level. So we are announcing govcloud Plus is fed ramp. I'm ready to go generally available and ready for customers. >>Excellent. Maybe bring us inside. You know what's different about how government agencies leverage sales force most companies out there, You know, Salesforce is a critical piece off how they manage, you know, number one, they're salesforce marketing and lots of other pieces, anything specific that we should understand about the public sector. >>Yeah, it's a great question because even our name Salesforce sounds like a commercial kind of thing to do. Governments don't think of themselves as selling, but if you break down to a level of detail about what governments actually do, it is the same kind of functions case management, its benefits delivery. It's communications and outreach. It's all the same kind of function that are necessary for commercial organizations to drive. And so that's what we do. We translate that into government ready terms so that they can serve child welfare, health information delivery record, former information, all kinds of services for the constituents of the public sector. And they might call them customers. They might call them citizens, residents constituent. But it's those they >>Yeah, well, what one of the things about Salesforce is, as you said, it's not just, you know, a sales tool. There's so much you've got a very broad and deep ecosystem. Their asses Well, as you know, people that know how to use it, they get underneath the covers. You know, when I think of not only a sales force. You know, the first company that I probably thought of and heard about that it was SAS. But if you talk about the AP economy, if you talk about how things integrate, Salesforce does a lot for developers. So I know one of the other pieces you had. There's everybody knows Dream Force. Maybe not as many people know, that trailhead DX show that that that Salesforce has had for developers. So bring us a little bit inside. What would Salesforce is doing for developers? And, of course, the government angle along those lines, too? >>Yeah, there's a lot going on in the developer world. We were glad to be able to host a virtual version of our trailhead developer conference and announced a lot of exciting new developments, including salesforce anywhere, which is really bringing an immersive voice, video and chat environment to collaborate in the developer environment and into the delivery environment. And you bring that into the public sector. And the benefits are amazing because one of the key challenges with government is keeping up with the pace of the public expectations. In a pace of change in the commercial world, all of the shop and bank and live on our mobile devices. And governments are being faced with the same expectations from the public to do any time anywhere personalized delivery as the code rapid development environments that force offers give public sector I t team the ability to quickly and respond to changing conditions like the code 19 pandemic and roll out applications that are not only fast to develop into boy but they also benefit from being in the govcloud environment. And so the compliance is party built in and that's another key challenges. Often it rises. The public sector is not almost building new applications and making sure they're secure with Salesforce all built in >>Yeah, sounds sounds a lot of sis similarity to what we hear in the private sector, of course, that the balance between what it is doing and how we enable developers, of course security, you mentioned super important anything specifically from the government sector that you'd say, Well, that might be different from what we see in the general enterprise world. >>You know, the but security is top of mind for the public sector, always because they're dealing with the most sensitive data they're dealing with the public trust and trust is really the currency of government. They're not dealing in profit and market share, but they are dealing in a public trust and protecting information like financial data, health data, personal data. And so it's essential that the government had the best in class commercial tools to make sure they are providing world class security for for their their constituents in their mission. And that's one reason we're so excited to be partnering with AWS on Golf Club was because Amazon AWS has already deployed the Fed Ramp I version of their infrastructures of service. And so, by riding on top of that, we inherit all of those existing controls at our own Fed ramp controls. And our customers benefit from the best in class security from two of the most trusted name in public Cloud >>Great. You know, absolutely. Govcloud has been a real boon for the entire industry. When it talks about how government agencies they're leveraging cloud, you talked about sitting on top of ah govcloud the government cloud plus, you know, leverages some of the certifications and like, can you bring us inside a little bit? How long did this effort take? to get anything specific in the integrations were, you know, functionality that that you might be able to highlight about this joint effort. >>Yeah, we've been working on for some time now because it's it's essential to really think from the ground up. And this is not just re platform ing our cloud solutions on AWS. It is rethinking the whole architecture so that we really are organically taking advantage of infrastructure services that AWS provides. So it is a really deep integration. And it's not only a technical tech, integration is the strategic partnership, and you're going to see a lot more now that's coming from both of us about the integration capabilities we're bringing together and a lot of the work we're going to be doing to continue to bring innovation to our joint customers. >>Excellent. You made reference to the pandemic. Uh, what are you hearing from your customers? How does this new offering impact them and support them both? Today is they're reacting to what happens as well as you know, going forward as we progress. >>Yes. Do you know the coveted 19 pandemic really exposed fault line in government programs that weren't scale to meet this demand. We saw website crashing when people were going to them and just overwhelming them with questions about the health situation. We saw benefits programs that only works where people could come in and sign up in a fly in person and obviously with government offices shut down, that wasn't an option. And a lot of government workers were sent home to tell a work without much notice, and their infrastructure just couldn't support it. And so just in general, there are a lot of breakdown along the way. But the good news is that a lot of public sector organizations and programs making that pivot quickly. For example, we worked with one state agency that experienced a 400% spikes in demand for applications for unemployment benefits. It makes sense people are out of work. They need unemployment benefit. But they just couldn't respond to that kind of surging demand. So we worked with them along with AWS and in less than a week stood up a virtual contact center with chatbots so that could meet the demand and provide those vital services to their residents at a time of real needs. So there's a lot to the optimistic about in the middle of this crisis, there is a lot of transformation happening. This kind of forcing function is producing a lot of innovation, transformation. And I think it's really going to make a fundamental shift in how we re imagined government in the future. >>Yeah. Okay, so you're absolutely right that this pandemic has shown a real spotlight on where you know what works and what doesn't, Um, and I think about not only government, but you know, a lot of how finances were often times you have your plans in place, you have your budgets in place. You have, you know, funding cycles. So you know what? What our sales force and Amazon doing to help those you talk about. They have to ramp things up a weight where they financially ready for this. Some companies Oh, wait. I have to temporarily dial things down. That's not in my 12 month or 36 month plan. So are there things that you're doing to help customers, you know, short term in and long term? Are you seeing some? Some change in how people think about their planning and how they could be ready for what change happens out there. >>Yeah, you know, one of the big findings from this whole experience, not just in the public sector but across every industry has been that digital transformation may in the past has been viewed as a nice to have. It is now really the only way to connect and serve both customers and employees, and so digital First, digital transformation is rapidly becoming an urgent imperative because this situation is is not going away overnight. And even when we get back to some state of normal, it's going to be different. It's a digital first and being able to move quickly to roll out services rapidly, to be able to start small and then scale rapidly. These are things that benefit any organization, whether it's government or commercial. >>Excellent. Okay, so I'll let you have the final word. What people want. What you want people to have is their take away of salesforce is participation in the AWS public sector online event. >>We are just so excited to be here with AWS to jointly come to our customers with govcloud plus the fed ramp. I authorized environment for the best in class theory, M and customer and employee services. Our partnership with AWS is one that we're excited about. You're going to see a lot more announcements coming to. It's not only a technology integration, it's also a strategic partnership. And we think our customers jointly. Just going to be really excited about the development. So thank you for the time and glad to be here. >>All right, well, thank you so much. Casey. Congratulations on the government cloud plus launch. And absolutely look forward to hearing more about it. >>Thank you. >>Alright. Be sure to stay tuned. Lots more coverage of the Cube at AWS Public Sector Summit online. I'm Stew Minimum. And thank you for watching the Cube. >>Yeah, Yeah, yeah, yeah.
SUMMARY :
AWS Public sector online brought to you by Amazon than in person in the District of Washington D. C Happy to welcome to the program First time Glad to be here. So first of all, maybe if you could give us a little bit of level set your role at You know, it's an ongoing journey and we help So it of course said that the public sector show a lot about the leverage runs on Amazon Web services in the govcloud in their govcloud environment. you know, number one, they're salesforce marketing and lots of other pieces, anything specific all kinds of services for the constituents of the public sector. So I know one of the other pieces you had. the code 19 pandemic and roll out applications that are not only fast to of course, that the balance between what it is doing and how we enable developers, so excited to be partnering with AWS on Golf Club was because Amazon in the integrations were, you know, functionality that that you might be able to highlight about And it's not only a technical tech, integration is the strategic to what happens as well as you know, going forward as we progress. And I think it's really going to make a fundamental shift in how we re imagined government in the future. a lot of how finances were often times you have your plans in place, you have your budgets in place. Yeah, you know, one of the big findings from this whole experience, not just in the public sector but across of salesforce is participation in the AWS public sector online event. We are just so excited to be here with AWS to jointly come And absolutely look forward to hearing more about it. And thank you for watching the Cube. Yeah, Yeah,
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Casey Coleman, Salesforce | AWS Public Sector Online
>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Online. Brought to you by Amazon Web Services. >> Hi, I'm Stu Miniman and this is theCUBE's coverage of AWS Public Sector's Summit Online. We've done this show for many years, of course, this time it's online rather than in person in the District of Washington, D.C. Happy to welcome to the program first time guest, a very good partner of AWS's, from Salesforce, it's Casey Coleman. She is the Senior Vice President of Global Government Solutions, once again, with Salesforce. Casey, thanks so much for joining us. >> Stu, thank you, glad to be here. >> All right, so first of all maybe, if you could, give us a little bit of level set, your role at Salesforce and obviously a long partnership with Amazon. Tell us a little bit about that. >> Yes, my role at Salesforce is to work with our customers in the public sector, globally, and really help them map out their digital transformation. You know, it's an ongoing journey and we help them understand how to break that down into actionable steps and really transform what they're doing to server their constituents and citizens better. >> Excellent, so of course, at the Public Sector Show a lot about leverage of GovCloud and the other services, all of the compliance that goes into that. Ahead of this event, you had a new update at Salesforce in partnership with AWS. Talk to us about it's the Government Cloud Plus. So what's entailed there? And tell us how AWS and Salesforce work together to launch this solution. >> Yeah, thanks Stu. We are so excited to announce the launch of GovCloud Plus which is Salesforce's Customer 360 CRM platform that runs on Amazon Web Services in the GovCloud in their GovCloud environment and we've just received a provisional APO provisional authority to operate from the FedRAMP Program office at the high security level. So we are announcing GovCloud Plus is FedRamp High, ready to go, generally available and ready for customers. >> Excellent, maybe bring us inside. What's different about how government agencies leverage Salesforce. For most companies out there, Salesforce is a critical piece of how they manage not only their sales force but marketing and lots of other pieces, anything specific that we should understand about the public sector. >> Yeah, it's a great question because even our name, Salesforce, sounds like a commercial kind of thing to do. Governments don't think of themselves as selling, but if you break down to a level of detail about what governments actually do, it is the same kind of functions. It's case management, it's benefits delivery, it's communications and outreach, it's all the same kind of functions that are necessary for commercial organizations to thrive. And so that's what we do, we translate that into government-ready terms so that they can serve child welfare, health information delivery, patient records, farmer information, all kinds of services for constituents of the public sector. And they might call them customers, they might call them citizens, residents, constituents, but it's those they serve. >> Yeah, well one of the things about Salesforce is, as you said, it's not just a sales tool, there's so much. You've got a very broad and deep ecosystem there as well as people that know how to use it. They get underneath the covers. When I think of not only is Salesforce the first company that I probably thought of and heard about that it was SaaS, but if you talk about the API economy, if you talk about how things integrate, Salesforce does a lot for developers. So I know one of the other pieces you had that everybody knows Dreamforce, maybe not as many people know the TrailheaDX Show that Salesforce just had for developers, so bring us a little bit inside what Salesforce is doing for developers and of course, the government angle along those lines, too. >> Yeah, there's a lot going on in the developer world. We were glad to be able to host a virtual version of our Trailhead Developer Conference and announce a lot of exciting, new developments, including Salesforce Anywhere which his really bringing an immersive voice, video, and chat environment to collaborate in the developer environment and in the delivery environment. And you bring that into the public sector and the benefits are amazing because one of the key challenges with government is keeping up with the pace of the public expectations at a pace of change in the commercial world. All of us shop and bank, and live on our mobile devices, and governments are being faced with the same expectations from the public to do anytime, anywhere, personalized service delivery. It's the (audio distortion) rapid development environment that Salesforce offers gives the public sector IT teams the ability to quickly respond to changing conditions like the COVID-19 pandemic, and rollout applications that are not only fast to develop and deploy, but they also benefit from being in the GovCloud environment, and so the compliance is already built in. And that's another key challenge that often arises, the public sector (audio distortion) is not only fielding new applications but making sure they're secure, and so with Salesforce, it's all built in. >> Yeah, it sounds a lot of system similarity to what we hear in the private sector. Of course, the balance between what IT is doing and how we enable developers. Of course, security, you mentioned, is super important. Anything, specifically, from the government sector that you'd say might be different from what we see in the general enterprise world? >> You know, the security is top of mind for the public sector, always, because they're dealing with the most sensitive data. They're dealing with the public trust. And trust is really the currency of government. They're not dealing in profit and market share, but they are dealing in a public trust and protecting information like financial data, health data, personal data, and so it's essential that the government has the best in class commercial tools to make sure they are providing world class security for their constituents and their mission. And that's one reason we're so excited to be partnering with AWS on GovCloud Plus because Amazon AWS has already deployed the FedRAMP High version of their infrastructures and service, and so by riding on top of that, we inherit all of those existing controls, add our own FedRAMP High controls, and our customers benefit from the best in class security from two of the most trusted names in the Public Cloud. >> Great, you know, absolutely, GovCloud has been a real boon for the entire industry when it talks about how government agencies are leveraging Cloud. You talked about sitting on top of GovCloud, the Government Cloud Plus leverages some of the certifications and the like. Can you bring us inside a little bit? How long did this effort take to get? Anything specific in the integrations or functionality that you might be able to highlight about this joint effort? >> Yeah we've been working on it for some time now, because it's essential to really think from the ground, up. And this is really not just re-platforming our Cloud solutions on AWS, it is rethinking the whole architecture so that we really are organically taking advantage of infrastructure services that AWS provides. So it is a really deep integration. And it's not only a tech integration, it's a strategic partnership too, and you're going to see a lot more announcements coming from both of us about the integration, the capabilities we're bringing together. And a lot of the work we're going to be doing continue to bring innovation to our joint customers. >> Excellent. You made reference to the pandemic. What are you hearing from your customers? How does this new offering impact them and support them both, today, as they're reacting to what happens as well as going forward, as we progress? >> Yeah, Stu, you know, the COVID-19 pandemic really exposed a fault line in government programs that weren't scaled to meet this demand. We saw Websites crashing when people were going to them, and just overwhelming them with questions about the health situation. We saw benefits programs that only worked when people could come in and sign up and apply in person, and obviously, with government offices shut down, that wasn't an option. And a lot of government workers were sent home to tele-work without much notice, and their infrastructure just couldn't support it. And so just in general, there was a lot of breakdowns along the way. But the good news is that a lot of public sector organizations and programs are making that pivot quickly. For example, we worked with one state agency that experienced a 400% spike in demand for applications for unemployment benefits. It makes sense. People are out of work, they need unemployment benefits, but they just couldn't respond to that kind of surge in demand. So we worked with them along with AWS and in less than a week, stood up a virtual contact center with chatbot so they could meet the demand and provide those vital services for their residents at a time of real need. So there's a lot to be optimistic about in the middle of this crisis; there is a lot of transformation happening. This kind of forcing function is producing a lot of innovation and transformation and I think it's really going to make a fundamental shift in how we reimagine government in the future. >> Yeah, Casey, you're absolutely right. This pandemic has shown a real spotlight on what works and what doesn't. And I think about not only government, but a lot of how finances work. Oftentimes, you have your plans in place, you have your budgets in place, you have funding cycles, so what are Salesforce and Amazon doing to help those customers? You talk about they have to ramp things up. Oh wait, were they financially ready for this? Some companies, "Oh wait, I have to temporarily "dial things down that's not in my 12-month "or 36-month plan." So are there things that you're doing to help customers short-term and long-term? Are you seeing some change in how people think about their planning and how they can be ready for what change happens out there? >> Yeah, one of the big findings from this while experience, not just in the public sector, but across every industry, has been that digital transformation may, in the past, have been viewed as a nice-to-have. It is now really the only way to connect and serve both the customers and employees, and so digital first, digital transformation is rapidly becoming an urgent imperative because this situation is not going away overnight. And even when we get back to some state of normal, it's going to be different. And so digital first and being able to move quickly to rollout services rapidly, to be able to start small and then scale rapidly, these are things that benefit any organization, whether it's government or commercial. >> Excellent, well Casey, I'll let you have the final word what you want people to have as their takeaway of Salesforce's participation in the AWS Private Sector Online Event. >> We are just so excited to be here with AWS to jointly come to our customers with GovCloud Plus, the FedRAMP High authorized environment for the best in class CRM, and customer and employee services. Our partnership with AWS is one that we're excited about. You're going to see a lot more announcements coming soon. It's not only a technology integration, it's also a strategic partnership, and we think our customers are, jointly, just going to be really excited about the development. So thank you for the time and glad to be here. >> All right, well thank you so much, Casey. Congratulations on the Government Cloud Plus launch and absolutely look forward to hearing more about it in the future. >> Thank you, Stu. >> All right, be sure to stay tuned. Lots more coverage of theCUBE at AWS Public Sector Summit Online. I'm Stu Miniman and thank you for watching theCUBE. (soft electronic music)
SUMMARY :
Brought to you by Amazon Web Services. in the District of Washington, D.C. a long partnership with Amazon. in the public sector, all of the compliance that goes into that. Services in the GovCloud about the public sector. for constituents of the public sector. and of course, the government from the public to do anytime, anywhere, from the government sector that the government has the best in class a real boon for the entire And a lot of the work to what happens as well as going forward, a lot of breakdowns along the way. but a lot of how finances work. not just in the public sector, but across in the AWS Private Sector Online Event. for the best in class CRM, and customer and absolutely look forward to hearing All right, be sure to stay tuned.
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Ben Cesare, Salesforce & Katie Dunlap, Bluewolf | IBM Think 2019
(upbeat music) >> Live from San Francisco it's theCUBE. Covering IBM Think 2019. Brought to you by IBM >> Welcome back to theCUBE. I'm Lisa Martin with John Furrier and we are on a rainy San Francisco day. Day three of theCUBE's coverage of IBM Think 2019 here to talk shopping. One of my favorite topics. We have Katie Dunlap VP of Global Unified rather Commerce and Marketing for Bluewolf part of IBM. Katie welcome to theCUBE. >> Welcome, thank you. >> And from Salesforce we have Ben Cesare Senior Director of Global Industry Retail Solutions. Ben it's great to have you on our program. >> How are you? >> Excellent. >> Good. >> Even though we are at the rejuvenated Moscone Center which is fantastic and I think all of the hybrid multi cloud have opened upon San Francisco. >> Right. >> It's a very soggy day. So Katie IBM announced a partnership with Salesforce a couple of years ago. >> Right. >> Just yesterday John and I were chatting. We heard Ginni Rometty your CEO talk about IBM is number one implementer of Salesforce. Talk to us a little bit about the partnership before we get into some specific examples with that. >> So we know that part of that partnership it's really to leverage the best of the technology from Salesforce as well as IBM and ways that we together married together create opportunities for the industry and specifically here today we're talking about retail. >> So on the retail side Salesforce as a great SAS company they keep on blowing the records on the numbers performance wise. SAS business has proven it's a cloud business but retails is a data business. >> Yes. >> So how does IBM look at that? What's the relationship with retail? What's the solution? >> Yeah. >> And what are people looking at Salesforce for retail. >> Yeah, I think it's really important to understand where our strengths are and I think when you talk about Salesforce you talk about Marketing Cloud and Commerce Cloud, Service Cloud. We call that the engagement layer. That's how we can really interact with our consumers with our shoppers. But at the same time to really have a great connection with consumers you need to have great data. You need have great insights. You need to understand what's happening with all the information that drives choices for retailers and that's why the relationship with IBM is absolutely so strong and it is a data driven relationship. Together I guess you can see the customers in the middle. So we have our engagement layer and a data layer. Together we satisfy the customer. >> Lisa what's the solution specifically because obviously you guys going to market together to explain the tactical relationship. You guys join sale, is it an integration? >> Sure. So what we have done given the disruption that's happening right now in the retail space and with the customer at the center of that conversation we've been looking at ways that what the native functionality for Salesforce is Einstein as an intelligent layer and for IBM it's Watson. So where do they complement one another? And so looking at retail with commerce and marketing and service as the center of that conversation and engagement layer. How are we activating and working with a customer from a collection of data information standpoint and activating that data all through supply chain. So the experience is not just the front experience that you and I have when we go to a site it's actually how and when is that delivered to me. If I have an issue how am I going to return that. So we've looked at the entire customer journey and looked at ways that we can support and engage along the way. So for us, we're looking at as you see retail and the way it's evolving is that we're no longer just talking about that one experience where you're actually adding to your cart and your buying. It goes all the way through servicing that customer returning and making sure that information that's specific to me. And if I can choose how I'm going to have that inventory sent to me and those products sent to me. That's exactly what we're looking to do. >> So then the retailer like a big clothing store is much more empowered than they've ever been. Probably really demanded by us consumers who want to be able to do any transaction anywhere started on my phone finish on a tablet, etc. So I can imagine maybe Ben is this like a Watson and Einstein working together to say take external data. Maybe it's weather data for example and combine those external data sources with what a retailer has within their customer database and Salesforce to create very personalized experiences for us shoppers as consumers. >> Right, and where retailers really can grow in terms of the future is really accessing all that data. I think if you look at some of the statistics retailers have up to 29 different systems of records and that's why some of our experiences are very good some of our experiences are not very good. So together if we can collapse that data in a uniform way that really drives personalization, contextual selling so you can actually see what you're buying why you're buying it, why it's just for me. That's the next level and I must say with all the changes in the industry there's some things that will never change and that is consumers want the right product, the right price, the right place and the right time. All enveloped in a great customer experience. That will never change but today we have data that can inform that strategy and when I was a senior merchant at Macy's years ago, I had no data. I had to do a lot of guessing and when mistakes are made that's when retailers have a problem. So if retailers are using data to it's benefit it just make sure that the customer experiences exceptional. And that's what we strive to do together. >> And I can build on that if we're thinking like specifically how we're engaged from a technology perspective. If I'm a merchandiser and I decide I want to run a promotion for New York and I want to make sure before I run that promotion that I have the right inventory and that I not only I'm I creating the right message but I have the information that I need in order to make that successful. One of the things that we partner with Salesforce on is the engagement layer being Salesforce. But in the back end we have access to something called Watson Embedded Business Agent and that business agent actually goes out and talks to all the disparate systems. So it doesn't have to be solutions that are necessarily a homegrown by IBM or Salesforce Watson could actually integrate directly with them and sits on top. So as a merchandiser I can ask the question and receive information back from supply chain. Yes there's enough product in New York for you to run this promotion. It can go out and check to see if there's any disruption that's expected and check in with weather so that as on the back end from an operation standpoint I'm empowered or the right data in order to run those promotions and be successful. >> It's interesting one of the things that comes up with her this expression from IBM. There's no AI without IA information architecture. You talk about systems of record all this silo databases. There's low latency you need to be real time in retail. So this is a data problem, right? So this is where AI really could fit in. I see that happening. The question that I have as a consumer is what's in it for me? Right? So Ben, tell us about the changes in retail because certainly online buying mobile is happening. But what are some of the new experiences that end users and consumers are seeing that are becoming new expectations? What's the big trend in retail? >> Well there's two paths they're your expectations as a consumer, then there's the retailer path and how they can meet your expectations. So let's talk about you first. So what you always want is a great customer experience. That's what you want. And what defines that is are they serving me the products I want when I want them? Are they delivering them on time? Do the products work? If I have a problem, how am I treated? How am I served? And these are all the things that we address with the Salesforce solutions. Now let's talk about the retailer. What's important to the retailer is next retailer myself. It was important that I understood what is my right assortment? And that's hard because you have a broad audience of consumers, you have regional or local requirements. So you want to understand what's the right assortment and working with IBM with their (mumbles) optimizer that helps us out in terms how we promote through our engagement later. That's number one. Number two, how about managing markdowns. This year there were over $300 billion in markdown through retailers. Half of those markdowns 150 billion were unplanned markdowns and that goes right to your P&L. So we want to make sure that the things we do satisfy the consumer but not at the expense of the retailer. The retailer has to succeed. So by using IBM supply chain data information we can properly service you. >> It's interesting we see the trend in retail I mean financial services for early on. >> Yeah. >> High-frequency trading, use of data. That kind of mindset is coming to retail where if you're not a data driven or data architecturally thinking about it. >> Yeah. >> The profit will drop. >> Yeah. >> Unplanned markdowns and other things and inventory variety of things. This is a critical new way to really reimagine retail. >> Yeah retail has become such a ubiquitous term there's retail banking, there's retail in every parts of our life. It's not just the store or online but it's retail everywhere and someone is selling their services to you. So I think the holy grail is really understanding you specifically. And it's not just about historical transact which you bought but behavioral data. What interests you. What are the trends and data has become a much broader term. It's just not numbers. Data is what are your trends? What are you saying on social media? What are you tweeting out? What are you reading.? What videos are you viewing? All that together really gives a retailer information to better serve you. So data is really become exponential in it's use and in it's form. >> So I'm curious what you guys see this retails it's very robust retail use case as driving in the future. We just heard yesterday one of the announcements Watson anywhere. I'm curious leveraging retail as an example and the consumerization of almost any industry because we expect to have things so readily and as you both point out data is commerce. Where do you think this will go from here with Watson Einstein and some of the other technologies? What's the next prime industry that really can benefit from what you're doing in retail? >> I think that I'll start and probably you can add that in as well. But I think that it's going to bleed into everything. So health and life sciences, consumer goods, product goods. We've talked about retail being all different kinds of things right now. Well CPG organizations are actually looking at ways to engage the customer directly and so having access utilizing Watson as a way of engaging and activating data to create insights that you've never thought of before. And so being able to stay a step ahead anticipate the needs stay on the bleeding edge of that interaction so that you're engaging customers in a whole new way is what we see and it's going to be proliferated into all kinds of different industries. >> Yes, every merchant every buyer wants to be able to predict. I mean won't that be incredible be able to see around the corner a bit and and while technologies don't give you the entire answer they can sure get you along the way to make better decisions. And I think with Watson and Einstein it does exactly that. It allows you to really predict what the customers want and that's very powerful. >> I want to get you guys perspective on some trend that we're seeing. We hear Ginni Rometty talk about chapter two of the cloud, you almost say there's a chapter two in retail, if you look at the certainly progressive way out front, doing all the new things. People doing the basics, getting an online presence, doing some basic things with mobile kind of setting the table a foundations, but they stare at the data problem. They almost like so it's a big problem. I know all this systems of record. How do I integrate it all in? So take us through a use case of how someone would attack that problem. Talking about an example a customer or a situation or use case that says okay guys help me. I'm staring at this data problem, I got the foundation set, I want to be a retail have to be efficient and innovative in retail, what do I do? Do I call IBM up, do I call Salesforce? How does that work? Take us through an example. >> So I think the first example that comes to mind is I think about Sally Beauty and how they're trying to approach the market and looking at who they are and many retailers right now because there's such a desire to understand data. Make sure that your cap. Everyone has enough data. But what is the right data to activate and use in that experience. So they came to us to kind of look at are we in the right space because right now everyone's trying to be everything to all people. So how do I pick the right place that I should be and am I in the right place with hair care and hair color? And the answer came back yes. You are in the right space. You need to just dive deeper into that and make sure that that experience online so they used a lot of information from their research on users to understand who their customers are, what they're expecting. And since they sell haircare product that is professional grade. How do I make sure that the customers are getting using it in the proper way. So they've actually created an entire infused way of deciding what exactly hair color you need and for me as a consumer, am I actually buying the right grade level from me and am I using that appropriately. And that data all came from doing the research because they are about to expand out and add in all kinds of things like (mumbles) where you're going into the makeup area but really helping them stay laser focused on what they need to do in order to be successful. >> Because you guys come and do like an audit engage with them on a professional service level. >> Yes, we went end-to-end >> And the buying SAS AI and then they plug in Salesforce. >> Yes, so they actually already had Salesforce. So they had the commerce solution marketing and service. They were fairly siloed so we go back to that whole conversation around data being held individually but not leveraging that as a unit in order to activate that experience for the consumer. What they have decided as a result of our work with them. So we came in and did a digital strategy. We're been involved as an agency of record to support them and how that entire brand strategy should be from an omnichannel perspective in the store, as well as that digital experience and then they actually just decided to go with IBM (mumbles) and use that as a way of activating from an omnichannel order orchestration standpoint. So all the way through that lifecycle we've been engaging them and supporting them and Watson obviously native to Salesforce's Einstein and they're leveraging that but they will be infusing Watson as part of their experience. >> So another benefit that Sally Beauty and imagine other retailers and other companies and other industries, we get is optimizing the use of Salesforce. It's a very ubiquitous tool but you mentioned, I think you mentioned Ben that in the previous days of many, many, many systems of records. So I imagine for Sally Beauty also not just to be able to deliver that personalized customer experience, track inventory but it's also optimizing their internal workforce productivity. But I'm curious-- >> Yes. >> For an organization of that size. What's the time to impact? They come in you guys do the joint implementation, go to market, the consulting, identify the phases of the project, how quickly did Sally Beauty start to see a positive impact on their business? >> I think they... Well there's immediate benefits, right? Because they are already Salesforce clients and so our team our IBM team was able to come in and infuse some best practices and their current existing site. So they've been able to leverage that and see that benefit through all the way through Black Friday and last holiday season. And now what they're seeing is they're on the verge of launching and relaunching their site in the next month and then implementing (mumbles) is a part of that. So they're still on the path in the journey to that success but they've already seen success based on the support that we've provided them. >> And what are some of the learnings you guys have seen with this? Obviously you got existing accounts. They take advantage of this, what are some of the learnings around this new engagement layer and with the data intelligence around AI? What's the learnings have you guys seen? >> Yeah I think the leading thing that I've learned is the power of personalization. It's incredibly powerful. And a good example is one of my favorite grocers and that's Kroger. If we really understand what Kroger has done, I'll talk about their business a bit. I'll talk about what they've been able to do. If you look at someone's shopper basket there's an amazing amount of things you can learn about that. You can learn if they're trying to be fit if they're on a diet. You can learn if their birthdays coming. You can learn if they just had a baby. You can learn so many different things. So with shopper basket analysis, you can understand exactly what coupons you send them. So when I get coupons digital or in my home they're all exactly what I buy. But to do that for 25-30 million top customers is a very difficult thing to do. So the ability to analyze the data, segment it and personalize it to you is extremely powerful and I think that's something that retailers and CPG organizations how they continue to try to do. We're not all the way there. Were probably 30% there I would say but personalization it's going to drive customer for life. That's what it's going to do and that's a massive learning for us. >> And the other thing too Ginni mentioned it in her keynote is the reasoning around the data. So it's knowing that the interest and around the personas, etc. But it's also those surprises. Knowing kind of in advance, maybe what someone might like given their situation-- >> Anticipating. >> And we were talking about this morning. Actually, we're talking about behavioral data and data has taken a different term. >> Data is again what are you doing online what are you talking about, what did you view. What video did you look at. For organizations that have access to that data tells me so much more about your interest right now today. And it's not just about a product but it's about a lifestyle. And if retails could understand your lifestyle that opens the door to so many products and services. So I think that's really what retailers are really into. >> My final question for you guys both of you get the answer. Answer will be great is what's the biggest thing that is going to happen in retail that people may not see coming that's going to be empowering and changing people's lives? What do you guys see as a trend that's knocking on the door or soon to be here and changing lives and empowering people and making them better in life. >> Yeah, I'll jump in on one real quick and I think it's already started but it's really phenomenon of commerce anywhere. Commerce used to be a very linear thing. You'd see an ad some would reach out to you and you buy something. The commerce now is happening wherever you are. You could be tweeting something on Instagram, you could be walking in an airport. You could be anywhere and you can actually execute a transaction. So I think the distance between media and commerce has totally collapsed. It's become real time and traditional media TV, print and radio is still a big part of media. A big part but there's distance. So I think it's the immediacy of media and a transaction. That's really going to take retailers and CPG customers by surprise. >> It changes the direct-to-consumer equation. >> It changes it. It does. >> And I think I would just build on that to say that people have relationships with their brands and the way that you can extend that in this and commerce anywhere is that people don't necessarily need to know they're in that commerce experience. They're actually having a relationship with that individual brand. They're seen for who they are as an individual not a segment. I don't fall into a segment that I'm kind of like this but I'm actually who I am and they're engaging. So the way that I think we're going to see things go as people thinking at more and more out of the box about how to make it more convenient for me and to not hide that it's a commerce experience but to make that more of an engagement conversation that-- >> People centric not person in a database. >> Exactly. >> That's right. >> Moving away from marketing from segmentation and more to individual conversations. >> Yeah I think you said it Ben it's the power of personalization. >> Power of personalization. >> Katie, Ben thanks so much for joining. >> Thank you. >> Talking about what you guys IBM and Salesforce are doing together and we're excited to see where that continues to go. >> Great. >> Thanks so much. >> Our pleasure, thank you. >> We want to thank you for watching theCUBE live from IBM Think 19 I'm Lisa Martin for John Furrier stick around on Express. We'll be joining us shortly. (upbeat music)
SUMMARY :
Brought to you by IBM and we are on a rainy San Francisco day. Ben it's great to have you on our program. and I think all of the hybrid multi cloud So Katie IBM announced a John and I were chatting. and ways that we together married together So on the retail side And what are people looking and I think when you talk about Salesforce to explain the tactical relationship. and the way it's evolving and Salesforce to create and that is consumers and talks to all the disparate systems. and consumers are seeing that and that goes right to your P&L. see the trend in retail That kind of mindset is coming to retail and other things and and in it's form. and the consumerization and it's going to be proliferated and that's very powerful. kind of setting the table a foundations, and am I in the right place and do like an audit And the buying SAS AI and and how that entire brand strategy that in the previous days of What's the time to impact? in the journey to that success What's the learnings have you guys seen? So the ability to analyze So it's knowing that the interest and data has taken a different term. that opens the door to so that is going to happen and you can actually It changes the It changes it. and the way that you People centric not and more to individual conversations. it's the power of personalization. IBM and Salesforce are doing together We want to thank you
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Peter Coffee, Salesforce | Innovation Master Class 2018
>> From Palo Alto, California, it's theCUBE, covering the Conference Board's Sixth Annual Innovation Master Class. (fast techno music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Innovation Master Collab at Xerox PARC. It's put on by the Conference Board, a relatively small event, but really, a lot of high-caliber individuals giving really great presentations. And we're excited about our next guest, he kicked the whole thing off this morning, and we could go for hours. We won't go for hours, we'll go about 10 minutes. But Peter Coffee, he's the VP of Strategic Research for Salesforce. Been there a long time, but you were a media guy before that for many, many years? So Peter, great to see you. >> It's good to be with you, thanks. >> So, you talk about so many things. So many things in your opening statement, and I have a ton of notes. But let's just jump into it, I think. One of the big things is you know, the future happens faster than we expect it. And we as humans have a really hard time with exponential growth, because it's not built that way. That's the way things move. >> So how do you as a businessperson kind of deal with that reality? Because the issue is you're never going to be ready for when they come. >> Yeah, well, it's not just humans as individuals, but the institutions and processes we've built. If you look at the process of getting a college degree, it's really seriously misaligned with the timeframe of change. By the time you're a senior, half of the subject matter in your field may be new since your freshman year, and conversely four years after you've graduated, perhaps a third of what you were taught will no longer be considered to be current information. Someone at Motorola once said, "a batch process "no matter how much you accelerate it "doesn't become a continuous flow process". You have to rethink what does a continuous flow look like, and that's useful conversation to have getting back to your actual opening question. When we're talking with customers, we say what are your unvoiced assumptions about the manner in which you have succession of technology, succession of product, and so on? Can we try to see what it would look like if that were a continuous process and not a project process? Many of our partners will tell us that their most difficult conversations with their customers are about getting away from a project mentality, a succession of Big Bang changes, into a process in which transformation is a way of life and not a bold initiative that will take a big sigh of relief and congratulate yourself on having transformed. No, dude, you've gotten your running shoes tied now you can begin to run. But now the hard part begins. >> Right, and the sun comes up tomorrow and you start to run again. You talked on big shifts count on new abundance and use horsepower. >> George Gilder's phrase, "errors are punctuated "by a dramatic change from a scarcity "to an abundance" so for example, horsepower or bandwidth or intelligence. >> So now we're coming into the era of massive big data we are asymptotically approaching free compute, free storage, and free networking. So how do you get business leaders to kind of rethink in an era where they have basically infinite resources, and it always goes back, so what would you build then? Because we're heading that way even if we're not there today. >> A Jedi mind trick that I often use with them is to say, let's not talk about the next couple of quarters, I want you to imagine the next Winter Olympics. When they light the torch four years from now I want you to try to visualize the world you're pretty sure you'll be living in four years from now and work backwards from that and say well if we all agree that within four years that's going to get done, well there's some implications about things we should be doing now and some things that we should stop doing now if we know that four years from now, the world is going to look like this. It helps free your mind from the pressures of incremental improvement and meeting next quarterly goals. And instead saying, ya know, that's not going to be a thing in four years and we should stop getting better at doing something that's simply not going to be relevant in that short of a time. >> So hard though, right? Innovators still, I mean, that's the classic conundrum especially if it's something that you have paying customers and you're driving great revenue to, it's hard to face the music that that may not be so important down the path. >> The willingness to acknowledge that someone will disrupt you, so it might as well be you, you might as well disrupt yourself, the conversation was had with IBM back in the days of the IBM PC, that they thought that that might be a quarter of a million machines they would sell, but whatever you do, don't touch the bread and butter of the 3270 terminal business, right? And they did not ultimately succeed in visualizing the impact of what they had done. Ironically, because they didn't think it was that important, they opened all the technology, and so things like Microsoft becoming what it is and the fact that the bios was open and allowed the compatibles industry like Compact to emerge was a side effect of IBM failing to realize how big of a door they were opening for the world. You can start off a spinoff operation. At Salesforce we have a product line called Essentials which is specifically tasked with create versions of Salesforce that are packaged and priced and supported in a way that's suitable to that small business. And that way you can kind of uncouple from that Clayton Christensen innovators dilemma thing by acknowledging it's a separate piece of the business, it can be measured differently, rewarded differently, and it's going to convey itself maybe even through a genuinely different brand. This is an example that was used once with Disney which when it decided it wanted to get away from family and children's entertainment, and start making movies aimed at more adult audiences, fine, they created the Touchstone brand so they could do that without getting in the way of, or maybe even polluting, a brand that they spent so much time building. So branding is important. A brand is a set of promises, and if you want to make different promises to different people, have a different brand. >> Right, so I'm shifting gears 'cause you touched on so many great things. A really popular thing that's going on now is the conversion of products to services. And repackaging your product as a service. And you talked about the don't taze me bro story which has so many elements of fun and interesting but I thought the best part of it, though, was now they took it to the next step. And we're only a stones throw away from Tesla, a lot of innovation but I think one of the most kind of not reported on benefits of these connected devices and a feedback loop back to the manufacturer is how people are actually using these things, checking in from home, being able to do these updates. And you talk about how the TASER company now is doing all the services, it's not even a service, it's a process. I thought it's awesome. >> Taking a product and selling it at a subscription price does not turn it into a service, even though some people will say, well see now we're moving to a services model. If you're still delivering a product in a lumpy, change-it-every-couple-of-years way, you haven't really achieved that transformation. So you have to go back into more of a sense of I mean, look at the expectation people have of the apps on their smartphones, that they just get better all the time, that the update process is low-burden, low-complexity, low-risk, and you have to achieve that same fluidity of continuous improvement. So that's one of the differences. You can't just take the thing you sell, bill for it on a monthly subscription, and think that you achieved that transition. The thing that they folks who were once TASER and now are Axon, of which TASER is a sub-brand, they managed to elevate their view from the device in a police officer's hand to a process of which that device is a part. Which is the incident that begins, is concluded, results in a report, maybe results in a criminal prosecution, and they broadened the scope of the Axon services package to the point that now it is selling the proposition of increased peace officer productivity rather than merely the piece of hardware that's part of that. So being able to zoom out and really see the environment in which your product is used, and this relates to yet another idea which is that people are saying you got to think outside your box. It doesn't help if you get outside your box, but all of the people with whom you might want to collaborate are all still inside their boxes. And so you may actually have to invest in the transformation and interface development of partners or maybe even competitors, and isn't that a wild idea. Elon Musk at Tesla open sourced a lot of their technology with the specific goal of growing that whole ecosystem of charging stations and other things so Tesla could be a great success. And the comment that I once made is it doesn't help if you're a perfect drop of artisanal oil in a world of water. You have to make the world capable of interacting with you and supporting you if you really want to grow. Or else you're an oddity, you're Betamax, which might have been technically superior but by failing to really build the ecosystem around it, wound up losing big time to VHS for a while. I may have to explain to all of your viewers under the age of 30 what VHS and Betamax even mean. >> I was sellin' those, I could tell you the whole Panasonic factory optimization story, which is whole 'nother piece of that puzzle. So that's good, so I'm going to shift gears again. >> You have to look a big perspective, you have to be prepared to forget that your excellence is your product, and start thinking of that as just the kernel of what needs to be your real proposition which is the need you meet, the pain you address, the process of which you become an inseparable part instead of a substitutable chunk of hardware. >> Well and I think too it's embracing the ongoing relationship as part of the process, versus selling something to your distribution and off it goes you cash the check and you build another one. >> Well that's another aspect, we've got whole industries where there's been a waterfall model. Automobiles were a particular example. Where manufacturers wholesaled cars to distributors who gave them the small markup to dealers who owned the buyer customer. And dealers would be very hostile to manufacturers trying to get involved in that relationship. But now because of the connected vehicles the manufacturer may know things about the manner of use of the vehicle and about the preliminary engagement of the prospective buyer with the manufacturers website. And so improving that relationship from a futile model, or a waterfall model, into a collaborative model is really necessary if all these great digital aspects are to have any value. >> Right, right, right. And as a distribution of information that desire to get a level of knowledge is no longer the case, there's so much more. >> Well it's scary how easy it is to do it wrong. IDC just did a study about the use in retail banking of technology like apps and websites. Which that industry was congratulating itself on adopting in ways that reduce the cost of things like bank office hours. And yet J.D. Power has found that the result is that customers no longer see differentiation among banks, are less loyal, more easily seduced by $50 to open a new bank account with direct deposit. And so innovation's a vector, and if you aim it at cost reduction, you'll get one set of results. And if you aim it at customer satisfaction improvement, you'll innovate differently, and ultimately I think much more successfully. >> Right, right, so we're almost out of time here. I want to go down one more path with you which I love. You talked a lot about visualization, you brought up some old NOPs, really talked about context, right? In the right context, this particular visualization is of value. And there's a lot of conversation about visualization especially with big data. And something I've been looking for, and maybe you've got an answer is, is there a visualization of a billion data point dataset that I can actually look at the visualization and see something, and see the insight. 'Cause most of the ones we see that are examples, they're very beautiful and there's a lot of compound shapes going on, but to actually pinpoint an actionable something out of that array, often times I don't see, I wonder if you have any good examples that you've seen out there where you can actually use visualization to drive insight from a really, really big dataset. >> Well if a big data exercise produces a table of numbers, then someone's going to have to apply an awful lot of understanding to know which numbers look odd. But a billion points, to use your initial question, well what is that? That's an array that's 1,000 by 1,000 by 1,000. We look at 1,000 by 1,000 two-dimensional screens all the time, visualizing a three-dimensional 1,000 by 1,000 cube is something we could do. And if there is use of color, use of motion, superposition of one over another with highlighting of what's changed, what people need most is for their attention to be drawn to what's changing or what's out of a range. And so it's tremendously important that people who are presenting the output of a big data exercise go beyond the high-resolution snapshot, if you will, and construct at least some sense of A B. Back in the ancient days of astronomy, they had a thing called the Blink Camera which would put two pictures side-by-side and simply let you flip back-and-forth between the images, and the human eye turned out to be amazingly good. There could be thousands of stars in that picture, the one dot that's moving and represents some new object, the one dot that suddenly appears, the human brain is very good at doing that. And there's a misperception that the human eye's just a camera. The eye does a lot of pre-processing before it ever sends stuff to the brain. And understanding what human vision does, it impressed the heck out of me the first time I had a consultation on the big data program at a university where the faculty waiting to meet with me turned out to be from the schools of Computer Science, Mathematics, Business, and Visual Arts. And having people with a sense of visual understanding and human perception in the room is going to be that critical link between having data and having understanding of opportunity threat or change. And that's really where it has to go. So if you just ask yourself, how can I add an element of color, or motion, or something else that the human eye and brain have millennia of evolution to get good at detecting, do that. And you will produce something that changes behavior and doesn't just give people facts >> Right, right. Well, Peter, thank you for taking a few minutes. We could go on, and on, and on. >> Happy to do chapters two, three, and four any time you like, yeah. >> We'll do chapter two at the new tower downtown. >> Any old time, thanks so much. >> Thanks for stoppin' by. >> My pleasure. >> He's Peter, I'm Jeff, you're watching theCUBE. We're at the Master Innovation Class at Xerox PARC put on by the Conference Board. Thanks for watching. (fast techno music)
SUMMARY :
it's theCUBE, covering the Conference Board's We are at the Innovation Master Collab at Xerox PARC. One of the big things is you know, Because the issue is you're never the manner in which you have succession Right, and the sun comes up tomorrow "by a dramatic change from a scarcity So how do you get business leaders to kind of couple of quarters, I want you to imagine that that may not be so important down the path. And that way you can kind of uncouple from that is the conversion of products to services. but all of the people with whom you might want to the whole Panasonic factory optimization story, the pain you address, the process and off it goes you cash the check But now because of the connected vehicles is no longer the case, there's so much more. Power has found that the 'Cause most of the ones we see the high-resolution snapshot, if you will, Well, Peter, thank you for taking a few minutes. any time you like, yeah. at Xerox PARC put on by the Conference Board.
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Ken Cavallon, Conga & Greg Gsell, Salesforce | Conga Connect West at Dreamforce 2018
>> Live from San Francisco, it's the Cube covering Conga, Connect West 2018 Brought to you by Conga. >> Hey, welcome back everybody Jeff Frick with the Cube, we're at Salesforce Dreamforce in downtown, San Francisco, 170,000 people. As I said before please take public transit, take a scooter, take a bird, but do not get on the roads. We're excited to be here. We have our first guest from Salesforce. We're so excited. It's Greg Gsell, he's the VP of Product Marketing, Salesforce CPQ and billing. Greg, great to see you. >> Really happy to be here. >> Along with him is Ken Cavallon the EDP of Conga. Great to see you. >> Nice to meet you as well, thanks for having us here. >> Oh, for sure. So first off, Greg, you've been with, said almost your 13th anniversary with Salesforce. >> That's correct, my 13th, been with the company for 12 years. First band I saw was Train and there was about 5000 people at the conference. >> I was going to say, I want to get your perspective. There was 5000 people at the conference. >> Yeah, maybe a little bit more than that, but it was right around there, so it was much smaller. We only had one of the Moscone buildings. We were still growing as fast as we could back then. >> Did they bring this cruise ship in this year? I can't remember. I remember Lynn Vojvodich brought the cruise ship in a couple of years ago for a room. >> The dream boat has not come back. It made one appearance and I have not been back for the conference yet. >> Okay, so a lot of stuff going on, obviously you guys work very close together, but today some big product announcements, over the last couple of days, what if you can kind of run through those for us? >> Yeah, it's been super exciting. So we've been working with Conga for a long time. They've been a great Salesforce partner since 2006, I think. Now we just announced a brand new product Conga quote generation for Salesforce CPQ and Conga invoice generation for Salesforce billing, which is a purpose filled application that allows our CPQ and billing customers to build pixel perfect quotes using Conga right inside a Salesforce CPQ. It's a great product announcement. >> So you've integrated the Conga functionality into the Salesforce application around that specific >> Exactly right. >> Application. >> Exactly right. >> So why did you go that way? Why didn't you just build it yourself? >> We do configure pricing quotes, you're generating a quote and a quote's not good unless it gets signed by a customer. So generating the documents is such an integral part of that process. Conga's one of the leaders so we decided to make this partnership to bring it all together. >> That's great. So Ken, you got to be pretty excited. You got to like that, huh? >> I'm extremely excited about this opportunity, I've been working with Salesforce for the last ten years, in many other capacities as a partner on the outside looking in. This has been an amazing experience, having Salesforce bring a partner to the inside saying help us solve these customers' problems. I mean Salesforce is all about customer success and helping customers be more successful. It was phenomenal to see an ecosystem owner like Salesforce realize that they could use a partner to actually drive more success for their customers. As the leader in document generation, on the Salesforce platform, we help make those pixel perfect format-friendly documents out of the customer's data in Salesforce applying their rules and their templates to their format, the way they want it. The CPQ team, the CPQ and billing team came to us and said as the best in doc gen we want you guys to produce the quotes that come out of our quoting system. The Salesforce CPQ system is amazing. We're also a customer. We use the technology, not just the Salesforce platform, but Salesforce CPQ as well. We know what it's like to actually need to satisfy a customer in getting that sale through the funnel faster. Being able to tie these two technologies together and allow the Salesforce themselves to take this to the customer, they now have one point of contact where they can get all of CPQ in the way they want it. >> It's really interesting as people think about the generation, kind of the mechanics of working through the configuration and all the options, that's a really simple thing to generate a document that somebody can actually sign. Pretty important step that a lot of people don't tie the whole bow back together. >> That's right, that's right. >> So now we've got the best of breed in both solutions coming together and being able to take to market by the Salesforce team. I actually am not really familiar with another opportunity where there's been a partner that can actually support Salesforce in that way. Generally Salesforce takes Salesforce products to market and then to have the us take to market on their price book and in their quotes to their customers is a great privilege. We treat it that way, working with Greg and his team on the product marketing side, with Dan and his team on the technology side, to build a new product on Lightning, as a Lightning component to take it to market. Great experience. >> So Greg, I'm just curious, that's a super development, you've been working on the CPQ and the billing for a while. What are some of the things on your road map, what are some of the priorities that you got as you look forward? >> Sure, on CPQ and billing we just launched billing about three weeks ago, so billing completes the last mile of the sales cycle, so it's where we've really been focused. Billing allows all of our customers to generate invoices to collect payment, to automate their renewals, it really transforms a new business model. Still enabling our customers to take advantage of the subscription based or recurring revenue based business model that we hear so much about in our consumer life. We're really bringing those business models into new companies and enabling them to launch new products. That's where our head's at, we've been really focused on billing, we're really excited to bring that to market here at Dreamforce. >> So I wonder if you can unpack some of the complexity around subscriptions and some of these new kind of business relationships between vendors and customers. Because it's not just the I buy it, get an invoice, and we can finish the transaction, but there's all these new variances. The subscription thing is huge and a growing piece of the economy. >> Subscriptions are nothing new, right, newspaper subscriptions have been around for hundreds of years. So it's not a new concept, but taking that and applying it in a B to B setting is actually is really new because it gets really complex. The devil is in the details here. A traditional back end systems, your ERP, were built to quote a widget, sell a widget, and bill for a widget, then you collect your money and you move on. It's not that recurring relationship. With billing, it was subscription based products and recurring relationships, now midway through that contract, you could upgrade, you could swap out a product, you could renew early. There's so many different variations that you could do and you actually have to go in and amend that contract. In the past, all of our customers had their contract, it's a piece of paper with an actual signature on it, long before Conga Sign, that sat in someone's folder, in a drawer in the basement. It's very, very difficult to actually go back in and amend that contract in your ERP system. So we see lots of challenges with scale, manual processes, manually updating data, that physically prevented companies from moving into this subscription model. But now with Salesforce billing, bill right on the Salesforce platform, we are able to unlock that, enable all these new dramatic changes. >> Then we talked earlier, Ken, with some other people from Conga about the contract management piece of that too that's got to dovetail in with the billing and everything else because the T's and C's depending on what you buy, how much you buy, and when you buy could be very, very different, right? >> It can govern the next sell. As Greg was talking about transforming that configure pricing quote process to modernize business, to allow for these new business models, Conga wraps around Salesforce CPQ and billing to help digially transform the sales business process. Better presentations, built out of data that are customized to a specific customer engagement, better proposals that can lead to the quoting process so that you can make sure that the customer really knows what they're buying and then is able to get a quote. Better set of reports that come afterwards to show that consumption and visualize for the customer and help them understand what to buy next. Then Conga invoice generation for Salesforce billing generates that actual invoice document for them. This entire sales business process digitally transformation journey, a lot of customers are in that journey today and they just really don't know how to do it and they can unlock the power of Salesforce and all that technology they've got with the custom master records so they can move that throughout the entire sales process. That's what Conga's here to do and we're here to do in partnership with Salesforce CPQ and billing. >> Just curious, how much of the push to these types of development to the application are driven by the customer request like hey, we want to do some of these new things, can you please put it in, or is it more, hey, now you have this, classic chicken and egg, now you can start to explore some of these transformative ways of doing business? What do you kind of see in the field, is it more of we want it, or here you have it, now we can do it? >> Different customers are at different points in their journey in that digital transformation. This is the fourth industrial revolution where we're going from where we were in the past of that transactional business where it starts and stops and you have to restart it again to a constant flow of business that they have with their customers. Depending upon where they are in that journey, depends on whether or not they're pulling us along, saying I've got to innovate further, or we have to go explore with them what's possible, the art of possible. I have to give Marc and the Salesforce team a lot of credit. Salesforce over the last 20 years has done such an amazing job at helping business figure out how to unlock that potential that they've got, and this platform has allowed Conga to thrive. Conga was born on the AppExchange a little more than 10 years ago, we've grown with the AppExchange ever since and as you can see from this great event we've got going on here today, we're able to solve a lot of customers' problems. To answer your question directly, it's where they are in that journey depends on whether or not they need a little push, or they're going to pull us. >> Right, so Greg, a little shifting gears. I'm just curious from a product marketing, product development point of view, when you operate with such a robust ecosystem and you're making decisions as to what do we buy, what do we partner, what do we integrate, of the 100,000 plus whatever people here, a whole lot of them are partners. It's a super robust ecosystem, so as you look at that, how do you prioritize or you kind of looking for partners for new things or you looking for them to fill holes, how do you fit that portfolio into what you're trying to build natively in the product? >> Sure I mean it always just comes back to customer success. We listen to our customers and we see what is available out there and we look to partners like Conga and the rest of the three, four thousand plus applications, I think on the AppExchange to make sure that we're filling all of our customers' needs. It's always about what is going to help our customers be the most successful in the fourth industrial revolution like Ken was saying. >> Ken, Greg, congrats on the announcement, on the integration. I'm sure it will have tremendous success for both of you. >> Thank you very much. >> He's Ken, he's Greg, I'm Jeff, you're watching the Cube. We're at Conga Connect West at Dreamforce at the Thirsty Bear, come on down, free food, free drink, and free I think entertainment. Thanks for watching.
SUMMARY :
Brought to you by Conga. It's Greg Gsell, he's the VP of Product Marketing, Great to see you. So first off, Greg, you've been with, at the conference. I was going to say, I want to get your perspective. We only had one of the Moscone buildings. I remember Lynn Vojvodich brought the cruise ship the conference yet. that allows our CPQ and billing customers to build pixel Conga's one of the leaders so we decided to make this So Ken, you got to be pretty excited. and allow the Salesforce themselves to take this to the the generation, kind of the mechanics of working through on their price book and in their quotes to their customers What are some of the things on your of the sales cycle, so it's where we've really been focused. of the economy. bill right on the Salesforce platform, we are able to unlock Better set of reports that come afterwards to show that saying I've got to innovate further, or we have to go of the 100,000 plus whatever people here, We listen to our customers and we see what is available Ken, Greg, congrats on the announcement, We're at Conga Connect West at Dreamforce at the
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Tiffani Bova, Salesforce | CUBEConversation, July 2018
(dramatic music) >> Hi I'm Peter Burris, and welcome to another CUBE Conversation from our wonderful Palo Alto studios in beautiful Palo Alto, California. Another great conversation today, this one's especially interesting to me, we've got Tiffani Bova who's the Global Growth and Innovation Evangelist of Salesforce. Just written a book, great book by the way, Growth IQ, I guess it's coming out later in August of 2018. But Tiffani, I want to talk a little bit about something that seems near and dear to your heart, the notion of customer engagement and how that gets turned into business strategy. So, let's start there. What is, in your two and half years at Saleforce, what have you learned about customer engagement and how actionable is it, really? >> Well, you know, Peter, it's a great question 'cause I'd say this, you know, I thought I knew the answer to that question when I stated two and a half years ago and I've had the wonderful pleasure of spending time with customers of ours around the world and now I have a different perspective on what that is. You know, Clayton Christensen wrote that new book, Competing Against Luck and it was all about, sort of, the job that you have to do, right? You're going to go from point A to point B, are you going to catch a taxi or you going to catch an Uber. And what makes the difference if the job is the same, regardless of what you're doing. In my mind, right, it's the experience or the engagement that that particular driver or brand has with the customer that is riding in the back of the car, satisfying the need for the job that needed to be done. And when I started to shift my thinking around it's really this experience layer and this engagement layer of how easy is it, how friction, you could apply it to all kinds of industries now, you know. Whether it's meal delivery, or buying a book or buy, you know, software from someone like Salesforce or consulting or watching this show. It used to be you had to go and watch it live or you'd have to watch it on television, now we have very different ways and means in which you can be engaged. So that has been super exciting to me to see it live and in person as brands are really focusing on this importance of the way in which they engage with, connect with and inspire customers to do things with them as their brand of choice. >> So, as I said, Tiffani, I like the book and there is three or points that really want to draw out. I want to start with the first one though. >> Okay. >> Let's go back to this notion of engagement. >> Yes. >> You make the observation in the book and I also have some, a background, thinking about customer engagement, customer experience but you make a great point in the book that your brand is the promise that you're make to the marketplace. Customer experience is the customer side of the engagement. >> Right. >> It seems as though if there's a significant miss-match between those two, that's the first indication you've got a problem. If your brand promise and what is being experienced are not aligned, that says something, have I got that right? >> Absolutely and what's fascinating about that is many brands feel like they're totally aligned and then in mass, you know, research from all kinds of people whether it's McKenzie or Bain or Gartner or Forester or anybody else, you're seeing this disconnection where the brand thinks it's great and the customer's going, it's not that great. The gap between those two things, unfortunately, even with all the advancements with technology, I feel like it's getting wider, right? Because their still sort of, brands are still sort pushing out what they think is interesting and engaging and customers are going, it's kind of not so much. And so, this really a way and I really dug into it in Growth IQ of how brands can figure out, how do I get closer to that by starting with the customer and working their way back in. I mean, it's a long discussed topic of outside in versus inside out, it's nothing new there but now we have this advancements of technology that actually allow us to know what that outside in is telling us at scale, without having to throw people at the problem. >> Yeah, through data collection and other types of things. >> Absolutely. >> But it all starts with that impedance miss-match. >> Yes. >> And as you said, if businesses don't accept that they have a problem they're not going to change. But that is a measurable, actionable thing. >> Right. >> So, if nothing else, if nobody reads anything else out of the book, just that simple idea that it's not MPV, it's not, you know, other types of measures, your Net Promoter Score or other types of measures but it's, basically, is there that disconnect. So the second thing is is that you've observed how it can be made actionable. Now, you've come up with 10, a recipe, or let's call them 10 ingredients of different ways of thinking about what you might be able to do from a growth standpoint. Now, rather than going through all of them, let's just say that they're there but the thing that's interesting is you've come up with a general framework for how you can imagine putting those things together. You call it context combination sequence, what does that mean? >> It, I think it's, I, when I decided to embark on this journey of writing this book I said, you know, what do I feel has been missing, or what did I notice as a pattern as I was having conversations since I was traveling around and talking to customers. And it wasn't the decision that they make of how they were going to grow that was interesting, it was actually the fact that it was rarely in isolation. It was never a single answer to a very complex problem, it was a combination of a number of things. So, if you're going to launch a new product, like that's going to be your growth strategy, well, are you going to launch it yourself? Are you going to do it with partners? Are you going to launch it direct to consumer online or you going to go into retail? You have to then combine the fact that you want to launch a new product with other things to help you grow. Or if you you're going to say I want to reduce turn, it's not just, well, I'm going to lower a price because that's going to be a reason for people to stay, it's, well wait a second, are the platforms easy to use? Can people open a ticket easily? It's always in combination. >> Do I have visibility into whom I churn? >> And to whom I churn, right? But the first place people fail to start, let's to back to your original question of this gap between what customers expect and what businesses are doing is the context in the market has significantly shifted over the last decade. You could say, well obvious technology advancements but I think far more disruptive than technology is actually the customers themselves demanding more from brands. I want you to be better to the environment. I want you to be better socially. I want you to give me more value for what I'm spending. I want it as a service not as a product. I want it in a monthly bill not a one-time bill. I want to pay usage. Whatever they're saying, the customer has changed the context of the market. And I think that's one of the big triggers in this, so you start with context, what's going on, next is what are you going to combine those efforts with. And then the third thing and equally import is sequence. The order in which you do things actually has implications to the likelihood of success of whatever it is you're doing. If you're going to launch into a new market with a new product, and you don't have the infrastructure for distribution or selling or service in place before you launch the product, probably the wrong order. >> Right. >> Right and so if you need to set up the partnerships and the distribution and support and sales and marketing, support within region or translate things to language or do the things that you need to do to marketing materials or websites before you get there because if you launch, the first impression is gone if it's not a good experience for the customer. >> Yeah, you only have one time to make a first impression. >> You only have one time and it doesn't need to be perfect, but you cannot be just completely off the mark because getting them to come back is more expensive than it would of been had you just taken a pause, gotten it right and launched at the appropriate time. >> And that notion of context is also especially important because you identify something you call timing which is related to sequence in the fact that you have to be very honest about what you can and cannot effect. There are some things you may want to sequencing, you may want to fall the sequence. >> Right. >> But if the market isn't going to respond favorably, tell us a little bit about timing and how context shapes and resets prioritization as it changes as well. >> Yeah so, if you think of somebody like a Netflix, if Netflix had started with streaming and not with DVD mail, you know, in the United States at least, not everybody had bandwidth, it was too expensive, it was in very specific neighborhoods and as bandwidth started to make its way into the households and the cost started to decline, then they could say, well, wait a second, is this the best way to do it or could we potentially stream it and start doing OTT types of services? But they had to wait for the technology as well as the customer to catch up with what was possible. So, had they not done mail and started with streaming, maybe they couldn't of held on long enough. And so, mail was a great way to do, I'm going to capture these customers, I'm going to penetrate this base, get them to order more movies and do more things with me. Now I'm going to introduce streaming. Now I have this base of customers which now may want to transition to a new kind of delivery or experience that they want to have with us. And you might be surprised that they still have hundreds of thousands of mail customer including my mom, she still gets DVDs in the mail. And it's a huge profit engine for them, actually allows them to reinvest in the business to expand the streaming services other places in the world which may never get mail service, right. But in the beachhead of it and just let the customers churn out, never getting rid of it, not marketing it but not getting rid of it. So, had those timings been offered different, they may not have been as successful. So, it really has implications to think about what is the customer looking for, what is the temperature socially, what can technology help me deliver? Putting those things together and going, knowing what I know, I don't need it to be perfect but I'm willing to test it and fail and iterate and keep going as long as I keep that context of the market in mind and then the customer, you know, as sort of my true north of making sure that I'm aligning those things again, like we were talking about. >> So let me see if I can summarize that, so you got to get the context, which is-- >> Yes. >> What's really in the marketplace, customer, regulatory, competitor, all those other thing we think about. >> All those things. >> You think about the combination of recipes or combination of responses and then how you're going to sequence them out. Then that sequencing decision then goes back and says and what do I need to redefine about my understanding of context. >> That's right, that's right. >> So I got that right? >> You've got that right and I would tell you that-- >> So your avoiding boiling the ocean. >> Yeah, and that's what always, sort of, when I was trying to figure out what did I want to say in this book. I did not want it to be a boil of the ocean. I picked 10 paths to growth, none of which I think will be a surprise to anybody. It's a modernization on what Ansoff had done around the Ansoff four, there's that. There are things that now we have at our disposal which we didn't have at our disposal in 1957 when Ansoff came out. >> Yeah. >> I mean, so, you have to obviously introduce new things. So like, just using something like socially conscious enterprise was not something we were talking about 10 years ago. >> Right. >> But it's being used as a growth path now. And so, I wanted to try to give 10 very distinct growth paths so that people didn't feel overwhelmed by the hundreds of choices they could make. So if I could get it to something that was digestible and then say now, how do I put those things together. So I made natural associations between paths so that people would say, oh, if I'm going to do product and customer diversification, I might need to do partnerships. If I have a churn problem, I may need to optimize sales. Those two things fit together, right? If I'm going to, customer experience is at the foundation of everything. >> Right. >> Right, and so I tried to tell the story that people could say, oh, we're already on this path, should be stay on this path? Is it the right path? Should we be moving? Am I doing everything I could be doing to make this path be more effective? And that's what I was hoping to get out this is that I don't want people to think this is something completely flip the chessboard over and start from scratch. >> Right. >> I want you to pivot ever so slowly and make adjustments in real time so that you're not having to do, this is kind of an evolutionary versus revolutionary kind of transformation. >> Yeah, the strategies that seem to work today, or feature three things and kind of comes from Cluetrain Manifesto, agile, the empirical, they're based on data, they're optimistic, they identify what really can be done and their irritative, they take smaller steps when they do that. >> Yes. >> So, let me return back to kind of the notion of engagement, just for a second. >> Sure. >> One of the reasons why this book has so much prescriptive power is because there is a dramatic shift globally in market power. It used to be the sellers had the market power and therefore the information at your disposal that you used to make a decision largely came from the sellers and now, you're able to move into communities where buyers can come together and identify themselves in each other and use that source of information to help you make decisions. Very, very significant and profound shift and that's in many respects what's driving experience. Historically though, we've talked about sales and marketing alignment. (Tiffani laughing) About how we got to get the right message out, we got to enable sales in the right way. But customers spend most of their time with a brand in the form of a product or service which suggests that he whole notion of customer service and sustaining alignment between expectations and actuality in the customer service function becomes especially important. Have I got that right? >> You nailed it, I mean, I would say also you know, and I'm actually a practitioner before I was at Gartner, so I actually ran a division of Gateway computers, I ran sales and marketing for them. Before that I worked in web hosting company, we were the largest web hoster in the United States, we were actually four times the size of Rackspace. I was the beta client for ALOQUA, I was the beta client for Constant Contact. I was socially selling in 2000. Our shared property is web.com, if you watch golf. And so I was super early, so I'm actually a sales, I sort of say I'm a recovering seller, I bleed sales blood. (Peter laughing) And so when I was running both sales and marketing, I could argue with myself. But when I was just selling, you know, I understand this, you know, marketing giving us leads, sales not doing something with it. Then when I had customer service, you know, those three things together, I think today, is where companies really miss an opportunity. That just getting new customers in the door and it's so much more expensive to recruit new customers and to pay to get them versus just mining the gold you've already got. So, that is something that I'd say over the last two and a half years now that I'm here and I see it at scale that I will have conversations with CMOs and heads of sales and then the head of customer service is not in the room or it's just marketing and sales. So the same way marketing enables sales, they need to enable customer service. >> And, very importantly, the information that is being generated out of customer service-- >> Absolutely. >> Need to enable sales and marketing. >> Absolutely-- >> And in products. >> So I would tell you that in my opinion, the disconnection between what customers expect and businesses are doing actually is a manifestation of the unintentional consequence of the disconnection of teams because of the disconnection of metrics. Sales is very much, like how much did you sell? I mean, I'm over simplifying but how much did you sell. Marketing, how many leads did you, good or bad, how many leads? Customer service, how quickly did you get someone off the phone and how many calls did, tickets did you close today? Those three things pull those three groups in very different directions. So, something like a Net Promoter Score or churn or lifetime values, something can thread those three groups together in a metric so that people know that they're all in this together even though they play different roles. And so, I think the fact that people try to own customer experience worries me because I think the whole company has to be very focused on... >> It's a CEO job. >> But then it's a cultural shift, right? >> Absolutely. >> It's about culture, it's about this customer wants to have me help their problem but I have to get off the phone in two minutes because that's my quota and so do I get off the phone in two minutes or do I help my customer? >> Okay, let me make one quick comment and I'm going to ask you one last question. >> Yeah. >> And the quick comment I'll make is very prominent CEO of a very, very large computing company once said to me, I asked this person, 'cause I thought that they had won large because of marketing. And I said, so, tell me about he role of marketing within your company. And this person said to me, oh, marketing is what I put between engineering and sales so they don't kill each other. (Tiffani laughing) And I think that needs, obviously that orientation needs to change. But the last thing I wanted to talk about is one of the patterns you noted is disruption or disruptive-- >> Yes. >> I don't remember exactly what you called it but it boiled down, it could mean a lot of things, but you specifically focused on and you've already mentioned it, social good. >> Yep. >> As part of a strategy, give us a, you know 30 second, 44 second, why is social good becoming a viable strategy or viable pattern, one of the combinations that's working today? >> Well, I'd say, Salesforce was founded on the 1-1-1 model, which was very much about sort of this doing well by doing good, or doing good by doing good. But I would say this that even if you've watched television commercials over the last year, especially since Super Bowl last year, you'll see brands actually making statements about how they do well for the environment, how they're giving back, how they're hiring veterans, how they're doing things for... You know, Starbucks just announced they're going to- >> How they're willing to fly immigrant children home if they need to. >> Yeah, Starbucks is not doing a Starbucks in DC that will be signing so for hearing impaired. So you see people really making pivots and actually using that as I'm trying to connect with my constituency and my customer base in a new and different way. I love the fact that social consciousness is now getting into Unilever and getting into, you know the 1-1-1 model spread across 3,000 companies now. Or the Tom's model, one for one, buy a shoe, a shoe gets donated. You see it happening with a lot of start ups now where they're trying to start the company that way. Now, if you have a company that didn't start that way, there's not reason why you can't start to find a place where you can inject it going forward. But I'm super excited about that. >> Tiffani Bova, Global Growth Innovation Evangelist Salesforce, talking about the book Growth IQ. Again, great book. >> Thank you. >> Very prescriptive and I mean, I generally hate business books, lot of case studies. Thanks very much for being on theCUBE. >> Thank you for having me, peter, it's been a pleasure. >> Absolutely, so once again, thanks for participating in our CUBE Conversation and until the next one, we'll see you soon. (dramatic music)
SUMMARY :
what have you learned about customer engagement sort of, the job that you have to do, right? and there is three or points that really want to draw out. but you make a great point in the book are not aligned, that says something, have I got that right? and then in mass, you know, research from that they have a problem they're not going to change. what you might be able to do from a growth standpoint. I said, you know, what do I feel has been missing, I want you to be better to the environment. or do the things that you need to do but you cannot be just completely off the mark you have to be very honest about what you can But if the market isn't going to respond favorably, and not with DVD mail, you know, What's really in the marketplace, and what do I need to redefine Yeah, and that's what always, sort of, I mean, so, you have to obviously introduce new things. So if I could get it to something that was digestible Is it the right path? I want you to pivot ever so slowly Yeah, the strategies that seem to work today, So, let me return back to kind of to help you make decisions. and it's so much more expensive to recruit new customers I mean, I'm over simplifying but how much did you sell. and I'm going to ask you one last question. is one of the patterns you noted is disruption I don't remember exactly what you called it television commercials over the last year, if they need to. there's not reason why you can't start to find a place talking about the book Growth IQ. I generally hate business books, and until the next one, we'll see you soon.
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Ken Byrnes, Dell Technologies & David Trigg, Dell Technologies | MWC Barcelona 2023
>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. >> All right, welcome back to the Fira in Barcelona. This is Dave Vellante with Dave Nicholson. Day 4 of coverage MWC 23. We've been talking all week about the disaggregation of the telco networks, how telcos need to increase revenue how they're not going to let the over the top providers do it again. They want to charge Netflix, right? And Netflix is punching back. There maybe are better ways to do revenue acceleration. We're going to talk to that topic with Dave Trigg who's the Global Vice President of Telecom systems business at Dell Technologies. And Ken Burns, who's a global telecom partner, sales lead. Guys, good to see you. >> Good to see you. Great to be here. >> Dave, you heard my, you're welcome. You heard my intro. It's got to be better ways to, for the telcos to make money. How can they accelerate revenue beyond taxing Netflix? >> Yeah, well, well first of all, sort of the promise of 5G, and a lot of people talk about 5G as the enterprise G. Right? So the promise of 5G is to really help drive revenue enterprise use cases. And so, it's sort of the promise of the next generation of technology, but it's not easy to figure out how we monetize that. And so we think Dell has a pretty significant role to play. It's a CEO conversation for every telco and how they accelerate. And so it's an area we're investing heavily into three different areas for telcos. One is the IT space. Dell's done that forever. 90% of the companies leaning in on that. The other places network, network's more about cost takeout. And the third area where we're investing in is working with what we call their line of businesses, but it's really their business units, right? How can we sit down with them and really understand what services do they take to market? Where do they go? So, we're making significant investments. So one way they can do it is working with Dell and and we're making big investments 'cause in most Geos we have a fairly significant sales force. We've brought in an industry leader to help us put it together. And we're getting very focused on this space and, you know, looking forward to talking more about it. >> So Ken, you know, the space inside and out, we just had at AT&T on... >> Dave Trigg: Yep. >> And they were saying we have to be hypersensitive because of our platinum brand to the use of personal information. >> Ken: Yeah. >> So we're not going to go there yet. We're not going to go directly monetize, but yet I'm thinking well, Netflix knows what I'm watching and they're making recommendations and they're, and and that's how they make money. And so the, the telcos are, are shy about doing that for right reasons, but they want to make better offers. They want to put, put forth better bundles. You know, they don't, they don't want to spend all their time trying to figure that out and not being able to change when they need to change. So, so what is the answer? If they're not going to go toward that direct monetization of data? >> Ken: Yeah. >> How do they get there? >> So I, I joined Dell in- at the end of June and brought on, as David said, to, to build and lead this what we call the line of business strategy, right? And ultimately what it is is tying together Dell technology solutions and the best of breed of what the telecoms bring to bear to solve the business outcomes of our joint customers. And there's a few jewels inside of Dell. One of it is that we have 35,000 sellers out there all touching enterprise business customers. And we have a really good understanding of what those customer needs are and you know what their outcomes needs to be. The other jewel is we have a really good understanding of how to solve those business outcomes. Dell is an open company. We work with thousands of integrators, and we have a really good insight in terms of how to solve those business outcomes, right? And so in my conversations with the telecom companies when you talk about, you know combining the best assets of Dell with their capabilities and we're all talking to the same customers, right? And if we're giving them the same story on these solutions solving business outcomes it's a beautiful thing. It's a time to market. >> What's an example of a, of a, of a situation where you'll partner with telcos that's going to drive revenue for, for both of you and value for the customer? >> Yeah, great question. So we've been laser focused on four key areas, cyber, well, let me start off with connected laptops, cyber, private mobility, and edge. Right? Now, the last two are a little bit squishy, but I'll I'll get to that in a bit, right? Because ultimately I feel like with this 5G market, we could actually make the market. And the way that we've been positioning this is almost, almost on a journey for IOT. When we talk about laptops, right? Dell is the, is the number one company in the world to sell business laptops. Well, if we start selling connected laptops the telcos are starting to say, well, you know what? If all of those laptops get connected to my network, that's a ton of 5G activations, right? We have the used cases on why having a connected workforce makes sense, right? So we're sharing that with the telcos to not simply sell a laptop, but to sell the company on why it makes sense to have that connected workforce. >> Dave Vellante: Why does it make sense? It could change the end customer. >> Ken: Yeah. So, you know, I'm probably not the best to answer that one right? But, but ultimately, you know Dell is selling millions and millions of laptops out there. And, and again, the Verizon's, the AT&T's, the T-mobile's, they're seeing the opportunity that, you know, connecting those laptops, give those the 5G activations right? But Dave, you know, the way that we've been positioning this is it's not simply a laptop could be really a Trojan horse into this IOT journey. Because ultimately, if you sell a thousand laptops to an enterprise company and you're connecting a thousand of their employees, you're connecting people, right? And we can give the analytics around that, what they're using it for, you know, making sure that the security, the bios, all of that is up to date. So now that you're connecting their people you could open up the conversation to why don't we we connect your place and, you know, allowing the telecom companies to come in and educate customers and the Dell sales force on why a private 5G mobility network makes sense to connecting places. That's a great opportunity. When you connect the place, the next part of that journey is connecting things in that place. Robotics, sensors, et cetera, right? And, and so really, so we're on the journey of people, places, things. >> So they got the cyber angle angle in there, Dave. That, that's clear benefit. If you, you know, if you got all these bespoke laptops and they're all at different levels you're going to get, you know, you're going to get hacked anyway. >> Ken: That's right. >> You're going to get hacked worse. >> Yeah. I'm curious, as you go to market, do you see significant differences? You don't have to name any names, but I imagine that there are behemoths that could be laggards because essentially they feel like they're the toll booth and all they have to do is collect, keep collecting the tolls. Whereas some of the smaller, more nimble, more agile entities that you might deal with might be more receptive to this message. That seems to be the sort of way the circle of life are. Are you seeing that? Are you seeing the big ones? Are you seeing the, you know, the aircraft carriers realizing that we got to turn into the wind guys and if we don't start turning into the wind now we're going to be in trouble. >> So this conference has been absolutely fantastic allowing us to speak with, you know, probably 30 plus telecom operators around this strategy, right? And all of the big guys, they've invested hundreds of billions of dollars in their 5G network and they haven't really seen the ROI. So when we're coming into them with a story about how Dell can help monetize their 5G network I got to tell you they're pretty excited >> Dave Nicholson: So they're receptive? >> Oh my God. They are very receptive >> So that's the big question, right? I mean is, who's, is anybody ever going to make any money off of 5G? And Ken, you were saying that private mobility and edge are a little fuzzy but I think from a strategy standpoint I mean that is a potential gold mine. >> Yeah, but it, for, for lot of the telcos and most telcos it's a pretty significant shift in mentality, right? Cause they are used to selling sim cards to some degree and how many sim cards are they selling and how many, what other used cases? And really to get to the point where they understand the use case, 'cause to get into the enterprise to really get into what can they do to help power a enterprise business more wholly. They've got to understand the use case. They got to understand the more complete solution. You know, Dell's been doing that for years. And that's where we can bring our Salesforce, our capabilities, our understanding of the customer. 'cause even your original question around AT&T and trying to understand the data, that's just really a how do you get better understanding of your customer, right? >> Right. Absolutely. >> And, and combined we're better together 'cause we bring a more complete picture of understanding our customers and then how can we help them understand what the edge is. Cause nobody's ever bought an Edge, right? They're buying an Edge to get a business outcome. You know, back in the day, nobody ever bought a data lake, right? Like, you know, they're buying an outcome. They want to use, use that data lake or they want to use the edge to deliver something. They want to use 5G. And 5G has very real capabilities. It's got intrinsic security, which, you know a lot of the wifi doesn't. It's got guaranteed on time, you know, for areas where you can't lose connectivity: autonomous vehicles, et cetera. So it's got very real capabilities that helps deliver that outcome. But you got to be able to translate that into the en- enterprise language to help them solve a problem. And that's where we think we need the help of the telcos. I think the telcos we can help them as well and, and really go drive that outcome. >> So Dell's bringing its go to market expertise and its technology. The telcos obviously have the the connectivity piece and what they do. There's no overlap in terms of the... >> Yeah. >> The, the equipment and the software that you're selling. I mean, they're going to, they're going to take your equipment and create new networks. Beautiful. And, and it's interesting you, like, you think about how Dell has transformed prior to EMC, Dell was, you know, PC maker with a subpar enterprise business, right? Kind of a wannabe enterprise business. Sorry Dell, it's the truth. And then EMC was largely, you know, a company sold storage boxes, but you owned VMware and then brought those two together. Now all of a sudden you had Dell powerhouse leader and Michael Dell, you had VMware incredibly strategic and important and it got EMC with amazing go to market. All of a sudden this Dell, Dell technologies became incredibly attractive to CIOs, C-level executives, board level. And you've come out of that transition VMware's now a separate company, right? And now, but now you have these relationships and you got the shops to be able to go into these edge locations at companies And actually go partner with the telcos. And you got a very compelling value proposition. >> Well, it's been interesting as in, in this show, again most telcos think of Dell as a server provider, you know? Important, but not overly strategic in their journey. But as we've started to invest in this business we've started to invest in things like automation. We've brought together things in our Infra Blocks and then we help them develop revenue. We're not only helping 'em take costs out of their network we're not helping 'em take risk out of deploying that network. We're helping them accelerate the deployment of that network. And then we're helping 'em drive revenue. We are having, you know, they're starting to see us in a new light. Not done yet, but, you know, you can start to see, one, how they're looking at Dell and two, and then how we can go to market. And you know, a big part of that is helping 'em drive and generate revenue. >> Yeah. Well, as, as a, as a former EMC person myself, >> Yeah? >> I will assert that that strategic DNA was injected into Dell by the acquisition of, of EMC. And I'm sticking... >> I won't say that. Okay I'll believe you on that. >> I'm sticking with the story. And it makes sense when you think about moving up market, that's the natural thing. What's, what's what's nearly impossible is to say, we sell semi-trucks but we want to get into the personal pickup truck market. That's that, that doesn't work. Going the other way works. >> Dave Trigg: Yeah. >> Now, now back to the conversation that you had with, with, with AT&T. I'm not buying this whole, no offense to AT&T, but I'm not buying this whole story that, you know, oh we're concerned about our branded customer data. That sounds like someone who's a little bit too comfortable with their existing revenue stream. If I'm out there, I want to be out partnering with folks who are truly aggressive about, about coming up with the next cool thing. You guys are talking about being connected in a laptop. Someone would say, well I got wifi. No, no, no. I'm thinking I want to sim in my laptop cause I don't want to screw around with wifi. Okay, fine. If I know I'm going to be somewhere with excellent wifi connectivity, great. But most of the time it's not excellent. >> That's right. >> So the idea that I could maybe hit F2 and have it switch over to my sim and know that anywhere that I've got coverage, I have high speed connections. Just the convenience of that. >> Ken: Absolutely. >> I'd pay extra for that as an end user consumer. >> Absolutely. >> And I pay for the service. >> Like I tell you, if it interests AT&T I think it's more not, they ask, they're comfortable. They don't know how to monetize that data. Now, of course, AT&T has a media >> Dave Nicholson: Business necessity is the mother of invention. If they don't see the necessity then they're not going to think about it. >> It's a mentality shift. Yes, but, but when you start talking about private mobility and edge, there's there's no concern about personal information there. You're going in with basically a business transformation. Hey, your, your business is, is not, not digital. It's not automated. Now we're going to automate that and digitize that. It's like the, the Dell booth with the beer guys. >> Right. >> You saw that, right? >> I mean that's, I mean that's a simple application. Yeah, a perfect example of how you network and use this technology. >> I mean, how many non-digital businesses are that that need to go digital? >> Dave Nicholson: Like, hundred percent of them. >> Everyone. >> Dave Nicholson: Pretty much. >> Yeah. And this, and this jewel that we have inside of Dell our global industries group, right, where we're investing really heavily in terms of what is the manufacturing industry looking for retail, finance, et cetera. So we have a CTO that came in, that it would be the CTO of manufacturing that gives us a really good opportunity to go to at AT&T or to Verizon or any telco out there, right? To, to say, these are the outcomes. There's Dell technology already in place. How do we connect it to your network? How do we leverage your assets, your manager professional services to provide a richer experience? So it's, there's, you said before Dave, there's really no overlap between Dell and, and our telecom partners. >> You guys making some serious investments here. I mean I, I've been, I was been critical over the years of, hey, you can't just take an X86 block, put a name on it that says edge something and throw it over the fence because that's what you were doing. >> Dave Trigg: And we would agree. >> Yeah. Right. But, of course, but that's all you had at the time. And so you put some... >> We may not have agreed then, but we would agree. >> You bought, brought some people in, you know, like Ken, who really know the business. You brought people into the technical side and you can really see it happening. It's not going to happen overnight. You know, I mean, you know if I were an investor in Dell, I'd be like, okay when are you going to start making money at this business? I'd be like, be patient. You know, it's going to take some time but look at the TAM. >> Yep. >> You know, you guys do a good, good TAM. Tennis is a pro at this stuff. >> We've been at, we've been at this two, three years and we're just now coming with some real material products. You've seen our server line really start to get more purpose-built, really start to get in there as we've started to put out some software that allows for quicker automation, quicker deployments. We have some telcos that are using it to deploy at 10,000 locations. They're literally turning up thousands of locations a week. And so yeah, we're starting to put out some real capability. Got a long way to go. A lot of exciting things on the roadmap. But to your point, it doesn't, you know the ship doesn't turn overnight, you know. >> It could be a really meaningful portion of Dell's business. I'm, I'm excited for the day that Tom Sweet starts reporting on it. Here's our telco business. Yeah. The telco business. But that's not going to happen overnight. But you know, Dell's pretty good at things like ROI. And so you guys do a lot of planning a lot of TAM analysis, a lot of technical analysis, bringing the ecosystem together. That's what this business needs. I, I just don't, it's, it feels unstoppable. You know, you're at this show everybody recognizes the need to open up. Some telcos are moving faster than others. The ones that move faster are going to disrupt. They're going to probably make some mistakes, you know but they're going to get there first. >> Well we've, we've seen the disruptors are making some mistakes and are kind of re- they're already at the phase where they're reevaluating, you know, their approach. Which is great. You know, you, you learn and adjust. You know, you run into a wall, you, you make a turn. And the interesting thing, one of the biggest learnings I've taken out of the show is talking to a bunch of the telcos that are a little bit more of the laggards. They're like, Nope, we, we don't believe in open. We don't think we can do it. We don't have the skillset. They're maybe in a geo that it's hard to find the skillset. As they've been talking to us, and we've been talking about, there's almost a glimmer of hope. They're not convinced yet, but they're like, well wait, maybe we can do this. Maybe open, you know, does give us choice. Maybe it can help us accelerate revenue. So it's been interesting to see a little bit of the, just a little bit, but a little bit of that shift. >> We all remember at 2010, 2011, you talked to banks and financial services companies about, the heck, the Cloud is happening, the Cloud's going to take over the world. We're never going to go into the Cloud. Now they're the biggest, you know Capital One's launching Cloud businesses, Western Union, I mean, they're all in the cloud, right? I mean, it's the same thing's going to happen here. Might, it might take a different pattern. Maybe it takes a little longer, but it's, it's it's a fate are completely >> I was in high school then, so I don't remember all that. >> Sorry, Dave. >> Wow, that was a low blow, like you know? >> But, but the, but the one thing that is for sure there's money to be made convincing people to get off of the backs of the dinosaurs they're riding. >> Dave Vellante: That's right. >> And also, the other thing that's a certainty is that it's not easy. And because it's not easy, there's opportunity there. So I know, I know it's, it, it, it, it, it all sounds great to talk about the the wonderful vision of the future, but I know how hard the the road is that you have to go down to get people, especially if you're comfortable with the revenue stream, if you're comfortable running the plumbing. If you're so comfortable that you can get up on stage and say, I want more money from you to pump your con- your content across my network. I love the Netflix retort, right Dave? >> Yeah, totally Dave. And, but the, the other thing is, telco's a great business. It's, they got monopolies that print money. So... >> Dave Nicholson: It's rational. It's rational. I understand. >> There's less of an incentive to move but what's going to be the incentive is guys like Dish Network coming in saying, we're going to, we're going to disrupt, we're going to build new apps. >> That's right. >> Yeah. >> Well and it's, you know, revenue acceleration, the board level, the CEO level know that they have to, you know, do things different. But to your point, it's just hard, and there's so much gravity there. There's hundreds of years literally of gravity of how they've operated their business. To your point, a lot of them, you know, lot- most of 'em were regulated and most Geos around the world at one point, right? They were government owned or government regulated entities. It's, it's a big ship to turn and it's really hard. We're not claiming we can help them turn the ship overnight but we think we can help evolve them. We think we can go along with the journey and we do think we are better together. >> IT the network and the line of business. Love the strategy. Guys, thanks so much for coming in theCUBE. >> Thank you so much. >> Thank you. >> All right, for Dave, Nicholson, Dave Vellante here, John Furrier is in our Palo Alto studio banging out all the news, keep it right there. TheCUBE's coverage of MWC 23. We'll be right back.
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that drive human progress. of the telco networks, how Great to be here. for the telcos to make money. 90% of the companies leaning in on that. So Ken, you know, the space of our platinum brand to the If they're not going to go toward that of how to solve those business outcomes. the telcos are starting to the end customer. allowing the telecom companies to come in and they're all at different levels and all they have to do is collect, I got to tell you they're pretty excited So that's the big question, right? And really to get Right. a lot of the wifi doesn't. the connectivity piece and what they do. And then EMC was largely, you know, And you know, a big part a former EMC person myself, into Dell by the acquisition I'll believe you on that. And it makes sense when you think about But most of the time it's not excellent. So the idea that I could I'd pay extra for that They don't know how to monetize that data. then they're not going to think about it. Yes, but, but when you start talking Yeah, a perfect example of how you network Dave Nicholson: Like, a really good opportunity to over the years of, hey, you And so you put some... then, but we would agree. You know, it's going to take some time You know, you guys do a good, good TAM. the ship doesn't turn overnight, you know. everybody recognizes the need to open up. of the telcos that are a little the Cloud's going to take over the world. I was in high school then, there's money to be made the road is that you have that print money. I understand. There's less of an incentive to move of them, you know, lot- the line of business. banging out all the news,
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Phil Kippen, Snowflake, Dave Whittington, AT&T & Roddy Tranum, AT&T | | MWC Barcelona 2023
(gentle music) >> Narrator: "TheCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hello everybody, welcome back to day four of "theCUBE's" coverage of MWC '23. We're here live at the Fira in Barcelona. Wall-to-wall coverage, John Furrier is in our Palo Alto studio, banging out all the news. Really, the whole week we've been talking about the disaggregation of the telco network, the new opportunities in telco. We're really excited to have AT&T and Snowflake here. Dave Whittington is the AVP, at the Chief Data Office at AT&T. Roddy Tranum is the Assistant Vice President, for Channel Performance Data and Tools at AT&T. And Phil Kippen, the Global Head Of Industry-Telecom at Snowflake, Snowflake's new telecom business. Snowflake just announced earnings last night. Typical Scarpelli, they beat earnings, very conservative guidance, stocks down today, but we like Snowflake long term, they're on that path to 10 billion. Guys, welcome to "theCUBE." Thanks so much >> Phil: Thank you. >> for coming on. >> Dave and Roddy: Thanks Dave. >> Dave, let's start with you. The data culture inside of telco, We've had this, we've been talking all week about this monolithic system. Super reliable. You guys did a great job during the pandemic. Everything shifting to landlines. We didn't even notice, you guys didn't miss a beat. Saved us. But the data culture's changing inside telco. Explain that. >> Well, absolutely. So, first of all IoT and edge processing is bringing forth new and exciting opportunities all the time. So, we're bridging the world between a lot of the OSS stuff that we can do with edge processing. But bringing that back, and now we're talking about working, and I would say traditionally, we talk data warehouse. Data warehouse and big data are now becoming a single mesh, all right? And the use cases and the way you can use those, especially I'm taking that edge data and bringing it back over, now I'm running AI and ML models on it, and I'm pushing back to the edge, and I'm combining that with my relational data. So that mesh there is making all the difference. We're getting new use cases that we can do with that. And it's just, and the volume of data is immense. >> Now, I love ChatGPT, but I'm hoping your data models are more accurate than ChatGPT. I never know. Sometimes it's really good, sometimes it's really bad. But enterprise, you got to be clean with your AI, don't you? >> Not only you have to be clean, you have to monitor it for bias and be ethical about it. We're really good about that. First of all with AT&T, our brand is Platinum. We take care of that. So, we may not be as cutting-edge risk takers as others, but when we go to market with an AI or an ML or a product, it's solid. >> Well hey, as telcos go, you guys are leaning into the Cloud. So I mean, that's a good starting point. Roddy, explain your role. You got an interesting title, Channel Performance Data and Tools, what's that all about? >> So literally anything with our consumer, retail, concenters' channels, all of our channels, from a data perspective and metrics perspective, what it takes to run reps, agents, all the way to leadership levels, scorecards, how you rank in the business, how you're driving the business, from sales, service, customer experience, all that data infrastructure with our great partners on the CDO side, as well as Snowflake, that comes from my team. >> And that's traditionally been done in a, I don't mean the pejorative, but we're talking about legacy, monolithic, sort of data warehouse technologies. >> Absolutely. >> We have a love-hate relationship with them. It's what we had. It's what we used, right? And now that's evolving. And you guys are leaning into the Cloud. >> Dramatic evolution. And what Snowflake's enabled for us is impeccable. We've talked about having, people have dreamed of one data warehouse for the longest time and everything in one system. Really, this is the only way that becomes a reality. The more you get in Snowflake, we can have golden source data, and instead of duplicating that 50 times across AT&T, it's in one place, we just share it, everybody leverages it, and now it's not duplicated, and the process efficiency is just incredible. >> But it really hinges on that separation of storage and compute. And we talk about the monolithic warehouse, and one of the nightmares I've lived with, is having a monolithic warehouse. And let's just go with some of my primary, traditional customers, sales, marketing and finance. They are leveraging BSS OSS data all the time. For me to coordinate a deployment, I have to make sure that each one of these units can take an outage, if it's going to be a long deployment. With the separation of storage, compute, they own their own compute cluster. So I can move faster for these people. 'Cause if finance, I can implement his code without impacting finance or marketing. This brings in CI/CD to more reality. It brings us faster to market with more features. So if he wants to implement a new comp plan for the field reps, or we're reacting to the marketplace, where one of our competitors has done something, we can do that in days, versus waiting weeks or months. >> And we've reported on this a lot. This is the brilliance of Snowflake's founders, that whole separation >> Yep. >> from compute and data. I like Dave, that you're starting with sort of the business flexibility, 'cause there's a cost element of this too. You can dial down, you can turn off compute, and then of course the whole world said, "Hey, that's a good idea." And a VC started throwing money at Amazon, but Redshift said, "Oh, we can do that too, sort of, can't turn off the compute." But I want to ask you Phil, so, >> Sure. >> it looks from my vantage point, like you're taking your Data Cloud message which was originally separate compute from storage simplification, now data sharing, automated governance, security, ultimately the marketplace. >> Phil: Right. >> Taking that same model, break down the silos into telecom, right? It's that same, >> Mm-hmm. >> sorry to use the term playbook, Frank Slootman tells me he doesn't use playbooks, but he's not a pattern matcher, but he's a situational CEO, he says. But the situation in telco calls for that type of strategy. So explain what you guys are doing in telco. >> I think there's, so, what we're launching, we launched last week, and it really was three components, right? So we had our platform as you mentioned, >> Dave: Mm-hmm. >> and that platform is being utilized by a number of different companies today. We also are adding, for telecom very specifically, we're adding capabilities in marketplace, so that service providers can not only use some of the data and apps that are in marketplace, but as well service providers can go and sell applications or sell data that they had built. And then as well, we're adding our ecosystem, it's telecom-specific. So, we're bringing partners in, technology partners, and consulting and services partners, that are very much focused on telecoms and what they do internally, but also helping them monetize new services. >> Okay, so it's not just sort of generic Snowflake into telco? You have specific value there. >> We're purposing the platform specifically for- >> Are you a telco guy? >> I am. You are, okay. >> Total telco guy absolutely. >> So there you go. You see that Snowflake is actually an interesting organizational structure, 'cause you're going after verticals, which is kind of rare for a company of your sort of inventory, I'll say, >> Absolutely. >> I don't mean that as a negative. (Dave laughs) So Dave, take us through the data journey at AT&T. It's a long history. You don't have to go back to the 1800s, but- (Dave laughs) >> Thank you for pointing out, we're a 149-year-old company. So, Jesse James was one of the original customers, (Dave laughs) and we have no longer got his data. So, I'll go back. I've been 17 years singular AT&T, and I've watched it through the whole journey of, where the monolithics were growing, when the consolidation of small, wireless carriers, and we went through that boom. And then we've gone through mergers and acquisitions. But, Hadoop came out, and it was going to solve all world hunger. And we had all the aspects of, we're going to monetize and do AI and ML, and some of the things we learned with Hadoop was, we had this monolithic warehouse, we had this file-based-structured Hadoop, but we really didn't know how to bring this all together. And we were bringing items over to the relational, and we were taking the relational and bringing it over to the warehouse, and trying to, and it was a struggle. Let's just go there. And I don't think we were the only company to struggle with that, but we learned a lot. And so now as tech is finally emerging, with the cloud, companies like Snowflake, and others that can handle that, where we can create, we were discussing earlier, but it becomes more of a conducive mesh that's interoperable. So now we're able to simplify that environment. And the cloud is a big thing on that. 'Cause you could not do this on-prem with on-prem technologies. It would be just too cost prohibitive, and too heavy of lifting, going back and forth, and managing the data. The simplicity the cloud brings with a smaller set of tools, and I'll say in the data space specifically, really allows us, maybe not a single instance of data for all use cases, but a greatly reduced ecosystem. And when you simplify your ecosystem, you simplify speed to market and data management. >> So I'm going to ask you, I know it's kind of internal organizational plumbing, but it'll inform my next question. So, Dave, you're with the Chief Data Office, and Roddy, you're kind of, you all serve in the business, but you're really serving the, you're closer to those guys, they're banging on your door for- >> Absolutely. I try to keep the 130,000 users who may or may not have issues sometimes with our data and metrics, away from Dave. And he just gets a call from me. >> And he only calls when he has a problem. He's never wished me happy birthday. (Dave and Phil laugh) >> So the reason I asked that is because, you describe Dave, some of the Hadoop days, and again love-hate with that, but we had hyper-specialized roles. We still do. You've got data engineers, data scientists, data analysts, and you've got this sort of this pipeline, and it had to be this sequential pipeline. I know Snowflake and others have come to simplify that. My question to you is, how is that those roles, how are those roles changing? How is data getting closer to the business? Everybody talks about democratizing business. Are you doing that? What's a real use example? >> From our perspective, those roles, a lot of those roles on my team for years, because we're all about efficiency, >> Dave: Mm-hmm. >> we cut across those areas, and always have cut across those areas. So now we're into a space where things have been simplified, data processes and copying, we've gone from 40 data processes down to five steps now. We've gone from five steps to one step. We've gone from days, now take hours, hours to minutes, minutes to seconds. Literally we're seeing that time in and time out with Snowflake. So these resources that have spent all their time on data engineering and moving data around, are now freed up more on what they have skills for and always have, the data analytics area of the business, and driving the business forward, and new metrics and new analysis. That's some of the great operational value that we've seen here. As this simplification happens, it frees up brain power. >> So, you're pumping data from the OSS, the BSS, the OKRs everywhere >> Everywhere. >> into Snowflake? >> Scheduling systems, you name it. If you can think of what drives our retail and centers and online, all that data, scheduling system, chat data, call center data, call detail data, all of that enters into this common infrastructure to manage the business on a day in and day out basis. >> How are the roles and the skill sets changing? 'Cause you're doing a lot less ETL, you're doing a lot less moving of data around. There were guys that were probably really good at that. I used to joke in the, when I was in the storage world, like if your job is bandaging lungs, you need to look for a new job, right? So, and they did and people move on. So, are you able to sort of redeploy those assets, and those people, those human resources? >> These folks are highly skilled. And we were talking about earlier, SQL hasn't gone away. Relational databases are not going away. And that's one thing that's made this migration excellent, they're just transitioning their skills. Experts in legacy systems are now rapidly becoming experts on the Snowflake side. And it has not been that hard a transition. There are certainly nuances, things that don't operate as well in the cloud environment that we have to learn and optimize. But we're making that transition. >> Dave: So just, >> Please. >> within the Chief Data Office we have a couple of missions, and Roddy is a great partner and an example of how it works. We try to bring the data for democratization, so that we have one interface, now hopefully know we just have a logical connection back to these Snowflake instances that we connect. But we're providing that governance and cleansing, and if there's a business rule at the enterprise level, we provide it. But the goal at CDO is to make sure that business units like Roddy or marketing or finance, that they can come to a platform that's reliable, robust, and self-service. I don't want to be in his way. So I feel like I'm providing a sub-level of platform, that he can come to and anybody can come to, and utilize, that they're not having to go back and undo what's in Salesforce, or ServiceNow, or in our billers. So, I'm sort of that layer. And then making sure that that ecosystem is robust enough for him to use. >> And that self-service infrastructure is predominantly through the Azure Cloud, correct? >> Dave: Absolutely. >> And you work on other clouds, but it's predominantly through Azure? >> We're predominantly in Azure, yeah. >> Dave: That's the first-party citizen? >> Yeah. >> Okay, I like to think in terms sometimes of data products, and I know you've mentioned upfront, you're Gold standard or Platinum standard, you're very careful about personal information. >> Dave: Yeah. >> So you're not trying to sell, I'm an AT&T customer, you're not trying to sell my data, and make money off of my data. So the value prop and the business case for Snowflake is it's simpler. You do things faster, you're in the cloud, lower cost, et cetera. But I presume you're also in the business, AT&T, of making offers and creating packages for customers. I look at those as data products, 'cause it's not a, I mean, yeah, there's a physical phone, but there's data products behind it. So- >> It ultimately is, but not everybody always sees it that way. Data reporting often can be an afterthought. And we're making it more on the forefront now. >> Yeah, so I like to think in terms of data products, I mean even if the financial services business, it's a data business. So, if we can think about that sort of metaphor, do you see yourselves as data product builders? Do you have that, do you think about building products in that regard? >> Within the Chief Data Office, we have a data product team, >> Mm-hmm. >> and by the way, I wouldn't be disingenuous if I said, oh, we're very mature in this, but no, it's where we're going, and it's somewhat of a journey, but I've got a peer, and their whole job is to go from, especially as we migrate from cloud, if Roddy or some other group was using tables three, four and five and joining them together, it's like, "Well look, this is an offer for data product, so let's combine these and put it up in the cloud, and here's the offer data set product, or here's the opportunity data product," and it's a journey. We're on the way, but we have dedicated staff and time to do this. >> I think one of the hardest parts about that is the organizational aspects of it. Like who owns the data now, right? It used to be owned by the techies, and increasingly the business lines want to have access, you're providing self-service. So there's a discussion about, "Okay, what is a data product? Who's responsible for that data product? Is it in my P&L or your P&L? Somebody's got to sign up for that number." So, it sounds like those discussions are taking place. >> They are. And, we feel like we're more the, and CDO at least, we feel more, we're like the guardians, and the shepherds, but not the owners. I mean, we have a role in it all, but he owns his metrics. >> Yeah, and even from our perspective, we see ourselves as an enabler of making whatever AT&T wants to make happen in terms of the key products and officers' trade-in offers, trade-in programs, all that requires this data infrastructure, and managing reps and agents, and what they do from a channel performance perspective. We still ourselves see ourselves as key enablers of that. And we've got to be flexible, and respond quickly to the business. >> I always had empathy for the data engineer, and he or she had to service all these different lines of business with no business context. >> Yeah. >> Like the business knows good data from bad data, and then they just pound that poor individual, and they're like, "Okay, I'm doing my best. It's just ones and zeros to me." So, it sounds like that's, you're on that path. >> Yeah absolutely, and I think, we do have refined, getting more and more refined owners of, since Snowflake enables these golden source data, everybody sees me and my organization, channel performance data, go to Roddy's team, we have a great team, and we go to Dave in terms of making it all happen from a data infrastructure perspective. So we, do have a lot more refined, "This is where you go for the golden source, this is where it is, this is who owns it. If you want to launch this product and services, and you want to manage reps with it, that's the place you-" >> It's a strong story. So Chief Data Office doesn't own the data per se, but it's your responsibility to provide the self-service infrastructure, and make sure it's governed properly, and in as automated way as possible. >> Well, yeah, absolutely. And let me tell you more, everybody talks about single version of the truth, one instance of the data, but there's context to that, that we are taking, trying to take advantage of that as we do data products is, what's the use case here? So we may have an entity of Roddy as a prospective customer, and we may have a entity of Roddy as a customer, high-value customer over here, which may have a different set of mix of data and all, but as a data product, we can then create those for those specific use cases. Still point to the same data, but build it in different constructs. One for marketing, one for sales, one for finance. By the way, that's where your data engineers are struggling. >> Yeah, yeah, of course. So how do I serve all these folks, and really have the context-common story in telco, >> Absolutely. >> or are these guys ahead of the curve a little bit? Or where would you put them? >> I think they're definitely moving a lot faster than the industry is generally. I think the enabling technologies, like for instance, having that single copy of data that everybody sees, a single pane of glass, right, that's definitely something that everybody wants to get to. Not many people are there. I think, what AT&T's doing, is most definitely a little bit further ahead than the industry generally. And I think the successes that are coming out of that, and the learning experiences are starting to generate momentum within AT&T. So I think, it's not just about the product, and having a product now that gives you a single copy of data. It's about the experiences, right? And now, how the teams are getting trained, domains like network engineering for instance. They typically haven't been a part of data discussions, because they've got a lot of data, but they're focused on the infrastructure. >> Mm. >> So, by going ahead and deploying this platform, for platform's purpose, right, and the business value, that's one thing, but also to start bringing, getting that experience, and bringing new experience in to help other groups that traditionally hadn't been data-centric, that's also a huge step ahead, right? So you need to enable those groups. >> A big complaint of course we hear at MWC from carriers is, "The over-the-top guys are killing us. They're riding on our networks, et cetera, et cetera. They have all the data, they have all the client relationships." Do you see your client relationships changing as a result of sort of your data culture evolving? >> Yes, I'm not sure I can- >> It's a loaded question, I know. >> Yeah, and then I, so, we want to start embedding as much into our network on the proprietary value that we have, so we can start getting into that OTT play, us as any other carrier, we have distinct advantages of what we can do at the edge, and we just need to start exploiting those. But you know, 'cause whether it's location or whatnot, so we got to eat into that. Historically, the network is where we make our money in, and we stack the services on top of it. It used to be *69. >> Dave: Yeah. >> If anybody remembers that. >> Dave: Yeah, of course. (Dave laughs) >> But you know, it was stacked on top of our network. Then we stack another product on top of it. It'll be in the edge where we start providing distinct values to other partners as we- >> I mean, it's a great business that you're in. I mean, if they're really good at connectivity. >> Dave: Yeah. >> And so, it sounds like it's still to be determined >> Dave: Yeah. >> where you can go with this. You have to be super careful with private and for personal information. >> Dave: Yep. >> Yeah, but the opportunities are enormous. >> There's a lot. >> Yeah, particularly at the edge, looking at, private networks are just an amazing opportunity. Factories and name it, hospital, remote hospitals, remote locations. I mean- >> Dave: Connected cars. >> Connected cars are really interesting, right? I mean, if you start communicating car to car, and actually drive that, (Dave laughs) I mean that's, now we're getting to visit Xen Fault Tolerance people. This is it. >> Dave: That's not, let's hold the traffic. >> Doesn't scare me as much as we actually learn. (all laugh) >> So how's the show been for you guys? >> Dave: Awesome. >> What're your big takeaways from- >> Tremendous experience. I mean, someone who doesn't go outside the United States much, I'm a homebody. The whole experience, the whole trip, city, Mobile World Congress, the technologies that are out here, it's been a blast. >> Anything, top two things you learned, advice you'd give to others, your colleagues out in general? >> In general, we talked a lot about technologies today, and we talked a lot about data, but I'm going to tell you what, the accelerator that you cannot change, is the relationship that we have. So when the tech and the business can work together toward a common goal, and it's a partnership, you get things done. So, I don't know how many CDOs or CIOs or CEOs are out there, but this connection is what accelerates and makes it work. >> And that is our audience Dave. I mean, it's all about that alignment. So guys, I really appreciate you coming in and sharing your story in "theCUBE." Great stuff. >> Thank you. >> Thanks a lot. >> All right, thanks everybody. Thank you for watching. I'll be right back with Dave Nicholson. Day four SiliconANGLE's coverage of MWC '23. You're watching "theCUBE." (gentle music)
SUMMARY :
that drive human progress. And Phil Kippen, the Global But the data culture's of the OSS stuff that we But enterprise, you got to be So, we may not be as cutting-edge Channel Performance Data and all the way to leadership I don't mean the pejorative, And you guys are leaning into the Cloud. and the process efficiency and one of the nightmares I've lived with, This is the brilliance of the business flexibility, like you're taking your Data Cloud message But the situation in telco and that platform is being utilized You have specific value there. I am. So there you go. I don't mean that as a negative. and some of the things we and Roddy, you're kind of, And he just gets a call from me. (Dave and Phil laugh) and it had to be this sequential pipeline. and always have, the data all of that enters into How are the roles and in the cloud environment that But the goal at CDO is to and I know you've mentioned upfront, So the value prop and the on the forefront now. I mean even if the and by the way, I wouldn't and increasingly the business and the shepherds, but not the owners. and respond quickly to the business. and he or she had to service Like the business knows and we go to Dave in terms doesn't own the data per se, and we may have a entity and really have the and having a product now that gives you and the business value, that's one thing, They have all the data, on the proprietary value that we have, Dave: Yeah, of course. It'll be in the edge business that you're in. You have to be super careful Yeah, but the particularly at the edge, and actually drive that, let's hold the traffic. much as we actually learn. the whole trip, city, is the relationship that we have. and sharing your story in "theCUBE." Thank you for watching.
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Paola Peraza Calderon & Viraj Parekh, Astronomer | Cube Conversation
(soft electronic music) >> Hey everyone, welcome to this CUBE conversation as part of the AWS Startup Showcase, season three, episode one, featuring Astronomer. I'm your host, Lisa Martin. I'm in the CUBE's Palo Alto Studios, and today excited to be joined by a couple of guests, a couple of co-founders from Astronomer. Viraj Parekh is with us, as is Paola Peraza-Calderon. Thanks guys so much for joining us. Excited to dig into Astronomer. >> Thank you so much for having us. >> Yeah, thanks for having us. >> Yeah, and we're going to be talking about the role of data orchestration. Paola, let's go ahead and start with you. Give the audience that understanding, that context about Astronomer and what it is that you guys do. >> Mm-hmm. Yeah, absolutely. So, Astronomer is a, you know, we're a technology and software company for modern data orchestration, as you said, and we're the driving force behind Apache Airflow. The Open Source Workflow Management tool that's since been adopted by thousands and thousands of users, and we'll dig into this a little bit more. But, by data orchestration, we mean data pipeline, so generally speaking, getting data from one place to another, transforming it, running it on a schedule, and overall just building a central system that tangibly connects your entire ecosystem of data services, right. So what, that's Redshift, Snowflake, DVT, et cetera. And so tangibly, we build, we at Astronomer here build products powered by Apache Airflow for data teams and for data practitioners, so that they don't have to. So, we sell to data engineers, data scientists, data admins, and we really spend our time doing three things. So, the first is that we build Astro, our flagship cloud service that we'll talk more on. But here, we're really building experiences that make it easier for data practitioners to author, run, and scale their data pipeline footprint on the cloud. And then, we also contribute to Apache Airflow as an open source project and community. So, we cultivate the community of humans, and we also put out open source developer tools that actually make it easier for individual data practitioners to be productive in their day-to-day jobs, whether or not they actually use our product and and pay us money or not. And then of course, we also have professional services and education and all of these things around our commercial products that enable folks to use our products and use Airflow as effectively as possible. So yeah, super, super happy with everything we've done and hopefully that gives you an idea of where we're starting. >> Awesome, so when you're talking with those, Paola, those data engineers, those data scientists, how do you define data orchestration and what does it mean to them? >> Yeah, yeah, it's a good question. So, you know, if you Google data orchestration you're going to get something about an automated process for organizing silo data and making it accessible for processing and analysis. But, to your question, what does that actually mean, you know? So, if you look at it from a customer's perspective, we can share a little bit about how we at Astronomer actually do data orchestration ourselves and the problems that it solves for us. So, as many other companies out in the world do, we at Astronomer need to monitor how our own customers use our products, right? And so, we have a weekly meeting, for example, that goes through a dashboard and a dashboarding tool called Sigma where we see the number of monthly customers and how they're engaging with our product. But, to actually do that, you know, we have to use data from our application database, for example, that has behavioral data on what they're actually doing in our product. We also have data from third party API tools, like Salesforce and HubSpot, and other ways in which our customer, we actually engage with our customers and their behavior. And so, our data team internally at Astronomer uses a bunch of tools to transform and use that data, right? So, we use FiveTran, for example, to ingest. We use Snowflake as our data warehouse. We use other tools for data transformations. And even, if we at Astronomer don't do this, you can imagine a data team also using tools like, Monte Carlo for data quality, or Hightouch for Reverse ETL, or things like that. And, I think the point here is that data teams, you know, that are building data-driven organizations have a plethora of tooling to both ingest the right data and come up with the right interfaces to transform and actually, interact with that data. And so, that movement and sort of synchronization of data across your ecosystem is exactly what data orchestration is responsible for. Historically, I think, and Raj will talk more about this, historically, schedulers like KRON and Oozie or Control-M have taken a role here, but we think that Apache Airflow has sort of risen over the past few years as the defacto industry standard for writing data pipelines that do tasks, that do data jobs that interact with that ecosystem of tools in your organization. And so, beyond that sort of data pipeline unit, I think where we see it is that data acquisition is not only writing those data pipelines that move your data, but it's also all the things around it, right, so, CI/CD tool and Secrets Management, et cetera. So, a long-winded answer here, but I think that's how we talk about it here at Astronomer and how we're building our products. >> Excellent. Great context, Paola. Thank you. Viraj, let's bring you into the conversation. Every company these days has to be a data company, right? They've got to be a software company- >> Mm-hmm. >> whether it's my bank or my grocery store. So, how are companies actually doing data orchestration today, Viraj? >> Yeah, it's a great question. So, I think one thing to think about is like, on one hand, you know, data orchestration is kind of a new category that we're helping define, but on the other hand, it's something that companies have been doing forever, right? You need to get data moving to use it, you know. You've got it all in place, aggregate it, cleaning it, et cetera. So, when you look at what companies out there are doing, right. Sometimes, if you're a more kind of born in the cloud company, as we say, you'll adopt all these cloud native tooling things your cloud provider gives you. If you're a bank or another sort of institution like that, you know, you're probably juggling an even wider variety of tools. You're thinking about a cloud migration. You might have things like Kron running in one place, Uzi running somewhere else, Informatics running somewhere else, while you're also trying to move all your workloads to the cloud. So, there's quite a large spectrum of what the current state is for companies. And then, kind of like Paola was saying, Apache Airflow started in 2014, and it was actually started by Airbnb, and they put out this blog post that was like, "Hey here's how we use Apache Airflow to orchestrate our data across all their sources." And really since then, right, it's almost been a decade since then, Airflow emerged as the open source standard, and there's companies of all sorts using it. And, it's really used to tie all these tools together, especially as that number of tools increases, companies move to hybrid cloud, hybrid multi-cloud strategies, and so on and so forth. But you know, what we found is that if you go to any company, especially a larger one and you say like, "Hey, how are you doing data orchestration?" They'll probably say something like, "Well, I have five data teams, so I have eight different ways I do data orchestration." Right. This idea of data orchestration's been there but the right way to do it, kind of all the abstractions you need, the way your teams need to work together, and so on and so forth, hasn't really emerged just yet, right? It's such a quick moving space that companies have to combine what they were doing before with what their new business initiatives are today. So, you know, what we really believe here at Astronomer is Airflow is the core of how you solve data orchestration for any sort of use case, but it's not everything. You know, it needs a little more. And, that's really where our commercial product, Astro comes in, where we've built, not only the most tried and tested airflow experience out there. We do employ a majority of the Airflow Core Committers, right? So, we're kind of really deep in the project. We've also built the right things around developer tooling, observability, and reliability for customers to really rely on Astro as the heart of the way they do data orchestration, and kind of think of it as the foundational layer that helps tie together all the different tools, practices and teams large companies have to do today. >> That foundational layer is absolutely critical. You've both mentioned open source software. Paola, I want to go back to you, and just give the audience an understanding of how open source really plays into Astronomer's mission as a company, and into the technologies like Astro. >> Mm-hmm. Yeah, absolutely. I mean, we, so we at Astronomers started using Airflow and actually building our products because Airflow is open source and we were our own customers at the beginning of our company journey. And, I think the open source community is at the core of everything we do. You know, without that open source community and culture, I think, you know, we have less of a business, and so, we're super invested in continuing to cultivate and grow that. And, I think there's a couple sort of concrete ways in which we do this that personally make me really excited to do my own job. You know, for one, we do things like we organize meetups and we sponsor the Airflow Summit and there's these sort of baseline community efforts that I think are really important and that reminds you, hey, there just humans trying to do their jobs and learn and use both our technology and things that are out there and contribute to it. So, making it easier to contribute to Airflow, for example, is another one of our efforts. As Viraj mentioned, we also employ, you know, engineers internally who are on our team whose full-time job is to make the open source project better. Again, regardless of whether or not you're a customer of ours or not, we want to make sure that we continue to cultivate the Airflow project in and of itself. And, we're also building developer tooling that might not be a part of the Apache Open Source project, but is still open source. So, we have repositories in our own sort of GitHub organization, for example, with tools that individual data practitioners, again customers are not, can use to make them be more productive in their day-to-day jobs with Airflow writing Dags for the most common use cases out there. The last thing I'll say is how important I think we've found it to build sort of educational resources and documentation and best practices. Airflow can be complex. It's been around for a long time. There's a lot of really, really rich feature sets. And so, how do we enable folks to actually use those? And that comes in, you know, things like webinars, and best practices, and courses and curriculum that are free and accessible and open to the community are just some of the ways in which I think we're continuing to invest in that open source community over the next year and beyond. >> That's awesome. It sounds like open source is really core, not only to the mission, but really to the heart of the organization. Viraj, I want to go back to you and really try to understand how does Astronomer fit into the wider modern data stack and ecosystem? Like what does that look like for customers? >> Yeah, yeah. So, both in the open source and with our commercial customers, right? Folks everywhere are trying to tie together a huge variety of tools in order to start making sense of their data. And you know, I kind of think of it almost like as like a pyramid, right? At the base level, you need things like data reliability, data, sorry, data freshness, data availability, and so on and so forth, right? You just need your data to be there. (coughs) I'm sorry. You just need your data to be there, and you need to make it predictable when it's going to be there. You need to make sure it's kind of correct at the highest level, some quality checks, and so on and so forth. And oftentimes, that kind of takes the case of ELT or ETL use cases, right? Taking data from somewhere and moving it somewhere else, usually into some sort of analytics destination. And, that's really what businesses can do to just power the core parts of getting insights into how their business is going, right? How much revenue did I had? What's in my pipeline, salesforce, and so on and so forth. Once that kind of base foundation is there and people can get the data they need, how they need it, it really opens up a lot for what customers can do. You know, I think one of the trendier things out there right now is MLOps, and how do companies actually put machine learning into production? Well, when you think about it you kind of have to squint at it, right? Like, machine learning pipelines are really just any other data pipeline. They just have a certain set of needs that might not not be applicable to ELT pipelines. And, when you kind of have a common layer to tie together all the ways data can move through your organization, that's really what we're trying to make it so companies can do. And, that happens in financial services where, you know, we have some customers who take app data coming from their mobile apps, and actually run it through their fraud detection services to make sure that all the activity is not fraudulent. We have customers that will run sports betting models on our platform where they'll take data from a bunch of public APIs around different sporting events that are happening, transform all of that in a way their data scientist can build models with it, and then actually bet on sports based on that output. You know, one of my favorite use cases I like to talk about that we saw in the open source is we had there was one company whose their business was to deliver blood transfusions via drone into remote parts of the world. And, it was really cool because they took all this data from all sorts of places, right? Kind of orchestrated all the aggregation and cleaning and analysis that happened had to happen via airflow and the end product would be a drone being shot out into a real remote part of the world to actually give somebody blood who needed it there. Because it turns out for certain parts of the world, the easiest way to deliver blood to them is via drone and not via some other, some other thing. So, these kind of, all the things people do with the modern data stack is absolutely incredible, right? Like you were saying, every company's trying to be a data-driven company. What really energizes me is knowing that like, for all those best, super great tools out there that power a business, we get to be the connective tissue, or the, almost like the electricity that kind of ropes them all together and makes so people can actually do what they need to do. >> Right. Phenomenal use cases that you just described, Raj. I mean, just the variety alone of what you guys are able to do and impact is so cool. So Paola, when you're with those data engineers, those data scientists, and customer conversations, what's your pitch? Why use Astro? >> Mm-hmm. Yeah, yeah, it's a good question. And honestly, to piggyback off of Viraj, there's so many. I think what keeps me so energized is how mission critical both our product and data orchestration is, and those use cases really are incredible and we work with customers of all shapes and sizes. But, to answer your question, right, so why use Astra? Why use our commercial products? There's so many people using open source, why pay for something more than that? So, you know, the baseline for our business really is that Airflow has grown exponentially over the last five years, and like we said has become an industry standard that we're confident there's a huge opportunity for us as a company and as a team. But, we also strongly believe that being great at running Airflow, you know, doesn't make you a successful company at what you do. What makes you a successful company at what you do is building great products and solving problems and solving pin points of your own customers, right? And, that differentiating value isn't being amazing at running Airflow. That should be our job. And so, we want to abstract those customers from meaning to do things like manage Kubernetes infrastructure that you need to run Airflow, and then hiring someone full-time to go do that. Which can be hard, but again doesn't add differentiating value to your team, or to your product, or to your customers. So, folks to get away from managing that infrastructure sort of a base, a base layer. Folks who are looking for differentiating features that make their team more productive and allows them to spend less time tweaking Airflow configurations and more time working with the data that they're getting from their business. For help, getting, staying up with Airflow releases. There's a ton of, we've actually been pretty quick to come out with new Airflow features and releases, and actually just keeping up with that feature set and working strategically with a partner to help you make the most out of those feature sets is a key part of it. And, really it's, especially if you're an organization who currently is committed to using Airflow, you likely have a lot of Airflow environments across your organization. And, being able to see those Airflow environments in a single place and being able to enable your data practitioners to create Airflow environments with a click of a button, and then use, for example, our command line to develop your Airflow Dags locally and push them up to our product, and use all of the sort of testing and monitoring and observability that we have on top of our product is such a key. It sounds so simple, especially if you use Airflow, but really those things are, you know, baseline value props that we have for the customers that continue to be excited to work with us. And of course, I think we can go beyond that and there's, we have ambitions to add whole, a whole bunch of features and expand into different types of personas. >> Right? >> But really our main value prop is for companies who are committed to Airflow and want to abstract themselves and make use of some of the differentiating features that we now have at Astronomer. >> Got it. Awesome. >> Thank you. One thing, one thing I'll add to that, Paola, and I think you did a good job of saying is because every company's trying to be a data company, companies are at different parts of their journey along that, right? And we want to meet customers where they are, and take them through it to where they want to go. So, on one end you have folks who are like, "Hey, we're just building a data team here. We have a new initiative. We heard about Airflow. How do you help us out?" On the farther end, you know, we have some customers that have been using Airflow for five plus years and they're like, "Hey, this is awesome. We have 10 more teams we want to bring on. How can you help with this? How can we do more stuff in the open source with you? How can we tell our story together?" And, it's all about kind of taking this vast community of data users everywhere, seeing where they're at, and saying like, "Hey, Astro and Airflow can take you to the next place that you want to go." >> Which is incredibly- >> Mm-hmm. >> and you bring up a great point, Viraj, that every company is somewhere in a different place on that journey. And it's, and it's complex. But it sounds to me like a lot of what you're doing is really stripping away a lot of the complexity, really enabling folks to use their data as quickly as possible, so that it's relevant and they can serve up, you know, the right products and services to whoever wants what. Really incredibly important. We're almost out of time, but I'd love to get both of your perspectives on what's next for Astronomer. You give us a a great overview of what the company's doing, the value in it for customers. Paola, from your lens as one of the co-founders, what's next? >> Yeah, I mean, I think we'll continue to, I think cultivate in that open source community. I think we'll continue to build products that are open sourced as part of our ecosystem. I also think that we'll continue to build products that actually make Airflow, and getting started with Airflow, more accessible. So, sort of lowering that barrier to entry to our products, whether that's price wise or infrastructure requirement wise. I think making it easier for folks to get started and get their hands on our product is super important for us this year. And really it's about, I think, you know, for us, it's really about focused execution this year and all of the sort of core principles that we've been talking about. And continuing to invest in all of the things around our product that again, enable teams to use Airflow more effectively and efficiently. >> And that efficiency piece is, everybody needs that. Last question, Viraj, for you. What do you see in terms of the next year for Astronomer and for your role? >> Yeah, you know, I think Paola did a really good job of laying it out. So it's, it's really hard to disagree with her on anything, right? I think executing is definitely the most important thing. My own personal bias on that is I think more than ever it's important to really galvanize the community around airflow. So, we're going to be focusing on that a lot. We want to make it easier for our users to get get our product into their hands, be that open source users or commercial users. And last, but certainly not least, is we're also really excited about Data Lineage and this other open source project in our umbrella called Open Lineage to make it so that there's a standard way for users to get lineage out of different systems that they use. When we think about what's in store for data lineage and needing to audit the way automated decisions are being made. You know, I think that's just such an important thing that companies are really just starting with, and I don't think there's a solution that's emerged that kind of ties it all together. So, we think that as we kind of grow the role of Airflow, right, we can also make it so that we're helping solve, we're helping customers solve their lineage problems all in Astro, which is our kind of the best of both worlds for us. >> Awesome. I can definitely feel and hear the enthusiasm and the passion that you both bring to Astronomer, to your customers, to your team. I love it. We could keep talking more and more, so you're going to have to come back. (laughing) Viraj, Paola, thank you so much for joining me today on this showcase conversation. We really appreciate your insights and all the context that you provided about Astronomer. >> Thank you so much for having us. >> My pleasure. For my guests, I'm Lisa Martin. You're watching this Cube conversation. (soft electronic music)
SUMMARY :
to this CUBE conversation Thank you so much and what it is that you guys do. and hopefully that gives you an idea and the problems that it solves for us. to be a data company, right? So, how are companies actually kind of all the abstractions you need, and just give the And that comes in, you of the organization. and analysis that happened that you just described, Raj. that you need to run Airflow, that we now have at Astronomer. Awesome. and I think you did a good job of saying and you bring up a great point, Viraj, and all of the sort of core principles and for your role? and needing to audit the and all the context that you (soft electronic music)
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Ed Walsh & Thomas Hazel | A New Database Architecture for Supercloud
(bright music) >> Hi, everybody, this is Dave Vellante, welcome back to Supercloud 2. Last August, at the first Supercloud event, we invited the broader community to help further define Supercloud, we assessed its viability, and identified the critical elements and deployment models of the concept. The objectives here at Supercloud too are, first of all, to continue to tighten and test the concept, the second is, we want to get real world input from practitioners on the problems that they're facing and the viability of Supercloud in terms of applying it to their business. So on the program, we got companies like Walmart, Sachs, Western Union, Ionis Pharmaceuticals, NASDAQ, and others. And the third thing that we want to do is we want to drill into the intersection of cloud and data to project what the future looks like in the context of Supercloud. So in this segment, we want to explore the concept of data architectures and what's going to be required for Supercloud. And I'm pleased to welcome one of our Supercloud sponsors, ChaosSearch, Ed Walsh is the CEO of the company, with Thomas Hazel, who's the Founder, CTO, and Chief Scientist. Guys, good to see you again, thanks for coming into our Marlborough studio. >> Always great. >> Great to be here. >> Okay, so there's a little debate, I'm going to put you right in the spot. (Ed chuckling) A little debate going on in the community started by Bob Muglia, a former CEO of Snowflake, and he was at Microsoft for a long time, and he looked at the Supercloud definition, said, "I think you need to tighten it up a little bit." So, here's what he came up with. He said, "A Supercloud is a platform that provides a programmatically consistent set of services hosted on heterogeneous cloud providers." So he's calling it a platform, not an architecture, which was kind of interesting. And so presumably the platform owner is going to be responsible for the architecture, but Dr. Nelu Mihai, who's a computer scientist behind the Cloud of Clouds Project, he chimed in and responded with the following. He said, "Cloud is a programming paradigm supporting the entire lifecycle of applications with data and logic natively distributed. Supercloud is an open architecture that integrates heterogeneous clouds in an agnostic manner." So, Ed, words matter. Is this an architecture or is it a platform? >> Put us on the spot. So, I'm sure you have concepts, I would say it's an architectural or design principle. Listen, I look at Supercloud as a mega trend, just like cloud, just like data analytics. And some companies are using the principle, design principles, to literally get dramatically ahead of everyone else. I mean, things you couldn't possibly do if you didn't use cloud principles, right? So I think it's a Supercloud effect, you're able to do things you're not able to. So I think it's more a design principle, but if you do it right, you get dramatic effect as far as customer value. >> So the conversation that we were having with Muglia, and Tristan Handy of dbt Labs, was, I'll set it up as the following, and, Thomas, would love to get your thoughts, if you have a CRM, think about applications today, it's all about forms and codifying business processes, you type a bunch of stuff into Salesforce, and all the salespeople do it, and this machine generates a forecast. What if you have this new type of data app that pulls data from the transaction system, the e-commerce, the supply chain, the partner ecosystem, et cetera, and then, without humans, actually comes up with a plan. That's their vision. And Muglia was saying, in order to do that, you need to rethink data architectures and database architectures specifically, you need to get down to the level of how the data is stored on the disc. What are your thoughts on that? Well, first of all, I'm going to cop out, I think it's actually both. I do think it's a design principle, I think it's not open technology, but open APIs, open access, and you can build a platform on that design principle architecture. Now, I'm a database person, I love solving the database problems. >> I'm waited for you to launch into this. >> Yeah, so I mean, you know, Snowflake is a database, right? It's a distributed database. And we wanted to crack those codes, because, multi-region, multi-cloud, customers wanted access to their data, and their data is in a variety of forms, all these services that you're talked about. And so what I saw as a core principle was cloud object storage, everyone streams their data to cloud object storage. From there we said, well, how about we rethink database architecture, rethink file format, so that we can take each one of these services and bring them together, whether distributively or centrally, such that customers can access and get answers, whether it's operational data, whether it's business data, AKA search, or SQL, complex distributed joins. But we had to rethink the architecture. I like to say we're not a first generation, or a second, we're a third generation distributed database on pure, pure cloud storage, no caching, no SSDs. Why? Because all that availability, the cost of time, is a struggle, and cloud object storage, we think, is the answer. >> So when you're saying no caching, so when I think about how companies are solving some, you know, pretty hairy problems, take MySQL Heatwave, everybody thought Oracle was going to just forget about MySQL, well, they come out with Heatwave. And the way they solve problems, and you see their benchmarks against Amazon, "Oh, we crush everybody," is they put it all in memory. So you said no caching? You're not getting performance through caching? How is that true, and how are you getting performance? >> Well, so five, six years ago, right? When you realize that cloud object storage is going to be everywhere, and it's going to be a core foundational, if you will, fabric, what would you do? Well, a lot of times the second generation say, "We'll take it out of cloud storage, put in SSDs or something, and put into cache." And that adds a lot of time, adds a lot of costs. But I said, what if, what if we could actually make the first read hot, the first read distributed joins and searching? And so what we went out to do was said, we can't cache, because that's adds time, that adds cost. We have to make cloud object storage high performance, like it feels like a caching SSD. That's where our patents are, that's where our technology is, and we've spent many years working towards this. So, to me, if you can crack that code, a lot of these issues we're talking about, multi-region, multicloud, different services, everybody wants to send their data to the data lake, but then they move it out, we said, "Keep it right there." >> You nailed it, the data gravity. So, Bob's right, the data's coming in, and you need to get the data from everywhere, but you need an environment that you can deal with all that different schema, all the different type of technology, but also at scale. Bob's right, you cannot use memory or SSDs to cache that, that doesn't scale, it doesn't scale cost effectively. But if you could, and what you did, is you made object storage, S3 first, but object storage, the only persistence by doing that. And then we get performance, we should talk about it, it's literally, you know, hundreds of terabytes of queries, and it's done in seconds, it's done without memory caching. We have concepts of caching, but the only caching, the only persistence, is actually when we're doing caching, we're just keeping another side-eye track of things on the S3 itself. So we're using, actually, the object storage to be a database, which is kind of where Bob was saying, we agree, but that's what you started at, people thought you were crazy. >> And maybe make it live. Don't think of it as archival or temporary space, make it live, real time streaming, operational data. What we do is make it smart, we see the data coming in, we uniquely index it such that you can get your use cases, that are search, observability, security, or backend operational. But we don't have to have this, I dunno, static, fixed, siloed type of architecture technologies that were traditionally built prior to Supercloud thinking. >> And you don't have to move everything, essentially, you can do it wherever the data lands, whatever cloud across the globe, you're able to bring it together, you get the cost effectiveness, because the only persistence is the cheapest storage persistent layer you can buy. But the key thing is you cracked the code. >> We had to crack the code, right? That was the key thing. >> That's where the plans are. >> And then once you do that, then everything else gets easier to scale, your architecture, across regions, across cloud. >> Now, it's a general purpose database, as Bob was saying, but we use that database to solve a particular issue, which is around operational data, right? So, we agree with Bob's. >> Interesting. So this brings me to this concept of data, Jimata Gan is one of our speakers, you know, we talk about data fabric, which is a NetApp, originally NetApp concept, Gartner's kind of co-opted it. But so, the basic concept is, data lives everywhere, whether it's an S3 bucket, or a SQL database, or a data lake, it's just a node on the data mesh. So in your view, how does this fit in with Supercloud? Ed, you've said that you've built, essentially, an enabler for that, for the data mesh, I think you're an enabler for the Supercloud-like principles. This is a big, chewy opportunity, and it requires, you know, a team approach. There's got to be an ecosystem, there's not going to be one Supercloud to rule them all, so where does the ecosystem fit into the discussion, and where do you fit into the ecosystem? >> Right, so we agree completely, there's not one Supercloud in effect, but we use Supercloud principles to build our platform, and then, you know, the ecosystem's going to be built on leveraging what everyone else's secret powers are, right? So our power, our superpower, based upon what we built is, we deal with, if you're having any scale, or cost effective scale issues, with data, machine generated data, like business observability or security data, we are your force multiplier, we will take that in singularly, just let it, simply put it in your object storage wherever it sits, and we give you uniformity access to that using OpenAPI access, SQL, or you know, Elasticsearch API. So, that's what we do, that's our superpower. So I'll play it into data mesh, that's a perfect, we are a node on a data mesh, but I'll play it in the soup about how, the ecosystem, we see it kind of playing, and we talked about it in just in the last couple days, how we see this kind of possibly. Short term, our superpowers, we deal with this data that's coming at these environments, people, customers, building out observability or security environments, or vendors that are selling their own Supercloud, I do observability, the Datadogs of the world, dot dot dot, the Splunks of the world, dot dot dot, and security. So what we do is we fit in naturally. What we do is a cost effective scale, just land it anywhere in the world, we deal with ingest, and it's a cost effective, an order of magnitude, or two or three order magnitudes more cost effective. Allows them, their customers are asking them to do the impossible, "Give me fast monitoring alerting. I want it snappy, but I want it to keep two years of data, (laughs) and I want it cost effective." It doesn't work. They're good at the fast monitoring alerting, we're good at the long-term retention. And yet there's some gray area between those two, but one to one is actually cheaper, so we would partner. So the first ecosystem plays, who wants to have the ability to, really, all the data's in those same environments, the security observability players, they can literally, just through API, drag our data into their point to grab. We can make it seamless for customers. Right now, we make it helpful to customers. Your Datadog, we make a button, easy go from Datadog to us for logs, save you money. Same thing with Grafana. But you can also look at ecosystem, those same vendors, it used to be a year ago it was, you know, its all about how can you grow, like it's growth at all costs, now it's about cogs. So literally we can go an environment, you supply what your customer wants, but we can help with cogs. And one-on one in a partnership is better than you trying to build on your own. >> Thomas, you were saying you make the first read fast, so you think about Snowflake. Everybody wants to talk about Snowflake and Databricks. So, Snowflake, great, but you got to get the data in there. All right, so that's, can you help with that problem? >> I mean we want simple in, right? And if you have to have structure in, you're not simple. So the idea that you have a simple in, data lake, schema read type philosophy, but schema right type performance. And so what I wanted to do, what we have done, is have that simple lake, and stream that data real time, and those access points of Search or SQL, to go after whatever business case you need, security observability, warehouse integration. But the key thing is, how do I make that click, click, click answer, and do it quickly? And so what we want to do is, that first read has to be fast. Why? 'Cause then you're going to do all this siloing, layers, complexity. If your first read's not fast, you're at a disadvantage, particularly in cost. And nobody says I want less data, but everyone has to, whether they say we're going to shorten the window, we're going to use AI to choose, but in a security moment, when you don't have that answer, you're in trouble. And that's why we are this service, this Supercloud service, if you will, providing access, well-known search, well-known SQL type access, that if you just have one access point, you're at a disadvantage. >> We actually talked about Snowflake and BigQuery, and a different platform, Data Bricks. That's kind of where we see the phase two of ecosystem. One is easy, the low-hanging fruit is observability and security firms. But the next one is, what we do, our super power is dealing with this messy data that schema is changing like night and day. Pipelines are tough, and it's changing all the time, but you want these things fast, and it's big data around the world. That's the next point, just use us alongside, or inside, one of their platforms, and now we get the best of both worlds. Our superpower is keeping this messy data as a streaming, okay, not a batch thing, allow you to do that. So, that's the second one. And then to be honest, the third one, which plays you to Supercloud, it also plays perfectly in the data mesh, is if you really go to the ultimate thing, what we have done is made object storage, S3, GCS, and blob storage, we made it a database. Put, get, complex query with big joins. You know, so back to your original thing, and Muglia teed it up perfectly, we've done that. Now imagine if that's an ecosystem, who would want that? If it's, again, it's uniform available across all the regions, across all the clouds, and it's right next to where you are building a service, or a client's trying, that's where the ecosystem, I think people are going to use Superclouds for their superpowers. We're really good at this, allows that short term. I think the Snowflakes and the Data Bricks are the medium term, you know? And then I think eventually gets to, hey, listen if you can make object storage fast, you can just go after it with simple SQL queries, or elastic. Who would want that? I think that's where people are going to leverage it. It's not going to be one Supercloud, and we leverage the super clouds. >> Our viewpoint is smart object storage can be programmable, and so we agree with Bob, but we're not saying do it here, do it here. This core, fundamental layer across regions, across clouds, that everyone has? Simple in. Right now, it's hard to get data in for access for analysis. So we said, simply, we'll automate the entire process, give you API access across regions, across clouds. And again, how do you do a distributed join that's fast? How do you do a distributed join that doesn't cost you an arm or a leg? And how do you do it at scale? And that's where we've been focused. >> So prior, the cloud object store was a niche. >> Yeah. >> S3 obviously changed that. How standard is, essentially, object store across the different cloud platforms? Is that a problem for you? Is that an easy thing to solve? >> Well, let's talk about it. I mean we've fundamentally, yeah we've extracted it, but fundamentally, cloud object storage, put, get, and list. That's why it's so scalable, 'cause it doesn't have all these other components. That complexity is where we have moved up, and provide direct analytical API access. So because of its simplicity, and costs, and security, and reliability, it can scale naturally. I mean, really, distributed object storage is easy, it's put-get anywhere, now what we've done is we put a layer of intelligence, you know, call it smart object storage, where access is simple. So whether it's multi-region, do a query across, or multicloud, do a query across, or hunting, searching. >> We've had clients doing Amazon and Google, we have some Azure, but we see Amazon and Google more, and it's a consistent service across all of them. Just literally put your data in the bucket of choice, or folder of choice, click a couple buttons, literally click that to say "that's hot," and after that, it's hot, you can see it. But we're not moving data, the data gravity issue, that's the other. That it's already natively flowing to these pools of object storage across different regions and clouds. We don't move it, we index it right there, we're spinning up stateless compute, back to the Supercloud concept. But now that allows us to do all these other things, right? >> And it's no longer just cheap and deep object storage. Right? >> Yeah, we make it the same, like you have an analytic platform regardless of where you're at, you don't have to worry about that. Yeah, we deal with that, we deal with a stateless compute coming up -- >> And make it programmable. Be able to say, "I want this bucket to provide these answers." Right, that's really the hope, the vision. And the complexity to build the entire stack, and then connect them together, we said, the fabric is cloud storage, we just provide the intelligence on top. >> Let's bring it back to the customers, and one of the things we're exploring in Supercloud too is, you know, is Supercloud a solution looking for a problem? Is a multicloud really a problem? I mean, you hear, you know, a lot of the vendor marketing says, "Oh, it's a disaster, because it's all different across the clouds." And I talked to a lot of customers even as part of Supercloud too, they're like, "Well, I solved that problem by just going mono cloud." Well, but then you're not able to take advantage of a lot of the capabilities and the primitives that, you know, like Google's data, or you like Microsoft's simplicity, their RPA, whatever it is. So what are customers telling you, what are their near term problems that they're trying to solve today, and how are they thinking about the future? >> Listen, it's a real problem. I think it started, I think this is a a mega trend, just like cloud. Just, cloud data, and I always add, analytics, are the mega trends. If you're looking at those, if you're not considering using the Supercloud principles, in other words, leveraging what I have, abstracting it out, and getting the most out of that, and then build value on top, I think you're not going to be able to keep up, In fact, no way you're going to keep up with this data volume. It's a geometric challenge, and you're trying to do linear things. So clients aren't necessarily asking, hey, for Supercloud, but they're really saying, I need to have a better mechanism to simplify this and get value across it, and how do you abstract that out to do that? And that's where they're obviously, our conversations are more amazed what we're able to do, and what they're able to do with our platform, because if you think of what we've done, the S3, or GCS, or object storage, is they can't imagine the ingest, they can't imagine how easy, time to glass, one minute, no matter where it lands in the world, querying this in seconds for hundreds of terabytes squared. People are amazed, but that's kind of, so they're not asking for that, but they are amazed. And then when you start talking on it, if you're an enterprise person, you're building a big cloud data platform, or doing data or analytics, if you're not trying to leverage the public clouds, and somehow leverage all of them, and then build on top, then I think you're missing it. So they might not be asking for it, but they're doing it. >> And they're looking for a lens, you mentioned all these different services, how do I bring those together quickly? You know, our viewpoint, our service, is I have all these streams of data, create a lens where they want to go after it via search, go after via SQL, bring them together instantly, no e-tailing out, no define this table, put into this database. We said, let's have a service that creates a lens across all these streams, and then make those connections. I want to take my CRM with my Google AdWords, and maybe my Salesforce, how do I do analysis? Maybe I want to hunt first, maybe I want to join, maybe I want to add another stream to it. And so our viewpoint is, it's so natural to get into these lake platforms and then provide lenses to get that access. >> And they don't want it separate, they don't want something different here, and different there. They want it basically -- >> So this is our industry, right? If something new comes out, remember virtualization came out, "Oh my God, this is so great, it's going to solve all these problems." And all of a sudden it just got to be this big, more complex thing. Same thing with cloud, you know? It started out with S3, and then EC2, and now hundreds and hundreds of different services. So, it's a complex matter for a lot of people, and this creates problems for customers, especially when you got divisions that are using different clouds, and you're saying that the solution, or a solution for the part of the problem, is to really allow the data to stay in place on S3, use that standard, super simple, but then give it what, Ed, you've called superpower a couple of times, to make it fast, make it inexpensive, and allow you to do that across clouds. >> Yeah, yeah. >> I'll give you guys the last word on that. >> No, listen, I think, we think Supercloud allows you to do a lot more. And for us, data, everyone says more data, more problems, more budget issue, everyone knows more data is better, and we show you how to do it cost effectively at scale. And we couldn't have done it without the design principles of we're leveraging the Supercloud to get capabilities, and because we use super, just the object storage, we're able to get these capabilities of ingest, scale, cost effectiveness, and then we built on top of this. In the end, a database is a data platform that allows you to go after everything distributed, and to get one platform for analytics, no matter where it lands, that's where we think the Supercloud concepts are perfect, that's where our clients are seeing it, and we're kind of excited about it. >> Yeah a third generation database, Supercloud database, however we want to phrase it, and make it simple, but provide the value, and make it instant. >> Guys, thanks so much for coming into the studio today, I really thank you for your support of theCUBE, and theCUBE community, it allows us to provide events like this and free content. I really appreciate it. >> Oh, thank you. >> Thank you. >> All right, this is Dave Vellante for John Furrier in theCUBE community, thanks for being with us today. You're watching Supercloud 2, keep it right there for more thought provoking discussions around the future of cloud and data. (bright music)
SUMMARY :
And the third thing that we want to do I'm going to put you right but if you do it right, So the conversation that we were having I like to say we're not a and you see their So, to me, if you can crack that code, and you need to get the you can get your use cases, But the key thing is you cracked the code. We had to crack the code, right? And then once you do that, So, we agree with Bob's. and where do you fit into the ecosystem? and we give you uniformity access to that so you think about Snowflake. So the idea that you have are the medium term, you know? and so we agree with Bob, So prior, the cloud that an easy thing to solve? you know, call it smart object storage, and after that, it's hot, you can see it. And it's no longer just you don't have to worry about And the complexity to and one of the things we're and how do you abstract it's so natural to get and different there. and allow you to do that across clouds. I'll give you guys and we show you how to do it but provide the value, I really thank you for around the future of cloud and data.
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Daren Brabham & Erik Bradley | What the Spending Data Tells us About Supercloud
(gentle synth music) (music ends) >> Welcome back to Supercloud 2, an open industry collaboration between technologists, consultants, analysts, and of course practitioners to help shape the future of cloud. At this event, one of the key areas we're exploring is the intersection of cloud and data. And how building value on top of hyperscale clouds and across clouds is evolving, a concept of course we call "Supercloud". And we're pleased to welcome our friends from Enterprise Technology research, Erik Bradley and Darren Brabham. Guys, thanks for joining us, great to see you. we love to bring the data into these conversations. >> Thank you for having us, Dave, I appreciate it. >> Yeah, thanks. >> You bet. And so, let me do the setup on what is Supercloud. It's a concept that we've floated, Before re:Invent 2021, based on the idea that cloud infrastructure is becoming ubiquitous, incredibly powerful, but there's a lack of standards across the big three clouds. That creates friction. So we defined over the period of time, you know, better part of a year, a set of essential elements, deployment models for so-called supercloud, which create this common experience for specific cloud services that, of course, again, span multiple clouds and even on-premise data. So Erik, with that as background, I wonder if you could add your general thoughts on the term supercloud, maybe play proxy for the CIO community, 'cause you do these round tables, you talk to these guys all the time, you gather a lot of amazing information from senior IT DMs that compliment your survey. So what are your thoughts on the term and the concept? >> Yeah, sure. I'll even go back to last year when you and I did our predictions panel, right? And we threw it out there. And to your point, you know, there's some haters. Anytime you throw out a new term, "Is it marketing buzz? Is it worth it? Why are you even doing it?" But you know, from my own perspective, and then also speaking to the IT DMs that we interview on a regular basis, this is just a natural evolution. It's something that's inevitable in enterprise tech, right? The internet was not built for what it has become. It was never intended to be the underlying infrastructure of our daily lives and work. The cloud also was not built to be what it's become. But where we're at now is, we have to figure out what the cloud is and what it needs to be to be scalable, resilient, secure, and have the governance wrapped around it. And to me that's what supercloud is. It's a way to define operantly, what the next generation, the continued iteration and evolution of the cloud and what its needs to be. And that's what the supercloud means to me. And what depends, if you want to call it metacloud, supercloud, it doesn't matter. The point is that we're trying to define the next layer, the next future of work, which is inevitable in enterprise tech. Now, from the IT DM perspective, I have two interesting call outs. One is from basically a senior developer IT architecture and DevSecOps who says he uses the term all the time. And the reason he uses the term, is that because multi-cloud has a stigma attached to it, when he is talking to his business executives. (David chuckles) the stigma is because it's complex and it's expensive. So he switched to supercloud to better explain to his business executives and his CFO and his CIO what he's trying to do. And we can get into more later about what it means to him. But the inverse of that, of course, is a good CSO friend of mine for a very large enterprise says the concern with Supercloud is the reduction of complexity. And I'll explain, he believes anything that takes the requirement of specific expertise out of the equation, even a little bit, as a CSO worries him. So as you said, David, always two sides to the coin, but I do believe supercloud is a relevant term, and it is necessary because the cloud is continuing to be defined. >> You know, that's really interesting too, 'cause you know, Darren, we use Snowflake a lot as an example, sort of early supercloud, and you think from a security standpoint, we've always pushed Amazon and, "Are you ever going to kind of abstract the complexity away from all these primitives?" and their position has always been, "Look, if we produce these primitives, and offer these primitives, we we can move as the market moves. When you abstract, then it becomes harder to peel the layers." But Darren, from a data standpoint, like I say, we use Snowflake a lot. I think of like Tim Burners-Lee when Web 2.0 came out, he said, "Well this is what the internet was always supposed to be." So in a way, you know, supercloud is maybe what multi-cloud was supposed to be. But I mean, you think about data sharing, Darren, across clouds, it's always been a challenge. Snowflake always, you know, obviously trying to solve that problem, as are others. But what are your thoughts on the concept? >> Yeah, I think the concept fits, right? It is reflective of, it's a paradigm shift, right? Things, as a pendulum have swung back and forth between needing to piece together a bunch of different tools that have specific unique use cases and they're best in breed in what they do. And then focusing on the duct tape that holds 'em all together and all the engineering complexity and skill, it shifted from that end of the pendulum all the way back to, "Let's streamline this, let's simplify it. Maybe we have budget crunches and we need to consolidate tools or eliminate tools." And so then you kind of see this back and forth over time. And with data and analytics for instance, a lot of organizations were trying to bring the data closer to the business. That's where we saw self-service analytics coming in. And tools like Snowflake, what they did was they helped point to different databases, they helped unify data, and organize it in a single place that was, you know, in a sense neutral, away from a single cloud vendor or a single database, and allowed the business to kind of be more flexible in how it brought stuff together and provided it out to the business units. So Snowflake was an example of one of those times where we pulled back from the granular, multiple points of the spear, back to a simple way to do things. And I think Snowflake has continued to kind of keep that mantle to a degree, and we see other tools trying to do that, but that's all it is. It's a paradigm shift back to this kind of meta abstraction layer that kind of simplifies what is the reality, that you need a complex multi-use case, multi-region way of doing business. And it sort of reflects the reality of that. >> And you know, to me it's a spectrum. As part of Supercloud 2, we're talking to a number of of practitioners, Ionis Pharmaceuticals, US West, we got Walmart. And it's a spectrum, right? In some cases the practitioner's saying, "You know, the way I solve multi-cloud complexity is mono-cloud, I just do one cloud." (laughs) Others like Walmart are saying, "Hey, you know, we actually are building an abstraction layer ourselves, take advantage of it." So my general question to both of you is, is this a concept, is the lack of standards across clouds, you know, really a problem, you know, or is supercloud a solution looking for a problem? Or do you hear from practitioners that "No, this is really an issue, we have to bring together a set of standards to sort of unify our cloud estates." >> Allow me to answer that at a higher level, and then we're going to hand it over to Dr. Brabham because he is a little bit more detailed on the realtime streaming analytics use cases, which I think is where we're going to get to. But to answer that question, it really depends on the size and the complexity of your business. At the very large enterprise, Dave, Yes, a hundred percent. This needs to happen. There is complexity, there is not only complexity in the compute and actually deploying the applications, but the governance and the security around them. But for lower end or, you know, business use cases, and for smaller businesses, it's a little less necessary. You certainly don't need to have all of these. Some of the things that come into mind from the interviews that Darren and I have done are, you know, financial services, if you're doing real-time trading, anything that has real-time data metrics involved in your transactions, is going to be necessary. And another use case that we hear about is in online travel agencies. So I think it is very relevant, the complexity does need to be solved, and I'll allow Darren to explain a little bit more about how that's used from an analytics perspective. >> Yeah, go for it. >> Yeah, exactly. I mean, I think any modern, you know, multinational company that's going to have a footprint in the US and Europe, in China, or works in different areas like manufacturing, where you're probably going to have on-prem instances that will stay on-prem forever, for various performance reasons. You have these complicated governance and security and regulatory issues. So inherently, I think, large multinational companies and or companies that are in certain areas like finance or in, you know, online e-commerce, or things that need real-time data, they inherently are going to have a very complex environment that's going to need to be managed in some kind of cleaner way. You know, they're looking for one door to open, one pane of glass to look at, one thing to do to manage these multi points. And, streaming's a good example of that. I mean, not every organization has a real-time streaming use case, and may not ever, but a lot of organizations do, a lot of industries do. And so there's this need to use, you know, they want to use open-source tools, they want to use Apache Kafka for instance. They want to use different megacloud vendors offerings, like Google Pub/Sub or you know, Amazon Kinesis Firehose. They have all these different pieces they want to use for different use cases at different stages of maturity or proof of concept, you name it. They're going to have to have this complexity. And I think that's why we're seeing this need, to have sort of this supercloud concept, to juggle all this, to wrangle all of it. 'Cause the reality is, it's complex and you have to simplify it somehow. >> Great, thanks you guys. All right, let's bring up the graphic, and take a look. Anybody who follows the breaking analysis, which is co-branded with ETR Cube Insights powered by ETR, knows we like to bring data to the table. ETR does amazing survey work every quarter, 1200 plus 1500 practitioners that that answer a number of questions. The vertical axis here is net score, which is ETR's proprietary methodology, which is a measure of spending momentum, spending velocity. And the horizontal axis here is overlap, but it's the presence pervasiveness, and the dataset, the ends, that table insert on the bottom right shows you how the dots are plotted, the net score and then the ends in the survey. And what we've done is we've plotted a bunch of the so-called supercloud suspects, let's start in the upper right, the cloud platforms. Without these hyperscale clouds, you can't have a supercloud. And as always, Azure and AWS, up and to the right, it's amazing we're talking about, you know, 80 plus billion dollar company in AWS. Azure's business is, if you just look at the IaaS is in the 50 billion range, I mean it's just amazing to me the net scores here. Anything above 40% we consider highly elevated. And you got Azure and you got Snowflake, Databricks, HashiCorp, we'll get to them. And you got AWS, you know, right up there at that size, it's quite amazing. With really big ends as well, you know, 700 plus ends in the survey. So, you know, kind of half the survey actually has these platforms. So my question to you guys is, what are you seeing in terms of cloud adoption within the big three cloud players? I wonder if you could could comment, maybe Erik, you could start. >> Yeah, sure. Now we're talking data, now I'm happy. So yeah, we'll get into some of it. Right now, the January, 2023 TSIS is approaching 1500 survey respondents. One caveat, it's not closed yet, it will close on Friday, but with an end that big we are over statistically significant. We also recently did a cloud survey, and there's a couple of key points on that I want to get into before we get into individual vendors. What we're seeing here, is that annual spend on cloud infrastructure is expected to grow at almost a 70% CAGR over the next three years. The percentage of those workloads for cloud infrastructure are expected to grow over 70% as three years as well. And as you mentioned, Azure and AWS are still dominant. However, we're seeing some share shift spreading around a little bit. Now to get into the individual vendors you mentioned about, yes, Azure is still number one, AWS is number two. What we're seeing, which is incredibly interesting, CloudFlare is number three. It's actually beating GCP. That's the first time we've seen it. What I do want to state, is this is on net score only, which is our measure of spending intentions. When you talk about actual pervasion in the enterprise, it's not even close. But from a spending velocity intention point of view, CloudFlare is now number three above GCP, and even Salesforce is creeping up to be at GCPs level. So what we're seeing here, is a continued domination by Azure and AWS, but some of these other players that maybe might fit into your moniker. And I definitely want to talk about CloudFlare more in a bit, but I'm going to stop there. But what we're seeing is some of these other players that fit into your Supercloud moniker, are starting to creep up, Dave. >> Yeah, I just want to clarify. So as you also know, we track IaaS and PaaS revenue and we try to extract, so AWS reports in its quarterly earnings, you know, they're just IaaS and PaaS, they don't have a SaaS play, a little bit maybe, whereas Microsoft and Google include their applications and so we extract those out and if you do that, AWS is bigger, but in the surveys, you know, customers, they see cloud, SaaS to them as cloud. So that's one of the reasons why you see, you know, Microsoft as larger in pervasion. If you bring up that survey again, Alex, the survey results, you see them further to the right and they have higher spending momentum, which is consistent with what you see in the earnings calls. Now, interesting about CloudFlare because the CEO of CloudFlare actually, and CloudFlare itself uses the term supercloud basically saying, "Hey, we're building a new type of internet." So what are your thoughts? Do you have additional information on CloudFlare, Erik that you want to share? I mean, you've seen them pop up. I mean this is a really interesting company that is pretty forward thinking and vocal about how it's disrupting the industry. >> Sure, we've been tracking 'em for a long time, and even from the disruption of just a traditional CDN where they took down Akamai and what they're doing. But for me, the definition of a true supercloud provider can't just be one instance. You have to have multiple. So it's not just the cloud, it's networking aspect on top of it, it's also security. And to me, CloudFlare is the only one that has all of it. That they actually have the ability to offer all of those things. Whereas you look at some of the other names, they're still piggybacking on the infrastructure or platform as a service of the hyperscalers. CloudFlare does not need to, they actually have the cloud, the networking, and the security all themselves. So to me that lends credibility to their own internal usage of that moniker Supercloud. And also, again, just what we're seeing right here that their net score is now creeping above AGCP really does state it. And then just one real last thing, one of the other things we do in our surveys is we track adoption and replacement reasoning. And when you look at Cloudflare's adoption rate, which is extremely high, it's based on technical capabilities, the breadth of their feature set, it's also based on what we call the ability to avoid stack alignment. So those are again, really supporting reasons that makes CloudFlare a top candidate for your moniker of supercloud. >> And they've also announced an object store (chuckles) and a database. So, you know, that's going to be, it takes a while as you well know, to get database adoption going, but you know, they're ambitious and going for it. All right, let's bring the chart back up, and I want to focus Darren in on the ecosystem now, and really, we've identified Snowflake and Databricks, it's always fun to talk about those guys, and there are a number of other, you know, data platforms out there, but we use those too as really proxies for leaders. We got a bunch of the backup guys, the data protection folks, Rubric, Cohesity, and Veeam. They're sort of in a cluster, although Rubric, you know, ahead of those guys in terms of spending momentum. And then VMware, Tanzu and Red Hat as sort of the cross cloud platform. But I want to focus, Darren, on the data piece of it. We're seeing a lot of activity around data sharing, governed data sharing. Databricks is using Delta Sharing as their sort of place, Snowflakes is sort of this walled garden like the app store. What are your thoughts on, you know, in the context of Supercloud, cross cloud capabilities for the data platforms? >> Yeah, good question. You know, I think Databricks is an interesting player because they sort of have made some interesting moves, with their Data Lakehouse technology. So they're trying to kind of complicate, or not complicate, they're trying to take away the complications of, you know, the downsides of data warehousing and data lakes, and trying to find that middle ground, where you have the benefits of a managed, governed, you know, data warehouse environment, but you have sort of the lower cost, you know, capability of a data lake. And so, you know, Databricks has become really attractive, especially by data scientists, right? We've been tracking them in the AI machine learning sector for quite some time here at ETR, attractive for a data scientist because it looks and acts like a lake, but can have some managed capabilities like a warehouse. So it's kind of the best of both worlds. So in some ways I think you've seen sort of a data science driver for the adoption of Databricks that has now become a little bit more mainstream across the business. Snowflake, maybe the other direction, you know, it's a cloud data warehouse that you know, is starting to expand its capabilities and add on new things like Streamlit is a good example in the analytics space, with apps. So you see these tools starting to branch and creep out a bit, but they offer that sort of neutrality, right? We heard one IT decision maker we recently interviewed that referred to Snowflake and Databricks as the quote unquote Switzerland of what they do. And so there's this desirability from an organization to find these tools that can solve the complex multi-headed use-case of data and analytics, which every business unit needs in different ways. And figure out a way to do that, an elegant way that's governed and centrally managed, that federated kind of best of both worlds that you get by bringing the data close to the business while having a central governed instance. So these tools are incredibly powerful and I think there's only going to be room for growth, for those two especially. I think they're going to expand and do different things and maybe, you know, join forces with others and a lot of the power of what they do well is trying to define these connections and find these partnerships with other vendors, and try to be seen as the nice add-on to your existing environment that plays nicely with everyone. So I think that's where those two tools are going, but they certainly fit this sort of label of, you know, trying to be that supercloud neutral, you know, layer that unites everything. >> Yeah, and if you bring the graphic back up, please, there's obviously big data plays in each of the cloud platforms, you know, Microsoft, big database player, AWS is, you know, 11, 12, 15, data stores. And of course, you know, BigQuery and other, you know, data platforms within Google. But you know, I'm not sure the big cloud guys are going to go hard after so-called supercloud, cross-cloud services. Although, we see Oracle getting in bed with Microsoft and Azure, with a database service that is cross-cloud, certainly Google with Anthos and you know, you never say never with with AWS. I guess what I would say guys, and I'll I'll leave you with this is that, you know, just like all players today are cloud players, I feel like anybody in the business or most companies are going to be so-called supercloud players. In other words, they're going to have a cross-cloud strategy, they're going to try to build connections if they're coming from on-prem like a Dell or an HPE, you know, or Pure or you know, many of these other companies, Cohesity is another one. They're going to try to connect to their on-premise states, of course, and create a consistent experience. It's natural that they're going to have sort of some consistency across clouds. You know, the big question is, what's that spectrum look like? I think on the one hand you're going to have some, you know, maybe some rudimentary, you know, instances of supercloud or maybe they just run on the individual clouds versus where Snowflake and others and even beyond that are trying to go with a single global instance, basically building out what I would think of as their own cloud, and importantly their own ecosystem. I'll give you guys the last thought. Maybe you could each give us, you know, closing thoughts. Maybe Darren, you could start and Erik, you could bring us home on just this entire topic, the future of cloud and data. >> Yeah, I mean I think, you know, two points to make on that is, this question of these, I guess what we'll call legacy on-prem players. These, mega vendors that have been around a long time, have big on-prem footprints and a lot of people have them for that reason. I think it's foolish to assume that a company, especially a large, mature, multinational company that's been around a long time, it's foolish to think that they can just uproot and leave on-premises entirely full scale. There will almost always be an on-prem footprint from any company that was not, you know, natively born in the cloud after 2010, right? I just don't think that's reasonable anytime soon. I think there's some industries that need on-prem, things like, you know, industrial manufacturing and so on. So I don't think on-prem is going away, and I think vendors that are going to, you know, go very cloud forward, very big on the cloud, if they neglect having at least decent connectors to on-prem legacy vendors, they're going to miss out. So I think that's something that these players need to keep in mind is that they continue to reach back to some of these players that have big footprints on-prem, and make sure that those integrations are seamless and work well, or else their customers will always have a multi-cloud or hybrid experience. And then I think a second point here about the future is, you know, we talk about the three big, you know, cloud providers, the Google, Microsoft, AWS as sort of the opposite of, or different from this new supercloud paradigm that's emerging. But I want to kind of point out that, they will always try to make a play to become that and I think, you know, we'll certainly see someone like Microsoft trying to expand their licensing and expand how they play in order to become that super cloud provider for folks. So also don't want to downplay them. I think you're going to see those three big players continue to move, and take over what players like CloudFlare are doing and try to, you know, cut them off before they get too big. So, keep an eye on them as well. >> Great points, I mean, I think you're right, the first point, if you're Dell, HPE, Cisco, IBM, your strategy should be to make your on-premise state as cloud-like as possible and you know, make those differences as minimal as possible. And you know, if you're a customer, then the business case is going to be low for you to move off of that. And I think you're right. I think the cloud guys, if this is a real problem, the cloud guys are going to play in there, and they're going to make some money at it. Erik, bring us home please. >> Yeah, I'm going to revert back to our data and this on the macro side. So to kind of support this concept of a supercloud right now, you know Dave, you and I know, we check overall spending and what we're seeing right now is total year spent is expected to only be 4.6%. We ended 2022 at 5% even though it began at almost eight and a half. So this is clearly declining and in that environment, we're seeing the top two strategies to reduce spend are actually vendor consolidation with 36% of our respondents saying they're actively seeking a way to reduce their number of vendors, and consolidate into one. That's obviously supporting a supercloud type of play. Number two is reducing excess cloud resources. So when I look at both of those combined, with a drop in the overall spending reduction, I think you're on the right thread here, Dave. You know, the overall macro view that we're seeing in the data supports this happening. And if I can real quick, couple of names we did not touch on that I do think deserve to be in this conversation, one is HashiCorp. HashiCorp is the number one player in our infrastructure sector, with a 56% net score. It does multiple things within infrastructure and it is completely agnostic to your environment. And if we're also speaking about something that's just a singular feature, we would look at Rubric for data, backup, storage, recovery. They're not going to offer you your full cloud or your networking of course, but if you are looking for your backup, recovery, and storage Rubric, also number one in that sector with a 53% net score. Two other names that deserve to be in this conversation as we watch it move and evolve. >> Great, thank you for bringing that up. Yeah, we had both of those guys in the chart and I failed to focus in on HashiCorp. And clearly a Supercloud enabler. All right guys, we got to go. Thank you so much for joining us, appreciate it. Let's keep this conversation going. >> Always enjoy talking to you Dave, thanks. >> Yeah, thanks for having us. >> All right, keep it right there for more content from Supercloud 2. This is Dave Valente for John Ferg and the entire Cube team. We'll be right back. (gentle synth music) (music fades)
SUMMARY :
is the intersection of cloud and data. Thank you for having period of time, you know, and evolution of the cloud So in a way, you know, supercloud the data closer to the business. So my general question to both of you is, the complexity does need to be And so there's this need to use, you know, So my question to you guys is, And as you mentioned, Azure but in the surveys, you know, customers, the ability to offer and there are a number of other, you know, and maybe, you know, join forces each of the cloud platforms, you know, the three big, you know, And you know, if you're a customer, you and I know, we check overall spending and I failed to focus in on HashiCorp. to you Dave, thanks. Ferg and the entire Cube team.
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Chat w/ Arctic Wolf exec re: budget restraints could lead to lax cloud security
>> Now we're recording. >> All right. >> Appreciate that, Hannah. >> Yeah, so I mean, I think in general we continue to do very, very well as a company. I think like everybody, there's economic headwinds today that are unavoidable, but I think we have a couple things going for us. One, we're in the cyberspace, which I think is, for the most part, recession proof as an industry. I think the impact of a recession will impact some vendors and some categories, but in general, I think the industry is pretty resilient. It's like the power industry, no? Recession or not, you still need electricity to your house. Cybersecurity is almost becoming a utility like that as far as the needs of companies go. I think for us, we also have the ability to do the security, the security operations, for a lot of companies, and if you look at the value proposition, the ROI for the cost of less than one to maybe two or three, depending on how big you are as a customer, what you'd have to pay for half to three security operations people, we can give you a full security operations. And so the ROI is is almost kind of brain dead simple, and so that keeps us going pretty well. And I think the other areas, we remove all that complexity for people. So in a world where you got other problems to worry about, handling all the security complexity is something that adds to that ROI. So for us, I think what we're seeing is mostly is some of the larger deals are taking a little bit longer than they have, some of the large enterprise deals, 'cause I think they are being a little more cautious about how they spend it, but in general, business is still kind of cranking along. >> Anything you can share with me that you guys have talked about publicly in terms of any metrics, or what can you tell me other than cranking? >> Yeah, I mean, I would just say we're still very, very high growth, so I think our financial profile would kind of still put us clearly in the cyber unicorn position, but I think other than that, we don't really share business metrics as a private- >> Okay, so how about headcount? >> Still growing. So we're not growing as fast as we've been growing, but I don't think we were anyway. I think we kind of, we're getting to the point of critical mass. We'll start to grow in a more kind of normal course and speed. I don't think we overhired like a lot of companies did in the past, even though we added, almost doubled the size of the company in the last 18 months. So we're still hiring, but very kind of targeted to certain roles going forward 'cause I do think we're kind of at critical mass in some of the other functions. >> You disclose headcount or no? >> We do not. >> You don't, okay. And never have? >> Not that I'm aware of, no. >> Okay, on the macro, I don't know if security's recession proof, but it's less susceptible, let's say. I've had Nikesh Arora on recently, we're at Palo Alto's Ignite, and he was saying, "Look," it's just like you were saying, "Larger deal's a little harder." A lot of times customers, he was saying customers are breaking larger deals into smaller deals, more POCs, more approvals, more people to get through the approval, not whole, blah, blah, blah. Now they're a different animal, I understand, but are you seeing similar trends, and how are you dealing with that? >> Yeah, I think the exact same trends, and I think it's just in a world where spending a dollar matters, I think a lot more oversight comes into play, a lot more reviewers, and can you shave it down here? Can you reduce the scope of the project to save money there? And I think it just caused a lot of those things. I think, in the large enterprise, I think most of those deals for companies like us and Palo and CrowdStrike and kind of the upper tier companies, they'll still go through. I think they'll just going to take a lot longer, and, yeah, maybe they're 80% of what they would've been otherwise, but there's still a lot of business to be had out there. >> So how are you dealing with that? I mean, you're talking about you double the size of the company. Is it kind of more focused on go-to-market, more sort of, maybe not overlay, but sort of SE types that are going to be doing more handholding. How have you dealt with that? Or have you just sort of said, "Hey, it is what it is, and we're not going to, we're not going to tactically respond to. We got long-term direction"? >> Yeah, I think it's more the latter. I think for us, it's we've gone through all these things before. It just takes longer now. So a lot of the steps we're taking are the same steps. We're still involved in a lot of POCs, we're involved in a lot of demos, and I don't think that changed. It's just the time between your POC and when someone sends you the PO, there's five more people now got to review things and go through a budget committee and all sorts of stuff like that. I think where we're probably focused more now is adding more and more capabilities just so we continue to be on the front foot of innovation and being relevant to the market, and trying to create more differentiators for us and the competitors. That's something that's just built into our culture, and we don't want to slow that down. And so even though the business is still doing extremely, extremely well, we want to keep investing in kind of technology. >> So the deal size, is it fair to say the initial deal size for new accounts, while it may be smaller, you're adding more capabilities, and so over time, your average contract values will go up? Are you seeing that trend? Or am I- >> Well, I would say I don't even necessarily see our average deal size has gotten smaller. I think in total, it's probably gotten a little bigger. I think what happens is when something like this happens, the old cream rises to the top thing, I think, comes into play, and you'll see some organizations instead of doing a deal with three or four vendors, they may want to pick one or two and really kind of put a lot of energy behind that. For them, they're maybe spending a little less money, but for those vendors who are amongst those getting chosen, I think they're doing pretty good. So our average deal size is pretty stable. For us, it's just a temporal thing. It's just the larger deals take a little bit longer. I don't think we're seeing much of a deal velocity difference in our mid-market commercial spaces, but in the large enterprise it's a little bit slower. But for us, we have ambitious plans in our strategy or on how we want to execute and what we want to build, and so I think we want to just continue to make sure we go down that path technically. >> So I have some questions on sort of the target markets and the cohorts you're going after, and I have some product questions. I know we're somewhat limited on time, but the historical focus has been on SMB, and I know you guys have gone in into enterprise. I'm curious as to how that's going. Any guidance you can give me on mix? Or when I talk to the big guys, right, you know who they are, the big managed service providers, MSSPs, and they're like, "Poo poo on Arctic Wolf," like, "Oh, they're (groans)." I said, "Yeah, that's what they used to say about the PC. It's just a toy. Or Microsoft SQL Server." But so I kind of love that narrative for you guys, but I'm curious from your words as to, what is that enterprise? How's the historical business doing, and how's the entrance into the enterprise going? What kind of hurdles are you having, blockers are you having to remove? Any color you can give me there would be super helpful. >> Yeah, so I think our commercial S&B business continues to do really good. Our mid-market is a very strong market for us. And I think while a lot of companies like to focus purely on large enterprise, there's a lot more mid-market companies, and a much larger piece of the IT puzzle collectively is in mid-market than it is large enterprise. That being said, we started to get pulled into the large enterprise not because we're a toy but because we're quite a comprehensive service. And so I think what we're trying to do from a roadmap perspective is catch up with some of the kind of capabilities that a large enterprise would want from us that a potential mid-market customer wouldn't. In some case, it's not doing more. It's just doing it different. Like, so we have a very kind of hands-on engagement with some of our smaller customers, something we call our concierge. Some of the large enterprises want more of a hybrid where they do some stuff and you do some stuff. And so kind of building that capability into the platform is something that's really important for us. Just how we engage with them as far as giving 'em access to their data, the certain APIs they want, things of that nature, what we're building out for large enterprise, but the demand by large enterprise on our business is enormous. And so it's really just us kind of catching up with some of the kind of the features that they want that we lack today, but many of 'em are still signing up with us, obviously, and in lieu of that, knowing that it's coming soon. And so I think if you look at the growth of our large enterprise, it's one of our fastest growing segments, and I think it shows anything but we're a toy. I would be shocked, frankly, if there's an MSSP, and, of course, we don't see ourself as an MSSP, but I'd be shocked if any of them operate a platform at the scale that ours operates. >> Okay, so wow. A lot I want to unpack there. So just to follow up on that last question, you don't see yourself as an MSSP because why, you see yourselves as a technology platform? >> Yes, I mean, the vast, vast, vast majority of what we deliver is our own technology. So we integrate with third-party solutions mostly to bring in that telemetry. So we've built our own platform from the ground up. We have our own threat intelligence, our own detection logic. We do have our own agents and network sensors. MSSP is typically cobbling together other tools, third party off-the-shelf tools to run their SOC. Ours is all homegrown technology. So I have a whole group called Arctic Wolf Labs, is building, just cranking out ML-based detections, building out infrastructure to take feeds in from a variety of different sources. We have a full integration kind of effort where we integrate into other third parties. So when we go into a customer, we can leverage whatever they have, but at the same time, we produce some tech that if they're lacking in a certain area, we can provide that tech, particularly around things like endpoint agents and network sensors and the like. >> What about like identity, doing your own identity? >> So we don't do our own identity, but we take feeds in from things like Okta and Active Directory and the like, and we have detection logic built on top of that. So part of our value add is we were XDR before XDR was the cool thing to talk about, meaning we can look across multiple attack surfaces and come to a security conclusion where most EDR vendors started with looking just at the endpoint, right? And then they called themselves XDR because now they took in a network feed, but they still looked at it as a separate network detection. We actually look at the things across multiple attack surfaces and stitch 'em together to look at that from a security perspective. In some cases we have automatic detections that will fire. In other cases, we can surface some to a security professional who can go start pulling on that thread. >> So you don't need to purchase CrowdStrike software and integrate it. You have your own equivalent essentially. >> Well, we'll take a feed from the CrowdStrike endpoint into our platform. We don't have to rely on their detections and their alerts, and things of that nature. Now obviously anything they discover we pull in as well, it's just additional context, but we have all our own tech behind it. So we operate kind of at an MSSP scale. We have a similar value proposition in the sense that we'll use whatever the customer has, but once that data kind of comes into our pipeline, it's all our own homegrown tech from there. >> But I mean, what I like about the MSSP piece of your business is it's very high touch. It's very intimate. What I like about what you're saying is that it's software-like economics, so software, software-like part of it. >> That's what makes us the unicorn, right? Is we do have, our concierges is very hands-on. We continue to drive automation that makes our concierge security professionals more efficient, but we always want that customer to have that concierge person as, is almost an extension to their security team, or in some cases, for companies that don't even have a security team, as their security team. As we go down the path, as I mentioned, one of the things we want to be able to do is start to have a more flexible model where we can have that high touch if you want it. We can have the high touch on certain occasions, and you can do stuff. We can have low touch, like we can span the spectrum, but we never want to lose our kind of unique value proposition around the concierge, but we also want to make sure that we're providing an interface that any customer would want to use. >> So given that sort of software-like economics, I mean, services companies need this too, but especially in software, things like net revenue retention and churn are super important. How are those metrics looking? What can you share with me there? >> Yeah, I mean, again, we don't share those metrics publicly, but all's I can continue to repeat is, if you looked at all of our financial metrics, I think you would clearly put us in the unicorn category. I think very few companies are going to have the level of growth that we have on the amount of ARR that we have with the net revenue retention and the churn and upsell. All those aspects continue to be very, very strong for us. >> I want to go back to the sort of enterprise conversation. So large enterprises would engage with you as a complement to their existing SOC, correct? Is that a fair statement or not necessarily? >> It's in some cases. In some cases, they're looking to not have a SOC. So we run into a lot of cases where they want to replace their SIEM, and they want a solution like Arctic Wolf to do that. And so there's a poll, I can't remember, I think it was Forrester, IDC, one of them did it a couple years ago, and they found out that 70% of large enterprises do not want to build the SOC, and it's not 'cause they don't need one, it's 'cause they can't afford it, they can't staff it, they don't have the expertise. And you think about if you're a tech company or a bank, or something like that, of course you can do it, but if you're an international plumbing distributor, you're not going to (chuckles), someone's not going to graduate from Stanford with a cybersecurity degree and go, "Cool, I want to go work for a plumbing distributor in their SOC," right? So they're going to have trouble kind of bringing in the right talent, and as a result, it's difficult to go make a multimillion-dollar investment into a SOC if you're not going to get the quality people to operate it, so they turn to companies like us. >> Got it, so, okay, so you're talking earlier about capabilities that large enterprises require that there might be some gaps, you might lack some features. A couple questions there. One is, when you do some of those, I inferred some of that is integrations. Are those integrations sort of one-off snowflakes or are you finding that you're able to scale those across the large enterprises? That's my first question. >> Yeah, so most of the integrations are pretty straightforward. I think where we run into things that are kind of enterprise-centric, they definitely want open APIs, they want access to our platform, which we don't do today, which we are going to be doing, but we don't do that yet today. They want to do more of a SIEM replacement. So we're really kind of what we call an open XDR platform, so there's things that we would need to build to kind of do raw log ingestion. I mean, we do this today. We have raw log ingestion, we have log storage, we have log searching, but there's like some of the compliance scenarios that they need out of their SIEM. We don't do those today. And so that's kind of holding them back from getting off their SIEM and going fully onto a solution like ours. Then the other one is kind of the level of customization, so the ability to create a whole bunch of custom rules, and that ties back to, "I want to get off my SIEM. I've built all these custom rules in my SIEM, and it's great that you guys do all this automatic AI stuff in the background, but I need these very specific things to be executed on." And so trying to build an interface for them to be able to do that and then also simulate it, again, because, no matter how big they are running their SIEM and their SOC... Like, we talked to one of the largest financial institutions in the world. As far as we were told, they have the largest individual company SOC in the world, and we operate almost 15 times their size. So we always have to be careful because this is a cloud-based native platform, but someone creates some rule that then just craters the performance of the whole platform, so we have to build kind of those guardrails around it. So those are the things primarily that the large enterprises are asking for. Most of those issues are not holding them back from coming. They want to know they're coming, and we're working on all of those. >> Cool, and see, just aside, I was talking to CISO the other day, said, "If it weren't for my compliance and audit group, I would chuck my SIEM." I mean, everybody wants to get rid of their SIEM. >> I've never met anyone who likes their SIEM. >> Do you feel like you've achieved product market fit in the larger enterprise or is that still something that you're sorting out? >> So I think we know, like, we're on a path to do that. We're on a provable path to do that, so I don't think there's any surprises left. I think everything that we know we need to do for that is someone's writing code for it today. It's just a matter of getting it through the system and getting into production. So I feel pretty good about it. I think that's why we are seeing such a high growth rate in our large enterprise business, 'cause we share that feedback with some of those key customers. We have a Customer Advisory Board that we share a lot of this information with. So yeah, I mean, I feel pretty good about what we need to do. We're certainly operate at large enterprise scales, so taking in the amount of the volume of data they're going to have and the types of integrations they need. We're comfortable with that. It's just more or less the interfaces that a large enterprise would want that some of the smaller companies don't ask for. >> Do you have enough tenure in the market to get a sense as to stickiness or even indicators that will lead toward retention? Have you been at it long enough in the enterprise or you still, again, figuring that out? >> Yeah, no, I think we've been at it long enough, and our retention rates are extremely high. If anything, kind of our net retention rates, well over 100% 'cause we have opportunities to upsell into new modules and expanding the coverage of what they have today. I think the areas that if you cornered enterprise that use us and things they would complain about are things I just told you about, right? There's still some things I want to do in my Splunk, and I need an API to pull my data out and put it in my Splunk and stuff like that, and those are the things we want to enable. >> Yeah, so I can't wait till you guys go public because you got Snowflake up here, and you got Veritas down here, and I'm very curious as to where you guys go. When's the IPO? You want to tell me that? (chuckling) >> Unfortunately, it's not up to us right now. You got to get the markets- >> Yeah, I hear you. Right, if the market were better. Well, if the market were better, you think you'd be out? >> Yeah, I mean, we'd certainly be a viable candidate to go. >> Yeah, there you go. I have a question for you because I don't have a SOC. I run a small business with my co-CEO. We're like 30, 40 people W-2s, we got another 50 or so contractors, and I'm always like have one eye, sleep with one eye open 'cause of security. What is your ideal SMB customer? Think S. >> Yeah. >> Would I fit? >> Yeah, I mean you're you're right in the sweet spot. I think where the company started and where we still have a lot of value proposition, which is companies like, like you said it, you sleep with one eye open, but you don't have necessarily the technical acumen to be able to do that security for yourself, and that's where we fit in. We bring kind of this whole security, we call it Security Operations Cloud, to bear, and we have some of the best professionals in the world who can basically be your SOC for less than it would cost you to hire somebody right out of college to do IT stuff. And so the value proposition's there. You're going to get the best of the best, providing you a kind of a security service that you couldn't possibly build on your own, and that way you can go to bed at night and close both eyes. >> So (chuckling) I'm sure something else would keep me up. But so in thinking about that, our Amazon bill keeps growing and growing and growing. What would it, and I presume I can engage with you on a monthly basis, right? As a consumption model, or how's the pricing work? >> Yeah, so there's two models that we have. So typically the kind of the monthly billing type of models would be through one of our MSP partners, where they have monthly billing capabilities. Usually direct with us is more of a longer term deal, could be one, two, or three, or it's up to the customer. And so we have both of those engagement models. Were doing more and more and more through MSPs today because of that model you just described, and they do kind of target the very S in the SMB as well. >> I mean, rough numbers, even ranges. If I wanted to go with the MSP monthly, I mean, what would a small company like mine be looking at a month? >> Honestly, I do not even know the answer to that. >> We're not talking hundreds of thousands of dollars a month? >> No. God, no. God, no. No, no, no. >> I mean, order of magnitude, we're talking thousands, tens of thousands? >> Thousands, on a monthly basis. Yeah. >> Yeah, yeah. Thousands per month. So if I were to budget between 20 and $50,000 a year, I'm definitely within the envelope. Is that fair? I mean, I'm giving a wide range >> That's fair. just to try to make- >> No, that's fair. >> And if I wanted to go direct with you, I would be signing up for a longer term agreement, correct, like I do with Salesforce? >> Yeah, yeah, a year. A year would, I think, be the minimum for that, and, yeah, I think the budget you set aside is kind of right in the sweet spot there. >> Yeah, I'm interested, I'm going to... Have a sales guy call me (chuckles) somehow. >> All right, will do. >> No, I'm serious. I want to start >> I will. >> investigating these things because we sell to very large organizations. I mean, name a tech company. That's our client base, except for Arctic Wolf. We should talk about that. And increasingly they're paranoid about data protection agreements, how you're protecting your data, our data. We write a lot of software and deliver it as part of our services, so it's something that's increasingly important. It's certainly a board level discussion and beyond, and most large organizations and small companies oftentimes don't think about it or try not to. They just put their head in the sand and, "We don't want to be doing that," so. >> Yeah, I will definitely have someone get in touch with you. >> Cool. Let's see. Anything else you can tell me on the product side? Are there things that you're doing that we talked about, the gaps at the high end that you're, some of the features that you're building in, which was super helpful. Anything in the SMB space that you want to share? >> Yeah, I think the biggest thing that we're doing technically now is really trying to drive more and more automation and efficiency through our operations, and that comes through really kind of a generous use of AI. So building models around more efficient detections based upon signal, but also automating the actions of our operators so we can start to learn through the interface. When they do A and B, they always do C. Well, let's just do C for them, stuff like that. Then also building more automation as far as the response back to third-party solutions as well so we can remediate more directly on third-party products without having to get into the consoles or having our customers do it. So that's really just trying to drive efficiency in the system, and that helps provide better security outcomes but also has a big impact on our margins as well. >> I know you got to go, but I want to show you something real quick. I have data. I do a weekly program called "Breaking Analysis," and I have a partner called ETR, Enterprise Technology Research, and they have a platform. I don't know if you can see this. They have a survey platform, and each quarter, they do a survey of about 1,500 IT decision makers. They also have a survey on, they call ETS, Emerging Technology Survey. So it's private companies. And I don't want to go into it too much, but this is a sentiment graph. This is net sentiment. >> Just so you know, all I see is a white- >> Yeah, just a white bar. >> Oh, that's weird. Oh, whiteboard. Oh, here we go. How about that? >> There you go. >> Yeah, so this is a sentiment graph. So this is net sentiment and this is mindshare. And if I go to Arctic Wolf... So it's typical security, right? The 8,000 companies. And when I go here, what impresses me about this is you got a decent mindshare, that's this axis, but you've also got an N in the survey. It's about 1,500 in the survey, It's 479 Arctic Wolf customers responded to this. 57% don't know you. Oh, sorry, they're aware of you, but no plan to evaluate; 19% plan to evaluate, 7% are evaluating; 11%, no plan to utilize even though they've evaluated you; and 1% say they've evaluated you and plan to utilize. It's a small percentage, but actually it's not bad in the random sample of the world about that. And so obviously you want to get that number up, but this is a really impressive position right here that I wanted to just share with you. I do a lot of analysis weekly, and this is a really, it's completely independent survey, and you're sort of separating from the pack, as you can see. So kind of- >> Well, it's good to see that. And I think that just is a further indicator of what I was telling you. We continue to have a strong financial performance. >> Yeah, in a good market. Okay, well, thanks you guys. And hey, if I can get this recording, Hannah, I may even figure out how to write it up. (chuckles) That would be super helpful. >> Yes. We'll get that up. >> And David or Hannah, if you can send me David's contact info so I can get a salesperson in touch with him. (Hannah chuckling) >> Yeah, great. >> Yeah, we'll work on that as well. Thanks so much for both your time. >> Thanks a lot. It was great talking with you. >> Thanks, you guys. Great to meet you. >> Thank you. >> Bye. >> Bye.
SUMMARY :
I think for us, we also have the ability I don't think we overhired And never have? and how are you dealing with that? I think they'll just going to that are going to be So a lot of the steps we're and so I think we want to just continue and the cohorts you're going after, And so I think if you look at the growth So just to follow up but at the same time, we produce some tech and Active Directory and the like, So you don't need to but we have all our own tech behind it. like about the MSSP piece one of the things we want So given that sort of of growth that we have on the So large enterprises would engage with you kind of bringing in the right I inferred some of that is integrations. and it's great that you guys do to get rid of their SIEM. I've never met anyone I think everything that we and expanding the coverage to where you guys go. You got to get the markets- Well, if the market were Yeah, I mean, we'd certainly I have a question for you and that way you can go to bed I can engage with you because of that model you just described, the MSP monthly, I mean, know the answer to that. No. God, no. Thousands, on a monthly basis. I mean, I'm giving just to try to make- is kind of right in the sweet spot there. Yeah, I'm interested, I'm going to... I want to start because we sell to very get in touch with you. doing that we talked about, of our operators so we can start to learn I don't know if you can see this. Oh, here we go. from the pack, as you can see. And I think that just I may even figure out how to write it up. if you can send me David's contact info Thanks so much for both your time. great talking with you. Great to meet you.
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Breaking Analysis: Cloud players sound a cautious tone for 2023
>> From the Cube Studios in Palo Alto in Boston bringing you data-driven insights from the Cube and ETR. This is Breaking Analysis with Dave Vellante. >> The unraveling of market enthusiasm continued in Q4 of 2022 with the earnings reports from the US hyperscalers, the big three now all in. As we said earlier this year, even the cloud is an immune from the macro headwinds and the cracks in the armor that we saw from the data that we shared last summer, they're playing out into 2023. For the most part actuals are disappointing beyond expectations including our own. It turns out that our estimates for the big three hyperscaler's revenue missed by 1.2 billion or 2.7% lower than we had forecast from even our most recent November estimates. And we expect continued decelerating growth rates for the hyperscalers through the summer of 2023 and we don't think that's going to abate until comparisons get easier. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we share our view of what's happening in cloud markets not just for the hyperscalers but other firms that have hitched a ride on the cloud. And we'll share new ETR data that shows why these trends are playing out tactics that customers are employing to deal with their cost challenges and how long the pain is likely to last. You know, riding the cloud wave, it's a two-edged sword. Let's look at the players that have gone all in on or are exposed to both the positive and negative trends of cloud. Look the cloud has been a huge tailwind for so many companies like Snowflake and Databricks, Workday, Salesforce, Mongo's move with Atlas, Red Hats Cloud strategy with OpenShift and so forth. And you know, the flip side is because cloud is elastic what comes up can also go down very easily. Here's an XY graphic from ETR that shows spending momentum or net score on the vertical axis and market presence in the dataset on the horizontal axis provision or called overlap. This is data from the January 2023 survey and that the red dotted lines show the positions of several companies that we've highlighted going back to January 2021. So let's unpack this for a bit starting with the big three hyperscalers. The first point is AWS and Azure continue to solidify their moat relative to Google Cloud platform. And we're going to get into this in a moment, but Azure and AWS revenues are five to six times that of GCP for IaaS. And at those deltas, Google should be gaining ground much faster than the big two. The second point on Google is notice the red line on GCP relative to its starting point. While it appears to be gaining ground on the horizontal axis, its net score is now below that of AWS and Azure in the survey. So despite its significantly smaller size it's just not keeping pace with the leaders in terms of market momentum. Now looking at AWS and Microsoft, what we see is basically AWS is holding serve. As we know both Google and Microsoft benefit from including SaaS in their cloud numbers. So the fact that AWS hasn't seen a huge downward momentum relative to a January 2021 position is one positive in the data. And both companies are well above that magic 40% line on the Y-axis, anything above 40% we consider to be highly elevated. But the fact remains that they're down as are most of the names on this chart. So let's take a closer look. I want to start with Snowflake and Databricks. Snowflake, as we reported from several quarters back came down to Earth, it was up in the 80% range in the Y-axis here. And it's still highly elevated in the 60% range and it continues to move to the right, which is positive but as we'll address in a moment it's customers can dial down consumption just as in any cloud. Now, Databricks is really interesting. It's not a public company, it never made it to IPO during the sort of tech bubble. So we don't have the same level of transparency that we do with other companies that did make it through. But look at how much more prominent it is on the X-axis relative to January 2021. And it's net score is basically held up over that period of time. So that's a real positive for Databricks. Next, look at Workday and Salesforce. They've held up relatively well, both inching to the right and generally holding their net scores. Same from Mongo, which is the brown dot above its name that says Elastic, it says a little gets a little crowded which Elastic's actually the blue dot above it. But generally, SaaS is harder to dial down, Workday, Salesforce, Oracles, SaaS and others. So it's harder to dial down because commitments have been made in advance, they're kind of locked in. Now, one of the discussions from last summer was as Mongo, less discretionary than analytics i.e. Snowflake. And it's an interesting debate but maybe Snowflake customers, you know, they're also generally committed to a dollar amount. So over time the spending is going to be there. But in the short term, yeah maybe Snowflake customers can dial down. Now that highlighted dotted red line, that bolded one is Datadog and you can see it's made major strides on the X-axis but its net score has decelerated quite dramatically. Openshift's momentum in the survey has dropped although IBM just announced that OpenShift has a a billion dollar ARR and I suspect what's happening there is IBM consulting is bundling OpenShift into its modernization projects. It's got a, that sort of captive base if you will. And as such it's probably not as top of mind to the respondents but I'll bet you the developers are certainly aware of it. Now the other really notable call out here is CloudFlare, We've reported on them earlier. Cloudflare's net score has held up really well since January of 2021. It really hasn't seen the downdraft of some of these others, but it's making major major moves to the right gaining market presence. We really like how CloudFlare is performing. And the last comment is on Oracle which as you can see, despite its much, much lower net score continues to gain ground in the market and thrive from a profitability standpoint. But the data pretty clearly shows that there's a downdraft in the market. Okay, so what's happening here? Let's dig deeper into this data. Here's a graphic from the most recent ETR drill down asking customers that said they were going to cut spending what technique they're using to do so. Now, as we've previously reported, consolidating redundant vendors is by far the most cited approach but there's two key points we want to make here. One is reducing excess cloud resources. As you can see in the bars is the second most cited technique and it's up from the previous polling period. The second we're not showing, you know directly but we've got some red call outs there. Reducing cloud costs jumps to 29% and 28% respectively in financial services and tech telco. And it's much closer to second. It's basically neck and neck with consolidating redundant vendors in those two industries. So they're being really aggressive about optimizing cloud cost. Okay, so as we said, cloud is great 'cause you can dial it up but it's just as easy to dial down. We've identified six factors that customers tell us are affecting their cloud consumption and there are probably more, if you got more we'd love to hear them but these are the ones that are fairly prominent that have hit our radar. First, rising mortgage rates mean banks are processing fewer loans means less cloud. The crypto crash means less trading activity and that means less cloud resources. Third lower ad spend has led companies to reduce not only you know, their ad buying but also their frequency of running their analytics and their calculations. And they're also often using less data, maybe compressing the timeframe of the corpus down to a shorter time period. Also very prominent is down to the bottom left, using lower cost compute instances. For example, Graviton from AWS or AMD chips and tiering storage to cheaper S3 or deep archived tiers. And finally, optimizing based on better pricing plans. So customers are moving from, you know, smaller companies in particular moving maybe from on demand or other larger companies that are experimenting using on demand or they're moving to spot pricing or reserved instances or optimized savings plans. That all lowers cost and that means less cloud resource consumption and less cloud revenue. Now in the days when everything was on prem CFOs, what would they do? They would freeze CapEx and IT Pros would have to try to do more with less and often that meant a lot of manual tasks. With the cloud it's much easier to move things around. It still takes some thinking and some effort but it's dramatically simpler to do so. So you can get those savings a lot faster. Now of course the other huge factor is you can cut or you can freeze. And this graphic shows data from a recent ETR survey with 159 respondents and you can see the meaningful uptick in hiring freezes, freezing new IT deployments and layoffs. And as we've been reporting, this has been trending up since earlier last year. And note the call out, this is especially prominent in retail sectors, all three of these techniques jump up in retail and that's a bit of a concern because oftentimes consumer spending helps the economy make a softer landing out of a pullback. But this is a potential canary in the coal mine. If retail firms are pulling back it's because consumers aren't spending as much. And so we're keeping a close eye on that. So let's boil this down to the market data and what this all means. So in this graphic we show our estimates for Q4 IaaS revenues compared to the "actual" IaaS revenues. And we say quote because AWS is the only one that reports, you know clean revenue and IaaS, Azure and GCP don't report actuals. Why would they? Because it would make them look even, you know smaller relative to AWS. Rather, they bury the figures in overall cloud which includes their, you know G-Suite for Google and all the Microsoft SaaS. And then they give us little tidbits about in Microsoft's case, Azure, they give growth rates. Google gives kind of relative growth of GCP. So, and we use survey data and you know, other data to try to really pinpoint and we've been covering this for, I don't know, five or six years ever since the cloud really became a thing. But looking at the data, we had AWS growing at 25% this quarter and it came in at 20%. So a significant decline relative to our expectations. AWS announced that it exited December, actually, sorry it's January data showed about a 15% mid-teens growth rate. So that's, you know, something we're watching. Azure was two points off our forecast coming in at 38% growth. It said it exited December in the 35% growth range and it said that it's expecting five points of deceleration off of that. So think 30% for Azure. GCP came in three points off our expectation coming in 35% and Alibaba has yet to report but we've shaved a bid off that forecast based on some survey data and you know what maybe 9% is even still not enough. Now for the year, the big four hyperscalers generated almost 160 billion of revenue, but that was 7 billion lower than what what we expected coming into 2022. For 2023, we're expecting 21% growth for a total of 193.3 billion. And while it's, you know, lower, you know, significantly lower than historical expectations it's still four to five times the overall spending forecast that we just shared with you in our predictions post of between 4 and 5% for the overall market. We think AWS is going to come in in around 93 billion this year with Azure closing in at over 71 billion. This is, again, we're talking IaaS here. Now, despite Amazon focusing investors on the fact that AWS's absolute dollar growth is still larger than its competitors. By our estimates Azure will come in at more than 75% of AWS's forecasted revenue. That's a significant milestone. AWS is operating margins by the way declined significantly this past quarter, dropping from 30% of revenue to 24%, 30% the year earlier to 24%. Now that's still extremely healthy and we've seen wild fluctuations like this before so I don't get too freaked out about that. But I'll say this, Microsoft has a marginal cost advantage relative to AWS because one, it has a captive cloud on which to run its massive software estate. So it can just throw software at its own cloud and two software marginal costs. Marginal economics despite AWS's awesomeness in high degrees of automation, software is just a better business. Now the upshot for AWS is the ecosystem. AWS is essentially in our view positioning very smartly as a platform for data partners like Snowflake and Databricks, security partners like CrowdStrike and Okta and Palo Alto and many others and SaaS companies. You know, Microsoft is more competitive even though AWS does have competitive products. Now of course Amazon's competitive to retail companies so that's another factor but generally speaking for tech players, Amazon is a really thriving ecosystem that is a secret weapon in our view. AWS happy to spin the meter with its partners even though it sells competitive products, you know, more so in our view than other cloud players. Microsoft, of course is, don't forget is hyping now, we're hearing a lot OpenAI and ChatGPT we reported last week in our predictions post. How OpenAI is shot up in terms of market sentiment in ETR's emerging technology company surveys and people are moving to Azure to get OpenAI and get ChatGPT that is a an interesting lever. Amazon in our view has to have a response. They have lots of AI and they're going to have to make some moves there. Meanwhile, Google is emphasizing itself as an AI first company. In fact, Google spent at least five minutes of continuous dialogue, nonstop on its AI chops during its latest earnings call. So that's an area that we're watching very closely as the buzz around large language models continues. All right, let's wrap up with some assumptions for 2023. We think SaaS players are going to continue to be sticky. They're going to be somewhat insulated from all these downdrafts because they're so tied in and customers, you know they make the commitment up front, you've got the lock in. Now having said that, we do expect some backlash over time on the onerous and generally customer unfriendly pricing models of most large SaaS companies. But that's going to play out over a longer period of time. Now for cloud generally and the hyperscalers specifically we do expect accelerating growth rates into Q3 but the amplitude of the demand swings from this rubber band economy, we expect to continue to compress and become more predictable throughout the year. Estimates are coming down, CEOs we think are going to be more cautious when the market snaps back more cautious about hiring and spending and as such a perhaps we expect a more orderly return to growth which we think will slightly accelerate in Q4 as comps get easier. Now of course the big risk to these scenarios is of course the economy, the FED, consumer spending, inflation, supply chain, energy prices, wars, geopolitics, China relations, you know, all the usual stuff. But as always with our partners at ETR and the Cube community, we're here for you. We have the data and we'll be the first to report when we see a change at the margin. Okay, that's a wrap for today. I want to thank Alex Morrison who's on production and manages the podcast, Ken Schiffman as well out of our Boston studio getting this up on LinkedIn Live. Thank you for that. Kristen Martin also and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our Editor-in-Chief over at siliconangle.com. He does some great editing for us. Thank you all. Remember all these episodes are available as podcast. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com, at siliconangle.com where you can see all the data and you want to get in touch. Just all you can do is email me david.vellante@siliconangle.com or DM me @dvellante if you if you got something interesting, I'll respond. If you don't, it's either 'cause I'm swamped or it's just not tickling me. You can comment on our LinkedIn post as well. And please check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for the Cube Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle upbeat music)
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From the Cube Studios and how long the pain is likely to last.
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Breaking Analysis: Enterprise Technology Predictions 2023
(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)
SUMMARY :
insights from the Cube and ETR, do for the community, Dave, good to see you. actually come back to me if you would. It just stays at the top. the most aggressive to cut. that have the most to lose What's the primary method still leads the way, you know, So in addition to what we're seeing here, And so I actually thank you I went through it for you. I'm going to ask you to explain and they're certainly not going to get it to you in a zero trust way. So all of that is the One is just the number of So come back to me in 12 So 52% of the ETR survey amount of money on the Metaverse and also in the data prep tools. the cloud expands to the biggest shock to me "Ah, it's, you know, really and Fastly is their really the folks said, you know, for a home in the enterprise, Yeah, and I got to be honest, in the community, you know, and I don't know if that's the right move and the vertical axis is shared net score. So that's really what you want Well, the way they compete So that's, you know, One of the problems, if and that's going to be obviously even Oracle is going to scale down. and the data pipelines and that, you know, Been a nuts three I love the fact I know you were really is so much data at ETR. and we'll see you next time
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Breaking Analysis: ChatGPT Won't Give OpenAI First Mover Advantage
>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)
SUMMARY :
bringing you data-driven and ChatGPT have taken the world by storm. So I asked it, give it to the large language models to do that. So to your point, it's So one of the problems with ChatGPT, and he simply gave the system the prompts, or the OS to help it do but it kind of levels the playing- and the answers were coming as the data you can get. Yeah, and leveled to certain extent. I check the facts, save me about maybe- and then I write a killer because like if the it's, the law is we, you know, I think that's true and I ask the set of similar question, What's your counter point? and not it's underestimated long term. That's what he said. for the first time, wow. the overhyped at the No, it was, it was I got, right I mean? the internet in the early days, and it's only going to get better." So you're saying it's bifurcated. and possibly the debate the first mobile device. So I mean. on the right with ChatGPT, and convicted by the Department of Justice the scrutiny from the Fed, right, so- And the privacy and thing to do what Sam Altman- So even though it'll get like, you know, it's- It's more than clever. I mean you write- I think that's a big thing. I think he was doing- I was not impressed because You know like. And he did the same thing he's got a lot of hyperbole. the browser moment to me, So OpenAI could stay on the right side You're right, it was terrible, They could be the Netscape Navigator, and in the horizontal axis's So I guess that's the other point is, I mean to quote IBM's So the data problem factors and the government's around the world, and they're slow to catch up. Yeah, and now they got years, you know, OpenAI. But the problem with government to kill Big Tech, and the 20% is probably relevant, back in the day, right? are they going to apply it? and also to write code as well, that the marketplace I don't, I don't see you had an interesting comment. No, no. First of all, the AI chops that Google has, right? are off the scales, right? I mean they got to be and the capacity to process that data, on some of the thinking So Lina Kahn is looming, and this is the third, could be a third rail. But the first thing What they will do out the separate company Is it to charge you for a query? it's cool to type stuff in natural language is the way and how many cents the and they're going through Google search results. It will, because there were It'll be like, you know, I mean. I never input the transcript. Wow, But it was a big lie. but I call it the vanilla content. Make your point, cause we And on the danger side as well, So the data By the way, that means at the Supercloud event, So one of the VCs actually What do you make of it? you were like "Hundreds of millions." not 10, not a billion. Clearly, the CapEx spending to build all But I think it's not that hard. It's, what, you know This is the new economics Look at the amount of And Sam is depositioning all the success, or 150 or 200 million to train the model. So I think to compute is different. not because of the software. Because you can, buy They built the moat And then, you know, well that the Hudson and it was over. that are going to be seeping out. Yeah, it's like horse race, 3 to 1, 2 to 1, that's pretty low odds. I mean you got to say, I mean. Oh there's no question. is what you're saying. and if they don't stumble on that, the money back to the public, to the, Okay, the odds are dropping. the model they have adopted. Supercloud and meshs versus, you know, on the heels of Supercloud
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Analyst Predictions 2023: The Future of Data Management
(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)
SUMMARY :
and pleased to tell you (Tony and Dave faintly speaks) that led them to their conclusion. down, the funding in VC IPO market. And I like how the fact And I happened to have tripped across I talked to Walmart in the prediction of graph databases. But I stand by the idea and maybe to the edge. You can apply graphs to great And so, it's going to streaming data permeates the landscape. and to be honest, I like the tough grading the next 20 to 25% of and of course, the degree of difficulty. that sits on the side, Thank you for that. And I have to disagree. So, the catalog becomes Do you have any stats for just the reasons that And a lot of those catalogs about the modern data stack. and more, the data lakehouse. and the application stack, So, the alternative is to have metadata that SQL is the killer app for big data. but in the perception of the marketplace, and I had to take the NoSQL, being up on stage with Curt Monash. (group laughing) is that the core need in the data lake, And your prediction is the and examine derivatives of the data to optimize around a set of KPIs. that folks in the content world (Dave and Carl laughing) going to say this... shifts the conversation to the consumers And essentially, one of the things (group laughing) the term that we'll remember today, to your last year's prediction, is headed to embedding. and going off to separate happening in the business, so that the analytics didn't And the thing that we're waiting for and that deep modeling. that the system can of decision has to be relevant And the fact that we're But in the end We see that's all over the You cache that data, and improvement of the and I love how it shapes the outcome here Thank you for watching.
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Harveer Singh, Western Union | Western Union When Data Moves Money Moves
(upbeat music) >> Welcome back to Supercloud 2, which is an open industry collaboration between technologists, consultants, analysts, and of course, practitioners, to help shape the future of cloud. And at this event, one of the key areas we're exploring is the intersection of cloud and data, and how building value on top of hyperscale clouds and across clouds is evolving, a concept we call supercloud. And we're pleased to welcome Harvir Singh, who's the chief data architect and global head of data at Western Union. Harvir, it's good to see you again. Thanks for coming on the program. >> Thanks, David, it's always a pleasure to talk to you. >> So many things stand out from when we first met, and one of the most gripping for me was when you said to me, "When data moves, money moves." And that's the world we live in today, and really have for a long time. Money has moved as bits, and when it has to move, we want it to move quickly, securely, and in a governed manner. And the pressure to do so is only growing. So tell us how that trend is evolved over the past decade in the context of your industry generally, and Western Union, specifically. Look, I always say to people that we are probably the first ones to introduce digital currency around the world because, hey, somebody around the world needs money, we move data to make that happen. That trend has actually accelerated quite a bit. If you look at the last 10 years, and you look at all these payment companies, digital companies, credit card companies that have evolved, majority of them are working on the same principle. When data moves, money moves. When data is stale, the money goes away, right? I think that trend is continuing, and it's not just the trend is in this space, it's also continuing in other spaces, specifically around, you know, acquisition of customers, communication with customers. It's all becoming digital, and it's, at the end of the day, it's all data being moved from one place or another. At the end of the day, you're not seeing the customer, but you're looking at, you know, the data that he's consuming, and you're making actionable items on it, and be able to respond to what they need. So I think 10 years, it's really, really evolved. >> Hmm, you operate, Western Union operates in more than 200 countries, and you you have what I would call a pseudo federated organization. You're trying to standardize wherever possible on the infrastructure, and you're curating the tooling and doing the heavy lifting in the data stack, which of course lessens the burden on the developers and the line of business consumers, so my question is, in operating in 200 countries, how do you deal with all the diversity of laws and regulations across those regions? I know you're heavily involved in AWS, but AWS isn't everywhere, you still have some on-prem infrastructure. Can you paint a picture of, you know, what that looks like? >> Yeah, a few years ago , we were primarily mostly on-prem, and one of the biggest pain points has been managing that infrastructure around the world in those countries. Yes, we operate in 200 countries, but we don't have infrastructure in 200 countries, but we do have agent locations in 200 countries. United Nations says we only have like 183 are countries, but there are countries which, you know, declare themselves countries, and we are there as well because somebody wants to send money there, right? Somebody has an agent location down there as well. So that infrastructure is obviously very hard to manage and maintain. We have to comply by numerous laws, you know. And the last few years, specifically with GDPR, CCPA, data localization laws in different countries, it's been a challenge, right? And one of the things that we did a few years ago, we decided that we want to be in the business of helping our customers move money faster, security, and with complete trust in us. We don't want to be able to, we don't want to be in the business of managing infrastructure. And that's one of the reasons we started to, you know, migrate and move our journey to the cloud. AWS, obviously chosen first because of its, you know, first in the game, has more locations, and more data centers around the world where we operate. But we still have, you know, existing infrastructure, which is in some countries, which is still localized because AWS hasn't reached there, or we don't have a comparable provider there. We still manage those. And we have to comply by those laws. Our data privacy and our data localization tech stack is pretty good, I would say. We manage our data very well, we manage our customer data very well, but it comes with a lot of complexity. You know, we get a lot of requests from European Union, we get a lot of requests from Asia Pacific every pretty much on a weekly basis to explain, you know, how we are taking controls and putting measures in place to make sure that the data is secured and is in the right place. So it's a complex environment. We do have exposure to other clouds as well, like Google and Azure. And as much as we would love to be completely, you know, very, very hybrid kind of an organization, it's still at a stage where we are still very heavily focused on AWS yet, but at some point, you know, we would love to see a world which is not reliant on a single provider, but it's more a little bit more democratized, you know, as and when what I want to use, I should be able to use, and pay-per-use. And the concept started like that, but it's obviously it's now, again, there are like three big players in the market, and, you know, they're doing their own thing. Would love to see them come collaborate at some point. >> Yeah, wouldn't we all. I want to double-click on the whole multi-cloud strategy, but if I understand it correctly, and in a perfect world, everything on-premises would be in the cloud is, first of all, is that a correct statement? Is that nirvana for you or not necessarily? >> I would say it is nirvana for us, but I would also put a caveat, is it's very tricky because from a regulatory perspective, we are a regulated entity in many countries. The regulators would want to see some control if something happens with a relationship with AWS in one country, or with Google in another country, and it keeps happening, right? For example, Russia was a good example where we had to switch things off. We should be able to do that. But if let's say somewhere in Asia, this country decides that they don't want to partner with AWS, and majority of our stuff is on AWS, where do I go from there? So we have to have some level of confidence in our own infrastructure, so we do maintain some to be able to fail back into and move things it needs to be. So it's a tricky question. Yes, it's nirvana state that I don't have to manage infrastructure, but I think it's far less practical than it said. We will still own something that we call it our own where we have complete control, being a financial entity. >> And so do you try to, I'm sure you do, standardize between all the different on-premise, and in this case, the AWS cloud or maybe even other clouds. How do you do that? Do you work with, you know, different vendors at the various places of the stack to try to do that? Some of the vendors, you know, like a Snowflake is only in the cloud. You know, others, you know, whether it's whatever, analytics, or storage, or database, might be hybrid. What's your strategy with regard to creating as common an experience as possible between your on-prem and your clouds? >> You asked a question which I asked when I joined as well, right? Which question, this is one of the most important questions is how soon when I fail back, if I need to fail back? And how quickly can I, because not everything that is sitting on the cloud is comparable to on-prem or is backward compatible. And the reason I say backward compatible is, you know, there are, our on-prem cloud is obviously behind. We haven't taken enough time to kind of put it to a state where, because we started to migrate and now we have access to infrastructure on the cloud, most of the new things are being built there. But for critical application, I would say we have chronology that could be used to move back if need to be. So, you know, technologies like Couchbase, technologies like PostgreSQL, technologies like Db2, et cetera. We still have and maintain a fairly large portion of it on-prem where critical applications could potentially be serviced. We'll give you one example. We use Neo4j very heavily for our AML use cases. And that's an important one because if Neo4j on the cloud goes down, and it's happened in the past, again, even with three clusters, having all three clusters going down with a DR, we still need some accessibility of that because that's one of the biggest, you know, fraud and risk application it supports. So we do still maintain some comparable technology. Snowflake is an odd one. It's obviously there is none on-prem. But then, you know, Snowflake, I also feel it's more analytical based technology, not a transactional-based technology, at least in our ecosystem. So for me to replicate that, yes, it'll probably take time, but I can live with that. But my business will not stop because our transactional applications can potentially move over if need to. >> Yeah, and of course, you know, all these big market cap companies, so the Snowflake or Databricks, which is not public yet, but they've got big aspirations. And so, you know, we've seen things like Snowflake do a deal with Dell for on-prem object store. I think they do the same thing with Pure. And so over time, you see, Mongo, you know, extending its estate. And so over time all these things are coming together. I want to step out of this conversation for a second. I just ask you, given the current macroeconomic climate, what are the priorities? You know, obviously, people are, CIOs are tapping the breaks on spending, we've reported on that, but what is it? Is it security? Is it analytics? Is it modernization of the on-prem stack, which you were saying a little bit behind. Where are the priorities today given the economic headwinds? >> So the most important priority right now is growing the business, I would say. It's a different, I know this is more, this is not a very techy or a tech answer that, you know, you would expect, but it's growing the business. We want to acquire more customers and be able to service them as best needed. So the majority of our investment is going in the space where tech can support that initiative. During our earnings call, we released the new pillars of our organization where we will focus on, you know, omnichannel digital experience, and then one experience for customer, whether it's retail, whether it's digital. We want to open up our own experience stores, et cetera. So we are investing in technology where it's going to support those pillars. But the spend is in a way that we are obviously taking away from the things that do not support those. So it's, I would say it's flat for us. We are not like in heavily investing or aggressively increasing our tech budget, but it's more like, hey, switch this off because it doesn't make us money, but now switch this on because this is going to support what we can do with money, right? So that's kind of where we are heading towards. So it's not not driven by technology, but it's driven by business and how it supports our customers and our ability to compete in the market. >> You know, I think Harvir, that's consistent with what we heard in some other work that we've done, our ETR partner who does these types of surveys. We're hearing the same thing, is that, you know, we might not be spending on modernizing our on-prem stack. Yeah, we want to get to the cloud at some point and modernize that. But if it supports revenue, you know, we'll invest in that, and get the, you know, instant ROI. I want to ask you about, you know, this concept of supercloud, this abstracted layer of value on top of hyperscale infrastructure, and maybe on-prem. But we were talking about the integration, for instance, between Snowflake and Salesforce, where you got different data sources and you were explaining that you had great interest in being able to, you know, have a kind of, I'll say seamless, sorry, I know it's an overused word, but integration between the data sources and those two different platforms. Can you explain that and why that's attractive to you? >> Yeah, I'm a big supporter of action where the data is, right? Because the minute you start to move, things are already lost in translation. The time is lost, you can't get to it fast enough. So if, for example, for us, Snowflake, Salesforce, is our actionable platform where we action, we send marketing campaigns, we send customer communication via SMS, in app, as well as via email. Now, we would like to be able to interact with our customers pretty much on a, I would say near real time, but the concept of real time doesn't work well with me because I always feel that if you're observing something, it's not real time, it's already happened. But how soon can I react? That's the question. And given that I have to move that data all the way from our, let's say, engagement platforms like Adobe, and particles of the world into Snowflake first, and then do my modeling in some way, and be able to then put it back into Salesforce, it takes time. Yes, you know, I can do it in a few hours, but that few hours makes a lot of difference. Somebody sitting on my website, you know, couldn't find something, walked away, how soon do you think he will lose interest? Three hours, four hours, he'll probably gone, he will never come back. I think if I can react to that as fast as possible without too much data movement, I think that's a lot of good benefit that this kind of integration will bring. Yes, I can potentially take data directly into Salesforce, but I then now have two copies of data, which is, again, something that I'm not a big (indistinct) of. Let's keep the source of the data simple, clean, and a single source. I think this kind of integration will help a lot if the actions can be brought very close to where the data resides. >> Thank you for that. And so, you know, it's funny, we sometimes try to define real time as before you lose the customer, so that's kind of real time. But I want to come back to this idea of governed data sharing. You mentioned some other clouds, a little bit of Azure, a little bit of Google. In a world where, let's say you go more aggressively, and we know that for instance, if you want to use Google's AI tools, you got to use BigQuery. You know, today, anyway, they're not sort of so friendly with Snowflake, maybe different for the AWS, maybe Microsoft's going to be different as well. But in an ideal world, what I'm hearing is you want to keep the data in place. You don't want to move the data. Moving data is expensive, making copies is badness. It's expensive, and it's also, you know, changes the state, right? So you got governance issues. So this idea of supercloud is that you can leave the data in place and actually have a common experience across clouds. Let's just say, let's assume for a minute Google kind of wakes up, my words, not yours, and says, "Hey, maybe, you know what, partnering with a Snowflake or a Databricks is better for our business. It's better for the customers," how would that affect your business and the value that you can bring to your customers? >> Again, I would say that would be the nirvana state that, you know, we want to get to. Because I would say not everyone's perfect. They have great engineers and great products that they're developing, but that's where they compete as well, right? I would like to use the best of breed as much as possible. And I've been a person who has done this in the past as well. I've used, you know, tools to integrate. And the reason why this integration has worked is primarily because sometimes you do pick the best thing for that job. And Google's AI products are definitely doing really well, but, you know, that accessibility, if it's a problem, then I really can't depend on them, right? I would love to move some of that down there, but they have to make it possible for us. Azure is doing really, really good at investing, so I think they're a little bit more and more closer to getting to that state, and I know seeking our attention than Google at this point of time. But I think there will be a revelation moment because more and more people that I talk to like myself, they're also talking about the same thing. I'd like to be able to use Google's AdSense, I would like to be able to use Google's advertising platform, but you know what? I already have all this data, why do I need to move it? Can't they just go and access it? That question will keep haunting them (indistinct). >> You know, I think, obviously, Microsoft has always known, you know, understood ecosystems. I mean, AWS is nailing it, when you go to re:Invent, it's all about the ecosystem. And they think they realized they can make a lot more money, you know, together, than trying to have, and Google's got to figure that out. I think Google thinks, "All right, hey, we got to have the best tech." And that tech, they do have the great tech, and that's our competitive advantage. They got to wake up to the ecosystem and what's happening in the field and the go-to-market. I want to ask you about how you see data and cloud evolving in the future. You mentioned that things that are driving revenue are the priorities, and maybe you're already doing this today, but my question is, do you see a day when companies like yours are increasingly offering data and software services? You've been around for a long time as a company, you've got, you know, first party data, you've got proprietary knowledge, and maybe tooling that you've developed, and you're becoming more, you're already a technology company. Do you see someday pointing that at customers, or again, maybe you're doing it already, or is that not practical in your view? >> So data monetization has always been on the charts. The reason why it hasn't seen the light is regulatory pressure at this point of time. We are partnering up with certain agencies, again, you know, some pilots are happening to see the value of that and be able to offer that. But I think, you know, eventually, we'll get to a state where our, because we are trying to build accessible financial services, we will be in a state that we will be offering those to partners, which could then extended to their customers as well. So we are definitely exploring that. We are definitely exploring how to enrich our data with other data, and be able to complete a super set of data that can be used. Because frankly speaking, the data that we have is very interesting. We have trends of people migrating, we have trends of people migrating within the US, right? So if a new, let's say there's a new, like, I'll give you an example. Let's say New York City, I can tell you, at any given point of time, with my data, what is, you know, a dominant population in that area from migrant perspective. And if I see a change in that data, I can tell you where that is moving towards. I think it's going to be very interesting. We're a little bit, obviously, sometimes, you know, you're scared of sharing too much detail because there's too much data. So, but at the end of the day, I think at some point, we'll get to a state where we are confident that the data can be used for good. One simple example is, you know, pharmacies. They would love to get, you know, we've been talking to CVS and we are talking to Walgreens, and trying to figure out, if they would get access to this kind of data demographic information, what could they do be better? Because, you know, from a gene pool perspective, there are diseases and stuff that are very prevalent in one community versus the other. We could probably equip them with this information to be able to better, you know, let's say, staff their pharmacies or keep better inventory of products that could be used for the population in that area. Similarly, the likes of Walmarts and Krogers, they would like to have more, let's say, ethnic products in their aisles, right? How do you enable that? That data is primarily, I think we are the biggest source of that data. So we do take pride in it, but you know, with caution, we are obviously exploring that as well. >> My last question for you, Harvir, is I'm going to ask you to do a thought exercise. So in that vein, that whole monetization piece, imagine that now, Harvir, you are running a P&L that is going to monetize that data. And my question to you is a there's a business vector and a technology vector. So from a business standpoint, the more distribution channels you have, the better. So running on AWS cloud, partnering with Microsoft, partnering with Google, going to market with them, going to give you more revenue. Okay, so there's a motivation for multi-cloud or supercloud. That's indisputable. But from a technical standpoint, is there an advantage to running on multiple clouds or is that a disadvantage for you? >> It's, I would say it's a disadvantage because if my data is distributed, I have to combine it at some place. So the very first step that we had taken was obviously we brought in Snowflake. The reason, we wanted our analytical data and we want our historical data in the same place. So we are already there and ready to share. And we are actually participating in the data share, but in a private setting at the moment. So we are technically enabled to share, unless there is a significant, I would say, upside to moving that data to another cloud. I don't see any reason because I can enable anyone to come and get it from Snowflake. It's already enabled for us. >> Yeah, or if somehow, magically, several years down the road, some standard developed so you don't have to move the data. Maybe there's a new, Mogli is talking about a new data architecture, and, you know, that's probably years away, but, Harvir, you're an awesome guest. I love having you on, and really appreciate you participating in the program. >> I appreciate it. Thank you, and good luck (indistinct) >> Ah, thank you very much. This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more great coverage from Supercloud 2. (uplifting music)
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Harvir, it's good to see you again. a pleasure to talk to you. And the pressure to do so is only growing. and you you have what I would call But we still have, you know, you or not necessarily? that I don't have to Some of the vendors, you and it's happened in the past, And so, you know, we've and our ability to compete in the market. and get the, you know, instant ROI. Because the minute you start to move, and the value that you can that, you know, we want to get to. and cloud evolving in the future. But I think, you know, And my question to you So the very first step that we had taken and really appreciate you I appreciate it. Ah, thank you very much.
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Bob Muglia, George Gilbert & Tristan Handy | How Supercloud will Support a new Class of Data Apps
(upbeat music) >> Hello, everybody. This is Dave Vellante. Welcome back to Supercloud2, where we're exploring the intersection of data analytics and the future of cloud. In this segment, we're going to look at how the Supercloud will support a new class of applications, not just work that runs on multiple clouds, but rather a new breed of apps that can orchestrate things in the real world. Think Uber for many types of businesses. These applications, they're not about codifying forms or business processes. They're about orchestrating people, places, and things in a business ecosystem. And I'm pleased to welcome my colleague and friend, George Gilbert, former Gartner Analyst, Wiki Bond market analyst, former equities analyst as my co-host. And we're thrilled to have Tristan Handy, who's the founder and CEO of DBT Labs and Bob Muglia, who's the former President of Microsoft's Enterprise business and former CEO of Snowflake. Welcome all, gentlemen. Thank you for coming on the program. >> Good to be here. >> Thanks for having us. >> Hey, look, I'm going to start actually with the SuperCloud because both Tristan and Bob, you've read the definition. Thank you for doing that. And Bob, you have some really good input, some thoughts on maybe some of the drawbacks and how we can advance this. So what are your thoughts in reading that definition around SuperCloud? >> Well, I thought first of all that you did a very good job of laying out all of the characteristics of it and helping to define it overall. But I do think it can be tightened a bit, and I think it's helpful to do it in as short a way as possible. And so in the last day I've spent a little time thinking about how to take it and write a crisp definition. And here's my go at it. This is one day old, so gimme a break if it's going to change. And of course we have to follow the industry, and so that, and whatever the industry decides, but let's give this a try. So in the way I think you're defining it, what I would say is a SuperCloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. >> Boom. Nice. Okay, great. I'm going to go back and read the script on that one and tighten that up a bit. Thank you for spending the time thinking about that. Tristan, would you add anything to that or what are your thoughts on the whole SuperCloud concept? >> So as I read through this, I fully realize that we need a word for this thing because I have experienced the inability to talk about it as well. But for many of us who have been living in the Confluence, Snowflake, you know, this world of like new infrastructure, this seems fairly uncontroversial. Like I read through this, and I'm just like, yeah, this is like the world I've been living in for years now. And I noticed that you called out Snowflake for being an example of this, but I think that there are like many folks, myself included, for whom this world like fully exists today. >> Yeah, I think that's a fair, I dunno if it's criticism, but people observe, well, what's the big deal here? It's just kind of what we're living in today. It reminds me of, you know, Tim Burns Lee saying, well, this is what the internet was supposed to be. It was supposed to be Web 2.0, so maybe this is what multi-cloud was supposed to be. Let's turn our attention to apps. Bob first and then go to Tristan. Bob, what are data apps to you? When people talk about data products, is that what they mean? Are we talking about something more, different? What are data apps to you? >> Well, to understand data apps, it's useful to contrast them to something, and I just use the simple term people apps. I know that's a little bit awkward, but it's clear. And almost everything we work with, almost every application that we're familiar with, be it email or Salesforce or any consumer app, those are applications that are targeted at responding to people. You know, in contrast, a data application reacts to changes in data and uses some set of analytic services to autonomously take action. So where applications that we're familiar with respond to people, data apps respond to changes in data. And they both do something, but they do it for different reasons. >> Got it. You know, George, you and I were talking about, you know, it comes back to SuperCloud, broad definition, narrow definition. Tristan, how do you see it? Do you see it the same way? Do you have a different take on data apps? >> Oh, geez. This is like a conversation that I don't know has an end. It's like been, I write a substack, and there's like this little community of people who all write substack. We argue with each other about these kinds of things. Like, you know, as many different takes on this question as you can find, but the way that I think about it is that data products are atomic units of functionality that are fundamentally data driven in nature. So a data product can be as simple as an interactive dashboard that is like actually had design thinking put into it and serves a particular user group and has like actually gone through kind of a product development life cycle. And then a data app or data application is a kind of cohesive end-to-end experience that often encompasses like many different data products. So from my perspective there, this is very, very related to the way that these things are produced, the kinds of experiences that they're provided, that like data innovates every product that we've been building in, you know, software engineering for, you know, as long as there have been computers. >> You know, Jamak Dagani oftentimes uses the, you know, she doesn't name Spotify, but I think it's Spotify as that kind of example she uses. But I wonder if we can maybe try to take some examples. If you take, like George, if you take a CRM system today, you're inputting leads, you got opportunities, it's driven by humans, they're really inputting the data, and then you got this system that kind of orchestrates the business process, like runs a forecast. But in this data driven future, are we talking about the app itself pulling data in and automatically looking at data from the transaction systems, the call center, the supply chain and then actually building a plan? George, is that how you see it? >> I go back to the example of Uber, may not be the most sophisticated data app that we build now, but it was like one of the first where you do have users interacting with their devices as riders trying to call a car or driver. But the app then looks at the location of all the drivers in proximity, and it matches a driver to a rider. It calculates an ETA to the rider. It calculates an ETA then to the destination, and it calculates a price. Those are all activities that are done sort of autonomously that don't require a human to type something into a form. The application is using changes in data to calculate an analytic product and then to operationalize that, to assign the driver to, you know, calculate a price. Those are, that's an example of what I would think of as a data app. And my question then I guess for Tristan is if we don't have all the pieces in place for sort of mainstream companies to build those sorts of apps easily yet, like how would we get started? What's the role of a semantic layer in making that easier for mainstream companies to build? And how do we get started, you know, say with metrics? How does that, how does that take us down that path? >> So what we've seen in the past, I dunno, decade or so, is that one of the most successful business models in infrastructure is taking hard things and rolling 'em up behind APIs. You take messaging, you take payments, and you all of a sudden increase the capability of kind of your median application developer. And you say, you know, previously you were spending all your time being focused on how do you accept credit cards, how do you send SMS payments, and now you can focus on your business logic, and just create the thing. One of, interestingly, one of the things that we still don't know how to API-ify is concepts that live inside of your data warehouse, inside of your data lake. These are core concepts that, you know, you would imagine that the business would be able to create applications around very easily, but in fact that's not the case. It's actually quite challenging to, and involves a lot of data engineering pipeline and all this work to make these available. And so if you really want to make it very easy to create some of these data experiences for users, you need to have an ability to describe these metrics and then to turn them into APIs to make them accessible to application developers who have literally no idea how they're calculated behind the scenes, and they don't need to. >> So how rich can that API layer grow if you start with metric definitions that you've defined? And DBT has, you know, the metric, the dimensions, the time grain, things like that, that's a well scoped sort of API that people can work within. How much can you extend that to say non-calculated business rules or governance information like data reliability rules, things like that, or even, you know, features for an AIML feature store. In other words, it starts, you started pragmatically, but how far can you grow? >> Bob is waiting with bated breath to answer this question. I'm, just really quickly, I think that we as a company and DBT as a product tend to be very pragmatic. We try to release the simplest possible version of a thing, get it out there, and see if people use it. But the idea that, the concept of a metric is really just a first landing pad. The really, there is a physical manifestation of the data and then there's a logical manifestation of the data. And what we're trying to do here is make it very easy to access the logical manifestation of the data, and metric is a way to look at that. Maybe an entity, a customer, a user is another way to look at that. And I'm sure that there will be more kind of logical structures as well. >> So, Bob, chime in on this. You know, what's your thoughts on the right architecture behind this, and how do we get there? >> Yeah, well first of all, I think one of the ways we get there is by what companies like DBT Labs and Tristan is doing, which is incrementally taking and building on the modern data stack and extending that to add a semantic layer that describes the data. Now the way I tend to think about this is a fairly major shift in the way we think about writing applications, which is today a code first approach to moving to a world that is model driven. And I think that's what the big change will be is that where today we think about data, we think about writing code, and we use that to produce APIs as Tristan said, which encapsulates those things together in some form of services that are useful for organizations. And that idea of that encapsulation is never going to go away. It's very, that concept of an API is incredibly useful and will exist well into the future. But what I think will happen is that in the next 10 years, we're going to move to a world where organizations are defining models first of their data, but then ultimately of their business process, their entire business process. Now the concept of a model driven world is a very old concept. I mean, I first started thinking about this and playing around with some early model driven tools, probably before Tristan was born in the early 1980s. And those tools didn't work because the semantics associated with executing the model were too complex to be written in anything other than a procedural language. We're now reaching a time where that is changing, and you see it everywhere. You see it first of all in the world of machine learning and machine learning models, which are taking over more and more of what applications are doing. And I think that's an incredibly important step. And learned models are an important part of what people will do. But if you look at the world today, I will claim that we've always been modeling. Modeling has existed in computers since there have been integrated circuits and any form of computers. But what we do is what I would call implicit modeling, which means that it's the model is written on a whiteboard. It's in a bunch of Slack messages. It's on a set of napkins in conversations that happen and during Zoom. That's where the model gets defined today. It's implicit. There is one in the system. It is hard coded inside application logic that exists across many applications with humans being the glue that connects those models together. And really there is no central place you can go to understand the full attributes of the business, all of the business rules, all of the business logic, the business data. That's going to change in the next 10 years. And we'll start to have a world where we can define models about what we're doing. Now in the short run, the most important models to build are data models and to describe all of the attributes of the data and their relationships. And that's work that DBT Labs is doing. A number of other companies are doing that. We're taking steps along that way with catalogs. People are trying to build more complete ontologies associated with that. The underlying infrastructure is still super, super nascent. But what I think we'll see is this infrastructure that exists today that's building learned models in the form of machine learning programs. You know, some of these incredible machine learning programs in foundation models like GPT and DALL-E and all of the things that are happening in these global scale models, but also all of that needs to get applied to the domains that are appropriate for a business. And I think we'll see the infrastructure developing for that, that can take this concept of learned models and put it together with more explicitly defined models. And this is where the concept of knowledge graphs come in and then the technology that underlies that to actually implement and execute that, which I believe are relational knowledge graphs. >> Oh, oh wow. There's a lot to unpack there. So let me ask the Colombo question, Tristan, we've been making fun of your youth. We're just, we're just jealous. Colombo, I'll explain it offline maybe. >> I watch Colombo. >> Okay. All right, good. So but today if you think about the application stack and the data stack, which is largely an analytics pipeline. They're separate. Do they, those worlds, do they have to come together in order to achieve Bob's vision? When I talk to practitioners about that, they're like, well, I don't want to complexify the application stack cause the data stack today is so, you know, hard to manage. But but do those worlds have to come together? And you know, through that model, I guess abstraction or translation that Bob was just describing, how do you guys think about that? Who wants to take that? >> I think it's inevitable that data and AI are going to become closer together? I think that the infrastructure there has been moving in that direction for a long time. Whether you want to use the Lakehouse portmanteau or not. There's also, there's a next generation of data tech that is still in the like early stage of being developed. There's a company that I love that is essentially Cross Cloud Lambda, and it's just a wonderful abstraction for computing. So I think that, you know, people have been predicting that these worlds are going to come together for awhile. A16Z wrote a great post on this back in I think 2020, predicting this, and I've been predicting this since since 2020. But what's not clear is the timeline, but I think that this is still just as inevitable as it's been. >> Who's that that does Cross Cloud? >> Let me follow up on. >> Who's that, Tristan, that does Cross Cloud Lambda? Can you name names? >> Oh, they're called Modal Labs. >> Modal Labs, yeah, of course. All right, go ahead, George. >> Let me ask about this vision of trying to put the semantics or the code that represents the business with the data. It gets us to a world that's sort of more data centric, where data's not locked inside or behind the APIs of different applications so that we don't have silos. But at the same time, Bob, I've heard you talk about building the semantics gradually on top of, into a knowledge graph that maybe grows out of a data catalog. And the vision of getting to that point, essentially the enterprise's metadata and then the semantics you're going to add onto it are really stored in something that's separate from the underlying operational and analytic data. So at the same time then why couldn't we gradually build semantics beyond the metric definitions that DBT has today? In other words, you build more and more of the semantics in some layer that DBT defines and that sits above the data management layer, but any requests for data have to go through the DBT layer. Is that a workable alternative? Or where, what type of limitations would you face? >> Well, I think that it is the way the world will evolve is to start with the modern data stack and, you know, which is operational applications going through a data pipeline into some form of data lake, data warehouse, the Lakehouse, whatever you want to call it. And then, you know, this wide variety of analytics services that are built together. To the point that Tristan made about machine learning and data coming together, you see that in every major data cloud provider. Snowflake certainly now supports Python and Java. Databricks is of course building their data warehouse. Certainly Google, Microsoft and Amazon are doing very, very similar things in terms of building complete solutions that bring together an analytics stack that typically supports languages like Python together with the data stack and the data warehouse. I mean, all of those things are going to evolve, and they're not going to go away because that infrastructure is relatively new. It's just being deployed by companies, and it solves the problem of working with petabytes of data if you need to work with petabytes of data, and nothing will do that for a long time. What's missing is a layer that understands and can model the semantics of all of this. And if you need to, if you want to model all, if you want to talk about all the semantics of even data, you need to think about all of the relationships. You need to think about how these things connect together. And unfortunately, there really is no platform today. None of our existing platforms are ultimately sufficient for this. It was interesting, I was just talking to a customer yesterday, you know, a large financial organization that is building out these semantic layers. They're further along than many companies are. And you know, I asked what they're building it on, and you know, it's not surprising they're using a, they're using combinations of some form of search together with, you know, textual based search together with a document oriented database. In this case it was Cosmos. And that really is kind of the state of the art right now. And yet those products were not built for this. They don't really, they can't manage the complicated relationships that are required. They can't issue the queries that are required. And so a new generation of database needs to be developed. And fortunately, you know, that is happening. The world is developing a new set of relational algorithms that will be able to work with hundreds of different relations. If you look at a SQL database like Snowflake or a big query, you know, you get tens of different joins coming together, and that query is going to take a really long time. Well, fortunately, technology is evolving, and it's possible with new join algorithms, worst case, optimal join algorithms they're called, where you can join hundreds of different relations together and run semantic queries that you simply couldn't run. Now that technology is nascent, but it's really important, and I think that will be a requirement to have this semantically reach its full potential. In the meantime, Tristan can do a lot of great things by building up on what he's got today and solve some problems that are very real. But in the long run I think we'll see a new set of databases to support these models. >> So Tristan, you got to respond to that, right? You got to, so take the example of Snowflake. We know it doesn't deal well with complex joins, but they're, they've got big aspirations. They're building an ecosystem to really solve some of these problems. Tristan, you guys are part of that ecosystem, and others, but please, your thoughts on what Bob just shared. >> Bob, I'm curious if, I would have no idea what you were talking about except that you introduced me to somebody who gave me a demo of a thing and do you not want to go there right now? >> No, I can talk about it. I mean, we can talk about it. Look, the company I've been working with is Relational AI, and they're doing this work to actually first of all work across the industry with academics and research, you know, across many, many different, over 20 different research institutions across the world to develop this new set of algorithms. They're all fully published, just like SQL, the underlying algorithms that are used by SQL databases are. If you look today, every single SQL database uses a similar set of relational algorithms underneath that. And those algorithms actually go back to system R and what IBM developed in the 1970s. We're just, there's an opportunity for us to build something new that allows you to take, for example, instead of taking data and grouping it together in tables, treat all data as individual relations, you know, a key and a set of values and then be able to perform purely relational operations on it. If you go back to what, to Codd, and what he wrote, he defined two things. He defined a relational calculus and relational algebra. And essentially SQL is a query language that is translated by the query processor into relational algebra. But however, the calculus of SQL is not even close to the full semantics of the relational mathematics. And it's possible to have systems that can do everything and that can store all of the attributes of the data model or ultimately the business model in a form that is much more natural to work with. >> So here's like my short answer to this. I think that we're dealing in different time scales. I think that there is actually a tremendous amount of work to do in the semantic layer using the kind of technology that we have on the ground today. And I think that there's, I don't know, let's say five years of like really solid work that there is to do for the entire industry, if not more. But the wonderful thing about DBT is that it's independent of what the compute substrate is beneath it. And so if we develop new platforms, new capabilities to describe semantic models in more fine grain detail, more procedural, then we're going to support that too. And so I'm excited about all of it. >> Yeah, so interpreting that short answer, you're basically saying, cause Bob was just kind of pointing to you as incremental, but you're saying, yeah, okay, we're applying it for incremental use cases today, but we can accommodate a much broader set of examples in the future. Is that correct, Tristan? >> I think you're using the word incremental as if it's not good, but I think that incremental is great. We have always been about applying incremental improvement on top of what exists today, but allowing practitioners to like use different workflows to actually make use of that technology. So yeah, yeah, we are a very incremental company. We're going to continue being that way. >> Well, I think Bob was using incremental as a pejorative. I mean, I, but to your point, a lot. >> No, I don't think so. I want to stop that. No, I don't think it's pejorative at all. I think incremental, incremental is usually the most successful path. >> Yes, of course. >> In my experience. >> We agree, we agree on that. >> Having tried many, many moonshot things in my Microsoft days, I can tell you that being incremental is a good thing. And I'm a very big believer that that's the way the world's going to go. I just think that there is a need for us to build something new and that ultimately that will be the solution. Now you can argue whether it's two years, three years, five years, or 10 years, but I'd be shocked if it didn't happen in 10 years. >> Yeah, so we all agree that incremental is less disruptive. Boom, but Tristan, you're, I think I'm inferring that you believe you have the architecture to accommodate Bob's vision, and then Bob, and I'm inferring from Bob's comments that maybe you don't think that's the case, but please. >> No, no, no. I think that, so Bob, let me put words into your mouth and you tell me if you disagree, DBT is completely useless in a world where a large scale cloud data warehouse doesn't exist. We were not able to bring the power of Python to our users until these platforms started supporting Python. Like DBT is a layer on top of large scale computing platforms. And to the extent that those platforms extend their functionality to bring more capabilities, we will also service those capabilities. >> Let me try and bridge the two. >> Yeah, yeah, so Bob, Bob, Bob, do you concur with what Tristan just said? >> Absolutely, I mean there's nothing to argue with in what Tristan just said. >> I wanted. >> And it's what he's doing. It'll continue to, I believe he'll continue to do it, and I think it's a very good thing for the industry. You know, I'm just simply saying that on top of that, I would like to provide Tristan and all of those who are following similar paths to him with a new type of database that can actually solve these problems in a much more architected way. And when I talk about Cosmos with something like Mongo or Cosmos together with Elastic, you're using Elastic as the join engine, okay. That's the purpose of it. It becomes a poor man's join engine. And I kind of go, I know there's a better answer than that. I know there is, but that's kind of where we are state of the art right now. >> George, we got to wrap it. So give us the last word here. Go ahead, George. >> Okay, I just, I think there's a way to tie together what Tristan and Bob are both talking about, and I want them to validate it, which is for five years we're going to be adding or some number of years more and more semantics to the operational and analytic data that we have, starting with metric definitions. My question is for Bob, as DBT accumulates more and more of those semantics for different enterprises, can that layer not run on top of a relational knowledge graph? And what would we lose by not having, by having the knowledge graph store sort of the joins, all the complex relationships among the data, but having the semantics in the DBT layer? >> Well, I think this, okay, I think first of all that DBT will be an environment where many of these semantics are defined. The question we're asking is how are they stored and how are they processed? And what I predict will happen is that over time, as companies like DBT begin to build more and more richness into their semantic layer, they will begin to experience challenges that customers want to run queries, they want to ask questions, they want to use this for things where the underlying infrastructure becomes an obstacle. I mean, this has happened in always in the history, right? I mean, you see major advances in computer science when the data model changes. And I think we're on the verge of a very significant change in the way data is stored and structured, or at least metadata is stored and structured. Again, I'm not saying that anytime in the next 10 years, SQL is going to go away. In fact, more SQL will be written in the future than has been written in the past. And those platforms will mature to become the engines, the slicer dicers of data. I mean that's what they are today. They're incredibly powerful at working with large amounts of data, and that infrastructure is maturing very rapidly. What is not maturing is the infrastructure to handle all of the metadata and the semantics that that requires. And that's where I say knowledge graphs are what I believe will be the solution to that. >> But Tristan, bring us home here. It sounds like, let me put pause at this, is that whatever happens in the future, we're going to leverage the vast system that has become cloud that we're talking about a supercloud, sort of where data lives irrespective of physical location. We're going to have to tap that data. It's not necessarily going to be in one place, but give us your final thoughts, please. >> 100% agree. I think that the data is going to live everywhere. It is the responsibility for both the metadata systems and the data processing engines themselves to make sure that we can join data across cloud providers, that we can join data across different physical regions and that we as practitioners are going to kind of start forgetting about details like that. And we're going to start thinking more about how we want to arrange our teams, how does the tooling that we use support our team structures? And that's when data mesh I think really starts to get very, very critical as a concept. >> Guys, great conversation. It was really awesome to have you. I can't thank you enough for spending time with us. Really appreciate it. >> Thanks a lot. >> All right. This is Dave Vellante for George Gilbert, John Furrier, and the entire Cube community. Keep it right there for more content. You're watching SuperCloud2. (upbeat music)
SUMMARY :
and the future of cloud. And Bob, you have some really and I think it's helpful to do it I'm going to go back and And I noticed that you is that what they mean? that we're familiar with, you know, it comes back to SuperCloud, is that data products are George, is that how you see it? that don't require a human to is that one of the most And DBT has, you know, the And I'm sure that there will be more on the right architecture is that in the next 10 years, So let me ask the Colombo and the data stack, which is that is still in the like Modal Labs, yeah, of course. and that sits above the and that query is going to So Tristan, you got to and that can store all of the that there is to do for the pointing to you as incremental, but allowing practitioners to I mean, I, but to your point, a lot. the most successful path. that that's the way the that you believe you have the architecture and you tell me if you disagree, there's nothing to argue with And I kind of go, I know there's George, we got to wrap it. and more of those semantics and the semantics that that requires. is that whatever happens in the future, and that we as practitioners I can't thank you enough John Furrier, and the
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Breaking Analysis: AI Goes Mainstream But ROI Remains Elusive
>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante. >> A decade of big data investments combined with cloud scale, the rise of much more cost effective processing power. And the introduction of advanced tooling has catapulted machine intelligence to the forefront of technology investments. No matter what job you have, your operation will be AI powered within five years and machines may actually even be doing your job. Artificial intelligence is being infused into applications, infrastructure, equipment, and virtually every aspect of our lives. AI is proving to be extremely helpful at things like controlling vehicles, speeding up medical diagnoses, processing language, advancing science, and generally raising the stakes on what it means to apply technology for business advantage. But business value realization has been a challenge for most organizations due to lack of skills, complexity of programming models, immature technology integration, sizable upfront investments, ethical concerns, and lack of business alignment. Mastering AI technology will not be a requirement for success in our view. However, figuring out how and where to apply AI to your business will be crucial. That means understanding the business case, picking the right technology partner, experimenting in bite-sized chunks, and quickly identifying winners to double down on from an investment standpoint. Hello and welcome to this week's Wiki-bond CUBE Insights powered by ETR. In this breaking analysis, we update you on the state of AI and what it means for the competition. And to do so, we invite into our studios Andy Thurai of Constellation Research. Andy covers AI deeply. He knows the players, he knows the pitfalls of AI investment, and he's a collaborator. Andy, great to have you on the program. Thanks for coming into our CUBE studios. >> Thanks for having me on. >> You're very welcome. Okay, let's set the table with a premise and a series of assertions we want to test with Andy. I'm going to lay 'em out. And then Andy, I'd love for you to comment. So, first of all, according to McKinsey, AI adoption has more than doubled since 2017, but only 10% of organizations report seeing significant ROI. That's a BCG and MIT study. And part of that challenge of AI is it requires data, is requires good data, data proficiency, which is not trivial, as you know. Firms that can master both data and AI, we believe are going to have a competitive advantage this decade. Hyperscalers, as we show you dominate AI and ML. We'll show you some data on that. And having said that, there's plenty of room for specialists. They need to partner with the cloud vendors for go to market productivity. And finally, organizations increasingly have to put data and AI at the center of their enterprises. And to do that, most are going to rely on vendor R&D to leverage AI and ML. In other words, Andy, they're going to buy it and apply it as opposed to build it. What are your thoughts on that setup and that premise? >> Yeah, I see that a lot happening in the field, right? So first of all, the only 10% of realizing a return on investment. That's so true because we talked about this earlier, the most companies are still in the innovation cycle. So they're trying to innovate and see what they can do to apply. A lot of these times when you look at the solutions, what they come up with or the models they create, the experimentation they do, most times they don't even have a good business case to solve, right? So they just experiment and then they figure it out, "Oh my God, this model is working. Can we do something to solve it?" So it's like you found a hammer and then you're trying to find the needle kind of thing, right? That never works. >> 'Cause it's cool or whatever it is. >> It is, right? So that's why, I always advise, when they come to me and ask me things like, "Hey, what's the right way to do it? What is the secret sauce?" And, we talked about this. The first thing I tell them is, "Find out what is the business case that's having the most amount of problems, that that can be solved using some of the AI use cases," right? Not all of them can be solved. Even after you experiment, do the whole nine yards, spend millions of dollars on that, right? And later on you make it efficient only by saving maybe $50,000 for the company or a $100,000 for the company, is it really even worth the experiment, right? So you got to start with the saying that, you know, where's the base for this happening? Where's the need? What's a business use case? It doesn't have to be about cost efficient and saving money in the existing processes. It could be a new thing. You want to bring in a new revenue stream, but figure out what is a business use case, how much money potentially I can make off of that. The same way that start-ups go after. Right? >> Yeah. Pretty straightforward. All right, let's take a look at where ML and AI fit relative to the other hot sectors of the ETR dataset. This XY graph shows net score spending velocity in the vertical axis and presence in the survey, they call it sector perversion for the October survey, the January survey's in the field. Then that squiggly line on ML/AI represents the progression. Since the January 21 survey, you can see the downward trajectory. And we position ML and AI relative to the other big four hot sectors or big three, including, ML/AI is four. Containers, cloud and RPA. These have consistently performed above that magic 40% red dotted line for most of the past two years. Anything above 40%, we think is highly elevated. And we've just included analytics and big data for context and relevant adjacentness, if you will. Now note that green arrow moving toward, you know, the 40% mark on ML/AI. I got a glimpse of the January survey, which is in the field. It's got more than a thousand responses already, and it's trending up for the current survey. So Andy, what do you make of this downward trajectory over the past seven quarters and the presumed uptick in the coming months? >> So one of the things you have to keep in mind is when the pandemic happened, it's about survival mode, right? So when somebody's in a survival mode, what happens, the luxury and the innovations get cut. That's what happens. And this is exactly what happened in the situation. So as you can see in the last seven quarters, which is almost dating back close to pandemic, everybody was trying to keep their operations alive, especially digital operations. How do I keep the lights on? That's the most important thing for them. So while the numbers spent on AI, ML is less overall, I still think the AI ML to spend to sort of like a employee experience or the IT ops, AI ops, ML ops, as we talked about, some of those areas actually went up. There are companies, we talked about it, Atlassian had a lot of platform issues till the amount of money people are spending on that is exorbitant and simply because they are offering the solution that was not available other way. So there are companies out there, you can take AoPS or incident management for that matter, right? A lot of companies have a digital insurance, they don't know how to properly manage it. How do you find an intern solve it immediately? That's all using AI ML and some of those areas actually growing unbelievable, the companies in that area. >> So this is a really good point. If you can you bring up that chart again, what Andy's saying is a lot of the companies in the ETR taxonomy that are doing things with AI might not necessarily show up in a granular fashion. And I think the other point I would make is, these are still highly elevated numbers. If you put on like storage and servers, they would read way, way down the list. And, look in the pandemic, we had to deal with work from home, we had to re-architect the network, we had to worry about security. So those are really good points that you made there. Let's, unpack this a little bit and look at the ML AI sector and the ETR data and specifically at the players and get Andy to comment on this. This chart here shows the same x y dimensions, and it just notes some of the players that are specifically have services and products that people spend money on, that CIOs and IT buyers can comment on. So the table insert shows how the companies are plotted, it's net score, and then the ends in the survey. And Andy, the hyperscalers are dominant, as you can see. You see Databricks there showing strong as a specialist, and then you got to pack a six or seven in there. And then Oracle and IBM, kind of the big whales of yester year are in the mix. And to your point, companies like Salesforce that you mentioned to me offline aren't in that mix, but they do a lot in AI. But what are your takeaways from that data? >> If you could put the slide back on please. I want to make quick comments on a couple of those. So the first one is, it's surprising other hyperscalers, right? As you and I talked about this earlier, AWS is more about logo blocks. We discussed that, right? >> Like what? Like a SageMaker as an example. >> We'll give you all the components what do you need. Whether it's MLOps component or whether it's, CodeWhisperer that we talked about, or a oral platform or data or data, whatever you want. They'll give you the blocks and then you'll build things on top of it, right? But Google took a different way. Matter of fact, if we did those numbers a few years ago, Google would've been number one because they did a lot of work with their acquisition of DeepMind and other things. They're way ahead of the pack when it comes to AI for longest time. Now, I think Microsoft's move of partnering and taking a huge competitor out would open the eyes is unbelievable. You saw that everybody is talking about chat GPI, right? And the open AI tool and ChatGPT rather. Remember as Warren Buffet is saying that, when my laundry lady comes and talk to me about stock market, it's heated up. So that's how it's heated up. Everybody's using ChatGPT. What that means is at the end of the day is they're creating, it's still in beta, keep in mind. It's not fully... >> Can you play with it a little bit? >> I have a little bit. >> I have, but it's good and it's not good. You know what I mean? >> Look, so at the end of the day, you take the massive text of all the available text in the world today, mass them all together. And then you ask a question, it's going to basically search through that and figure it out and answer that back. Yes, it's good. But again, as we discussed, if there's no business use case of what problem you're going to solve. This is building hype. But then eventually they'll figure out, for example, all your chats, online chats, could be aided by your AI chat bots, which is already there, which is not there at that level. This could build help that, right? Or the other thing we talked about is one of the areas where I'm more concerned about is that it is able to produce equal enough original text at the level that humans can produce, for example, ChatGPT or the equal enough, the large language transformer can help you write stories as of Shakespeare wrote it. Pretty close to it. It'll learn from that. So when it comes down to it, talk about creating messages, articles, blogs, especially during political seasons, not necessarily just in US, but anywhere for that matter. If people are able to produce at the emission speed and throw it at the consumers and confuse them, the elections can be won, the governments can be toppled. >> Because to your point about chatbots is chatbots have obviously, reduced the number of bodies that you need to support chat. But they haven't solved the problem of serving consumers. Most of the chat bots are conditioned response, which of the following best describes your problem? >> The current chatbot. >> Yeah. Hey, did we solve your problem? No. Is the answer. So that has some real potential. But if you could bring up that slide again, Ken, I mean you've got the hyperscalers that are dominant. You talked about Google and Microsoft is ubiquitous, they seem to be dominant in every ETR category. But then you have these other specialists. How do those guys compete? And maybe you could even, cite some of the guys that you know, how do they compete with the hyperscalers? What's the key there for like a C3 ai or some of the others that are on there? >> So I've spoken with at least two of the CEOs of the smaller companies that you have on the list. One of the things they're worried about is that if they continue to operate independently without being part of hyperscaler, either the hyperscalers will develop something to compete against them full scale, or they'll become irrelevant. Because at the end of the day, look, cloud is dominant. Not many companies are going to do like AI modeling and training and deployment the whole nine yards by independent by themselves. They're going to depend on one of the clouds, right? So if they're already going to be in the cloud, by taking them out to come to you, it's going to be extremely difficult issue to solve. So all these companies are going and saying, "You know what? We need to be in hyperscalers." For example, you could have looked at DataRobot recently, they made announcements, Google and AWS, and they are all over the place. So you need to go where the customers are. Right? >> All right, before we go on, I want to share some other data from ETR and why people adopt AI and get your feedback. So the data historically shows that feature breadth and technical capabilities were the main decision points for AI adoption, historically. What says to me that it's too much focus on technology. In your view, is that changing? Does it have to change? Will it change? >> Yes. Simple answer is yes. So here's the thing. The data you're speaking from is from previous years. >> Yes >> I can guarantee you, if you look at the latest data that's coming in now, those two will be a secondary and tertiary points. The number one would be about ROI. And how do I achieve? I've spent ton of money on all of my experiments. This is the same thing theme I'm seeing across when talking to everybody who's spending money on AI. I've spent so much money on it. When can I get it live in production? How much, how can I quickly get it? Because you know, the board is breathing down their neck. You already spend this much money. Show me something that's valuable. So the ROI is going to become, take it from me, I'm predicting this for 2023, that's going to become number one. >> Yeah, and if people focus on it, they'll figure it out. Okay. Let's take a look at some of the top players that won, some of the names we just looked at and double click on that and break down their spending profile. So the chart here shows the net score, how net score is calculated. So pay attention to the second set of bars that Databricks, who was pretty prominent on the previous chart. And we've annotated the colors. The lime green is, we're bringing the platform in new. The forest green is, we're going to spend 6% or more relative to last year. And the gray is flat spending. The pinkish is our spending's going to be down on AI and ML, 6% or worse. And the red is churn. So you don't want big red. You subtract the reds from the greens and you get net score, which is shown by those blue dots that you see there. So AWS has the highest net score and very little churn. I mean, single low single digit churn. But notably, you see Databricks and DataRobot are next in line within Microsoft and Google also, they've got very low churn. Andy, what are your thoughts on this data? >> So a couple of things that stands out to me. Most of them are in line with my conversation with customers. Couple of them stood out to me on how bad IBM Watson is doing. >> Yeah, bring that back up if you would. Let's take a look at that. IBM Watson is the far right and the red, that bright red is churning and again, you want low red here. Why do you think that is? >> Well, so look, IBM has been in the forefront of innovating things for many, many years now, right? And over the course of years we talked about this, they moved from a product innovation centric company into more of a services company. And over the years they were making, as at one point, you know that they were making about majority of that money from services. Now things have changed Arvind has taken over, he came from research. So he's doing a great job of trying to reinvent themselves as a company. But it's going to have a long way to catch up. IBM Watson, if you think about it, that played what, jeopardy and chess years ago, like 15 years ago? >> It was jaw dropping when you first saw it. And then they weren't able to commercialize that. >> Yeah. >> And you're making a good point. When Gerstner took over IBM at the time, John Akers wanted to split the company up. He wanted to have a database company, he wanted to have a storage company. Because that's where the industry trend was, Gerstner said no, he came from AMEX, right? He came from American Express. He said, "No, we're going to have a single throat to choke for the customer." They bought PWC for relatively short money. I think it was $15 billion, completely transformed and I would argue saved IBM. But the trade off was, it sort of took them out of product leadership. And so from Gerstner to Palmisano to Remedi, it was really a services led company. And I think Arvind is really bringing it back to a product company with strong consulting. I mean, that's one of the pillars. And so I think that's, they've got a strong story in data and AI. They just got to sort of bring it together and better. Bring that chart up one more time. I want to, the other point is Oracle, Oracle sort of has the dominant lock-in for mission critical database and they're sort of applying AI there. But to your point, they're really not an AI company in the sense that they're taking unstructured data and doing sort of new things. It's really about how to make Oracle better, right? >> Well, you got to remember, Oracle is about database for the structure data. So in yesterday's world, they were dominant database. But you know, if you are to start storing like videos and texts and audio and other things, and then start doing search of vector search and all that, Oracle is not necessarily the database company of choice. And they're strongest thing being apps and building AI into the apps? They are kind of surviving in that area. But again, I wouldn't name them as an AI company, right? But the other thing that that surprised me in that list, what you showed me is yes, AWS is number one. >> Bring that back up if you would, Ken. >> AWS is number one as you, it should be. But what what actually caught me by surprise is how DataRobot is holding, you know? I mean, look at that. The either net new addition and or expansion, DataRobot seem to be doing equally well, even better than Microsoft and Google. That surprises me. >> DataRobot's, and again, this is a function of spending momentum. So remember from the previous chart that Microsoft and Google, much, much larger than DataRobot. DataRobot more niche. But with spending velocity and has always had strong spending velocity, despite some of the recent challenges, organizational challenges. And then you see these other specialists, H2O.ai, Anaconda, dataiku, little bit of red showing there C3.ai. But these again, to stress are the sort of specialists other than obviously the hyperscalers. These are the specialists in AI. All right, so we hit the bigger names in the sector. Now let's take a look at the emerging technology companies. And one of the gems of the ETR dataset is the emerging technology survey. It's called ETS. They used to just do it like twice a year. It's now run four times a year. I just discovered it kind of mid-2022. And it's exclusively focused on private companies that are potential disruptors, they might be M&A candidates and if they've raised enough money, they could be acquirers of companies as well. So Databricks would be an example. They've made a number of investments in companies. SNEAK would be another good example. Companies that are private, but they're buyers, they hope to go IPO at some point in time. So this chart here, shows the emerging companies in the ML AI sector of the ETR dataset. So the dimensions of this are similar, they're net sentiment on the Y axis and mind share on the X axis. Basically, the ETS study measures awareness on the x axis and intent to do something with, evaluate or implement or not, on that vertical axis. So it's like net score on the vertical where negatives are subtracted from the positives. And again, mind share is vendor awareness. That's the horizontal axis. Now that inserted table shows net sentiment and the ends in the survey, which informs the position of the dots. And you'll notice we're plotting TensorFlow as well. We know that's not a company, but it's there for reference as open source tooling is an option for customers. And ETR sometimes like to show that as a reference point. Now we've also drawn a line for Databricks to show how relatively dominant they've become in the past 10 ETS surveys and sort of mind share going back to late 2018. And you can see a dozen or so other emerging tech vendors. So Andy, I want you to share your thoughts on these players, who were the ones to watch, name some names. We'll bring that data back up as you as you comment. >> So Databricks, as you said, remember we talked about how Oracle is not necessarily the database of the choice, you know? So Databricks is kind of trying to solve some of the issue for AI/ML workloads, right? And the problem is also there is no one company that could solve all of the problems. For example, if you look at the names in here, some of them are database names, some of them are platform names, some of them are like MLOps companies like, DataRobot (indistinct) and others. And some of them are like future based companies like, you know, the Techton and stuff. >> So it's a mix of those sub sectors? >> It's a mix of those companies. >> We'll talk to ETR about that. They'd be interested in your input on how to make this more granular and these sub-sectors. You got Hugging Face in here, >> Which is NLP, yeah. >> Okay. So your take, are these companies going to get acquired? Are they going to go IPO? Are they going to merge? >> Well, most of them going to get acquired. My prediction would be most of them will get acquired because look, at the end of the day, hyperscalers need these capabilities, right? So they're going to either create their own, AWS is very good at doing that. They have done a lot of those things. But the other ones, like for particularly Azure, they're going to look at it and saying that, "You know what, it's going to take time for me to build this. Why don't I just go and buy you?" Right? Or or even the smaller players like Oracle or IBM Cloud, this will exist. They might even take a look at them, right? So at the end of the day, a lot of these companies are going to get acquired or merged with others. >> Yeah. All right, let's wrap with some final thoughts. I'm going to make some comments Andy, and then ask you to dig in here. Look, despite the challenge of leveraging AI, you know, Ken, if you could bring up the next chart. We're not repeating, we're not predicting the AI winter of the 1990s. Machine intelligence. It's a superpower that's going to permeate every aspect of the technology industry. AI and data strategies have to be connected. Leveraging first party data is going to increase AI competitiveness and shorten time to value. Andy, I'd love your thoughts on that. I know you've got some thoughts on governance and AI ethics. You know, we talked about ChatGBT, Deepfakes, help us unpack all these trends. >> So there's so much information packed up there, right? The AI and data strategy, that's very, very, very important. If you don't have a proper data, people don't realize that AI is, your AI is the morals that you built on, it's predominantly based on the data what you have. It's not, AI cannot predict something that's going to happen without knowing what it is. It need to be trained, it need to understand what is it you're talking about. So 99% of the time you got to have a good data for you to train. So this where I mentioned to you, the problem is a lot of these companies can't afford to collect the real world data because it takes too long, it's too expensive. So a lot of these companies are trying to do the synthetic data way. It has its own set of issues because you can't use all... >> What's that synthetic data? Explain that. >> Synthetic data is basically not a real world data, but it's a created or simulated data equal and based on real data. It looks, feels, smells, taste like a real data, but it's not exactly real data, right? This is particularly useful in the financial and healthcare industry for world. So you don't have to, at the end of the day, if you have real data about your and my medical history data, if you redact it, you can still reverse this. It's fairly easy, right? >> Yeah, yeah. >> So by creating a synthetic data, there is no correlation between the real data and the synthetic data. >> So that's part of AI ethics and privacy and, okay. >> So the synthetic data, the issue with that is that when you're trying to commingle that with that, you can't create models based on just on synthetic data because synthetic data, as I said is artificial data. So basically you're creating artificial models, so you got to blend in properly that that blend is the problem. And you know how much of real data, how much of synthetic data you could use. You got to use judgment between efficiency cost and the time duration stuff. So that's one-- >> And risk >> And the risk involved with that. And the secondary issues which we talked about is that when you're creating, okay, you take a business use case, okay, you think about investing things, you build the whole thing out and you're trying to put it out into the market. Most companies that I talk to don't have a proper governance in place. They don't have ethics standards in place. They don't worry about the biases in data, they just go on trying to solve a business case >> It's wild west. >> 'Cause that's what they start. It's a wild west! And then at the end of the day when they are close to some legal litigation action or something or something else happens and that's when the Oh Shit! moments happens, right? And then they come in and say, "You know what, how do I fix this?" The governance, security and all of those things, ethics bias, data bias, de-biasing, none of them can be an afterthought. It got to start with the, from the get-go. So you got to start at the beginning saying that, "You know what, I'm going to do all of those AI programs, but before we get into this, we got to set some framework for doing all these things properly." Right? And then the-- >> Yeah. So let's go back to the key points. I want to bring up the cloud again. Because you got to get cloud right. Getting that right matters in AI to the points that you were making earlier. You can't just be out on an island and hyperscalers, they're going to obviously continue to do well. They get more and more data's going into the cloud and they have the native tools. To your point, in the case of AWS, Microsoft's obviously ubiquitous. Google's got great capabilities here. They've got integrated ecosystems partners that are going to continue to strengthen through the decade. What are your thoughts here? >> So a couple of things. One is the last mile ML or last mile AI that nobody's talking about. So that need to be attended to. There are lot of players in the market that coming up, when I talk about last mile, I'm talking about after you're done with the experimentation of the model, how fast and quickly and efficiently can you get it to production? So that's production being-- >> Compressing that time is going to put dollars in your pocket. >> Exactly. Right. >> So once, >> If you got it right. >> If you get it right, of course. So there are, there are a couple of issues with that. Once you figure out that model is working, that's perfect. People don't realize, the moment you decide that moment when the decision is made, it's like a new car. After you purchase the value decreases on a minute basis. Same thing with the models. Once the model is created, you need to be in production right away because it starts losing it value on a seconds minute basis. So issue number one, how fast can I get it over there? So your deployment, you are inferencing efficiently at the edge locations, your optimization, your security, all of this is at issue. But you know what is more important than that in the last mile? You keep the model up, you continue to work on, again, going back to the car analogy, at one point you got to figure out your car is costing more than to operate. So you got to get a new car, right? And that's the same thing with the models as well. If your model has reached a stage, it is actually a potential risk for your operation. To give you an idea, if Uber has a model, the first time when you get a car from going from point A to B cost you $60. If the model decayed the next time I might give you a $40 rate, I would take it definitely. But it's lost for the company. The business risk associated with operating on a bad model, you should realize it immediately, pull the model out, retrain it, redeploy it. That's is key. >> And that's got to be huge in security model recency and security to the extent that you can get real time is big. I mean you, you see Palo Alto, CrowdStrike, a lot of other security companies are injecting AI. Again, they won't show up in the ETR ML/AI taxonomy per se as a pure play. But ServiceNow is another company that you have have mentioned to me, offline. AI is just getting embedded everywhere. >> Yep. >> And then I'm glad you brought up, kind of real-time inferencing 'cause a lot of the modeling, if we can go back to the last point that we're going to make, a lot of the AI today is modeling done in the cloud. The last point we wanted to make here, I'd love to get your thoughts on this, is real-time AI inferencing for instance at the edge is going to become increasingly important for us. It's going to usher in new economics, new types of silicon, particularly arm-based. We've covered that a lot on "Breaking Analysis", new tooling, new companies and that could disrupt the sort of cloud model if new economics emerge. 'Cause cloud obviously very centralized, they're trying to decentralize it. But over the course of this decade we could see some real disruption there. Andy, give us your final thoughts on that. >> Yes and no. I mean at the end of the day, cloud is kind of centralized now, but a lot of this companies including, AWS is kind of trying to decentralize that by putting their own sub-centers and edge locations. >> Local zones, outposts. >> Yeah, exactly. Particularly the outpost concept. And if it can even become like a micro center and stuff, it won't go to the localized level of, I go to a single IOT level. But again, the cloud extends itself to that level. So if there is an opportunity need for it, the hyperscalers will figure out a way to fit that model. So I wouldn't too much worry about that, about deployment and where to have it and what to do with that. But you know, figure out the right business use case, get the right data, get the ethics and governance place and make sure they get it to production and make sure you pull the model out when it's not operating well. >> Excellent advice. Andy, I got to thank you for coming into the studio today, helping us with this "Breaking Analysis" segment. Outstanding collaboration and insights and input in today's episode. Hope we can do more. >> Thank you. Thanks for having me. I appreciate it. >> You're very welcome. All right. I want to thank Alex Marson who's on production and manages the podcast. Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and our newsletters. And Rob Hoof is our editor-in-chief over at Silicon Angle. He does some great editing for us. Thank you all. Remember all these episodes are available as podcast. Wherever you listen, all you got to do is search "Breaking Analysis" podcast. I publish each week on wikibon.com and silicon angle.com or you can email me at david.vellante@siliconangle.com to get in touch, or DM me at dvellante or comment on our LinkedIn posts. Please check out ETR.AI for the best survey data and the enterprise tech business, Constellation Research. Andy publishes there some awesome information on AI and data. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching everybody and we'll see you next time on "Breaking Analysis". (gentle closing tune plays)
SUMMARY :
bringing you data-driven Andy, great to have you on the program. and AI at the center of their enterprises. So it's like you found a of the AI use cases," right? I got a glimpse of the January survey, So one of the things and it just notes some of the players So the first one is, Like a And the open AI tool and ChatGPT rather. I have, but it's of all the available text of bodies that you need or some of the others that are on there? One of the things they're So the data historically So here's the thing. So the ROI is going to So the chart here shows the net score, Couple of them stood out to me IBM Watson is the far right and the red, And over the course of when you first saw it. I mean, that's one of the pillars. Oracle is not necessarily the how DataRobot is holding, you know? So it's like net score on the vertical database of the choice, you know? on how to make this more Are they going to go IPO? So at the end of the day, of the technology industry. So 99% of the time you What's that synthetic at the end of the day, and the synthetic data. So that's part of AI that blend is the problem. And the risk involved with that. So you got to start at data's going into the cloud So that need to be attended to. is going to put dollars the first time when you that you can get real time is big. a lot of the AI today is I mean at the end of the day, and make sure they get it to production Andy, I got to thank you for Thanks for having me. and manages the podcast.
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Nir Zuk, Palo Alto Networks | Palo Alto Networks Ignite22
>> Presenter: theCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Hey guys and girls. Welcome back to theCube's live coverage at Palo Alto Ignite '22. We're live at the MGM Grand Hotel in beautiful Las Vegas. Lisa Martin here with Dave Vellante. This is day one of our coverage. We've been talking with execs from Palo Alto, Partners, but one of our most exciting things is talking with Founders day. We get to do that next. >> The thing is, it's like I wrote this weekend in my breaking analysis. Understanding the problem in cybersecurity is really easy, but figuring out how to fix it ain't so much. >> It definitely isn't. >> So I'm excited to have Nir here. >> Very excited. Nir Zuk joins us, the founder and CTO of Palo Alto Networks. Welcome, Nir. Great to have you on the program. >> Thank you. >> So Palo Alto Networks, you founded it back in 2005. It's hard to believe that's been 18 years, almost. You did something different, which I want to get into. But tell us, what was it back then? Why did you found this company? >> I thought the world needed another cybersecurity company. I thought it's because there were so many cybersecurity vendors in the world, and just didn't make any sense. This industry has evolved in a very weird way, where every time there was a new challenge, rather than existing vendors dealing with a challenge, you had new vendors dealing with it, and I thought I could put a stop to it, and I think I did. >> You did something differently back in 2005, looking at where you are now, the leader, what was different in your mind back then? >> Yeah. When you found a new company, you have really two good options. There's also a bad option, but we'll skip that. You can either disrupt an existing market, or you can create a new market. So first, I decided to disrupt an existing market, go into an existing market first, network security, then cyber security, and change it. Change the way it works. And like I said, the challenges that every problem had a new vendor, and nobody just stepped back and said, "I think I can solve it with the platform." Meaning, I think I can spend some time not solving a specific problem, but building a platform that then can be used to solve many different problems. And that's what I've done, and that's what Palo Alto Networks has done, and that's where we are today. >> So you look back, you call it now, I think you call it a next gen firewall, but nothing in 2005, can it be next gen? Do you know the Silicon Valley Show? Do you know the show Silicon Valley? >> Oh! Yeah. >> Yeah, of course. >> You got to have a box. But it was a different kind of box- >> Actually. >> Explain that. >> Actually, it's exactly the same thing. You got to have a box. So I actually wanted to call it a necessary evil. Marketing wouldn't go for that. >> No. >> And the reason I wanted to call it a necessary evil, because one of the things that we've done in order to platform our cyber security, again, first network security now, also cloud security, and security operations, is to turn it into a SaaS delivered industry. Today every cyber security professional knows that, when they buy cyber security, they buy usually a SaaS delivered service. Back then, people thought I was crazy to think that customers are going to send their data to their vendor in order to process, and they wanted everything on premise and so on, but I said, "No, customers are going to send information to us for processing, because we have much more processing power than they have." And we needed something in the infrastructure to send us the information. So that's why I wanted to call it the necessary evil. We ended up calling it next generation firewall, which was probably a better term. >> Well, even Veritas. Remember Veritas? They had the no hardware agenda. Even they have a box. So it is like you say, you got to have it. >> It's necessary. >> Okay. You did this, you started this on your own cloud, kind of like Salesforce, ServiceNow. >> Correct. >> Similar now- >> Build your own data centers. >> Build your own data center. Okay, I call it a cloud, but no. >> No, it's the same. There's no cloud, it's just someone else's computer. >> According to Larry Ellison, he was actually probably right about that. But over time, you've had this closer partnership with the public clouds. >> Correct. >> What does that bring you and your customers, and how hard was that to navigate? >> It wasn't that hard for us, because we didn't have that many services. Usually it's harder. Of course, we didn't do a lift and shift, which is their own thing to do with the cloud. We rebuild things for the cloud, and the benefits, of course, are time to market, scale, agility, and in some cases also, cost. >> Yeah, some cases. >> In some cases. >> So you have a sort of a hybrid model today. You still run your own data centers, do you not? >> Very few. >> Really? >> There are very, very few things that we have to do on hardware, like simulating malware and things that cannot be done in a virtual machine, which is pretty much the only option you have in the cloud. They provide bare metal, but doesn't serve our needs. I think that we don't view cloud, and your viewers should not be viewing cloud, as a place where they're going to save money. It's a place where they're going to make money. >> I like that. >> You make much more money, because you're more agile. >> And that's why this conversation is all about, your cost of goods sold they're going to be so high, you're going to have to come back to your own data centers. That's not on your mind right now. What's on your mind is advancing the unit, right? >> Look, my own data center would limit me in scale, would limit my agility. If you want to build something new, you don't have all the PaaS services, the platform as a service, services like database, and AI, and so on. I have to build them myself. It takes time. So yeah, it's going to be cheaper, but I'm not going to be delivering the same thing. So my revenues will be much lower. >> Less top line. What can humans do better than machines? You were talking about your keynote... I'm just going to chat a little bit. You were talking about your keynote. Basically, if you guys didn't see the keynote, that AI is going to run every soc within five years, that was a great prediction that you made. >> Correct. >> And they're going to do things that you can't do today, and then in the future, they're going to do things that you can't... Better than you can do. >> And you just have to be comfortable with that. >> So what do you think humans can do today and in the future better than machines? >> Look, humans can always do better than machines. The human mind can do things that machines cannot do. We are conscious, I don't think machines will be conscious. And you can do things... My point was not that machines can do things that humans cannot do. They can just do it better. The things that humans do today, machines can do better, once machines do that, humans will be free to do things that they don't do today, that machines cannot do. >> Like what? >> Like finding the most difficult, most covert attacks, dealing with the most difficult incidents, things that machines just can't do. Just that today, humans are consumed by finding attacks that machines can find, by dealing with incidents that machines can deal with. It's a waste of time. We leave it to the machines and go and focus on the most difficult problems, and then have the machines learn from you, so that next time or a hundred or a thousand times from now, they can do it themselves, and you focus on the even more difficult. >> Yeah, just like after 9/11, they said that we lack the creativity. That's what humans have, that machines don't, at least today. >> Machines don't. Yeah, look, every airplane has two pilots, even though airplanes have been flying themselves for 30 years now, why do you have two pilots, to do the things that machines cannot do? Like land on the Hudson, right? You always need humans to do the things that machines cannot do. But to leave the things that machines can do to the machines, they'll do it better. >> And autonomous vehicles need breaks. (indistinct) >> In your customer conversations, are customers really grappling with that, are they going, "Yeah, you're right?" >> It depends. It's hard for customers to let go of old habits. First, the habit of buying a hundred different solutions from a hundred different vendors, and you know what? Why would I trust one vendor to do everything, put all my eggs in the same basket? They have all kind of slogans as to why not to do that, even though it's been proven again and again that, doing everything in one system with one brain, versus a hundred systems with a hundred brains, work much better. So that's one thing. The second thing is, we always have the same issue that we've had, I think, since the industrial revolution, of what machines are going to take away my job. No, they're just going to make your job better. So I think that some of our customers are also grappling with that, like, "What do I do if the machines take over?" And of course, like we've said, the machines aren't taking over. They're going to do the benign work, you're going to do the interesting work. You should embrace it. >> When I think about your history as a technology pro, from Check Point, a couple of startups, one of the things that always frustrated you, is when when a larger company bought you out, you ended up getting sucked into the bureaucratic vortex. How do you avoid that at Palo Alto Networks? >> So first, you mean when we acquire company? >> Yes. >> The first thing is that, when we acquire companies, we always acquire for integration. Meaning, we don't just buy something and then leave it on the side, and try to sell it here and there. We integrate it into the core of our products. So that's very important, so that the technology lives, thrives and continues to grow as part of our bigger platform. And I think that the second thing that is very important, from past experience what we've learned, is to put the people that we acquire in key positions. Meaning, you don't buy a company and then put the leader of that company five levels below the CEO. You always put them in very senior positions. Almost always, we have the leaders of the companies that we acquire, be two levels below the CEO, so very senior in the company, so they can influence and make changes. >> So two questions related to that. One is, as you grow your team, can you be both integrated? And second part of the question, can you be both integrated and best of breed? Second part of the question is, do you even have to be? >> So I'll answer it in the third way, which is, I don't think you can be best of breed without being integrated in cybersecurity. And the reason is, again, this split brain that I've mentioned twice. When you have different products do a part of cybersecurity and they don't talk to each other, and they don't share a single brain, you always compromise. You start looking for things the wrong way. I can be a little bit technical here, but please. Take the example of, traditionally you would buy an IDS/IPS, separately from your filtering, separately from DNS security. One of the most important things we do in network security is to find combining control connections. Combining control connections where the adversaries controlling something behind your firewall and is now going around your network, is usually the key heel of the attack. That's why attacks like ransomware, that don't have a commanding control connection, are so difficult to deal with, by the way. So commanding control connections are a key seal of the attacks, and there are three different technologies that deal with it. Neural filtering for neural based commanding control, DNS security for DNS based commanding control, and IDS/IPS for general commanding control. If those are three different products, they'll be doing the wrong things. The oral filter will try to find things that it's not really good at, that the IPS really need to find, and the DN... It doesn't work. It works much better when it's one product doing everything. So I think the choice is not between best of breed and integrated. I think the only choice is integrated, because that's the only way to be best of breed. >> And behind that technology is some kind of realtime data store, I'll call it data lake, database. >> Yeah. >> Whatever. >> It's all driven by the same data. All the URLs, all the domain graph. Everything goes to one big data lake. We collect about... I think we collect about, a few petabytes per day. I don't write the exact number of data. It's all going to the same data lake, and all the intelligence is driven by that. >> So you mentioned in a cheeky comment about, why you founded the company, there weren't enough cybersecurity companies. >> Yeah. >> Clearly the term expansion strategy that Palo Alto Networks has done has been very successful. You've been, as you talked about, very focused on integration, not just from the technology perspective, but from the people perspective as well. >> Correct. >> So why are there still so many cybersecurity companies, and what are you thinking Palo Alto Networks can do to change that? >> So first, I think that there are a lot of cybersecurity companies out there, because there's a lot of money going into cybersecurity. If you look at the number of companies that have been really successful, it's a very small percentage of those cybersecurity companies. And also look, we're not going to be responsible for all the innovation in cybersecurity. We need other people to innovate. It's also... Look, always the question is, "Do you buy something or do you build it yourself?" Now we think we're the smartest people in the world. Of course, we can build everything, but it's not always true that we can build everything. Know that we're the smartest people in the world, for sure. You see, when you are a startup, you live and die by the thing that you build. Meaning if it's good, it works. If it's not good, you die. You run out of money, you shut down, and you just lost four years of your life to this, at least. >> At least. >> When you're a large company, yeah, I can go and find a hundred engineers and hire them. And especially nowadays, it becomes easier, as it became easier, and give them money, and have them go and build the same thing that the startup is building, but they're part of a bigger company, and they'll have more coffee breaks, and they'll be less incentive to go and do that, because the company will survive with or without them. So that's why startups can do things much better, sometimes than larger companies. We can do things better than startups, when it comes to being data driven because we have the data, and nobody can compete against the amount of data that we have. So we have a good combination of finding the right startups that have already built something, already proven that it works with some customers, and of course, building a lot of things internally that we cannot do outside. >> I heard you say in one of the, I dunno, dozens of videos I've listened to you talked to. The industry doesn't need or doesn't want another IoT stovepipe. Okay, I agree. So you got on-prem, AWS, Azure, Google, maybe Alibaba, IoT is going to be all over the place. So can you build, I call it the security super cloud, in other words, a consistent experience with the same policies and edicts across all my estates, irrespective of physical location? Is that technically feasible? Is it what you are trying to do? >> Certainly, what we're trying to do with Prisma Cloud, with our cloud security product, it works across all the clouds that you mentioned, and Oracle as well. It's almost entirely possible. >> Almost. >> Almost. Well, the things that... What you do is you normalize the language that the different cloud scale providers use, into one language. This cloud calls it a S3, and so, AWS calls it S3, and (indistinct) calls it GCS, and so on. So you normalize their terminology, and then build policy using a common terminology that your customers have to get used to. Of course, there are things that are different between the different cloud providers that cannot be normalized, and there, it has to be cloud specific. >> In that instance. So is that, in part, your strategy, is to actually build that? >> Of course. >> And does that necessitate running on all the major clouds? >> Of course. It's not just part of our strategy, it's a major part of our strategy. >> Compulsory. >> Look, as a standalone vendor that is not a cloud provider, we have two advantages. The first one is we're security product, security focused. So we can do much better than them when it comes to security. If you are a AWS, GCP, Azure, and so on, you're not going to put your best people on security, you're going to put them on the core business that you have. So we can do much better. Hey, that's interesting. >> Well, that's not how they talk. >> I don't care how they talk. >> Now that's interesting. >> When something is 4% of your business, you're not going to put it... You're not going to put your best people there. It's just, why would you? You put your best people on 96%. >> That's not driving their revenue. >> Look, it's simple. It's not what we- >> With all due respect. With all due respect. >> So I think we do security much better than them, and they become the good enough, and we become the premium. But certainly, the second thing that give us an advantage and the right to be a standalone security provider, is that we're multicloud, private cloud and all the major cloud providers. >> But they also have a different role. I mean, your role is not the security, the Nitro card or the Graviton chip, or is it? >> They are responsible for securing up to the operating system. We secure everything. >> They do a pretty good job of that. >> No, they do, certainly they have to. If they get bridged at that level, it's not just that one customer is going to suffer, the entire customer base. They have to spend a lot of time and money on it, and frankly, that's where they put their best security people. Securing the infrastructure, not building some cloud security feature. >> Absolutely. >> So Palo Alto Networks is, as we wrap here, on track to nearly double its revenues to nearly seven billion in FY '23, just compared to 2020, you were quoted in the press by saying, "We will be the first $100 billion cyber company." What is next for Palo Alto to achieve that? >> Yeah, so it was Nikesh, our CEO and chairman, that was quoted saying that, "We will double to a hundred billion." I don't think he gave it a timeframe, but what it takes is to double the sales, right? We're at 50 billion market cap right now, so we need to double sales. But in reality, you mentioned that we're growing the turn by doing more and more cybersecurity functions, and taking away pieces. Still, we have a relatively small, even though we're the largest cybersecurity vendor in the world, we have a very low market share that shows you how fragmented the market is. I would also like to point out something that is less known. Part of what we do with AI, is really take the part of the cybersecurity industry, which are service oriented, and that's about 50% of the cybersecurity industry services, and turn it into products. I mean, not all of it. But a good portion of what's provided today by people, and tens of billions of dollars are spent on that, can be done with products. And being one of the very, very few vendors that do that, I think we have a huge opportunity at turning those tens of billions of dollars in human services to AI. >> It's always been a good business taking human labor and translating into R and D, vendor R and D. >> Especially- >> It never fails if you do it well. >> Especially in difficult times, difficult economical times like we are probably experiencing right now around the world. We, not we, but we the world. >> Right, right. Well, congratulations. Coming up on the 18th anniversary. Tremendous amount of success. >> Thank you. >> Great vision, clear vision, STEM expansion strategy, really well underway. We are definitely going to continue to keep our eyes. >> Big company, a hundred billion, that's market capital, so that's a big company. You said you didn't want to work for a big company unless you founded it, is that... >> Unless it acts like a small company. >> There's the caveat. We'll keep our eye on that. >> Thank you very much. >> It's such a pleasure having you on. >> Thank you. >> Same here, thank you. >> All right, for our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live emerging and enterprise tech coverage. (upbeat music)
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brought to you by Palo Alto Networks. We get to do that next. but figuring out how to Great to have you on the program. It's hard to believe that's and I thought I could put a stop to it, So first, I decided to Yeah. You got to have a box. You got to have a box. because one of the things that we've done So it is like you say, you got to have it. You did this, you started Build your own data center. No, it's the same. According to Larry Ellison, and the benefits, of So you have a sort option you have in the cloud. You make much more money, back to your own data centers. but I'm not going to be that was a great prediction that you made. things that you can't do today, And you just have to And you can do things... and you focus on the even more difficult. they said that we lack the creativity. to do the things that machines cannot do? And autonomous vehicles need breaks. to make your job better. one of the things that of the companies that we acquire, One is, as you grow your team, and they don't talk to each other, And behind that technology is some kind and all the intelligence So you mentioned in not just from the technology perspective, and you just lost four years that the startup is building, listened to you talked to. clouds that you mentioned, and there, it has to be cloud specific. is to actually build that? It's not just part of our strategy, core business that you have. You're not going to put It's not what we- With all due respect. and the right to be a the Nitro card or the They are responsible for securing customer is going to suffer, just compared to 2020, and that's about 50% of the and D, vendor R and D. experiencing right now around the world. Tremendous amount of success. We are definitely going to You said you didn't want There's the caveat. the leader in live emerging
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Nikesh Arora, Palo Alto Networks | Palo Alto Networks Ignite22
Upbeat music plays >> Voice Over: TheCUBE presents Ignite 22, brought to you by Palo Alto Networks. >> Good morning everyone. Welcome to theCUBE. Lisa Martin here with Dave Vellante. We are live at Palo Alto Networks Ignite. This is the 10th annual Ignite. There's about 3,000 people here, excited to really see where this powerhouse organization is taking security. Dave, it's great to be here. Our first time covering Ignite. People are ready to be back. They.. and security is top. It's a board level conversation. >> It is the other Ignite, I like to call it cuz of course there's another big company has a conference name Ignite, so I'm really excited to be here. Palo Alto Networks, a company we've covered for a number of years, as we just wrote in our recent breaking analysis, we've called them the gold standard but it's not just our opinion, we've backed it up with data. The company's on track. We think to do close to 7 billion in revenue by 2023. That's double it's 2020 revenue. You can measure it with execution, market cap M and A prowess. I'm super excited to have the CEO here. >> We have the CEO here, Nikesh Arora joins us from Palo Alto Networks. Nikesh, great to have you on theCube. Thank you for joining us. >> Well thank you very much for having me Lisa and Dave >> Lisa: It was great to see your keynote this morning. You said that, you know fundamentally security is a data problem. Well these days every company has to be a data company. Grocery stores, gas stations, car dealers. How is Palo Alto networks making customers, these data companies, more secure? >> Well Lisa, you know, (coughs) I've only done cybersecurity for about four, four and a half years so when I came to the industry I was amazed to see how security is so reactive as opposed to proactive. We should be able to stop bad threats, right? as they're happening. But I think a lot of threats get through because we don't have the right infrastructure and the right tooling and right products in there. So I think we've been working hard for the last four and a half years to turn it around so we can have consistent data flow across an enterprise and then mine that data for threats and anomalous behavior and try and protect our customers. >> You know the problem, I wrote this, this weekend, the problem in cybersecurity is well understood, you put up that Optiv graph and it's like 8,000 companies >> Yes >> and I think you mentioned your keynote on average, you know 30 to 40 tools, maybe 50, at least 20, >> Yes. >> from the folks that I talked to. So, okay, great, but actually solving that problem is not trivial. To be a consolidator, I mean, everybody wants to consolidate tools. So in your three to four years and security as you well know, it's, you can't fake security. It's a really, really challenging topic. So when you joined Palo Alto Networks and you heard that strategy, I know you guys have been thinking about this for some time, what did you see as the challenges to actually executing on that and how is it that you've been able to sort of get through that knot hole. >> So Dave, you know, it's interesting if you look at the history of cybersecurity, I call them the flavor of the decade, a flare, you know a new threat vector gets created, very large market gets created, a solution comes through, people flock, you get four or five companies will chase that opportunity, and then they become leaders in that space whether it's firewalls or endpoints or identity. And then people stick to their swim lane. The problem is that's a very product centric approach to security. It's not a customer-centric approach. The customer wants a more secure enterprise. They don't want to solve 20 different solutions.. problems with 20 different point solutions. But that's kind of how the industry's grown up, and it's been impossible for a large security company in one category, to actually have a substantive presence in the next category. Now what we've been able to do in the last four and a half years is, you know, from our firewall base we had resources, we had intellectual capability from a security perspective and we had cash. So we used that to pay off our technical debt. We acquired a bunch of companies, we created capability. In the last three years, four years we've created three incremental businesses which are all on track to hit a billion dollars the next 12 to 18 months. >> Yeah, so it's interesting on Twitter last night we had a little conversation about acquirers and who was a good, who was not so good. It was, there was Oracle, they came up actually very high, they'd done pretty, pretty good Job, VMware was on the list, IBM, Cisco, ServiceNow. And if you look at IBM and Cisco's strategy, they tend to be very services heavy, >> Mm >> right? How is it that you have been able to, you mentioned get rid of your technical debt, you invested in that. I wonder if you could, was it the, the Cloud, even though a lot of the Cloud was your own Cloud, was that a difference in terms of your ability to integrate? Because so many companies have tried it in the past. Oracle I think has done a good job, but it took 'em 10 to 12 years, you know, to, to get there. What was the sort of secret sauce? Is it culture, is it just great engineering? >> Dave it's a.. thank you for that. I think, look, it's, it's a mix of everything. First and foremost, you know, there are certain categories we didn't play in so there was nothing to integrate. We built a capability in a category in automation. We didn't have a product, we acquired a company. It's a net new capability in instant response. We didn't have a capability. It was net new capability. So there was, there was, other than integrating culturally and into the organization into our core to market processes there was no technical integration needed. Most of our technical integration was needed in our Cloud platform, which we bought five or six companies, we integrated then we just bought one recently called cyber security as well, which is going to get integrated in the Cloud platform. >> Dave: Yeah. >> And the thing is like, the Cloud platform is net new in the industry. We.. nobody's created a Cloud security platform yet, so we're working hard to create it because we don't want to replicate the mistakes of the past, that were made in enterprise security, in Cloud security. So it's a combination of cultural integration it's a combination of technical integration. The two things we do differently I think, than most people in the industry is look, we have no pride of, you know of innovations. Like, if somebody else has done it, we respect it and we'll acquire it, but we always want to acquire number one or number two in their category. I don't want number three or four. There's three or four for a reason and there still leaves one or two out there to compete with. So we've always acquired one or two, one. And the second thing, which is as important is most of these companies are in the early stage of development. So it's very important for the founding team to be around. So we spend a lot of time making sure they stick around. We actually make our people work for them. My principle is, listen, if they beat us in the open market with all our resources and our people, then they deserve to run this as opposed to us. So most of our new product categories are run by founders of companies required. >> So a little bit of Jack Welch, a little bit of Franks Lubens is a, you know always deference to the founders. But go ahead Lisa. >> Speaking of cultural transformation, you were mentioning your keynote this morning, there's been a significant workforce transformation at Palo Alto Networks. >> Yeah >> Talk a little bit about that, cause that's a big challenge, for many organizations to achieve. Sounds like you've done it pretty well. >> Well you know, my old boss, Eric Schmidt, used to say, 'revenue solves all known problems'. Which kind of, you know, it is a part joking, part true, but you know as Dave mentioned, we've doubled or two and a half time the revenues in the last four and a half years. That allows you to grow, that allows you to increase headcount. So we've gone from four and a half thousand people to 14,000 people. Good news is that's 9,500 people are net new to the company. So you can hire a whole new set of people who have new skills, new capabilities and there's some attrition four and a half thousand, some part of that turns over in four and a half years, so we effectively have 80% net new people, and the people we have, who are there from before, are amazing because they've built a phenomenal firewall business. So it's kind of been right sized across the board. It's very hard to do this if you're not growing. So you got to focus on growing. >> Dave: It's like winning in sports. So speaking of firewalls, I got to ask you does self-driving cars need brakes? So if I got a shout out to my friend Zeus Cararvela so like that's his line about why you need firewalls, right? >> Nikesh: Yes. >> I mean you mentioned it in your keynote today. You said it's the number one question that you get. >> and I don't get it why P industry observers don't go back and say that's, this is ridiculous. The network traffic is doubling or tripling. (clears throat) In fact, I gave an interesting example. We shut down our data centers, as I said, we are all on Google Cloud and Amazon Cloud and then, you know our internal team comes in, we'd want a bigger firewall. I'm like, why do you want a bigger firewall? We shut down our data centers as well. The traffic coming in and out of our campus is doubled. We need a bigger firewall. So you still need a firewall even if you're in the Cloud. >> So I'm going to come back to >> Nikesh: (coughs) >> the M and A strategy. My question is, can you be both best of breed and develop a comprehensive suite number.. part one and part one A of that is do you even have to, because generally sweets win out over best of breed. But what, how do you, how do you respond? >> Well, you know, this is this age old debate and people get trapped in that, I think in my mind, and let me try and expand the analogy which I tried to do up in my keynote. You know, let's assume that Oracle, Microsoft, Dynamics and Salesforce did not exist, okay? And you were running a large company of 50,000 people and your job was to manage the customer process which easier to understand than security. And I said, okay, guess what? I have a quoting system and a lead system but the lead system doesn't talk to my coding system. So I get leads, but I don't know who those customers. And I write codes for a whole new set of customers and I have a customer database. Then when they come as purchase orders, I have a new database with all the customers who've bought something from me, and then when I go get them licensing I have a new database and when I go have customer support, I have a fifth database and there are customers in all five databases. You'll say Nikesh you're crazy, you should have one customer database, otherwise you're never going to be able to make this work. But security is the same problem. >> Dave: Mm I should.. I need consistency in data from suit to nuts. If it's in Cloud, if you're writing code, I need to understand the security flaws before they go into deployment, before they go into production. We for somehow ridiculously have bought security like IT. Now the difference between IT and security is, IT is required to talk to each other, so a Dell server and HP server work very similarly but a Palo Alto firewall and a Checkpoint firewall Fortnight firewall work formally differently. And then how that transitions into endpoints is a whole different ball game. So you need consistency in data, as Lisa was saying earlier, it's a data problem. You need consistency as you traverse to the enterprise. And that's why that's the number one need. Now, when you say best of breed, (coughs) best of breed, if it's fine, if it's a specific problem that you're trying to solve. But if you're trying to make sure that's the data flow that happens, you need both best of breed, you know, technology that stops things and need integration on data. So what we are trying to do is we're trying to give people best to breed solutions in the categories they want because otherwise they won't buy us. But we're also trying to make sure we stitch the data. >> But that definition of best of breed is a little bit of nuance than different in security is what I'm hearing because that consistency >> Nikesh: (coughs) Yes, >> across products. What about across Cloud? You mentioned Google and Amazon. >> Yeah so that's great question. >> Dave: Are you building the security super Cloud, I call it, above the Cloud? >> It's, it's not, it's, less so a super Cloud, It's more like Switzerland and I used to work at Google for 10 years, not a secret. And we used to sell advertising and we decided to go into pub into display ads or publishing, right. Now we had no publishing platform so we had to be good at everybody else's publishing platform >> Dave: Mm >> but we never were able to search ads for everybody else because we only focus on our own platform. So part of it is when the Cloud guys they're busy solving security for their Cloud. Google is not doing anything about Amazon Cloud or Microsoft Cloud, Microsoft's Azure, right? AWS is not doing anything about Google Cloud or Azure. So what we do is we don't have a Cloud. Our job in providing Cloud securities, be Switzerland make sure it works consistently across every Cloud. Now if you try to replicate what we offer Prisma Cloud, by using AWS, Azure and GCP, you'd have to first of all, have three panes of glass for all three of them. But even within them they have four panes of glass for the capabilities we offer. So you could end up with 12 different interfaces to manage a development process, we give you one. Now you tell me which is better. >> Dave: Sounds like a super Cloud to me Lisa (laughing) >> He's big on super Cloud >> Uber Cloud, there you >> Hey I like that, Uber Cloud. Well, so I want to understand Nikesh, what's realistic. You mentioned in your keynote Dave, brought it up that the average organization has 30 to 50 tools, security tools. >> Nikesh: Yes, yes >> On their network. What is realistic for from a consolidation perspective where Palo Alto can come in and say, let me make this consistent and simple for you. >> Well, I'll give you your own example, right? (clears throat) We're probably sub 10 substantively, right? There may be small things here and there we do. But on a substantive protecting the enterprise perspective you be should be down to eight or 10 vendors, and that is not perfect but it's a lot better than 50, >> Lisa: Right? >> because don't forget 50 tools means you have to have capability to understand what those 50 tools are doing. You have to have the capability to upgrade them on a constant basis, learn about their new capabilities. And I just can't imagine why customers have two sets of firewalls right. Now you got to learn both the files on how to deploy both them. That's silly because that's why we need 7 million more people. You need people to understand, so all these tools, who work for companies. If you had less tools, we need less people. >> Do you think, you know I wrote about this as well, that the security industry is anomalous and that the leader has, you know, single digit, low single digit >> Yes >> market shares. Do you think that you can change that? >> Well, you know, when I started that was exactly the observation I had Dave, which you highlighted in your article. We were the largest by revenue, by small margin. And we were one and half percent of the industry. Now we're closer to three, three to four percent and we're still at, you know, like you said, going to be around $7 billion. So I see a path for us to double from here and then double from there, and hopefully as we keep doubling and some point in time, you know, I'd like to get to double digits to start with. >> One of the things that I think has to happen is this has to grow dramatically, the ecosystem. I wonder if you could talk about the ecosystem and your strategy there. >> Well, you know, it's a matter of perspective. I think we have to get more penetrated in our largest customers. So we have, you know, 1800 of the top 2000 customers in the world are Palo Alto customers. But we're not fully penetrated with all our capabilities and the same customers set, so yes the ecosystem needs to grow, but the pandemic has taught us the ecosystem can grow wherever they are without having to come to Vegas. Which I don't think is a bad thing to be honest. So the ecosystem is growing. You are seeing new players come to the ecosystem. Five years ago you didn't see a lot of systems integrators and security. You didn't see security offshoots of telecom companies. You didn't see the Optivs, the WWTs, the (indistinct) of the world (coughs) make a concerted shift towards consolidation or services and all that is happening >> Dave: Mm >> as we speak today in the audience you will find people from Google, Amazon Microsoft are sitting in the audience. People from telecom companies are sitting in the audience. These people weren't there five years ago. So you are seeing >> Dave: Mm >> the ecosystem's adapting. They're, they want to be front and center of solving the customer's problem around security and they want to consolidate capability, they need. They don't want to go work with a hundred vendors because you know, it's like, it's hard. >> And the global system integrators are key. I always say they like to eat at the trough and there's a lot of money in security. >> Yes. >> Dave: (laughs) >> Well speaking of the ecosystem, you had Thomas Curry and Google Cloud CEO in your fireside chat in the keynote. Talk a little bit about how Google Cloud plus Palo Alto Networks, the Zero Trust Partnership and what it's enable customers to achieve. >> Lisa, that's a great question. (clears his throat) Thank you for bringing it up. Look, you know the, one of the most fundamental shifts that is happening is obviously the shift to the Cloud. Now when that shift fully, sort of, takes shape you will realize if your network has changed and you're delivering everything to the Cloud you need to go figure out how to bring the traffic to the Cloud. You don't have to bring it back to your data center you can bring it straight to the Cloud. So in that context, you know we use Google Cloud and Amazon Cloud, to be able to carry our traffic. We're going from a product company to a services company in addition, right? Cuz when we go from firewalls to SASE we're not carrying your traffic. When we carry our traffic, we need to make sure we have underlying capability which is world class. We think GCP and AWS and Azure run some of the biggest and best networks in the world. So our partnership with Google is such that we use their public Cloud, we sit on top of their Cloud, they give us increased enhanced functionality so that our customers SASE traffic gets delivered in priority anywhere in the world. They give us tooling to make sure that there's high reliability. So you know, we partner, they have Beyond Corp which is their version of Zero Trust which allows you to take unmanaged devices with browsers. We have SASE, which allows you to have managed devices. So the combination gives our collective customers the ability for Zero Trust. >> Do you feel like there has to be more collaboration within the ecosystem, the security, you know, landscape even amongst competitors? I mean I think about Google acquires Mandiant. You guys have Unit 42. Should and will, like, Wendy Whitmore and maybe they already are, Kevin Mandia talk more and share more data. If security's a data problem is all this data >> Nikesh: Yeah look I think the industry shares threat data, both in private organizations as well as public and private context, so that's not a problem. You know the challenge with too much collaboration in security is you never know. Like you know, the moment you start sharing your stuff at third parties, you go out of Secure Zone. >> Lisa: Mm >> Our biggest challenge is, you know, I can't trust a third party competitor partner product. I have to treat it with as much suspicion as anything else out there because the only way I can deliver Zero Trust is to not trust anything. So collaboration in Zero Trust are a bit of odds with each other. >> Sounds like another problem you can solve >> (laughs) >> Nikesh last question for you. >> Yes >> Favorite customer or example that you think really articulates the value of what Palo Alto was delivering? >> Look you know, it's a great question, Lisa. I had this seminal conversation with a customer and I explained all those things we were talking about and the customer said to me, great, okay so what do I need to do? I said, fun, you got to trust me because you know, we are on a journey, because in the past, customers have had to take the onus on themselves of integrating everything because they weren't sure a small startup will be independent, be bought by another cybersecurity company or a large cybersecurity company won't get gobbled up and split into pieces by private equity because every one of the cybersecurity companies have had a shelf life. So you know, our aspiration is to be the evergreen cybersecurity company. We will always be around and we will always tackle innovation and be on the front line. So the customer understood what we're doing. Over the last three years we've been working on a transformation journey with them. We're trying to bring them, or we have brought them along the path of Zero Trust and we're trying to work with them to deliver this notion of reducing their meantime to remediate from days to minutes. Now that's an outcome based approach that's a partnership based approach and we'd like, love to have more and more customers of that kind. I think we weren't ready to be honest as a company four and a half years ago, but I think today we're ready. Hence my keynote was called The Perfect Storm. I think we're at the right time in the industry with the right capabilities and the right ecosystem to be able to deliver what the industry needs. >> The perfect storm, partners, customers, investors, employees. Nikesh, it's been such a pleasure having you on theCUBE. Thank you for coming to talk to Dave and me right after your keynote. We appreciate that and we look forward to two days of great coverage from your executives, your customers, and your partners. Thank you. >> Well, thank you for having me, Lisa and Dave and thank you >> Dave: Pleasure >> for what you guys do for our industry. >> Our pleasure. For Nikesh Arora and Dave Vellante, I'm Lisa Martin, you're watching theCUBE live at MGM Grand Hotel in Las Vegas, Palo Alto Ignite 22. Stick around Dave and I will be joined by our next guest in just a minute. (cheerful music plays out)
SUMMARY :
brought to you by Palo Alto Networks. Dave, it's great to be here. I like to call it cuz Nikesh, great to have you on theCube. You said that, you know and the right tooling and and you heard that strategy, So Dave, you know, it's interesting And if you look at IBM How is it that you have been able to, First and foremost, you know, of, you know of innovations. Lubens is a, you know you were mentioning your for many organizations to achieve. and the people we have, So speaking of firewalls, I got to ask you I mean you mentioned and then, you know our that is do you even have to, Well, you know, this So you need consistency in data, and Amazon. so that's great question. and we decided to go process, we give you one. that the average organization and simple for you. Well, I'll give you You have to have the Do you think that you can change that? and some point in time, you know, I wonder if you could So we have, you know, 1800 in the audience you will find because you know, it's like, it's hard. And the global system and Google Cloud CEO in your So in that context, you security, you know, landscape Like you know, the moment I have to treat it with as much suspicion for you. and the customer said to me, great, okay Thank you for coming Arora and Dave Vellante,
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