>>Hey guys and girls, welcome back to Motor City, Lisa Martin here with John Furrier on the Cube's third day of coverage of Coon Cloud Native Con North America. John, we've had some great conversations over the last two and a half days. We've been talking about identity and security management as a critical need for enterprises within the cloud native space. We're gonna have another quick conversation >>On that. Yeah, we got a great segment coming up from someone who's been in the industry, a long time expert, running a great company. Now it's gonna be one of those pieces that fits into what we call super cloud. Others are calling cloud operating system. Some are calling just Cloud 2.0, 3.0. But there's definitely a major trend happening around how cloud is going Next generation. We've been covering it. So this segment should be >>Great. Let's unpack those trends. One of our alumni is back with us, O Rika Zi, co-founder and CEO of Aerio. Omri. Great to have you back on the >>Cube. Thank you. Great to be here. >>So identity move to the cloud, Access authorization did not talk to us about why you found it assertive, what you guys are doing and how you're flipping that script. >>Yeah, so back 15 years ago, I helped start Azure at Microsoft. You know, one of the first few folks that you know, really focused on enterprise services within the Azure family. And at the time I was working for the guy who ran all of Windows server and you know, active directory. He called it the linchpin workload for the Windows Server franchise, like big words. But what he meant was we had 95% market share and all of these new SAS applications like ServiceNow and you know, Workday and salesforce.com, they had to invent login and they had to invent access control. And so we were like, well, we're gonna lose it unless we figure out how to replace active directory. And that's how Azure Active Directory was born. And the first thing that we had to do as an industry was fix identity, right? Yeah. So, you know, we worked on things like oof Two and Open, Id Connect and SAML and Jot as an industry and now 15 years later, no one has to go build login if you don't want to, right? You have companies like Odd Zero and Okta and one login Ping ID that solve that problem solve single sign-on, on the web. But access Control hasn't really moved forward at all in the last 15 years. And so my co-founder and I who were both involved in the early beginnings of Azure Active directory, wanted to go back to that problem. And that problem is even bigger than identity and it's far from >>Solved. Yeah, this is huge. I think, you know, self-service has been a developer thing that's, everyone knows developer productivity, we've all experienced click sign in with your LinkedIn or Twitter or Google or Apple handle. So that's single sign on check. Now the security conversation kicks in. If you look at with this no perimeter and cloud, now you've got multi-cloud or super cloud on the horizon. You've got all kinds of opportunities to innovate on the security paradigm. I think this is kind of where I'm hearing the most conversation around access control as well as operationally eliminating a lot of potential problems. So there's one clean up the siloed or fragmented access and two streamlined for security. What's your reaction to that? Do you agree? And if not, where, where am I missing that? >>Yeah, absolutely. If you look at the life of an IT pro, you know, back in the two thousands they had, you know, l d or active directory, they add in one place to configure groups and they'd map users to groups. And groups typically corresponded to roles and business applications. And it was clunky, but life was pretty simple. And now they live in dozens or hundreds of different admin consoles. So misconfigurations are rampant and over provisioning is a real problem. If you look at zero trust and the principle of lease privilege, you know, all these applications have these course grained permissions. And so when you have a breach, and it's not a matter of if, it's a matter of when you wanna limit the blast radius of you know what happened, and you can't do that unless you have fine grained access control. So all those, you know, all those reasons together are forcing us as an industry to come to terms with the fact that we really need to revisit access control and bring it to the age of cloud. >>You guys recently, just this week I saw the blog on Topaz. Congratulations. Thank you. Talk to us about what that is and some of the gaps that's gonna help sarto to fill for what's out there in the marketplace. >>Yeah, so right now there really isn't a way to go build fine grains policy based real time access control based on open source, right? We have the open policy agent, which is a great decision engine, but really optimized for infrastructure scenarios like Kubernetes admission control. And then on the other hand, you have this new, you know, generation of access control ideas. This model called relationship based access control that was popularized by Google Zanzibar system. So Zanzibar is how they do access control for Google Docs and Google Drive. If you've ever kind of looked at a Google Doc and you know you're a viewer or an owner or a commenter, Zanzibar is the system behind it. And so what we've done is we've married these two things together. We have a policy based system, OPPA based system, and at the same time we've brought together a directory, an embedded directory in Topaz that allows you to answer questions like, does this user have this permission on this object? And bringing it all together, making it open sources a real game changer from our perspective, real >>Game changer. That's good to hear. What are some of the key use cases that it's gonna help your customers address? >>So a lot of our customers really like the idea of policy based access management, but they don't know how to bring data to that decision engine. And so we basically have a, you know, a, a very opinionated way of how to model that data. So you import data out of your identity providers. So you connect us to Okta or oze or Azure, Azure Active directory. And so now you have the user data, you can define groups and then you can define, you know, your object hierarchy, your domain model. So let's say you have an applicant tracking system, you have nouns like job, you know, know job descriptions or candidates. And so you wanna model these things and you want to be able to say who has access to, you know, the candidates for this job, for example. Those are the kinds of rules that people can express really easily in Topaz and in assertive. >>What are some of the challenges that are happening right now that dissolve? What, what are you looking at to solve? Is it complexity, sprawl, logic problems? What's the main problem set you guys >>See? Yeah, so as organizations grow and they have more and more microservices, each one of these microservices does authorization differently. And so it's impossible to reason about the full surface area of, you know, permissions in your application. And more and more of these organizations are saying, You know what, we need a standard layer for this. So it's not just Google with Zanzibar, it's Intuit with Oddy, it's Carta with their own oddy system, it's Netflix, you know, it's Airbnb with heed. All of them are now talking about how they solve access control extracted into its own service to basically manage complexity and regain agility. The other thing is all about, you know, time to market and, and tco. >>So, so how do you work with those services? Do you replace them, you unify them? What is the approach that you're taking? >>So basically these organizations are saying, you know what? We want one access control service. We want all of our microservices to call that thing instead of having to roll out our own. And so we, you know, give you the guts for that service, right? Topaz is basically the way that you're gonna go implement an access control service without having to go build it the same way that you know, large companies like Airbnb or Google or, or a car to >>Have. What's the competition look like for you guys? I'm not really seeing a lot of competition out there. Are there competitors? Are there different approaches? What makes you different? >>Yeah, so I would say that, you know, the biggest competitor is roll your own. So a lot of these companies that find us, they say, We're sick and tired of investing 2, 3, 4 engineers, five engineers on this thing. You know, it's the gift that keeps on giving. We have to maintain this thing and so we can, we can use your solution at a fraction of the cost a, a fifth, a 10th of what it would cost us to maintain it locally. There are others like Sty for example, you know, they are in the space, but more in on the infrastructure side. So they solve the problem of Kubernetes submission control or things like that. So >>Rolling your own, there's a couple problems there. One is do they get all the corner cases who built a they still, it's a company. Exactly. It's heavy lifting, it's undifferentiated, you just gotta check the box. So probably will be not optimized. >>That's right. As Bezo says, only focus on the things that make your beer taste better. And access control is one of those things. It's part of your security, you know, posture, it's a critical thing to get right, but you know, I wanna work on access control, said no developer ever, right? So it's kind of like this boring, you know, like back office thing that you need to do. And so we give you the mechanisms to be able to build it securely and robustly. >>Do you have a, a customer story example that is one of your go-tos that really highlights how you're improving developer productivity? >>Yeah, so we have a couple of them actually. So there's the largest third party B2B marketplace in the us. Free retail. Instead of building their own, they actually brought in aer. And what they wanted to do with AER was be the authorization layer for both their externally facing applications as well as their internal apps. So basically every one of their applications now hooks up to AER to do authorization. They define users and groups and roles and permissions in one place and then every application can actually plug into that instead of having to roll out their own. >>I'd like to switch gears if you don't mind. I get first of all, great update on the company and progress. I'd like to get your thoughts on the cloud computing market. Obviously you were your legendary position, Azure, I mean look at the, look at the progress over the past few years. Just been spectacular from Microsoft and you set the table there. Amazon web service is still, you know, thundering away even though earnings came out, the market's kind of soft still. You know, you see the cloud hyperscalers just continuing to differentiate from software to chips. Yep. Across the board. So the hyperscalers kicking ass taking names, doing great Microsoft right up there. What's the future? Cuz you now have the conversation where, okay, we're calling it super cloud, somebody calling multi-cloud, somebody calling it distributed computing, whatever you wanna call it. The old is now new again, it just looks different as cloud becomes now the next computer industry, >>You got an operating system, you got applications, you got hardware, I mean it's all kind of playing out just on a massive global scale, but you got regions, you got all kinds of connected systems edge. What's your vision on how this plays out? Because things are starting to fall into place. Web assembly to me just points to, you know, app servers are coming back, middleware, Kubernetes containers, VMs are gonna still be there. So you got the progression. What's your, what's your take on this? How would you share, share your thoughts to a friend or the industry, the audience? So what's going on? What's, what's happening right now? What's, what's going on? >>Yeah, it's funny because you know, I remember doing this quite a few years ago with you probably in, you know, 2015 and we were talking about, back then we called it hybrid cloud, right? And it was a vision, but it is actually what's going on. It just took longer for it to get here, right? So back then, you know, the big debate was public cloud or private cloud and you know, back when we were, you know, talking about these ideas, you know, we said, well you know, some applications will always stay on-prem and some applications will move to the cloud. I was just talking to a big bank and they basically said, look, our stated objective now is to move everything we can to the public cloud and we still have a large private cloud investment that will never go away. And so now we have essentially this big operating system that can, you know, abstract all of this stuff. So we have developer platforms that can, you know, sit on top of all these different pieces of infrastructure and you know, kind of based on policy decide where these applications are gonna be scheduled. So, you know, the >>Operating schedule shows like an operating system function. >>Exactly. I mean like we now, we used to have schedulers for one CPU or you know, one box, then we had schedulers for, you know, kind of like a whole cluster and now we have schedulers across the world. >>Yeah. My final question before we kind of get run outta time is what's your thoughts on web assembly? Cuz that's getting a lot of hype here again to kind of look at this next evolution again that's lighter weight kind of feels like an app server kind of direction. What's your, what's your, it's hyped up now, what's your take on that? >>Yeah, it's interesting. I mean back, you know, what's, what's old is new again, right? So, you know, I remember back in the late nineties we got really excited about, you know, JVMs and you know, this notion of right once run anywhere and yeah, you know, I would say that web assembly provides a pretty exciting, you know, window into that where you can take the, you know, sandboxing technology from the JavaScript world, from the browser essentially. And you can, you know, compile an application down to web assembly and have it real, really truly portable. So, you know, we see for example, policies in our world, you know, with opa, one of the hottest things is to take these policies and can compile them to web assemblies so you can actually execute them at the edge, you know, wherever it is that you have a web assembly runtime. >>And so, you know, I was just talking to Scott over at Docker and you know, they're excited about kind of bringing Docker packaging, OCI packaging to web assemblies. So we're gonna see a convergence of all these technologies right now. They're kind of each, each of our, each of them are in a silo, but you know, like we'll see a lot of the patterns, like for example, OCI is gonna become the packaging format for web assemblies as it is becoming the packaging format for policies. So we did the same thing. We basically said, you know what, we want these policies to be packaged as OCI assembly so that you can sign them with cosign and bring the entire ecosystem of tools to bear on OCI packages. So convergence is I think what >>We're, and love, I love your attitude too because it's the open source community and the developers who are actually voting on the quote defacto standard. Yes. You know, if it doesn't work, right, know people know about it. Exactly. It's actually a great new production system. >>So great momentum going on to the press released earlier this week, clearly filling the gaps there that, that you and your, your co-founder saw a long time ago. What's next for the assertive business? Are you hiring? What's going on there? >>Yeah, we are really excited about launching commercially at the end of this year. So one of the things that we were, we wanted to do that we had a promise around and we delivered on our promise was open sourcing our edge authorizer. That was a huge thing for us. And we've now completed, you know, pretty much all the big pieces for AER and now it's time to commercially launch launch. We already have customers in production, you know, design partners, and you know, next year is gonna be the year to really drive commercialization. >>All right. We will be watching this space ery. Thank you so much for joining John and me on the keep. Great to have you back on the program. >>Thank you so much. It was a pleasure. >>Our pleasure as well For our guest and John Furrier, I'm Lisa Martin, you're watching The Cube Live. Michelle floor of Con Cloud Native Con 22. This is day three of our coverage. We will be back with more coverage after a short break. See that.

>>the Cube presents H P E discovered 2022. Brought to you by H P E. >>Hi, buddy Dave Balon and Jon Ferrier Wrapping up the cubes. Coverage of day two, hp Discover 2022. We're live from Las Vegas. Vishal Lall is here. He's the senior vice president and general manager for HP ES Green Lake Cloud Services Solutions. Michelle, good to see you again. >>Likewise. David, good to see you. It was about a year ago that we met here. Or maybe nine months >>ago. That's right. Uh, September of last year. A new role >>for you. Is that right? I was starting that new role when I last met you. Yeah, but it's been nine months. Three quarters? What have you learned so far? I mean, it's been quite a right, right? I mean, when I was starting off, I had, you know, about three priorities we've executed on on all of them. So, I mean, if you remember back then they we talked about, you know, improving a cloud experience. We talked about data and analytics being a focus area and then building on the marketplace. I think you heard a lot of that over the last couple of days here. Right? So we've enhanced our cloud experience. We added a private cloud, which was the big announcement yesterday or day before yesterday that Antonio made so that's been I mean, we've been testing that with customers. Great feedback so far. Right? And we're super excited about that. And, uh, you know, uh, down there, the test drive section people are testing that. So we're getting really, really good feedback. Really good acceptance from customers on the data and Analytics side. We you know, we launched the S three connector. We also had the analytics platform. And then we launched data fabric as a service a couple of days ago, right, which is kind of like back into that hybrid world. And then on the marketplace side, we've added a tonne of partners going deep with them about 80 plus partners now different SVS. So again, I think, uh, great. I think we've accomplished a lot over the last three quarters or so lot more to be done. Though >>the marketplace is really interesting to us because it's a hallmark of cloud. You've got to have a market price. Talk about how that's evolving and what your vision is for market. Yes, >>you're exactly right. I mean, having a broad marketplace provides a full for the platform, right? It's a chicken and egg. You need both. You need a good platform on which a good marketplace can set, but the vice versa as well. And what we're doing two things there, Right? One Is we expanding coverage of the marketplace. So we're adding more SVS into the marketplace. But at the same time, we're adding more capabilities into the marketplace. So, for example, we just demoed earlier today quickly deploy capabilities, right? So we have an I S p in the marketplace, they're tested. They are, uh, the work with the solution. But now you can you can collect to deploy directly on our infrastructure over time, the lad, commerce capabilities, licencing capabilities, etcetera. But again, we are super excited about that capability because I think it's important from a customer perspective. >>I want to ask you about that, because that's again the marketplace will be the ultimate arbiter of value creation, ecosystem and marketplace. Go hand in hand. What's your vision for what a successful ecosystem looks like? What's your expectation now that Green Lake is up and running. I stay up and running, but like we've been following the announcement, it just gets better. It's up to the right. So we're anticipating an ecosystem surge. Yeah. What are you expecting? And what's your vision for? How the ecosystem is going to develop out? Yeah. I >>mean, I've been meeting with a lot of our partners over the last couple of days, and you're right, right? I mean, I think of them in three or four buckets right there. I s V s and the I S P is coming to two forms right there. Bigger solutions, right? I think of being Nutanix, right, Home wall, big, bigger solutions. And then they are smaller software packages. I think Mom would think about open source, right? So again, one of them is targeted to developers, the other to the I t. Tops. But that's kind of one bucket, right? I s P s, uh, the second is around the channel partners who take this to market and they're asking us, Hey, this is fantastic. Help us understand how we can help you take this to market. And I think the other bucket system indicators right. I met with a few today and they're all excited about. They're like, Hey, we have some tooling. We have the manage services capabilities. How can we take your cloud? Because they build great practise around extent around. Sorry. Aws around? Uh, sure. So they're like, how can we build a similar practise around Green Lake? So again, those are the big buckets. I would say. Yeah, >>that's a great answer. Great commentary. I want to just follow up on that real quick. You don't mind? So a couple things we're seeing observing I want to get your reaction to is with a i machine learning. And the promise of that vertical specialisation is creating unique opportunities on with these platforms. And the other one is the rise of the managed service provider because expertise are hard to come by. You want kubernetes? Good luck finding talent. So managed services seem to be exploding. How does that fit into the buckets? Or is it all three buckets or you guys enable that? How do you see that coming? And then the vertical piece? >>A really good question. What we're doing is through our software, we're trying to abstract a lot of the complexity of take communities, right? So we are actually off. We have actually automated a whole bunch of communities functionality in our software, and then we provide managed services around it with very little. I would say human labour associated with it is is software manage? But at the same time we are. What we are trying to do is make sure that we enable that same functionality to our partners. So a lot of it is software automation, but then they can wrap their services around it, and that way we can scale the business right. So again, our first principle is automated as much as we can to software right abstract complexity and then as needed, uh, at the Manus Services. >>So you get some functionality for HP to have it and then encourage the ecosystem to fill it in or replicated >>or replicated, right? I mean, I don't think it's either or it should be both right. We can provide many services or we should have our our partners provide manage services. That's how we scale the business. We are the end of the day. We are product and product company, right, and it can manifest itself and services. That discussion was consumed, but it's still I p based. So >>let's quantify, you know, some of that momentum. I think the last time you call your over $800 million now in a are are you gotta You're growing at triple digits. Uh, you got a big backlog. Forget the exact number. Uh, give us a I >>mean, the momentum is fantastic Day. Right. So we have about $7 billion in total contract value, Right? Significant. We have 1600 customers now. Unique customers are running Green Lake. We have, um, your triple dip growth year over year. So the last quarter, we had 100% growth year over year. So again, fantastic momentum. I mean, the other couple, like one other metric I would like to talk about is the, um the stickiness factor associated tension in our retention, right? As renewal's is running in, like, high nineties, right? So if you think about it, that's a reflection of the value proposition of, like, >>that's that's kind of on a unit basis, if you will. That's the number >>on the revenue basis on >>revenue basis. Okay? >>And the 1600 customers. He's talking about the size and actually big numbers. Must be large companies that are. They're >>both right. So I'll give you some examples, right? So I mean, there are large companies. They come from different industries. Different geography is we're seeing, like, the momentum across every single geo, every single industry. I mean, just to take some examples. BMW, for example. Uh, I mean, they're running the entire electrical electric car fleet data collection on data fabric on Green Lake, right? Texas Children's Health on the on the healthcare side. Right On the public sector side, I was with with Carl Hunt yesterday. He's the CEO of County of Essex, New Jersey. So they are running the entire operations on Green Lake. So just if you look at it, Barclays the financial sector, right? I mean, they're running 100,000 workloads of three legs. So if you just look at the scale large companies, small companies, public sector in India, we have Steel Authority of India, which is the largest steel producer there. So, you know, we're seeing it across multiple industries. Multiple geography is great. Great uptake. >>Yeah. We were talking yesterday on our wrap up kind of dissecting through the news. I want to ask you the question that we were riffing on and see if we can get some clarity on it. If I'm a customer, CI or C so or buyer HP have been working with you or your team for for years. What's the value proposition? Finish this sentence. I work with HPV because blank because green like, brings new value proposition. What is that? Fill in that blank for >>me. So I mean, as we, uh, talked with us speaking with customers, customers are looking at alternatives at all times, right? Sometimes there's other providers on premises, sometimes as public cloud. And, uh, as we look at it, uh, I mean, we have value propositions across both. Right. So from a public cloud perspective, some of the challenges that our customers cr around latency around, uh, post predictability, right? That variability cost is really kind of like a challenge. It's around compliance, right? Uh, things of that nature is not open systems, right? I mean, sometimes, you know, they feel locked into a cloud provider, especially when they're using proprietary services. So those are some of the things that we have solved for them as compared to kind of like, you know, the other on premises vendors. I would say the marketplace that we spoke about earlier is huge differentiator. We have this huge marketplace. Now that's developing. Uh, we have high levels of automation that we have built, right, which is, uh, you know, which tells you about the TCO that we can drive for the customers. What? The other thing that is really cool that be introduced in the public in the private cloud is fungible itty across infrastructure. Right? So basically on the same infrastructure you can run. Um, virtual machines, containers, bare metals, any application he wants, you can decommission and commission the infrastructure on the fly. So what it does, is it no matter where it is? Uh, on premises, right? Yeah, earlier. I mean, if you think about it, the infrastructure was dedicated for a certain application. Now we're basically we have basically made it compose herbal, right? And that way, what? Really? Uh, that doesnt increases utilisation so you can get increased utilisation. High automation. What drives lower tco. So you've got a >>horizontal basically platform now that handle a variety of work and >>and these were close. Can sit anywhere to your point, right? I mean, we could have a four node workload out in a manufacturing setting multiple racks in a data centre, and it's all run by the same cloud prints, same software train. So it's really extensive. >>And you can call on the resources that you need for that particular workload. >>Exactly what you need them exactly. Right. >>Excellent. Give you the last word kind of takeaways from Discover. And where when we talk, when we sit down and talk next year, it's about where do you want to be? >>I mean, you know, I think, as you probably saw from discovered, this is, like, very different. Antonio did a live demo of our product, right? Uh, visual school, right? I mean, we haven't done that in a while, so I mean, you started. It >>didn't die like Bill Gates and demos. No, >>no, no, no. I think, uh, so I think you'll see more of that from us. I mean, I'm focused on three things, right? I'm focused on the cloud experience we spoke about. So what we are doing now is making sure that we increase the time for that, uh, make it very, you know, um, attractive to different industries to certifications like HIPAA, etcetera. So that's kind of one focus. So I just drive harder at that adoption of that of the private out, right across different industries and different customer segments. The second is more on the data and analytics I spoke about. You will have more and more analytic capabilities that you'll see, um, building upon data fabric as a service. And this is a marketplace. So that's like it's very specific is the three focus areas were driving hard. All right, we'll be watching >>number two. Instrumentation is really keen >>in the marketplace to I mean, you mentioned Mongo. Some other data platforms that we're going to see here. That's going to be, I think. Critical for Monetisation on the on on Green Lake. Absolutely. Uh, Michelle, thanks so much for coming back in the Cube. >>Thank you. Thanks for coming. All >>right, keep it right. There will be John, and I'll be back up to wrap up the day with a couple of heavies from I d. C. You're watching the cube. Mhm. Mm mm. Mhm.

(gentle music) >> Hey everyone. Welcome to theCUBE's coverage of the AWS Startup Showcase. Season two, episode three. This is about MarTech, emerging cloud scale customer experience. This is our ongoing series that you know and love hopefully that feature a great number of AWS ecosystem partners. I'm your host, Lisa Martin. Got a great guest here from Branch. Michelle Lerner joins me, the senior director of business development. She's going to be talking about Branch but also about one of your favorite brands, Peet's, yep, the coffee place, and how they supercharged loyalty and app adoption with Branch. Michelle, it's great to have you on the program. >> Yeah. Great to be here. Thank you so much for having me. >> Tell us a little bit about Branch, what you guys do for the modern mobile marketer. >> Yeah, absolutely. So you can think about Branch as a mobile linking platform. So what that means is we offer a seamless deep linking experience and insightful campaign measurement across every single marketing channel and platform on mobile. We exist so that we can break down walled gardens to help our customers engage with their customers in the most optimal way across any device and from every marketing channel. Our products are specifically designed to help create an amazing user experience, but also provide full picture holistic downstream measurement across any paid, owned, and earned channels so that brands can actually see what's working. So what that really means is that we make it really easy to scale our links across every single marketing channel, which then route the users to the right place at any device through even past install so that they can get to the context that they expect for a seamless experience. We then provide that cross channel analytics back to the brand so that they could see what's working and they can make better business decisions. So kind of summing it up, our industry leading mobile linking actually powers those deep links, also supports that measurement so that brands can build a sophisticated experience that actually delight their users but also improve their metrics and conversion rates. >> Those two things that you said are key. We expected to be delighted with whatever experience we're having and we also want to make sure, and obviously, the brands want to make sure that they're doing that but also that from an attribution perspective, from a campaign conversion perspective, that they can really understand the right tactics and the right strategic elements that are driving those conversions. That's been a challenge for marketers for a long time. Speaking of challenges, we've all been living through significant challenges. There's no way to say it nicely. The last two years, every industry completely affected by the pandemic talk. We're going to talk about Peet's Coffee. And I want to understand some of the challenges that you saw in the quick service restaurant or QSR industry at large. Talk to me about those industry challenges and then we'll dig into the Peet's story. >> Yeah, absolutely. So obviously the pandemic changed so much in our lives whether it's going to work or commuting or taking our kids to school or even getting our morning coffee. So when you think about Peet's, specifically within the QSR industry, they knew that they needed to innovate in order to make sure that they could provide their customers with their daily cups of coffee in a really safe and effective way. So they thought really quickly on their feet, they engaged us at Branch to help launch their order ahead messaging across their online and offline channels. They really wanted to maintain their commitment to an excellent customer experience but in a way that obviously would be safe and effective. >> That was one of the things that I missed the very most in the very beginning of the pandemic was going to my local Peet's. I missed that experience. Talk me about, you mentioned the online and offline, I'm very familiar with the online as an app user, mobile app user, but what were some of the challenges that they were looking to Branch to resolve on the offline experiences? People were queuing outside or for those folks that were they trying to get folks to convert to using the mobile app that maybe weren't users already? What was that online and offline experience? What were some of the challenges they were looking to resolve? >> Yeah, absolutely. The modern marketer is really both, like you said, online and offline, there is a heavy focus within the app and Peet's kind of wanted to bridge those two by pushing users into the app to provide a better experience there. So what they ended up doing was they used our deep linking capabilities to seamlessly route their customers to their loyalty program and their rewards catalog and other menu offerings within the app so that they could actually get things done in real time, but also in real time was the ability to then measure across those different campaigns so that they had visibility, Peet's, into kind of the way that they could optimize that campaign performance but also still give that great experience to their users. And they actually saw higher loyalty adoption, order values, and attributed purchases when they were able to kind of see in real time where these users were converting. But another thing that we're actually seeing across the board and Peet's did a great job of this was leveraging Branch power QR codes where we are seeing like the rebirth of the QR code. They're back, they're here to stay. They actually used that across multiple channels. So they used it with their in-store signage. You might have even seen it on their to go cups, coffee cards that were handed out by baristas. They were all encouraging customers to go order ahead using the Peet's coffee app. But that was kind of just the beginning for them. The creation of unique links for those QR codes actually spread for them to create Branch links across everything from emails to ads on Instagram. So before long, most of Peet's retail marketing were actually Branch links just because of the ease of creation and reliability, but more so again, going back to that customer experience, it really provided that good experience for the customers to make sure that they were getting within the mobile app so that they can take action and order their coffee. Another way that Branch kind of bridges the different platforms is actually between mobile web and app. Peet used Branch Journeys and that's a product of ours. It's a way that they can convert their mobile web users into app users. So they used deferred deep links with the ultimate goal of then converting those users into high value app users. So the Peet's team actually tested different creative and interstitials across the mobile site which would then place those users into the key pages, like either the homepage or the store locator, or the menu pages within the app. So that also helped them kind of build up not just their mobile app order online but also their delivery business so they could hire new trials of seasonal beverages. They could pair them with a free delivery offering. So they knew that they were able to leverage that at scale across multiple initiatives. >> I love those kinds of stories where it's kind of like a land and expand where there was obviously a global massive problem. They saw that recognized our customers are still going to be is demanding. Maybe if not more than they were before with I want my coffee, I want it now, you mentioned real time. I think one of the things we learned during the pandemic is access to realtime data isn't a nice to have anymore. We expect it as consumers even in our business lives, but the ability to be able to measure, course correct, but then see, wow, this is driving average order value up, we're getting more folks using our mobile app, maybe using delivery. Let's expand the usage of Branch across what we're doing in marketing can really help transform our marketing organization and a business at the brand level. >> Absolutely. And it also helps predict that brand loyalty. Because like you said, we, as consumers expect that that brands are going to kind of follow us where we are in our life cycle as consumers and if you don't do that, then you're going to be left in the dust unfortunately. >> I think one of the memories that will always stick with me, Michelle, during the last couple years is that first cup of Peet's that I didn't have to make at home myself. Just finally getting the courage to go back in, use the app, go in there, but oh man, that was probably the best taste of coffee I probably will ever have. You mentioned some of the products, you mentioned Journeys, and that allows them to do AB testing, looking at different CTAs, being able to kind of course correct and adjust campaigns in real time. >> Yeah, absolutely. So Journeys, what it does is it's basically a banner or a full page interstitial that is populated on the mobile web. So if you go to let's say Peets.com, you could get served as a user, either different creative or depending on where you are, location wise, you could be in the store, maybe there's a promotion. So it's triggered by all these different targeting capabilities. And so what that does is it takes me as a user. I can click that and go into the app where, like we said before, we have higher order value, higher lifetime value of a customer. And all my credit card information is saved. It just makes it so much more seamless for me to convert as a user within the app. And obviously Peet's likes that as well because then their conversion rates are actually higher. There's also kind of fun ways to play around with it. So if I am already a loyal customer and I have the app, you probably would target different creative for me than you would for someone who doesn't have the app. So you could say, hey, download our app, get $5 off of your next mobile order. Things like that you could play around with and you can see really does help increase that loyalty. But actually they were able to take, they kind of are experimenting with the geotargeted journeys in different key markets with different Peet's. And actually it was helping ultimately get their reinstalls growing. So for customers who maybe had the app before but needed to reinstall it because now there's such a bigger focus, they saw it both on the acquisition and the re-engagement side as well. >> So Branch has been pretty transformative, not in my estimation to Peet's marketing, but to Peet's as a business I'm hearing absolutely customer loyalty, revenue obviously impacted, brand loyalty, brand reputation. These are things that really kind of boil up to the top of the organization. So we're not just talking about benefits to the marketing and the sales folks. This is the overall massive business outcomes that you guys are enabling organizations like Peet's to generate. >> Yeah, definitely. And that's kind of what we tell our customers when they come to Branch. We want them to think about what their overall business objectives are versus if you think just campaign by campaign, okay, that's fine. But ultimately what are we trying to achieve? How could we help the bottom line? And then how can we also kind of help integrate with other mobile marketing technology or the modern tech stack that they're using? How do we integrate into that and actually provide not just a seamless experience for their end user, but with their marketing orgs, their product orgs, whoever's kind of touching the business as well? >> Have you noticed along those lines in the last couple of years as things like customer delight, seamless experience, the ability to translate, if I start on my iPad and I go to my laptop and then I finish a transaction on my phone, have you noticed your customer conversations increasing up to the C-suite level? Is this much more of a broad organizational objective around we've got to make sure that we have a really strong digital user experience? >> Yeah, absolutely. Like we were talking about before, it really does help affect the bottom line when you're providing a great experience with Branch being a mobile linking platform, our links just work. We outperform everybody else in the space and it might sound like really simple, okay, a link is working getting me from point A to point B, but doing it the right way and being consistent actually will increase performance over time of all these campaigns. So it's just an addition to providing that experience, you're seeing those key business results every single time. >> Talk about attribution for a minute because I've been in marketing for a long time in the tech industry. And that's always one of the challenges is we want to know what lever did the customer pull that converted them from opportunity to a lead to whatnot? Talk about the ability for Branch from an attribution perspective to really tell those marketers and the organization exactly, tactically, down to the tactical level, this is what's working. This is what's not working. Even if it's a color combination for example. That science is critical. >> Yeah, absolutely. Because we are able to cover the entire marketing life cycle of that they're trying to reach their customers. We cover off on email. We have mobile web to app. We have organic, we have search. No matter what you can look at that purview under a Branch lens. So we are just providing not just the accurate attribution down to the post-install, what happens after that, but also a more holistic view of everything that's happening on mobile. So then you can stitch all that together and really look at which ones are actually performing so you could see exactly which campaigns attributed directly to what amount of spend or which campaigns helped us understand the true lifetime long term value of customers, let's say in this case who ordered delivery or pickup. So to the kind of customer persona, it really helped. And also they actually were able to see Peet's because of our attribution, they saw actually a four and a half time increase in attributed purchases at the peak of the pandemic. And even since then, they're still seeing a three times increase in monthly attributed purchases. So because they actually have the view across everything that they're doing, we're able to provide that insight. >> That insight is so critical these days, like we mentioned earlier talking about real time data. Well we expect the experiences to be real time. And I expect that when I go back on the app they're going to know what I ordered last time. Maybe I want that again. Maybe I want to be able to change that, but I want them to know enough about me in a non creepy way. Give me that seamless experience that I'm expecting because of course that drives me to come back over and over again and spend way too much money there which I'm guilty of, guilty as charged. >> Coffee is totally fine. >> Right? Thank you. Thank you so much for validating that. I appreciate that. But talk to me about, as we are kind of wrapping things up here, the brick and mortars, it was such a challenge globally, especially the mom and pops to be able to convert quickly and figure out how do we reach a digital audience? How do we get our customers to be loyal? What's some of the advice that you have for the brick and mortars or those quick service restaurants like Peet's who've been navigating this the last couple years now here we are in this interesting semi post pandemic I would like to believe world? >> Yeah, we're getting there slowly but surely, but yeah, it's really important for them to adapt as we kind of move into this semi post pandemic world, we're kind of in the middle of like a hybrid online, offline, are we in stores, are we ordering online? These brand and customer relationships are super complex. I think the mobile app is just one part of that. Customers really shouldn't have any problems getting from the content or item they're looking for, no matter if they're in the store, if they're in the app, if they're on the desktop, if they're checking their email, if they're perusing TikTok, the best customer relationships really are omnichannel in nature. So what I would say, the need for providing the stellar customer experience isn't going to go away. It's actually really key. Whether it's driving users from their mobile properties to the app, providing a great in-store experience, like the QR codes, customers are expecting a lot more than they did before the pandemic. So they're not really seeing these brand touch points as little silos. They're seeing one brand. So it really should feel like one brand you should speak to the customers as if it's one brand across every single device, channel, and platform, and really unify that experience for them. >> Absolutely. That's going to be I think for so many different brands, whether it's a brick and mortar QSR, that's going to be one of the defining competitive advantages. If they can give their end users a single brand experience across channels, and you mentioned TikTok, those channels are only going to grow. As are I think or expectations. I don't think anybody's going to go back to wanting less than they did two years ago, right? >> Absolutely. Absolutely. >> Well this has been great, Michelle, thank you so much for joining me, talking about Branch, what you guys are doing, mobile linking platform, mobile measurement platform, the deep links, what you were able to do with Peet's Coffee, a beloved brand since the 60s and so many others. We appreciate your insights, your time and the story that you shared. >> Thank you so much, Lisa. I hope you have a great rest of your day. >> You as well. For Michelle Lerner, I'm Lisa Martin. You're watching theCUBE's coverage of the AWS Showcase. Keep it right here. More great content coming up from theCUBE, the leader in live tech coverage. (gentle music)

>> 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. >> Last year, we noted in a breaking analysis that the cloud ecosystem is innovating beyond the idea or notion of multi-cloud. We've said for years that multi-cloud is really not a strategy but rather a symptom of multi-vendor. And we coined this term supercloud to describe an abstraction layer that lives above the hyperscale infrastructure that hides the underlying complexities, the APIs, and the primitives of each of the respective clouds. It interconnects whether it's On-Prem, AWS, Azure, Google, stretching out to the edge and creates a value layer on top of that. So our vision is that supercloud is more than running an individual service in cloud native mode within an individual individual cloud rather it's this new layer that builds on top of the hyperscalers. And does things irrespective of location adds value and we'll get into that in more detail. Now it turns out that we weren't the only ones thinking about this, not surprisingly, the majority of the technology ecosystem has been working towards this vision in various forms, including some examples that actually don't try to hide the underlying primitives. And we'll talk about that, but give a consistent experience across the DevSecOps tool chain. Hello, and welcome to this week's Wikibon, Cube insights powered by ETR. In this breaking analysis, we're going to share some recent examples and direct quotes about supercloud from the many Cube guests that we've had on over the last several weeks and months. And we've been trying to test this concept of supercloud. Is it technically feasible? Is it business rational? Is there business case for it? And we'll also share some recent ETR data to put this into context with some of the players that we think are going after this opportunity and where they are in their supercloud build out. And as you can see I'm not in the studio, everybody's got COVID so the studios shut down temporarily but breaking analysis continues. So here we go. Now, first thing is we uncovered an article from earlier this year by Lori MacVittie, is entitled, Supercloud: The 22 Answer to Multi-Cloud Challenges. What a great title. Of course we love it. Now, what really interested us here is not just the title, but the notion that it really doesn't matter what it's called, who cares? Supercloud, distributed cloud, someone even called it Metacloud recently, and we'll get into that. But Lori is a technologist. She's a developer by background. She works at F-Five and she's partial to the supercloud definition that was put forth by Cornell. You can see it here. That's a cloud architecture that enables application migration as a service across different availability zones or cloud providers, et cetera. And that the supercloud provides interfaces to allocate, migrate and terminate resources... And can span all major public cloud providers as well as private clouds. Now, of course, we would take that as well to the edge. So sure. That sounds about right and provides further confirmation that something new is really happening out there. And that was our initial premise when we put this fourth last year. Now we want to dig deeper and hear from the many Cube guests that we've interviewed recently probing about this topic. We're going to start with Chuck Whitten. He's Dell's new Co-COO and most likely part of the Dell succession plan, many years down the road hopefully. He coined the phrase multi-cloud by default versus multi-cloud by design. And he provides a really good business perspective. He's not a deep technologist. We're going to hear from Chuck a couple of times today including one where John Furrier asks him about leveraging hyperscale CapEx. That's an important concept that's fundamental to supercloud. Now, Ashesh Badani heads products at Red Hat and he talks about what he calls Metacloud. Again, it doesn't matter to us what you call it but it's the ecosystem gathering and innovating and we're going to get his perspective. Now we have a couple of clips from Danny Allan. He is the CTO of Veeam. He's a deep technologist and super into the weeds, which we love. And he talks about how Veeam abstracts the cloud layer. Again, a concept that's fundamental to supercloud and he describes what a supercloud is to him. And we also bring with Danny the edge discussion to the conversation. Now the bottom line from Danny is we want to know is supercloud technically feasible? And is it a thing? And then we have Jeff Clarke. Jeff Clark is the Co-COO and Vice Chairman of Dell super experienced individual. He lays out his vision of supercloud and what John Furrier calls a business operating system. You're going to hear from John a couple times. And he, Jeff Clark has a dropped the mic moment, where he says, if we can do this X, we'll describe what X is, it's game over. Okay. So of course we wanted to then go to HPE, one of Dell's biggest competitors and Patrick Osborne is the vice president of the storage business unit at Hewlett Packet Enterprise. And so given Jeff Clarke's game over strategy, we want to understand how HPE sees supercloud. And the bottom line, according to Patrick Osborne is that it's real. So you'll hear from him. And now Raghu Raghuram is the CEO of VMware. He threw a curve ball at this supercloud concept. And he flat out says, no, we don't want to hide the underlying primitives. We want to give developers access to those. We want to create a consistent developer experience in that DevsSecOps tool chain and Kubernetes runtime environments, and connect all the elements in the application development stack. So that's a really interesting perspective that Raghu brings. And then we end on Itzik Reich. Itzik is a technologist and a technical team leader who's worked as a go between customers and product developers for a number of years. And we asked Itzik, is supercloud technically feasible and will it be a reality? So let's hear from these experts and you can decide for yourselves how real supercloud is today and where it is, run the sizzle >> Operative phrase is multi-cloud by default that's kind of the buzz from your keynote. What do you mean by that? >> Well, look, customers have woken up with multiple clouds, multiple public clouds, On-Premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was, it can be and it should be. It is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they want to maintain an On-Premise cloud, On-Premise clouds are not going away, I mentioned edge clouds, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud by default we mean that's the state of the world today. Our goal is to bring multi-cloud by design as you heard. >> Really great question, actually, since you and I talked, Dave, I've been spending some time noodling just over that. And you're right. There's probably some terminology, something that will get developed either by us or in collaboration with the industry. Where we sort of almost have the next almost like a Metacloud that we're working our way towards. >> So we manage both the snapshots and we convert it into the Veeam portable data format. And here's where the supercloud comes into play. Because if I can convert it into the Veeam portable data format, I can move that OS anywhere. I can move it from physical to virtual, to cloud, to another cloud, back to virtual, I can put it back on physical if I want to. It actually abstracts the cloud layer. There are things that we do when we go between cloud some use BIOS, some use UEFI, but we have the data in backup format, not snapshot format, that's theirs, but we have it in backup format that we can move around and abstract workloads across all of the infrastructure. >> And your catalog is control in control of that. Is that right? Am I thinking about that the right way? >> Yeah it is, 100%. And you know what's interesting about our catalog, Dave, the catalog is inside the backup. Yes. So here's, what's interesting about the edge, two things, on the edge you don't want to have any state, if you can help it. And so containers help with that You can have stateless environments, some persistent data storage But we not not only provide the portability in operating systems, we also do this for containers. And that's true. If you go to the cloud and you're using say EKS with relational database services RDS for the persistent data later, we can pick that up and move it to GKE or move it to OpenShift On-Premises. And so that's why I call this the supercloud, we have all of this data. Actually, I think you termed the term supercloud. >> Yeah. But thank you for... I mean, I'm looking for a confirmation from a technologist that it's technically feasible. >> It is technically feasible and you can do it today. >> You said also technology and business models are tied together and enabler. If you believe that then you have to believe that it's a business operating system that they want. They want to leverage whatever they can. And at the end of the day, they have to differentiate what they do. >> Well, that's exactly right. If I take that in what Dave was saying and I summarize it the following way, if we can take these cloud assets and capabilities, combine them in an orchestrated way to deliver a distributed platform, game over. >> We have a number of platforms that are providing whether it's compute or networking or storage, running those workloads that they plum up into the cloud they have an operational experience in the cloud and they now they have data services that are running in the cloud for us in GreenLake. So it's a reality, we have a number of platforms that support that. We're going to have a a set of big announcements coming up at HPE Discover. So we led with Electra and we have a block service. We have VM backup as a service and DR on top of that. So that's something that we're providing today. GreenLake has over, I think it's actually over 60 services right now that we're providing in the GreenLake platform itself. Everything from security, single sign on, customer IDs, everything. So it's real. We have the proofpoint for it. >> Yeah. So I want to clarify something that you said because this tends to be very commonly confused by customers. I use the word abstraction. And usually when people think of abstraction, they think it hides capabilities of the cloud providers. That's not what we are trying to do. In fact, that's the last thing we are trying to do. What we are trying to do is to provide a consistent developer experience regardless of where you want to build your application. So that you can use the cloud provider services if that's what you want to use. But the DevSecOp tool chain, the runtime environment which turns out to be Kubernetes and how you control the Kubernetes environment, how do you manage and secure and connect all of these things. Those are the places where we are adding the value. And so really the VMware value proposition is you can build on the cloud of your choice but providing these consistent elements, number one, you can make better use of us, your scarce developer or operator resources and expertise. And number two, you can move faster. And number three, you can just spend less as a result of this. So that's really what we are trying to do. We are not... So I just wanted to clarify the word abstraction. In terms of where are we? We are still, I would say, in the early stages. So if you look at what customers are trying to do, they're trying to build these greenfield applications. And there is an entire ecosystem emerging around Kubernetes. There is still, Kubernetes is not a developer platform. The developer experience on top of Kubernetes is highly inconsistent. And so those are some of the areas where we are introducing new innovations with our Tanzu Application Platform. And then if you take enterprise applications, what does it take to have enterprise applications running all the time be entirely secure, et cetera. >> Well, look, the multi-cloud by default today are isolated clouds. They don't work together. Your data is siloed. It's locked up and it is expensive to move and make sense of it. So I think the word you and I were batting around before, this is an interconnected tissue. That's what the world needs. They need the clouds to work together as a single platform. That's the problem that we're trying to solve. And you saw it in some of our announcements here that we're starting to make steps on that journey to make multi-cloud work together much simpler. >> It's interesting, you mentioned the hyperscalers and all that CapEx investments. Why wouldn't you want to take advantage of a cloud and build on the CapEx and then ultimately have the solutions machine learning as one area. You see some specialization with the clouds. But you start to see the rise of superclouds, Dave calls them, and that's where you can innovate on a cloud then go to the multiple clouds. Snowflakes is one, we see a lot of examples of supercloud... >> Project Alpine was another one. I mean, it's early, but it's its clearly where you're going. The technology is just starting to come around. I mean it's real. >> Yeah. I mean, why wouldn't you want to take advantage of all of the cloud innovation out there? >> Is that something that's, that supercloud idea is a reality from a technologist perspective. >> I think it is. So for example Katie Gordon, which I believe you've interviewed earlier this week, was demonstrating the Kubernetes data mobility aspect which is another project. That's exactly part of the it's rationale, the rationale of customers being able to move some of their Kubernetes workloads to the cloud and back and between different clouds. Why are we doing? Because customers wants to have the ability to move between different cloud providers, using a common API that will be able to orchestrate all of those things with a self-service that may be offered via the APEX console itself. So it's all around enabling developers and meeting them where they are today and also meeting them into tomorrow's world where they actually may have changed their mind to do those things. So yes we are walking on all of those different aspects. >> Okay. Let's take a quick look at some of the ETR data. This is an X-Y graph. You've seen it a number of times on breaking analysis, it plots the net score or spending momentum on the Y-axis and overlap or pervasiveness in the ETR dataset on the X-axis, used to be called market share. I think that term was off putting to some people, but anyway it's an indicator of presence in the dataset. Now that red dotted line that's rarefied air where anything above that line is considered highly elevated. Now you can see we've plotted Azure and AWS in the upper right. GCP is in there and Kubernetes. We've done that as reference points. They're not necessarily building supercloud platforms. We'll see if they ever want to do so. And Kubernetes of course not a company, but we put 'em in there for context. And we've cherry picked a few players that we believe are building out or are important for supercloud build out. Let's start with Snowflake. We've talked a lot about this company. You can see they're highly elevated on the vertical axis. We see the data cloud as a supercloud in the making. You've got pure storage in there. They made the public, the early part of its supercloud journey at Accelerate 2019 when it unveiled a hybrid block storage service inside of AWS, it connects its On-Prem to AWS and creates that singular experience for pure customers. We see Hashi, HashiCorp as an enabling infrastructure, as code. So they're enabling infrastructure as code across different clouds and different locations. You see Nutanix. They're embarking on their multi-cloud strategy but it's doing so in a way that we think is supercloud, like now. Now Veeam, we were just at VeeamON. And this company has tied Dell for the number one revenue player in data protection. That's according to IDC. And we don't think it won't be long before it holds that position alone at the top as it's growing faster than in Dell in the space. We'll see, Dell is kind of waking up a little bit and putting more resource on that. But Veeam, they're a pure play vendor in data protection. And you heard their CTO, Danny Allan's view on Supercloud, they're doing it today. And we heard extensive comments as well from Dell that's clearly where they're headed, project Alpine was an early example from Dell technologies world of Supercloud in our view. And HPE with GreenLake. Finally beginning to talk about that cross cloud experience. I think it in initially HPE has been more focused on the private cloud, we'll continue to probe. We'll be at HPE discover later on the spring, actually end of June. And we'll continue to probe to see what HPE is doing specifically with GreenLake. Now, finally, Cisco, we put them on the chart. We don't have direct quotes from recent shows and events but this data really shows you the size of Cisco's footprint within the ETR data set that's on the X-axis. Now the cut of this ETR data includes all sectors across the ETR taxonomy which is not something that we commonly show but you can see the magnitude of Cisco's presence. It's impressive. Now, they had better, Cisco that is, had better be building out a supercloud in our view or they're going to be left behind. And I'm quite certain that they're actually going to do so. So we have a lot of evidence that we're putting forth here and seeing in the marketplace what we said last year, the ecosystem is take taking shape, supercloud is forming and becoming a thing. And really in our view, is the future of cloud. But there are always risks to these predictive scenarios and we want to acknowledge those. So first, look, we could end up with a bunch of bespoke superclouds. Now one supercloud is better than three separate cloud native services that do fundamentally the same thing from the same vendor. One for AWS, one for GCP and one for Azure. So maybe that's not all that bad. But to point number two, we hope there evolves a set of open standards for self-service infrastructure, federated governance, and data sharing that will evolve as a horizontal layer versus a set of proprietary vendor specific tools. Now, maybe a company like Veeam will provide that as a data management layer or some of Veeam's competitors or maybe it'll emerge again as open source. As well, and this next point, we see the potential for edge disruptions, changing the economics of the data center. Edge in fact could evolve on its own, independent of the cloud. In fact, David Floria sees the edge somewhat differently from Danny Allan. Floria says he sees a requirement for distributed stateful environments that are ephemeral where recovery is built in. And I said, David, stateful? Ephemeral? Stateful ephemeral? Isn't that an oxymoron? And he responded that, look, if it's not ephemeral the costs are going to be prohibitive. He said the biggest mistake the companies could make is thinking that the edge is simply an extension of their current cloud strategies. We're seeing that a lot. Dell largely talks about the edge as retail. Now, and Telco is a little bit different, but back to Floria's comments, he feels companies have to completely reimagine an integrated file and recovery system which is much more data efficient. And he believes that the technology will evolve with massive volumes and eventually seep into enterprise cloud and distributed data centers with better economics. In other words, as David Michelle recently wrote, we're about 15 years into the most recent cloud cycle and history shows that every 15 years or so, something new comes along that is a blind spot and highly disruptive to existing leaders. So number four here is really important. Remember, in 2007 before AWS introduced the modern cloud, IBM outpost, sorry, IBM outspent Amazon and Google and RND and CapEx and was really comparable to Microsoft. But instead of inventing cloud, IBM spent hundreds of billions of dollars on stock buybacks and dividends. And so our view is that innovation rewards leaders. And while it's not without risks, it's what powers the technology industry it always has and likely always will. So we'll be watching that very closely, how companies choose to spend their free cash flow. Okay. That's it for now. Thanks for watching this episode of The Cube Insights, powered by ETR. Thanks to Stephanie Chan who does some of the background research? Alex Morrison is on production and is going to compile all this stuff. Thank you, Alex. We're all remote this week. Kristen Nicole and Cheryl Knight do Cube distribution and social distribution and get the word out, so thank you. Robert Hof is our editor in chief. Don't forget the checkout etr.ai for all the survey action. Remember I publish each week on wikibon.com and siliconangle.com and you can check out all the breaking analysis podcasts. All you can do is search breaking analysis podcast so you can pop in the headphones and listen while you're on a walk. You can email me at david.vellante@siliconangle.com. If you want to get in touch or DM me at DVellante, you can always hit me up into a comment on our LinkedIn posts. This is Dave Vellante. Thank you for watching this episode of break analysis, stay safe, be well and we'll see you next time. (upbeat music)

>>Welcome to the cubes coverage of Dell tech world 2022. My name is Dave Volante and I'm currently in our studios outside of Boston. As we prepare to gather for the first in person Dell technologies world since 20 19, 1 of the major structural change and the technology business during the pandemic was IBM's spin out of Kendra. A world class technology services provider that lived inside of IBM. Kendra is a large business with trailing 12 month revenues north of 18 billion. It's got 90,000 employees worldwide. Kendra has long term predictable cash flows. And in my view is one of the most undervalued companies in the technology sector. As a separate company, Kendra is able to turn many of its former internal IBM roadblocks into tailwinds and ecosystem. Partnerships are one of the best examples of new opportunities that are opening up for the newly separate at company. In this next segment, we're gonna dig into a new partnership between Kendra and Dell technologies and what is the most critical priority for organizations today? Cyber resiliency and with me are two really impressive and talented guests. Chris Lovejoy is global security and resiliency practice leader at Kendra. Michelle Weston is vice president of, of global offerings for security and resiliency also at kindred ladies. Welcome to the cube. Thanks for coming on and spending some time with us. >>Thank thank you. >>Okay. Let's zoom out a little bit and start with a big picture. What would you say are, are the one or two major trends or changes even in cyber that you've seen since the pandemic, maybe Chris, you could start us off and Michelle, you can chime in. >>Sure. Happy to. And, um, you know, I think part of this actually preceded the pandemic and, um, you know, the fact is, you know, a lot of organizations have been engaging in the adoption of new technologies, you know, be it cloud AI IOT, what, what, whatever that may be. Um, and they've been introducing that technology without, um, adequate security control and during the COVID pandemic, um, when, you know, technology transformation happened for existential reasons, what we were seeing is organizations throwing at even more technology at cyclic, right, with absolutely no security control whatsoever. And in the meantime, the regulators who are, you know, watching this in, you know, horror are introducing new requirements in and around, um, what we're calling cyber resilience today. And it's all based on this concept that, you know, conventional cybersecurity assume that the adversaries could be kept out of organizations. >>Um, you could protect the organization and sort of block it, um, as rising numbers of disruptive attacks, like, you know, ransomware attacks have shown those approaches don't work. And so, um, what we're seeing is that the market is really moving toward this concept of cyber resiliency, which goes beyond cybersecurity. It assumes that the advanced a adversaries are frankly, many adversaries can overcome, um, conventional protections and that, um, they, that organization need to prepare to recover. Um, so our approach, the approach that we're taking to the market is really to help organizations in binding security plus continuity plus disaster recovery, then giving them the ability to anticipate, protect, um, with stand and recovery from any adverse condition associated with their cyber real estate. Um, and this is why we're so excited to work with Dell, uh, because they're really, uh, paving the roads for us to actually, you know, work together in solving these needs for our clients. >>Got it. That makes sense. And now Michelle, as Chris was saying, these worlds are coming together. What used to be adjacencies, oftentimes they, after thoughts, bolted on, and now you've got the work from home and, and hybrid work, not to mention, as Chris was saying, you're injecting AI and all this data, you know, this is a complicated situation for a lot of people, isn't it? >>Yeah. And it was only even more complicated during, during the pandemic as well. I think, uh, another trend that we saw was the end enterprise was outside the enterprise, right? Uh, everyone was working from home. They weren't in the data centers, their own resiliency and security protocols were already at risk because they were so manual and people intensive. And yet we know, you know, the bad actors actually took advantage of, of that right. Uh, data centers were, uh, less monitored. Um, we had all of the employees working from home. Now, the enterprise is outside of the enterprise, but you still need security and resiliency for all of those endpoints. Right. And I think that's driving a higher need, um, coming out of the PA the pandemic and even with this hybrid model, okay. We'll return to work, but not, not in the same fashion that we did prior to the pandemic. >>That's the new reality. The other thing that I would say is that those customers that had adopted cloud already and cloud enabled their business, they were able to fare, um, the best during the pandemic. They were able to sustain their businesses. Um, alternatively, and it's kind of a different lens to it. I think the pandemic actually drove new ways of working and some really creative solutions. I mean, if you look at, um, you know, food delivery services that, uh, proliferated during the pandemic, or, uh, that are now offering fitness online, um, fitness classes online, people had to think, um, intelligently and, and creatively on how they sustain their businesses. So I think all of that's coming together, but certainly this need of, as you said, not thinking of security and resiliency as an afterthought, but as a forethought planning for those things efficiently and effectively, that we find customers that do that, uh, do it the best. And, uh, I think that Kendra offers a unique value pro in here because bringing both together is a journey that we started a couple of years ago that we've only accelerated with the, uh, spin of the Kendra company. >>Yeah. Interesting. So I wanted to talk about that partnership because mm-hmm, <affirmative>, you know, Dell's got this massive channel, it's got infrastructure technology expertise, uh, but Dell, you know, Dell's a product company, Kendra is a services company, so it's a really good match in that sense. Right. Uh, maybe you could talk about how the partnership came together and, you know, what are the critical aspects that folks need to be aware of? >>Yeah. I would say Dell's an excellent partner for us and they have been for a number of years. So in a lot of ways that's not new. Okay. Uh, we've been partnering in market together for quite quite some time. In fact, the solution that we'll talk about today was first put into market in 2018. And you're absolutely right. We, we come together in the best ways. They're leveraging our strengths with regard to manage services, professional services. And we are certainly looking at them as a key technology provider, um, for our portfolio, we've worked together for years. Uh, we manage backup environments based on their data protection solutions, including data domain, but what was unique. And I think we were both ahead of the market at the time, um, was the 2018 solution that we put in to market and have only enhanced and augmented it ever since it's, it's called, um, cyber volt is, is the solution from Dell technologies. >>We certainly manage that solution in market for them today. And then we have unique differentiation in our Kindra portfolio that we've integrated with that and add to, um, their cyber incident recovery features, um, Dell initially put the solution in market coming out of, um, some of the ransomware attacks that they had cyber attacks that they had. They realized there was a need to protect the large data domain install base around the world. Um, they developed some proprietary solar solution, uh, software on top of their large data domain boxes and, and any cyber incident recovery solution. You need a, a few things you need the ability to assure imutable storage, a, a copy that you can assure has not been altered so that when you initiate the recovery, you know that you've got a clean copy and you're not propagating whatever is there. Um, so the solution has that, um, it has the other component that you need, which is the ability to scan the data for anomalies, right? >>So they're scanning the backup files continuously to look for anomalies. And then lastly, you need some form of data mover, which the data domain, um, solution offers. So they came to us in 2018 and said, look, we've got this solution. We think we're ahead of the market. Uh, we were also investing in cyber incident recovery with a key asset that we acquired in market in 2015, um, that we've continued to bake cyber incident recovery features and functions into, and they said, let's marry the two. And let's have you provide all of the managed services capabilities around this for clients. Um, that is a key piece because when it comes to cyber, uh, there's always a level of confidence that customers have, right? Yes. I can recover from any adverse condition. If you ask them, can you recover from a cyber attack with a hundred percent assurance? I don't think there's a customer today that could say given how sophisticated and how much these, these attack vectors are changing, that, that they, they have that a hundred percent confidence level. So a managed service provider, a phone, a friend in the event of is a, is a unique value proposition. Um, and that's what the two companies are bringing together, uh, for customers today. >>Got it. Thank you. So, so Chris, maybe as a services company, you, you, you have to be ignite, you know, to technology, you know, the best fit, et cetera. But, but prior to the spin, we never would've heard it, something like this. And so what, maybe you could talk about the partnership from your perspective. >>Yeah, no, absolutely. And I, I do wanna, um, you know, sort of double click on this a little bit, you, and you mentioned it in your opening, you know, headwinds being wins now. And I think this is important, incredibly important. You know, what people don't realize about Kendra is that, you know, we were never able to, as the services organization, um, that was really focused on strategic outsourcing and providing other kinds of services to, uh, clients while under the IBM banner are really never able to talk about the technical depth that we had across any number of platforms, including, um, the hyperscalers. And we have thousands upon thousands of people with hyperscaler certifications. Um, we have experience with pretty much every security and resilience technology out there. Um, we have broad and early with organizations like yours, that we were never able to speak about now, you know, when it comes to a client, you know, let's be realistic. >>Everybody is engaged in some sort of it modernization program. And while, and we have to realize also that those it modernization programs, you know, oftentimes they have no destination per se. You know, we talk about them as a journey, but we, if no destination, they just keep going and going and going. And the directions change every day, depending on, you know, what the strategic, uh, requirements are from whatever C-suite, you have, you know, sitting at the table, uh, what the competitive trends are, what the market is telling you, et cetera. And so what clients are saying to us is that the value we offer is that we can untangle the mess. That is their environment. We can meet them where they are, we can get them where they wanna go. And so, you know, when it comes to a relationship with Dell, you know, we believe that, you know, particularly in the area of security, in resilience, that there is a unique proposition to be had around the services and the cross platform experience and certifications and skills that our, um, our teams have married with the technology advances that Dell has made in the, in, in the world, as well as our experience in, you know, sort of the two that has have been frankly, hidden over the past few years. >>I think we have some, uh, something unique that we can offer to the market. Particularly, as I said, in this space of security and resilience, where all of our clients are, you know, looking for some sort of solution to this, you know, gee, I can't spend enough money to protect myself. I need to make sure that if the worst happens that I can bring myself back again, that's what we can do for our clients. >>Great. Thank you, Michelle. I wanna go back to the solution for a moment. You mentioned a number of things, integrations. I got like a zillion questions here. I'm interested in what kind of integrations you talked about imutability where does, where does that occur? Is that in the cloud? Is that the, you know, Dell technology is scan for anomalies again, what is that? Is that some kind of, you know, AI magic, you got a high speed data mover. Is there an air gap involved, maybe help me fill in some of those gaps. >>Yeah. And I think you, I think you've netted out the solution. Any cyber incident recovery solution in my mind would have those three things. They have some form of imutable storage. Uh, this could be cloud object storage in the case of the Dell solution, they're actually using their retention lock feature on the large data domain devices. Right? So think of this solution as having two data domains, they both have this retention lock feature. That's the imutable storage. They're able to move data and forth between the two, uh, that's another key piece. And then finally, for any incident recovery solution, you need the ability to scan and make sure that there aren't anomalies, um, in this case, in the backup files. So they're using a, a third party to scan thatno scan those files for anomalies. And when when's detected, that kind of gives the indication that something may be there and then they can go in and triage it and, and, and clean the environment if needed. >>Um, so we certainly manage that end to end, and that is one approach. It is an on-premise approach. It uses the data domain, uh, technologies. We know that clients have a lot more than that, right? So where Kendra comes in with its cyber incident recovery solution that also integrates with Dell's cyber incident recovery solution is we support cloud, um, multiple infrastructure. We have also imutable storage that we leverage. Um, and then in terms of our anomaly scanning capabilities, in this case, we're using technology that we had originally developed in IBM research that we integrated into the software product. Um, again, this is on an acquisition we did in market five years ago, called son Nobi. It's a software product. Um, it ingests and automates all of your workflows in the, in, in the event of any failover failback, any, uh, outage, including cyber and that technology underpin a lot of what we do on the incident recovery perspective, Dells use data domain. >>We've used the software, all both solutions have all three components of the cyber incident recovery, uh, solution when they're integrated, there's real power there, right? Because now you're looking at protection, not just of the backup environ, um, but all environments, including production, you're looking at being able to scale beyond OnPrem. Um, and more importantly, you're looking at the speed to recover, right? The not needing to rehydrate the data, but to be able to recover with the RTOs and RPOs that are expected, um, of our customers on the resiliency orchestration side, the Kendra solution. Um, this is, this is push of a button fail over, fail back in the event of an outage. Um, you can recover the entire hybrid estate in the matter of minutes and what we know with respect to any outage it's costly. We know know that downtime is costly, but with respect to cyber, we know that that's more costly than a typical outage, sometimes four X, um, you don't always recover from the brand damage from the loss of customers. So being down and, and coming up as quickly as you can, with the additional data verification, data validation and assurance that you're not propagating, whatever is there is the value prop, um, that both CU, both companies are really serving. >>And where does an air gap fit in into this equation? Is that yet another layer of protection what's best practice there? >>Um, so think of the air gap is just between the data movement and the immune storage, right? You need to be able to cut connection in a way, right. That is an air gap solution. And it's based on the imutable storage that both have. >>Okay. And that would be, it could be local, I guess, but it also could be, it should be maybe remote. Yes. Mm-hmm >><affirmative> okay. Exactly. And, and the ability to manage and orchestrate that air gap is a key value prop again, of the Kendra solution. >>Okay. And so I've mentioned local or remote. I mean, obviously the trade off is recovery time, you know, uh, I guess RTO, um, but, but <laugh> and RPO. So a lot of layers is, is what I'm hearing is that's always security pros in this framework. >>Let me give you another example, the reason why this is so important. Um, most of our Dr. Processes today, they all rely on people, right? We had a large client that was impacted when we were IBM. They were impacted with pet. They had a great Dr plan. They were a customer of ours. Um, we managed that service for them. Their Dr. Plan was still people intensive. And when that attack happened, it took out the badge readers to the people that you've invested in. Can't get on site to manage the incident, can't bring up the environment. And then if you look at going back to the very beginning of our conversation, COVID being sort of, uh, another way that that happened with access and the ability to continuously monitor and have the people on site that ability was impacted. So this is where you need to invest in technology, uh, P and processes to make sure that you are as robust as you can be. And as Chris said, your ability to anticipate with stand and recover from any adverse condition, that's, that's the value prop that our global practice brings. Yeah. >>To your, to your point, the adversary is well funded and motivated. Chris, we'll give you the last word, where do, where do you wanna see this partnership go? You know, kinda what what's next? What should we look for in the coming months and in, in years? >>Yeah. I'm, you know, I think, you know, very simply, and I'm going put my CISO hat on right. For a minute, because I think it's important to speak, you know, for the customer as a customer, you know, at the end of the day, I, I think most C-suite executives do don't realize the extent to which security, continuity and disaster recovery have been separate silos. And what is shocking to our clients when they get into a ransomware event in particular is the fact that they have their, um, systems, their services, their data is locked up, their backups have been sort of implemented or have, have been, you know, sort of subverted. They call in the pros, they call in the folks that help them with the incident response. The incident responders are able to identify the ransomware strain. They're able to contain the ransomware strain, but the damage is done. >>Now, what, how do you bring the environment back? How do you that the data is good? How do you, how do you find the system configurations and load them again? In what order do you load them? What they don't realize is that security and recovery, they have to be merged together. And so what I think that we can do it, it's not just, you know, build customer demand is not just sell a solution. We can really help clients. And so my hope is that we are able to bring cyber resilience into every organization, every large enterprise out there that needs to, you know, continually service their clients and their employees. They need to stay in business that we're able to bring the solution to them in such a way that they're able to, you know, bring back their environments to serve their clients when the worst does happen. >>Great. Yes. Thank you. We're definitely seeing that data protection world and the cybersecurity world. They, they adjacencies, but they really are coming together and part of a comprehensive plan. Okay. We have to leave it there. Thanks so much folks for coming on the cube really appreciate your time and your insights. >>Thanks for having us. And >>Thank you. Thank you for watching the Cube's coverage of Dell technologies world 2022. Keep it right there. We're running all week with live coverage from the show floor. We're pumping in deep dives like this one throughout the week. So don't go away.

>> From "theCUBE" studios in Palo Alto, in Boston, bringing you data driven insights from "theCUBE" and ETR. This is "Breaking Analysis" with Dave Vellante. >> There are very few political issues that get bipartisan support these days, nevermind consensus spanning geopolitical boundaries. But whether we're talking across the aisle or over the pond, there seems to be common agreement that the power of big tech firms should be regulated. But the government's track record when it comes to antitrust aimed at big tech is actually really mixed, mixed at best. History has shown that market forces rather than public policy have been much more effective at curbing monopoly power in the technology industry. Hello, and welcome to this week's "Wikibon CUBE" insights powered by ETR. In this "Breaking Analysis" we welcome in frequent "CUBE" contributor Dave Moschella, author and senior fellow at the Information Technology and Innovation Foundation. Dave, welcome, good to see you again. >> Hey, thanks Dave, good to be here. >> So you just recently published an article, we're going to bring it up here and I'll read the title, "Theory Aside, Antitrust Advocates Should Keep Their "Big Tech" Ambitions Narrow". And in this post you argue that big sweeping changes like breaking apart companies to moderate monopoly power in the tech industry have been ineffective compared to market forces, but you're not saying government shouldn't be involved rather you're suggesting that more targeted measures combined with market forces are the right answer. Can you maybe explain a little bit more the premise behind your research and some of your conclusions? >> Sure, and first let's go back to that title, when I said, theory aside, that is referring to a huge debate that's going on in global antitrust circles these days about whether antitrust should follow the traditional path of being invoked when there's real harm, demonstrable harm to consumers or a new theory that says that any sort of vast monopoly power inevitably will be bad for competition and consumers at some point, so your best to intervene now to avoid harms later. And that school, which was a very minor part of the antitrust world for many, many years is now quite ascendant and the debate goes on doesn't matter which side of that you're on the questions sort of there well, all right, well, if you're going to do something to take on big tech and clearly many politicians, regulators are sort of issuing to do something, what would you actually do? And what are the odds that that'll do more good than harm? And that was really the origins of the piece and trying to take a historical view of that. >> Yeah, I learned a new word, thank you. Neo-brandzian had to look it up, but basically you're saying that traditionally it was proving consumer harm versus being proactive about the possibility or likelihood of consumer harm. >> Correct, and that's a really big shift that a lot of traditional antitrust people strongly object to, but is now sort of the trendy and more send and view. >> Got it, okay, let's look a little deeper into the history of tech monopolies and government action and see what we can learn from that. We put together this slide that we can reference. It shows the three historical targets in the tech business and now the new ones. In 1969, the DOJ went after IBM, Big Blue and it's 13 years later, dropped its suit. And then in 1984 the government broke Ma Bell apart and in the late 1990s, went after Microsoft, I think it was 1998 in the Wintel monopoly. And recently in an interview with tech journalist, Kara Swisher, the FTC chair Lena Khan claimed that the government played a major role in moderating the power of tech giants historically. And I think she even specifically referenced Microsoft or maybe Kara did and basically said the industry and consumers from the dominance of companies like Microsoft. So Dave, let's briefly talk about and Kara by the way, didn't really challenge that, she kind of let it slide. But let's talk about each of these and test this concept a bit. Were the government actions in these instances necessary? What were the outcomes and the consequences? Maybe you could start with IBM and AT&T. >> Yeah, it's a big topic and there's a lot there and a lot of history, but I might just sort of introduce by saying for whatever reasons antitrust has been part of the entire information technology industry history from mainframe to the current period and that slide sort of gives you that. And the reasons for that are I think once that we sort of know the economies of scale, network effects, lock in safe choices, lot of things that explain it, but the good bit about that is we actually have so much history of this and we can at least see what's happened in the past and when you look at IBM and AT&T they both were massive antitrust cases. The one against IBM was dropped and it was dropped in as you say, in 1980. Well, what was going on in at that time, IBM was sort of considered invincible and unbeatable, but it was 1981 that the personal computer came around and within just a couple of years the world could see that the computing paradigm had change from main frames and minis to PCs lines client server and what have you. So IBM in just a couple of years went from being unbeatable, you can't compete with them, we have to break up with them to being incredibly vulnerable and in trouble and never fully recovered and is sort of a shell of what it once was. And so the market took care of that and no action was really necessary just by everybody thinking there was. The case of AT&T, they did act and they broke up the company and I would say, first question is, was that necessary? Well, lots of countries didn't do that and the reality is 1980 breaking it up into long distance and regional may have made some sense, but by the 1990 it was pretty clear that the telecom world was going to change dramatically from long distance and fixed wires services to internet services, data services, wireless services and all of these things that we're going to restructure the industry anyways. But AT& T one to me is very interesting because of the unintended consequences. And I would say that the main unintended consequence of that was America's competitiveness in telecommunications took a huge hit. And today, to this day telecommunications is dominated by European, Chinese and other firms. And the big American sort of players of the time AT&T which Western Electric became Lucent, Lucent is now owned by Nokia and is really out of it completely and most notably and compellingly Bell Labs, the Bell Labs once the world's most prominent research institution now also a shell of itself and as it was part of Lucent is also now owned by the Finnish company Nokia. So that restructuring greatly damaged America's core strength in telecommunications hardware and research and one can argue we've never recovered right through this 5IG today. So it's a very good example of the market taking care of, the big problem, but meddling leading to some unintended consequences that have hurt the American competitiveness and as we'll talk about, probably later, you can see some of that going on again today and in the past with Microsoft and Intel. >> Right, yeah, Bell Labs was an American gem, kind of like Xerox PARC and basically gone now. You mentioned Intel and Microsoft, Microsoft and Intel. As many people know, some young people don't, IBM unwillingly handed its monopoly to Intel and Microsoft by outsourcing the micro processor and operating system, respectively. Those two companies ended up with IBM ironically, agreeing to take OS2 which was its proprietary operating system and giving Intel, Microsoft Windows not realizing that its ability to dominate a new disruptive market like PCs and operating systems had been vaporized to your earlier point by the new Wintel ecosystem. Now Dave, the government wanted to break Microsoft apart and split its OS business from its application software, in the case of Intel, Intel only had one business. You pointed out microprocessors so it couldn't bust it up, but take us through the history here and the consequences of each. >> Well, the Microsoft one is sort of a classic because the antitrust case which was raging in the sort of mid nineties and 1998 when it finally ended, those were the very, once again, everybody said, Bill Gates was unstoppable, no one could compete with Microsoft they'd buy them, destroy them, predatory pricing, whatever they were accusing of the attacks on Netscape all these sort of things. But those the very years where it was becoming clear first that Microsoft basically missed the early big years of the internet and then again, later missed all the early years of the mobile phone business going back to BlackBerrys and pilots and all those sorts of things. So here we are the government making the case that this company is unstoppable and you can't compete with them the very moment they're entirely on the defensive. And therefore wasn't surprising that that suit eventually was dropped with some minor concessions about Microsoft making it a little bit easier for third parties to work with them and treating people a little bit more, even handling perfectly good things that they did. But again, the more market took care of the problem far more than the antitrust activities did. The Intel one is also interesting cause it's sort of like the AT& T one. On the one hand antitrust actions made Intel much more likely and in fact, required to work with AMD enough to keep that company in business and having AMD lowered prices for consumers certainly probably sped up innovation in the personal computer business and appeared to have a lot of benefits for those early years. But when you look at it from a longer point of view and particularly when look at it again from a global point of view you see that, wow, they not so clear because that very presence of AMD meant that there's a lot more pressure on Intel in terms of its pricing, its profitability, its flexibility and its volumes. All the things that have made it harder for them to A, compete with chips made in Taiwan, let alone build them in the United States and therefore that long term effect of essentially requiring Intel to allow AMD to exist has undermined Intel's position globally and arguably has undermined America's position in the long run. And certainly Intel today is far more vulnerable to an ARM and Invidia to other specialized chips to China, to Taiwan all of these things are going on out there, they're less capable of resisting that than they would've been otherwise. So, you thought we had some real benefits with AMD and lower prices for consumers, but the long term unintended consequences are arguably pretty bad. >> Yeah, that's why we recently wrote in Intel two "Strategic To Fail", we'll see, Okay. now we come to 2022 and there are five companies with anti-trust targets on their backs. Although Microsoft seems to be the least susceptible to US government ironically intervention at this this point, but maybe not and we show "The Cincos Comas Club" in a homage to Russ Hanneman of the show "Silicon Valley" Apple, Microsoft, Google, and Amazon all with trillion dollar plus valuations. But meta briefly crossed that threshold like Mr. Hanneman lost a comma and is now well under that market cap probably around five or 600 million, sorry, billion. But under serious fire nonetheless Dave, people often don't realize the immense monopoly power that IBM had which relatively speaking when measured its percent of industry revenue or profit dwarf that of any company in tech ever, but the industry is much smaller then, no internet, no cloud. Does it call for a different approach this time around? How should we think about these five companies their market power, the implications of government action and maybe what you suggested more narrow action versus broad sweeping changes. >> Yeah, and there's a lot there. I mean, if you go back to the old days IBM had what, 70% of the computer business globally and AT&T had 90% or so of the American telecom market. So market shares that today's players can only dream of. Intel and Microsoft had 90% of the personal computer market. And then you look at today the big five and as wealthy and as incredibly successful as they've been, you sort of have almost the argument that's wrong on the face of it. How can five companies all of which compete with each other to at least some degree, how can they all be monopolies? And the reality is they're not monopolies, they're all oligopolies that are very powerful firms, but none of them have an outright monopoly on anything. There are competitors in all the spaces that they're in and increasing and probably increasingly so. And so, yeah, I think people conflate the extraordinary success of the companies with this belief that therefore they are monopolist and I think they're far less so than those in the past. >> Great, all right, I want to do a quick drill down to cloud computing, it's a key component of digital business infrastructure in his book, "Seeing Digital", Dave Moschella coined a term the matrix or the key which is really referred to the key technology platforms on which people are going to build digital businesses. Dave, we joke you should have called it the metaverse you were way ahead of your time. But I want to look at this ETR chart, we show spending momentum or net score on the vertical access market share or pervasiveness in the dataset on the horizontal axis. We show this view a lot, we put a dotted line at the 40% mark which indicates highly elevated spending. And you can sort of see Microsoft in the upper right, it's so far up to the right it's hidden behind the January 22 and AWS is right there. Those two dominate the cloud far ahead of the pack including Google Cloud. Microsoft and to a lesser extent AWS they dominate in a lot of other businesses, productivity, collaboration, database, security, video conferencing. MarTech with LinkedIn PC software et cetera, et cetera, Googles or alphabets of business of course is ads and we don't have similar spending data on Apple and Facebook, but we know these companies dominate their respective business. But just to give you a sense of the magnitude of these companies, here's some financial data that's worth looking at briefly. The table ranks companies by market cap in trillions that's the second column and everyone in the club, but meta and each has revenue well over a hundred billion dollars, Amazon approaching half a trillion dollars in revenue. The operating income and cash positions are just mind boggling and the cash equivalents are comparable or well above the revenues of highly successful tech companies like Cisco, Dell, HPE, Oracle, and Salesforce. They're extremely profitable from an operating income standpoint with the clear exception of Amazon and we'll come back to that in a moment and we show the revenue multiples in the last column, Apple, Microsoft, and Google, just insane. Dave, there are other equally important metrics, CapX is one which kind of sets the stage for future scale and there are other measures. >> Yeah, including our research and development where those companies are spending hundreds of billions of dollars over the years. And I think it's easy to look at those numbers and just say, this doesn't seem right, how can any companies have so much and spend so much? But if you think of what they're actually doing, those companies are building out the digital infrastructure of essentially the entire world. And I remember once meeting some folks at Google, and they said, beyond AI, beyond Search, beyond Android, beyond all the specific things we do, the biggest thing we're actually doing is building a physical infrastructure that can deliver search results on any topic in microseconds and the physical capacity they built costs those sorts of money. And when people start saying, well, we should have lots and lots of smaller companies well, that sounds good, yeah, it's all right, but where are those companies going to get the money to build out what needs to be built out? And every country in the world is trying to build out its digital infrastructure and some are going to do it much better than others. >> I want to just come back to that chart on Amazon for a bit, notice their comparatively tiny operating profit as a percentage of revenue, Amazon is like Bezos giant lifestyle business, it's really never been that profitable like most retail. However, there's one other financial data point around Amazon's business that we want to share and this chart here shows Amazon's operating profit in the blue bars and AWS's in the orange. And the gray line is the percentage of Amazon's overall operating profit that comes from AWS. That's the right most access, so last quarter we were well over a hundred percent underscoring the power of AWS and the horrendous margins in retail. But AWS is essentially funding Amazon's entrance into new markets, whether it's grocery or movies, Bezos moves into space. Dave, a while back you collaborated with us and we asked our audience, what could disrupt Amazon? And we came up with your detailed help, a number of scenarios as shown here. And we asked the audience to rate the likelihood of each scenario in terms of its likelihood of disrupting Amazon with a 10 being highly likely on average the score was six with complacency, arrogance, blindness, you know, self-inflicted wounds really taking the top spot with 6.5. So Dave is breaking up Amazon the right formula in your view, why or why not? >> Yeah, there's a couple of things there. The first is sort of the irony that when people in the sort of regulatory world talk about the power of Amazon, they almost always talk about their power in consumer markets, whether it's books or retail or impact on malls or main street shops or whatever and as you say that they make very little money doing that. The interest people almost never look at the big cloud battle between Amazon, Microsoft and lesser extent Google, Alibaba others, even though that's where they're by far highest market share and pricing power and all those things are. So the regulatory focus is sort of weird, but you know, the consumer stuff obviously gets more appeal to the general public. But that survey you referred to me was interesting because one of the challenges I sort of sent myself I was like okay, well, if I'm going to say that IBM case, AT&T case, Microsoft's case in all those situations the market was the one that actually minimized the power of those firms and therefore the antitrust stuff wasn't really necessary. Well, how true is that going to be again, just cause it's been true in the past doesn't mean it's true now. So what are the possible scenarios over the 2020s that might make it all happen again? And so each of those were sort of questions that we put out to others, but the ones that to me by far are the most likely I mean, they have the traditional one of company cultures sort of getting fat and happy and all, that's always the case, but the more specific ones, first of all by far I think is China. You know, Amazon retail is a low margin business. It would be vulnerable if it didn't have the cloud profits behind it, but imagine a year from now two years from now trade tensions with China get worse and Christmas comes along and China just says, well, you know, American consumers if you want that new exercise bike or that new shoes or clothing, well, anything that we make well, actually that's not available on Amazon right now, but you can get that from Alibaba. And maybe in America that's a little more farfetched, but in many countries all over the world it's not farfetched at all. And so the retail divisions vulnerability to China just seems pretty obvious. Another possible disruption, Amazon has spent billions and billions with their warehouses and their robots and their automated inventory systems and all the efficiencies that they've done there, but you could argue that maybe someday that's not really necessary that you have Search which finds where a good is made and a logistical system that picks that up and delivers it to customers and why do you need all those warehouses anyways? So those are probably the two top one, but there are others. I mean, a lot of retailers as they get stronger online, maybe they start pulling back some of the premium products from Amazon and Amazon takes their cut of whatever 30% or so people might want to keep more of that in house. You see some of that going on today. So the idea that the Amazon is in vulnerable disruption is probably is wrong and as part of the work that I'm doing, as part of stuff that I do with Dave and SiliconANGLE is how's that true for the others too? What are the scenarios for Google or Apple or Microsoft and the scenarios are all there. And so, will these companies be disrupted as they have in the past? Well, you can't say for sure, but the scenarios are certainly plausible and I certainly wouldn't bet against it and that's what history tells us. And it could easily happen once again and therefore, the antitrust should at least be cautionary and humble and realize that maybe they don't need to act as much as they think. >> Yeah, now, one of the things that you mentioned in your piece was felt like narrow remedies, were more logical. So you're not arguing for totally Les Affaire you're pushing for remedies that are more targeted in scope. And while the EU just yesterday announced new rules to limit the power of tech companies and we showed the article, some comments here the regulators they took the social media to announce a victory and they had a press conference. I know you watched that it was sort of a back slapping fest. The comments however, that we've sort of listed here are mixed, some people applauded, but we saw many comments that were, hey, this is a horrible idea, this was rushed together. And these are going to result as you say in unintended consequences, but this is serious stuff they're talking about applying would appear to be to your point or your prescription more narrowly defined restrictions although a lot of them to any company with a market cap of more than 75 billion Euro or turnover of more than 77.5 billion Euro which is a lot of companies and imposing huge penalties for violations up to 20% of annual revenue for repeat offenders, wow. So again, you've taken a brief look at these developments, you watched the press conference, what do you make of this? This is an application of more narrow restrictions, but in your quick assessment did they get it right? >> Yeah, let's break that down a little bit, start a little bit of history again and then get to Europe because although big sweeping breakups of the type that were proposed for IBM, Microsoft and all weren't necessary that doesn't mean that the government didn't do some useful things because they did. In the case of IBM government forces in Europe and America basically required IBM to make it easier for companies to make peripherals type drives, disc drives, printers that worked with IBM mainframes. They made them un-bundle their software pricing that made it easier for database companies and others to sell their of products. With AT&T it was the government that required AT&T to actually allow other phones to connect to the network, something they argued at the time would destroy security or whatever that it was the government that required them to allow MCI the long distance carrier to connect to the AT network for local deliveries. And with that Microsoft and Intel the government required them to at least treat their suppliers more even handly in terms of pricing and policies and support and such things. So the lessons out there is the big stuff wasn't really necessary, but the little stuff actually helped a lot and I think you can see the scenarios and argue in the piece that there's little stuff that can be done today in all the cases for the big five, there are things that you might want to consider the companies aren't saints they take advantage of their power, they use it in ways that sometimes can be reigned in and make for better off overall. And so that's how it brings us to the European piece of it. And to me, the European piece is much more the bad scenario of doing too much than the wiser course of trying to be narrow and specific. What they've basically done is they have a whole long list of narrow things that they're all trying to do at once. So they want Amazon not to be able to share data about its selling partners and they want Apple to open up their app store and they don't want people Google to be able to share data across its different services, Android, Search, Mail or whatever. And they don't want Facebook to be able to, they want to force Facebook to open up to other messaging services. And they want to do all these things for all the big companies all of which are American, and they want to do all that starting next year. And to me that looks like a scenario of a lot of difficult problems done quickly all of which might have some value if done really, really well, but all of which have all kinds of risks for the unintended consequence we've talked before and therefore they seem to me being too much too soon and the sort of problems we've seen in the past and frankly to really say that, I mean, the Europeans would never have done this to the companies if they're European firms, they're doing this because they're all American firms and the sort of frustration of Americans dominance of the European tech industry has always been there going back to IBM, Microsoft, Intel, and all of them. But it's particularly strong now because the tech business is so big. And so I think the politics of this at a time where we're supposedly all this great unity of America and NATO and Europe in regards to Ukraine, having the Europeans essentially go after the most important American industry brings in the geopolitics in I think an unavoidable way. And I would think the story is going to get pretty tense over the next year or so and as you say, the Europeans think that they're taking massive actions, they think they're doing the right thing. They think this is the natural follow on to the GDPR stuff and even a bigger version of that and they think they have more to come and they see themselves as the people taming big tech not just within Europe, but for the world and absent any other rules that they may pull that off. I mean, GDPR has indeed spread despite all of its flaws. So the European thing which it doesn't necessarily get huge attention here in America is certainly getting attention around the world and I would think it would get more, even more going forward. >> And the caution there is US public policy makers, maybe they can provide, they will provide a tailwind maybe it's a blind spot for them and it could be a template like you say, just like GDPR. Okay, Dave, we got to leave it there. Thanks for coming on the program today, always appreciate your insight and your views, thank you. >> Hey, thanks a lot, Dave. >> All right, don't forget these episodes are all available as podcast, wherever you listen. All you got to do is search, "Breaking Analysis Podcast". Check out ETR website, etr.ai. We publish every week on wikibon.com and siliconangle.com. And you can email me david.vellante@siliconangle.com or DM me @davevellante. Comment on my LinkedIn post. This is Dave Vellante for Dave Michelle for "theCUBE Insights" powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (slow tempo music)

>>we'll go back to the cubes. Coverage of the age of US Executive Summit at Davis. Reinvent made possible by Accenture My name is Dave Volunteer. We're gonna talk about the arm nation advantage, embraced the future of productivity, improve speed quality and customer experience through artificial intelligence. And we herewith Bhaskar goes, Who's the chief strategy Officer X censure in Rajendra RP Prasad is the senior managing director in Global Automation. The Accenture guys walk into the Cube. Get to seal. >>Thank you. >>Hey, congratulations on the new book. I know it's like giving birth, but it's a mini version. If the well, the automation advantage embraced a future of productivity, improve speed, quality and customer experience to artificial intelligence. What inspired you to write this book? Can you tell us a little bit more about it and how businesses are going to be able to take advantage of the information that's in there? Maybe you could start, >>so I think you know, if we say that what inspired as primarily the two things really style, you know, over inspired have to start this project in first of all is the technology change step change in the technology. Second is the mile maturity of the buyer maturity of the market when it's a little more, you know, when I talk about the technology change, automation is nothing new in the industry. In the starting from the Industrial Revolution, always, industry adopted the automation. But last few years would happen. That there is a significant change in the technology in terms of not of new technologies are coming together like cloud data, artificial intelligence, machine learning and they are gearing match you, and that created a huge opportunity in the industry. So that is number one second if fighting the maturity of the buyer. So buyers are always buying automation, adopting the automation. So when I talked to this different by a different industrial wire, suddenly we realise they're not asking about workings automation, how that will help. But primarily they're talking about how they can scaling. They have all have done the pilot, the prototype, how they can take the full advantage in their enterprise through scheme and talking to few client few of our clients, and he realised that it's best to write this boat and film all our clients to take advantage of this new technologies to skill up their business. If I give a little more than inside that one, exactly we are trying to do in this boat primarily, we dealt with three things. One is the individual automation which deals with the human efficiency. Second is the industrial automation who visited a group efficiency. And third is the intelligent automation. We deal city business, official efficiency while business value. So we believe that this is what will really change their business and help our client help the automation. It users to really make clear an impact in their business. >>Yeah, And so you talked about that? The maturity of the customer. And and I like the way you should describe that spectrum ending with intelligent automation. So the point is you not just paving the cow path, if you will, automating processes that maybe were invented decades ago. You're really trying to rethink the best approach. And that's where you going to get the most business value, our peace In thinking about the maturity, I think the a pre pandemic people were maybe a little reluctant s Bhaskar was saying maybe needed some education. But But how? If things change me, obviously the penned Emmick has had a huge impact. It's accelerated things, but but what's changed in the business environment? In terms of the need to implement automation? R. P >>thank you Well, that is an excellent question. As even through the pandemic, most of the enterprises accelerated what I call as the digital transformation, technology transformation and the war all time that it takes to do. The transformation is compressed in our most land prices. Now do compress transformation. The core of it is innovation and innovation, led technology and technology based solutions. To drive this transformation automation. Artificial intelligence becomes hot of what we do while we are implementing this accelerators. Innovation enablers within the enterprises, most of the enterprises prior to the pandemic we're looking automation and I as a solution for cost efficiency. Saving cost in DePina deriving capacity efficiency does if they do the transformation when we press the fast forward but draw the transformation journey liberating automation. What happens is most of the enterprises which the focus from cost efficiency to speed to market application availability and system resiliency at the core. When I speaking to most of the sea woes Corrine Wall in the tech transformation they have now embrace automation and air as a Conan able to bribe this journeys towards, you know, growth, innovation, lead application, availability and transformation and sustainability of the applications through the are A book addresses all of these aspects, including the most important element of which is compute storeys and the enablement that it can accomplish through cloud transformation, cloud computing services and how I I and Michelle learning take log technologies can in a benefit from transformation to the block. In addition, we also heard person talk about automation in the cloud zero automation taking journey towards the cloud on automation Once you're in the clouds, water the philosophy and principles he should be following to drive the motivation. We also provide holy holistic approach to dry automation by focusing process technology that includes talent and change management and also addressing automation culture for the organisations in the way they work as they go forward. >>You mentioned a couple things computing, storage and when we look at our surveys, guys is it is interesting to see em, especially since the pandemic, four items have popped up where all the spending momentum is cloud province reasons scale and in resource and, you know, be able the report to remotely containers because a lot of people have work loads on Prem that they just can automatically move in the company, want to do development in the cloud and maybe connect to some of those on from work clothes. R P A. Which is underscores automation in, of course, and R. P. You mentioned a computing storage and, of course, the other pieces. Data's We have always data, but so my question is, how has the cloud and eight of us specifically influenced changes in automation? In a >>brilliant question and brilliant point, I say no winner. I talked to my clients. One of the things that I always says, Yeah, I I is nothing but y for the data that is the of the data. So that date of place underlying a very critical part of applying intelligence, artificial intelligence and I in the organization's right as the organisation move along their automation journey. Like you said, promoting process automation to contain a realisation to establishing data, building the data cubes and managing the massive data leveraging cloud and how Yebda please can help in a significant way to help the data stratification Dana Enablement data analysis and not data clustering classification All aspects of the what we need to do within the between the data space that helps for the Lord scale automation effort, the cloud and and ablest place a significant role to help accelerate and enable the data part. Once you do that, building mission learning models on the top of it liberating containers clusters develops techniques to drive, you know the principles on the top of it is very makes it easier to drive that on foster enablement advancement through cloud technologists. Alternatively, using automation itself to come enable the cloud transformation data transformation data migration aspects to manage the complexity, speed and scale is very important. The book stresses the very importance of fuelling the motion of the entire organisation to agility, embracing new development methods like automation in the cloud develops Davis a cop's and the importance of oral cloud adoptions that bills the foundational elements of, you know, making sure you're automation and air capabilities are established in a way that it is scalable and sustainable within the organisations as they move forward, >>Right? Thank you for that r p vast crime want to come back to this notion of maturity and and just quite automation. So Andy Jossy made the phrase undifferentiated, heavy lifting popular. But that was largely last decade. Apply to it. And now we're talking about deeper business integration. And so you know, automation certainly is solves the problem of Okay, I can take Monday and cast like provisioning storage in compute and automate that great. But what is some of the business problems, that deeper business integration that we're solving through things? And I want to use the phrase they used earlier intelligent automation? What is that? Can you give an example? >>Let's a very good question as we said, that the automation is a journey, you know, if we talk to any blind, so everybody wants to use data and artificial intelligence to transform their business, so that is very simple. But the point is that you cannot reach their anti unless you follow the steps. So in our book, we have explained that the process that means you know, we defined in a five steps. We said that everybody has to follow the foundation, which is primarily tools driven optimise, which is process drivel. An official see improvement, which is primarily are driven. Then comes predictive capability, the organisation, which is data driven, and then intelligence, which is primarily artificial intelligence driven. Now, when I talked about the use of artificial intelligence and this new intelligent in the business, what the what I mean is basically improved decision making in every level in the organisation and give the example. We have given multiple example in this, both in a very simple example, if I take suppose, a financial secretary organisation, they're selling wealth management product to the client, so they have a number of management product, and they have number of their number of clients a different profile. But now what is happening? This artificial intelligence is helping their agents to target the night product for the night customers. So then, at the success rate is very high. So that is a change that is a change in the way they do business. Now some of the platform companies like Amazon on Netflix. He will see that this this killed is a very native skill for them. They used the artificial intelligence try to use everywhere, but there a lot of other companies who are trying to adopt this killed today. Their fundamental problem is they do not have the right data. They do not have the capability. They do not have all the processes so that they can inject the decision making artificial intelligence capability in every decision making to empower their workforce. And that is what we have written in this book. To provide the guidance to this in this book. How they can use the better business decision improved the create, the more business value using artificial intelligence and intelligent automation. >>Interesting. Bhaskar are gonna stay with you, you know, in their book in the middle of last decade, Erik Brynjolfsson and Andy McAfee wrote the second Machine Age, and they made a point in the book that machines have always replaced humans in instead of various tasks. But for the first time ever, we're seeing machines replacing human in cognitive task that scares a lot of people so hardy you inspire employees to embrace the change that automation can bring. What what are you seeing is the best ways to do that? >>This is a very good question. The intelligent automation implementation is not, Iet Project is primarily change management. It's primarily change in the culture, the people in the organisation into embrace this change and how they will get empowered with the machine. It is not about the replacing people by machine, which has happened historically into the earlier stages of automation, which I explained. But in this intelligent automation, it is basically empowering people to do the better. Dwelled the example. That is the thing we have written in the book about about a newspaper, 100 years old newspaper in Italy. And you know, this industry has gone through multiple automation and changes black and white printing, printing to digital. Everything happened. And now what is happening? They're using artificial intelligence, so they're writers are using those technologies to write faster. So when they are writing immediately, they're getting supported with the later they're supporting with the related article they are supporting with this script, even they're supported to the heading of this article. So the question is that it is not replacing the news, you know, the content writer, but is basically empowering them so that they can produce the better quality of product they can, better writing in a faster time. So is very different approach and that is why is, um, needs a change management and it's a cultural change. >>Garden R P What's it for me? Why should we read the automation advantage? Maybe you can talk about some of the key takeaways and, you know, maybe the best places to start on an automation journey. >>Very will cut the fastest MP, Newer automation journey and Claude Adoption Journey is to start simple and start right if you know what's have free one of the process, Guru says, If you don't know where you are on a map, a map won't help you, so to start right, a company needs to know where they are on a map today, identify the right focus areas, create a clear roadmap and then move forward with the structured approach for successful our option. The other important element is if you automate an inefficient process, we are going to make your inefficiency run more efficiently. So it is very important to baseline, and then I established the baseline and know very or on the journey map. This is one of the key teams we discuss in the Automation Advantis book, with principles and tips and real world examples on how to approach each of these stages. We also stress the importance of building the right architecture is for intelligent automation, cloud enablement, security at the core of automation and the platform centric approach. Leading enterprises can fade out adopters and Iraq, whether they are in the early stages of the automation, journey or surrender advanced stage the formation journey. They can look at the automation advantage book and build and take the best practises and and what is provided as a practical tips within the book to drive there. Automation journey. This also includes importance of having right partners in the cloud space, like a loveliest who can accelerate automation, journey and making sure accompanies cloud migration. Strategy includes automation, automation, lead, yea and data as part of their journey. Management. >>That's great. Good advice there. Bring us home. Maybe you can wrap it up with the final final world. >>So, lefty, keep it very simple. This book will help you to create difference in your business with the power of automation and artificial intelligence. >>That's a simple message and will governor what industry you're in? There is a disruptions scenario for your industry and that disruption scenarios going to involve automation, so you better get ahead of editor game. They're The book is available, of course, at amazon dot com. You can get more information. X censure dot com slash automation advantage. Gosh, thanks so much for coming in the Cube. Really appreciate your time. >>Thank you. Thank >>you. >>Eh? Thank you for watching this episode of the eight of US Executive Summit of reinvent made possible by Accenture. Keep it right there for more discussions that educating spy inspire You're watching the queue.

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 five publicly traded u.s based companies have market valuations over or just near a trillion dollars as of october 29th apple and microsoft topped the list each with 2.5 trillion followed by alphabet at 2 trillion amazon at 1.7 and facebook now meta at just under a trillion off from a tie of 1.1 trillion prior to its recent troubles these companies have reached extraordinary levels of success and power what if anything could disrupt their market dominance in his book seeing digital author david micheller made three key points that i want to call out first in the technology industry disruptions of the norm the waves of mainframes minis pcs mobile and the internet all saw new companies emerge and power structures that dwarfed previous eras of innovation is that dynamic changing second every industry has a disruption scenario not just the technology industry and third silicon valley broadly defined to include seattle or at least amazon has a dual disruption agenda the first being horizontally disrupting the technology industry and the second as digital disruptors in virtually any industry how relevant is that to the future power structure of the digital industry generally in amazon specifically hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome in author speaker researcher and thought leader david michela to assess what could possibly disrupt today's trillionaire companies and we're going to start with amazon dave good to see you welcome thanks dave good to see you yeah so dave approached us about a month or so ago he was working on these disruption scenarios and we agreed to make this a community research project where we're going to tap the knowledge of the cube crowd and its adjacent communities and to that end we're initiating a community survey that asks folks to rate the likelihood of seven plus one disruption scenarios so we have a slide here that sort of shows what that survey structure is going to look like and so as i say there's seven plus another one which is kind of an open open-ended and we're going to start with amazon as the disruptee so dave you've been writing about the technology industry for decades and digital disruption and china and automation and hundreds of other topics what prompted you to start this project yeah it's a great question you know as you said that the whole history of our business has been you know every decade or so you have a new set of leaders ibm digital microsoft the internet companies etc but when i started looking at it you know that seems in some ways to have actually stopped that you know microsoft is now 40 years old amazon is what 1995 is getting towards 30. you know google's been a dominant company for 20 years and you know apple of course and facebook more recently so so whatever reason this sort of longevity of these firms has been longer than we've seen in the past so i sort of say well is there anything that's going to change that so part of it and we'll get into it is what could happen to disrupt those big five but then the sort of second question was well maybe the uh disruptive energies of the of the tech business have moved elsewhere they've moved to crypto currencies or they've moved to tesla and so you start to sort of broaden your sense of disruption and when you talked about that dual disruption agenda that whole ability of tech to disrupt other sectors banking health care insurance automobiles whatever is sort of a second wave of disruption so uh we started coming out all right what sort of scenarios are we really looking at over say for the 2020s what might shake up the big five as we know them and how might disruption spread to sort of more industry specific parts of the world and that was really the the genesis of the project and really just my own thinking of all right what scenarios can i come up with and then reaching out to companies like yourselves to figure out okay how can we get more input on that how can we crowdsource it how can we get a sense of of what the community thinks of all this it's great love it and as you know we're very open to do that so we're going to crowdsource this we're going to open it up to virtually anyone and use multiple channels so let's go through some of the scenarios all of them actually and explain the reasoning behind their inclusion the first one the govern government mandated separation divestment and or limits on amazon's cloud computing retail media credit card and or in-house product groups it probably no coincidence that this was the first one you chose today but why start here well i think the government interest in doing something to get back at big tech is is pretty clear and probably one of the few things that has bipartisan support in washington these days and also government interventions have always been an enormous part of the tech industry's history the the antitrust efforts against ibm and att in particular and more recently microsoft a smaller one but it's it's always been there there's a vibe to do it now and when you look at all the big ones but particularly amazon you can see that potential divestments and breakups are sitting there right in front of you the separation of retail and aws uh perhaps breaking out credit card or music or media businesses these sorts of things are all on the surface at least relatively clean things to do and i think when you look at the formation of an alphabet or a meta those companies themselves are starting to see their own businesses as consisting of multiple firms yeah so i just want to kind of drill into the cloud piece just to emphasize the importance of aws in the context of amazon amazon announced earnings thursday night after the close aws is now a 64 billion revenue run rate company and they're growing at 39 percent year over year that's actually an accelerated growth rate from q3 2020 when the company was grew at 29 it's astounding think about a company this size moreover aws accounted for more than actually but 100 of amazon's operating profit last quarter so the aws cloud is obviously crucial as a funding vehicle and ecosystem accelerant for amazon and i just wanted to share some data points dave before we move on to these other scenarios yeah and just on that uh i think that is the fundamental point it's very easy to see aws on its own as a powerhouse but i think you know if you figure how much freedom aws money has given the retail business or the credit card business or the music businesses to launch themselves and to essentially make no money for very long periods of time uh you see that you know if you're a walmart trying to compete with amazon as a retailer well that money from aws is is an awful big problem and and so when they look at separation that's the sort of stuff people talk about right so i just want to i want to put that into context just in in terms of the the cloud business so this chart is one from our etr surveys that isolates the four hyperscale cloud providers and adds in oracle and ibm we both own public clouds but don't you know don't have nearly the the scale we don't have apple or facebook they have clouds as well and we can talk about that in a moment but the chart shows net score or spending momentum on the vertical axis and market share or pervasiveness in the survey on the horizontal axis it's it's really mentioned share not dollar market share but it's an indicator and the red line is an indicator of elevated spending momentum and you can see azure and aws they're up and to the right i mean amazon is 64 billion you know uh azure will claim larger because they're including their application business but just their their their i asked business obviously smaller than amazon's but you can see in the survey the respondents define cloud they include that sas business so they they both impressively have this high spending momentum on the vertical axis well above that 40 line despite their size google obviously well behind those to the left and then alibaba which has a small sample in the etr survey it's you know it's not as prominent in china but even though it's ias cloud businesses larger than google's by probably a couple billion dollars now the point is these four hyperscalers and there really are only four in my view anyway they have a presence that allows them to build new businesses and disrupt ecosystems and enact that dual disruption agenda should they choose to do so at least in the case of amazon oracle and ibm are not in a position to do that it's not part of their agenda they don't they don't have that scale but dave can you talk about your dual disruption scenario very clearly amazon fits in there and i would think alibaba as well but what about microsoft facebook apple google yeah i mean you know people often say what's the biggest difference between microsoft and amazon from from a cloud point of view and the answer is pretty clear that microsoft goes out of its way to assure its customers that it really doesn't have any interest in competing directly about them so you don't see microsoft going into the retail business or the banking business or the healthcare business all that seriously in contrast that's really what amazon is all about is taking its capabilities to essentially any industry it likes and therefore as one is as great as the service aws provides it's often being provided to people who amazon is actually competing with at least some degree or another and you know that's a huge part of microsoft's sales pitch and it's certainly a potential vulnerability down the road uh it's very hard in the end to be an essential supplier and a direct competitor at the same time but so far they've managed to do that yeah so we put together just another sort of aside here this little thought experiment to see what aws would look like as a separate entity and so it's a chart that looks at a number of tech companies and lays out their revenue run rate the growth rates gross margin probably should have done operating margin might have been more relevant but market cap and revenue multiple again given the size of aws at 64 billion run rate and accelerating growth trajectory it's just it's remarkable and so we we figured this out based on industry norms and today's valuations it's not inconceivable that aws could be you know in the trillionaire club or close to it so based on that discussion we had earlier amazon amazon's dual disruption agenda being funded by empowered by aws as we just discussed dave yeah and just keep in mind nothing that you or i are saying are predictions or saying that anything is going to happen they are possible scenarios of what might happen that seem to make some plausible sense so that when amazon is making the sort of profits that it's making aws naturally that's going to attract other companies because there's margin to to be had there and similarly you know look at uh you look at microsoft for all those years the profits it made in windows or in office software allowed it to do all kinds of other things and essentially that's what amazon is doing today but if a google or a microsoft could cut into those profits through some sort of aggressive pricing and perhaps we'll talk about that you know that would have a lot of impact on amazon as a whole all right so let's quickly go through the other description scenarios and maybe make some comments the next one sort of major companies increasingly choose to do their own cloud computing and or sell their products directly for competitive cost security or other reasons so dave i saw this and look at a company like walmart and others no way they're going to run their business on aws walmart as we know is building out its own cloud and maybe it doesn't have the size of a hyperscaler but it's very large it's got the technical chops it can most likely do it a lot cheaper than renting cloud space what was your thinking in this scenario yeah the broader thing here is essentially one of that computing paradigms have been proven to go in cycles you know a long time ago people shared computers and called timeshare and then people ran their own and now they're sharing again through the cloud and who knows it's possible that the cycle could shift again through some innovation and you know a lot of companies today look at the bills they're getting for cloud or for various sas services and some of them are pretty high and a lot of them will look at and say hey maybe we actually can do some of this stuff cheaper so the scenario is that essentially the the cycle shifts once again uh and it makes more sense to do stuff in-house again that's not a prediction but uh certainly something that's happened before and couldn't plausibly happen again yeah there's a lot of discussion about that in the industry of martine casado and sarah wong wrote that piece about the you know the trillion dollar basically sucking sound basically saying the the scenario was the the the premise rather was the that that sas companies their cost of goods sold are increasingly going to be you know chewed up by cloud costs and then of course mark andreessen says every company is going to be a sas company so as the sassification of business occurs that's something to consider okay next scenario is environmental policies raise costs change packaging delivery recycling rules and or consumer preferences can you comment dave on your thinking on this scenario yeah first i'll just back up a bit we're used to thinking of technology is the great disrupter and clearly that's still important but there are now other forces out there china which will talk about uh the environment uh various cultural forces and and here with the environment you see all kinds of things that could change that you know if you look at amazon and its model of very high levels of packaging lots of delivery vehicles and all the things it is doing are those necessarily the best environmentally and will there potentially be various taxes carbon metrics or things that might work against that model and tend to favor more traditional stores where people go to pick them up that seems to be a plausible scenario and i think everybody here knows that desire to do something in the in the climate environmental spaces is pretty strong and you know if you look at you know just throws aside the recycling industry itself has arguably been quite a failure in that much of what is so-called recycled is basically put in tankers and shipped to the third world which no longer wants it uh and so the backlog of packaging and concerns about packaging and uh what to do with all that you know those those issues are rising and and will be real and i i don't know whether amazon has a good answer to that they're you know they obviously are very aware of it they're working very hard to do everything they can in that space but their fundamental model of essentially packaging every good in its own little box or envelope or whatever is arguably not the greenest way of doing business got it thank you so okay so the next one is price in slash trade wars with the u.s and or china cloud and e-commerce giant so protectionism favors national players so we talking here about for example google bombing prices or alibaba or trade policy making it difficult for amazon to do business in certain parts of the world can you add some color on this one yeah all those things and i would just start with with china itself you know you could argue that covet has been the biggest disruptor of the last couple years but if you look out the next five or eight you had to look at all these things you'd probably say china the size of the chinese market the power of its vendors players like alibaba clearly can rival amazon in many different ways uh you know it's no secret that it'd be hard for amazon to they're not going to be a big success in china uh but you can see it in harder ways that you imagine across asia or other markets where alibaba is strong and you're in today's sort of environment where there's scarce goods and maybe certain products well maybe they go chinese may probably go to alibaba first and you want to buy that product well amazon doesn't have it but alibaba has it you know those sort of scenarios if you get into a sharp trade war with china or even if the current tensions continue it's quite easy to see how that could uh play some havoc with amazon's supply chains in many ways the whole amazon retail model is based on a steady flow of goods manufactured in china and that clearly is not as stable as it was right got it the next one actually caught my attention and this is a big part of the reason why we want to survey the community to see how plausible folks think this is in its its technology related scenario so that would potentially disrupt aws and by fault by default hit amazon so that's major computing innovations such as quantum edge machine machine would obsolete today's cloud architectures okay so so here what you're thinking just as aws changed the game in i.t some future innovations or new business models that we haven't conceived yet could disrupt the prevailing cloud computing model right yeah absolutely i mean you know again we'll go back to where we started that new technologies have always been the main disruptors and here we're looking at some potentially very powerful uh new technologies you know your guess is good in mind about what's gonna happen with quantum is clearly a very different way of computing quite possibly led by other vendors possibly even led by china which would be a huge issue you look at the cloud well cloud's not very good at sort of edge stuff or machine to a machine stuff or sort of near field things out cars in the highway talking to each other uh you know again amazon's totally aware of these things and they are working on it but they have a huge investment in other ways of doing things and historically that inertia that need to protect existing bases of activity and practices has made it difficult for a lot of companies to adjust to new things and so that could happen again uh and there's certainly a puzzle but yeah in all these cases so far amazon has been aware of it is trying to do it but you can still see the scenario playing out and in a truly disruptive technology it's not always possible for the incumbent to effectively cope with it okay the next scenario speaks to i think some of the work that you've done in automation and related areas software replaces centralized warehouses as delivery services are directly connected to suppliers and factories so dave this is like cut out the middle man right software and automation changes the nature of the route absolutely i mean you know in a world of ubiquitous delivery services and product standardization metrics and products being built and shipped from all over the world the concept of running them all through a centralized warehouse is at least at a minimum uh seems like something that might be uh obsoleted and replaced and you know imagine if google built a significant taxonomy of of core products that could be traced directly to where they are either manufactured supplied or brought into the country from whatever company that tries to sell them and the delivery service connected directly to that uh and so that model has always been out there i think at various times people have looked at it it hasn't happened so far and i think amazon itself is is is looking at this particularly as it gets more into food that the idea of shipping all fresh food any sort of centralized warehouse is a pretty bad idea uh and so you know that model of software essentially replacing giant automated warehouses uh is out there and and seems to me uh likely and i just say that you know alibaba for the record doesn't really use that warehouse model it uses a network of suppliers and does it that way and and there do seem to be uh some efficiencies that would likely come with that the next one is was really interesting from a historian's perspective and it's the penultimate uh scenario and that's the proverbial self-inflicted wound and you and i certainly remember ibm's you know fateful decision to outsource the microprocessor and operating system to intel and and and and and microsoft sorry ibm's decision to do that lotus you might recall it refused to allow 123 to run on windows back in the day novell buying word perfect jim barksdale a lot of young people the audience won't of course remember this but jim barksdale poo-pooing microsoft's decision to bundle internet explorer into the operating system all those were kind of self-inflicted or blind spots so this one is complacency arrogance blindness abuse of power loss of trust so much more than the examples i gave consumer and or employee backlash you're seeing some of that at facebook now and i guess this is taking their eye off the customer ball losing the day zero in amazon's case forgetting that customer obsession formula they're working backwards culture and i think this is a big reason why andy jassy was put in charge so this wouldn't happen but we've seen time and time again as the examples i just gave blind spots have absolutely killed companies haven't they dave absolutely he listed many of the most famous but perhaps my favorite of all was kennels and the founder of digital equipment corporation one of the great tech visionaries of his time who stated over and over again why would anybody want a home computer or eunuch's snake oil was his other beautiful all of those things and and so there's the blindness uh there's the area ibm who just came to the view that they and att both came to the view that they were invincible and nothing could ever crack their control of their customer base so we've seen all that i think uh more recently i think some of these things can actually go from the bottom up and you know what's happening to facebook today well they're being hurt by former employees speaking out uh you know this never really happened too much to in the ibm and t days but people calling into question amazon's work labor practices and such things is certainly a possible scenario and the whole sort of you know in the end you know people talk about a cultural backlash against technology i'm not sure i believe it'll happen but it certainly is possible that people will start to rebel against these firms you see it more likely with facebook is fairly well along there uh amazon's still popular but you know in the end and as you i think you said the the core thing that companies routinely fail on is they lose their customer focus and they get caught up in other things their financial numbers their their power inside their position of their company but they they lose track of staying close to the customer has need and terrific job of staying close to the customers over the years uh so if anyone you know was maybe less vulnerable that they they would be well along that that line but it can happen to anyone and new management is often you know one of the real tests and there's many examples of that through history when a new executive comes in will they have that same focus that same thing particularly you know as the first generation's employees get wealthy and retired in a new set of people come in you know you look at microsoft the new people who came in well they're not going to be multi-millionaires they may have missed the great runs they're there to work and and the culture of companies changes when you get to that state the m is not that there yet but you can envision that comings soon enough so you know cultural issues have always been a factor and it's hard to imagine there won't be some sort of factor going forward well and you know you talk about that the the succession of founders and ceos i mean that's what to me makes microsoft so astounding because during the bomber years it was unclear that they were ever going to become relevant again and so nadella has done a masterful job but of course they had the margins from the pc software business that allowed them to buy that time but look at intel and the troubles it's going through uh and so many other examples of companies that just sort of said all right well we're going to pack it in and either sell the company or which is again what i think makes think companies like oracle and dell which you know founder-led ceos not ceo in the case of oracle but still running the business uh so quite uh significant yeah yeah and you know we've talked a lot about things that might hurt answers but you gotta recognize how in many ways how amazing they are and most tech companies a lot of them anyways have essentially been one trick ponies i mean google still makes overwhelming amount of its money selling ads and the things it's tried to do in cars and healthcare and various things you know they've often struggled you know apple still makes the core of its money around it's it's cell phone platform amazon's one of the few that continually generates entirely new huge businesses and and you have to give them an enormous amount of credit for that you know microsoft uh was a they failed repeatedly over and over again with internet stuff and phone stuff and all these things and it really wasn't until you know satya came in and really focused on their customers and their need for enterprise services that he that he really got the company on the right track so you know amazon has always been good listeners customers and if they continue to do so it bodes well but history says other stuff comes along okay and the last scenario is open-ended dave included uh you know what did we miss is there another scenario that we haven't put forth that you could feel it could be disruptive to amazon right i mean you've got to have the at least what'd we miss yeah i mean you know these are things that me and you and i just sort of made up the top of our head these are things we see that that might happen but you know in your huge audience of people in this community every day i'm sure there are other people out there who have thoughts of what might shake things up or even doing things that might shake things up already uh and you know one of the things you do for you guys is get this sort of material out there and and see what ideas surface so hopefully people will uh participate in this and we'll see what comes out of it all right so what happens from here is we're going to publish the the link to the survey in this video description and in our posts we ask you to take the survey please tell your friends we're going to publish the results as always we do in an open and free david michelle thanks so much for putting your brain power on this and collaborating with us i'm really excited to see the results and and and run through the other giants with you as well once we see what this survey says yeah thanks david great and yeah if we can make this one work be fun to do it for for google and microsoft and facebook and apple and see where it all goes thanks a lot all right okay that's it for today remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcast i publish each week on wikibon.com and siliconangle.com etr.plus is where all the cool survey data lives they just dropped their october survey with some great findings so do check that out you can reach me on twitter at d velante he's at d michelle or comment on my linkedin post or email me at david.vellante at siliconangle.com this is dave vellante for dave michelle thanks for watching thecube insights powered by etr be well and we'll see you next time

>>From the Bellagio hotel in Las Vegas. It's the cube covering UI path forward for brought to you by UI path. >>Good morning. Welcome to the cubes coverage of UI path forward for day two. Live from the Bellagio in Las Vegas. I'm Lisa Martin with Dave Velante, Dave. We had a great action packed day yesterday. We're going to have another action packed day today. We've got the CEO coming on. We've got customers coming on, but there's been a lot in the news last 24 hours. Facebook, what are your thoughts? >>Yeah, so wall street journal today, headline Facebook hearing fuels call for rain in on big tech. All right, everybody's going after big tech. Uh, for those of you who missed it, 60 minutes had a, uh, an interview with the whistleblower. Her name is, uh, Francis Haugen. She's very credible, just a little background. I'll give you my take. I mean, she was hired to help set Facebook straight and protect privacy of individuals, of children. And I really feel like, again, she, she didn't come across as, as bitter or antagonistic, but, but I feel as though she feels betrayed, right, I think she was hired to do a job. They lured her in to say, Hey, this is again, just my take to say, Hey, we want your help in earnest to protect the privacy of our users, our citizens, et cetera. And I think she feels betrayed because she's now saying, listen, this is not cool. >>You hired us to do a job. We in earnest, went in and tried to solve this problem. And you guys kind of ignored it and you put profit ahead of safety. And I think that is the fundamental crux of this. Now she made a number of really good points in her hearing yesterday and I'll, and we'll try to summarize, I mean, there's a lot of putting advertising revenue ahead of children's safety and, and, and others. The examples they're using are during the 2020 election, they shut down any sort of negative conversations. They would be really proactive about that, but after the election, they turned it back on and you know, we all know what happened on January 6th. So there's sort of, you know, the senators are trying that night. Um, the second thing is she talked about Facebook as a wall garden, and she made the point yesterday at the congressional hearings that Google actually, you can data scientists, anybody can go download all the data that Google has on you. >>You and I can do that. Right? There's that website that we've gone to and you look at all the data Google has and you kind of freak out. Yeah, you can't do that with Facebook, right? It's all hidden. So it's kind of this big black box. I will say this it's interesting. The calls for breaking up big tech, Bernie Sanders tweeted something out yesterday said that, uh, mark Zuckerberg was worth, I don't know. I think 9 billion in 2007 or eight or nine, whatever it was. And he's worth 122 billion today, which of course is mostly tied up in Facebook stock, but still he's got incredible wealth. And then Bernie went on his red it's time to break up big tech. It's time to get people to pay their fair share, et cetera. I'm intrigued that the senators don't have as much vigilance around other industries, whether it's big pharma, food companies addicting children to sugar and the like, but that doesn't let Facebook. >>No, it doesn't, but, but you ha you bring up a good point. You and I were chatting about this yesterday. What the whistleblower is identifying is scary. It's dangerous. And the vast majority, I think of its users, don't understand it. They're not aware of it. Um, and why is big tech being maybe singled out and use as an example here, when, to your point, you know, the addiction to sugar and other things are, uh, have very serious implications. Why is big tech being singled out here as the poster child for what's going wrong? >>Well, and they're comparing it to big tobacco, which is the last thing you want to be compared to as big tobacco. But the, but the, but the comparison is, is valid in that her claim, the whistleblower's claim was that Facebook had data and research that it knew, it knows it's hurting, you know, you know, young people. And so what did it do? It created, you know, Instagram for kids, uh, or it had 600,000. She had another really interesting comment or maybe one of the senators did. Facebook said, look, we scan our records and you know, kids lie. And we, uh, we kicked 600,000 kids off the network recently who were underaged. And the point was made if you have 600,000 people on your network that are underage, you have to go kill. That's a problem. Right? So now the flip side of this, again, trying to be balanced is Facebook shut down Donald Trump and his nonsense, uh, and basically took him off the platform. >>They kind of thwarted all the hunter Biden stuff, right. So, you know, they did do some, they did. It's not like they didn't take any actions. Uh, and now they're up, you know, in front of the senators getting hammered. But I think the Zuckerberg brings a lot of this on himself because he put out an Instagram he's on his yacht, he's drinking, he's having fun. It's like he doesn't care. And he, you know, who knows, he probably doesn't. She also made the point that he owns an inordinate percentage and controls an inordinate percentage of the stock, I think 52% or 53%. So he can kind of do what he wants. And I guess, you know, coming back to public policy, there's a lot of narrative of, I get the billionaires and I get that, you know, the Mo I'm all for billionaires paying more taxes. >>But if you look at the tax policies that's coming out of the house of representatives, it really doesn't hit the billionaires the way billionaires can. We kind of know the way that they protect their wealth is they don't sell and they take out low interest loans that aren't taxed. And so if you look at the tax policies that are coming out, they're really not going after the billionaires. It's a lot of rhetoric. I like to deal in facts. And so I think, I think there's, there's a lot of disingenuous discourse going on right now at the same time, you know, Facebook, they gotta, they gotta figure it out. They have to really do a better job and become more transparent, or they are going to get broken up. And I think that's a big risk to the, to their franchise and maybe Zuckerberg doesn't care. Maybe he just wants to give it a, give it to the government, say, Hey, are you guys are on? It >>Happens. What do you think would happen with Amazon, Google, apple, some of the other big giants. >>That's a really good question. And I think if you look at the history of the us government, in terms of ant anti monopolistic practices, it spent decade plus going after IBM, you know, at the end of the day and at the same thing with Microsoft at the end of the day, and those are pretty big, you know, high profiles. And then you look at, at T and T the breakup of at T and T if you take IBM, IBM and Microsoft, they were slowed down by the U S government. No question I've in particular had his hands shackled, but it was ultimately their own mistakes that caused their problems. IBM misunderstood. The PC market. It gave its monopoly to Intel and Microsoft, Microsoft for its part. You know, it was hugging windows. They tried to do the windows phone to try to jam windows into everything. >>And then, you know, open source came and, you know, the world woke up and said, oh, there's this internet that's built on Linux. You know, that kind of moderated by at T and T was broken up. And then they were the baby bells, and then they all got absorbed. And now you have, you know, all this big, giant telcos and cable companies. So the history of the U S government in terms of adjudicating monopolistic behavior has not been great at the same time. You know, if companies are breaking the law, they have to be held accountable. I think in the case of Amazon and Google and apple, they, a lot of lawyers and they'll fight it. You look at what China's doing. They just cut right to the chase and they say, don't go to the, they don't litigate. They just say, this is what we're doing. >>Big tech, you can't do a, B and C. We're going to fund a bunch of small startups to go compete. So that's an interesting model. I was talking to John Chambers about this and he said, you know, he was flat out that the Western way is the right way. And I believe in, you know, democracy and so forth. But I think if, to answer your question, I think they'll, they'll slow it down in courts. And I think at some point somebody's going to figure out a way to disrupt these big companies. They always do, you know, >>You're right. They always do >>Right. I mean, you know, the other thing John Chambers points out is that he used to be at 1 28, working for Wang. There is no guarantee that the past is prologue that because you succeeded in the past, you're going to succeed in the future. So, so that's kind of the Facebook break up big tech. I'd like to see a little bit more discussion around, you know, things like food companies and the, like >>You bring up a great point about that, that they're equally harmful in different ways. And yet they're not getting the visibility that a Facebook is getting. And maybe that's because of the number of users that it has worldwide and how many people depend on it for communication, especially in the last 18 months when it was one of the few channels we had to connect and engage >>Well. And, and the whistleblower's point, Facebook puts out this marketing narrative that, Hey, look at all this good we're doing in reality. They're all about the, the, the advertising profits. But you know, I'm not sure what laws they're breaking. They're a public company. They're, they're, they have a responsibility to shareholders. So that's, you know, to be continued. The other big news is, and the headline is banks challenge, apple pay over fees for transactions, right? In 2014, when apple came up with apple pay, all the banks lined up, oh, they had FOMO. They didn't want to miss out on this. So they signed up. Now. They don't like the fact that they have to pay apple fees. They don't like the fact that apple introduced its own credit card. They don't like the fact that they have to pay fees on monthly recurring charges on your, you know, your iTunes. >>And so we talked about this and we talk about it a lot on the cube is that, that in, in, in, in his book, seeing digital David, Michelle, or the author talked about Silicon valley broadly defined. So he's including Seattle, Microsoft, but more so Amazon, et cetera, has a dual disruption agenda. They're not only trying to disrupt horizontally the technology industry, but they're also disrupting industry. We talked about this yesterday, apple and finances. The example here, Amazon, who was a bookseller got into cloud and is in grocery and is doing content. And you're seeing these a large companies, traverse industry value chains, which have historically been very insulated right from that type of competition. And it's all because of digital and data. So it's a very, pretty fascinating trends going on. >>Well, from a financial services perspective, we've been seeing the unbundling of the banks for a while. You know, the big guys with B of A's, those folks are clearly concerned about the smaller, well, I'll say the smaller FinTech disruptors for one, but, but the non FinTech folks, the apples of the world, for example, who aren't in that industry who are now to your point, disrupting horizontally and now going after individual specific industries, ultimately I think as consumers we want, whatever is going to make our lives easier. Um, do you ever, ever, I always kind of scratch my nose when somebody doesn't take apple pay, I'm like, you don't take apple pay so easy. It's so easy to make this easy for me. >>Yeah. Yeah. So it's, it's going to be really interesting to see how this plays out. I, I do think, um, you know, it begs the question when will banks or Willbanks lose control of the payment systems. They seem to be doing that already with, with alternative forms of payment, uh, whether it's PayPal or Stripe or apple pay. And then crypto is, uh, with, with, with decentralized finance is a whole nother topic of disruption and innovation, >>Right? Well, these big legacy institutions, these organizations, and we've spoke with some of them yesterday, we're going to be speaking with some of them today. They need to be able to be agile, to transform. They have to have the right culture in order to do that. That's the big one. They have to be willing. I think an open to partner with the broader ecosystem to unlock more opportunities. If they want to be competitive and retain the trust of the clients that they've had for so long. >>I think every industry has a digital disruption scenario. We used to always use the, don't get Uber prized example Uber's coming on today, right? And, and there isn't an industry, whether it's manufacturing or retail or healthcare or, or government that isn't going to get disrupted by digital. And I think the unique piece of this is it's it's data, data, putting data at the core. That's what the big internet giants have done. That's what we're hearing. All these incumbents try to do is to put data. We heard this from Coca-Cola yesterday, we're putting data at the core of our company and what we're enabling through automation and other activities, uh, digital, you know, a company. And so, you know, can these, can these giants, these hundred plus year old giants compete? I think they can because they don't have to invent AI. They can work with companies like UI path and embed AI into their business and focused on, on what they do best. Now, of course, Google and Amazon and Facebook and Microsoft there may be going to have the best AI in the world. But I think ultimately all these companies are on a giant collision course, but the market is so huge that I think there's a lot of, >>There's a tremendous amount of opportunity. I think one of the things that was exciting about talking to one, the female CIO of Coca-Cola yesterday, a hundred plus old organization, and she came in with a very transformative, very different mindset. So when you see these, I always appreciate when I say legacy institutions like Coca-Cola or Merck who was on yesterday, blue cross blue shield who's on today, embracing change, cultural change going. We can't do things the way we used to do, because there are competitors in that review mirror who are smaller, they're more nimble, they're faster. They're going to be, they're going to take our customers away from us. We have to deliver this exceptional customer and employee experience. And Coca-Cola is a great example of one that really came in with CA brought in a disruptor in order to align digital with the CEO's thoughts and processes and organization. These are >>Highly capable companies. We heard from the head of finance at, at applied materials today. He was also coming on. I was quite, I mean, this is a applied materials is really strong company. They're talking about a 20 plus billion dollar company with $120 billion market cap. They supply semiconductor equipment and they're a critical component of the semiconductor supply chain. And we all know what's going on in semiconductors today with a huge shortage. So they're a really important company, but I was impressed with, uh, their finance leaders vision on how they're transforming the company. And it was not like, you know, 10 years out, these were not like aspirational goals. This is like 20, 19, 20, 22. Right. And, and really taking costs out of the business, driving new innovation. And, and it's, it was it's, it's refreshing to me Lisa, to see CFOs, you know, typically just bottom line finance focused on these industry transformations. Now, of course, at the end of the day, it's all about the bottom line, but they see technology as a way to get there. In fact, he put technology right in the middle of his stack. I want to ask him about that too. I actually want to challenge him a little bit on it because he had that big Hadoop elephant in the middle and this as an elephant in the room. And that picture, >>The strategy though, that applied materials had, it was very well thought out, but it was also to your point designed to create outcomes year upon year upon year. And I was looking at some of the notes. I took that in year one, alone, 274 automations in production. That's a lot, 150,000 in annual work hours automated 124 use cases they tackled in one year. >>So I want to, I want to poke at that a little bit too. And I, and I did yesterday with some guests. I feel like, well, let's see. So, um, I believe it was, uh, I forget what guests it was, but she said we don't put anything forward that doesn't hit the income statement. Do you remember that? Yes, it was Chevron because that was pushing her. I'm like, well, you're not firing people. Right. And we saw from IDC data today, only 13% of organizations are saying, or, or, or the organizations at 13% of the value was from reduction in force. And a lot of that was probably in plan anyway, and they just maybe accelerated it. So they're not getting rid of headcount, but they're counting hours saved. So that says to me, there's gotta be an normally or often CFOs say, well, it's that soft dollars because we're redeploying folks. But she said, no, it hits the income statement. So I don't, I want to push a little bit and see how they connect the dots, because if you're going to save hours, you're going to apply people to new work. And so either they're generating revenue or cutting costs somewhere. So, so there's another layer that I want to appeal to understand how that hits the income state. >>Let's talk about some of that IDC data. They announced a new white paper this morning sponsored by UI path. And I want to get your perspectives on some of the stats that they talked about. They were painting a positive picture, an optimistic picture. You know, we can't talk about automation without talking about the fear of job loss. They've been in a very optimistic picture for the actual gains over a few year period. What are your thoughts about that? Especially when we saw that stat 41% slowed hiring. >>Yeah. So, well, first of all, it's a sponsored study. So, you know, and of course the conferences, so it's going to be, be positive, but I will say this about IDC. IDC is a company I would put, you know, forest they're similar. They do sponsored research and they're credible. They don't, they, they have the answer to their audience, so they can't just out garbage. And so it has to be defensible. So I give them credit there that they won't just take whatever the vendor wants them to write and then write it. I've used to work there. And I, and I know the culture and there's a great deal of pride in being able to defend what you do. And if the answer doesn't come out, right, sorry, this is the answer. You know, you could pay a kill fee or I dunno how they handle it today. >>But, but, so my point is I think, and I know the people who did that study, many of them, and I think they're pretty credible. I, I thought by the way, you, to your 41% point. So the, the stat was 13% are gonna reduce head count, right? And then there were two in the middle and then 41% are gonna reduce or defer hiring in the future. And this to me, ties into the Erik Brynjolfsson and, and, and, uh, and, and McAfee work. Andy McAfee work from MIT who said, look, initially actually made back up. They said, look at machines, have always replaced humans. Historically this was in their book, the second machine age and what they said was, but for the first time in history, machines are replacing humans with cognitive functions. And this is sort of, we've never seen this before. It's okay. That's cool. >>And their, their research suggests that near term, this is going to be a negative economic impact, sorry, negative impact on jobs and salaries. And we've, we've generally seen this, the average salary, uh, up until recently has been flat in the United States for years and somewhere in the mid fifties. But longterm, their research shows that, and this is consistent. I think with IDC that it's going to help hiring, right? There's going to be a boost buddy, a net job creator. And there's a, there's a, there's a chasm you've got across, which is education training and skill skillsets, which Brynjolfsson and McAfee focused on things that humans can do that machines can't. And you have this long list and they revisited every year. Like they used to be robots. Couldn't walk upstairs. Well, you see robots upstairs all the time now, but it's empathy, it's creativity. It's things like that. >>Contact that humans are, are much better at than machines, uh, even, even negotiations. And, and so, so that's, those are skills. I don't know where you get those skills. Do you teach those and, you know, MBA class or, you know, there's these. So their point is there needs to be a new thought process around education, public policy, and the like, and, and look at it. You can't protect the past from the future, right? This is inevitable. And we've seen this in terms of economic activity around the world countries that try to protect, you know, a hundred percent employment and don't let competition, they tend to fall behind competitively. You know, the U S is, is not of that category. It's an open market. So I think this is inevitable. >>So a lot about upskilling yesterday, and the number of we talked with PWC about, for example, about what they're doing and a big focus on upscaling. And that was part of the IDC data that was shared this morning. For example, I'll share a stat. This was a survey of 518 people. 68% of upscaled workers had higher salaries than before. They also shared 57% of upskilled workers had higher roles and their enterprises then before. So some, again, two point it's a sponsored study, so it's going to be positive, but there, there was a lot of discussion of upskilling yesterday and the importance on that education, because to your point, we can't have one without the other. You can't give these people access to these tools and not educate them on how to use it and help them help themselves become more relevant to the organization. Get rid of the mundane tasks and be able to start focusing on more strategic business outcome, impacting processes. >>We talked yesterday about, um, I use the example of, of SAP. You, you couldn't have predicted SAP would have won the ERP wars in the early to mid 1990s, but if you could have figured out who was going to apply ERP to their businesses, you know what, you know, manufacturing companies and these global firms, you could have made a lot of money in the stock market by, by identifying those that were going to do that. And we used to say the same thing about big data, and the reason I'm bringing all this up is, you know, the conversations with PWC, Deloitte and others. This is a huge automation, a huge services opportunity. Now, I think the difference between this and the big data era, which is really driven by Hadoop is it was big data was so complicated and you had a lack of data scientists. >>So you had to hire these services firms to come in and fill those gaps. I think this is an enormous services opportunity with automation, but it's not because the software is hard to get to work. It's all around the organizational processes, rethinking those as people process technology, it's about the people in the process, whereas Hadoop and the big data era, it was all about the tech and they would celebrate, Hey, this stuff works great. There are very few companies really made it through that knothole to dominate as we've seen with the big internet giants. So you're seeing all these big services companies playing in this market because as I often say, they like to eat at the trough. I know it's kind of a pejorative, but it's true. So it's huge, huge market, but I'm more optimistic about the outcomes for a broader audience with automation than I was with, you know, big data slash Hadoop, because I think the software as much, as much more adoptable, easier to use, and you've got the cloud and it's just a whole different ball game. >>That's certainly what we heard yesterday from Chevron about the ease of use and that you should be able to see results and returns very quickly. And that's something too that UI path talks about. And a lot of their marketing materials, they have a 96, 90 7% retention rate. They've done a great job building their existing customers land and expand as we talked about yesterday, a great use case for that, but they've done so by making things easy, but hearing that articulated through the voice of their customers, fantastic validation. >>So, you know, the cube is like a little, it's like a interesting tip of the spirits, like a probe. And I will tell you when I, when we first started doing the cube and the early part of the last decade, there were three companies that stood out. It was Splunk service now and Tableau. And the reason they stood out is because they were able to get customers to talk about how great they were. And the light bulb went off for us. We were like, wow, these are three companies to watch. You know, I would tell all my wall street friends, Hey, watch these companies. Yeah. And now you see, you know, with Frank Slootman at snowflake, the war, the cat's out of the bag, everybody knows it's there. And they're expecting, you know, great things. The stock is so priced to perfection. You could argue, it's overpriced. >>The reason I'm bringing this up is in terms of customer loyalty and affinity and customer love. You're getting it here. Absolutely this ecosystem. And the reason I bring that up is because there's a lot of questions in the, in the event last night, it was walking around. I saw a couple of wall street guys who came up to me and said, Hey, I read your stuff. It was good. Let's, let's chat. And there's a lot of skepticism on, on wall street right now about this company. Right? And to me, that's, that's good news for you. Investors who want to do some research, because the words may be not out. You know, they, they, they gotta prove themselves here. And to me, the proof is in the customer and the lifetime value of that customer. So, you know, again, we don't give stock advice. We, we kind of give fundamental observations, but this stock, I think it's trading just about 50. >>Now. I don't think it's going to go to 30, unless the market just tanks. It could have some, you know, if that happens, okay, everything will go down. But I actually think, even though this is a richly priced stock, I think the future of this company is very bright. Obviously, if they continue to execute and we're going to hear from the CEO, right? People don't know Daniel, Denise, right? They're like, who is this guy? You know, he started this company and he's from Eastern Europe. And we know he's never have run a public company before, so they're not diving all in, you know? And so that to me is something that really pay attention to, >>And we can unpack that with him later today. And we've got some great customers on the program. You mentioned Uber's here. Spotify is here, applied materials. I feel like I'm announcing something on Saturday night. Live Uber's here. Spotify is here. All right, Dave, looking forward to a great action packed today. We're going to dig more into this and let's get going. Shall we let's do it. All right. For David Dante, I'm Lisa Martin. This is the cube live in Las Vegas. At the Bellagio. We are coming to you presenting UI path forward for come back right away. Our first guest comes up in just a second.

>>Welcome back to our coverage of HBs. Green Lake announcement's gonna talk about transformation acceleration, who doesn't wanna go faster as they're transforming, right? Everybody is transforming and they want to go as fast as possible to get time to value keith White is here, he's the senior vice president and general manager of Green Lakes commercial business at HP. Michelle LaU is Green Lake cloud services solutions at HP gents. Welcome. Good to see you >>awesome to be here. Thanks so much. Great to be here. >>Dave keith, we've we've been talking virtually for >>quite some time now. >>Q three earnings beaten raise uh focusing on, you know, some real momentum uh want to understand where it's coming from. A r I've said it's headed toward a billion, I think you said 700 million was where you were at last quarter, 1100 customers, orders were up 46%,, Last quarter revenue up over 30%. Where's the momentum >>coming from? No, it's fantastic. And I think what you're seeing is, you know, the world is hybrid. So in essence customers are looking for that solution that says, hey, mere my public cloud with my on premise scenario and give me that hybrid solution and we're just seeing just tremendous momentum and interest across a variety of workloads across a variety of vertical solutions and frankly we're seeing customers basically uh lean in on really running their business on HP. Green Lake, so you know, we had a pretty exciting announcement with the s a a couple weeks back, $2 billion deal, um but again, this shows the value of what Green Lake and the on prem requirements are high level of security, high level of capability? They're doing analytics on all the data that's out there. I mean this is the number one intelligence agency in the world. Right? So super excited about that and it just validates our strategy and validates where we're going. Um the other thing that's really exciting is we're seeing a lot of customers with this whole S. A. P migration, right? Um so ongc, one of the largest oil and gas companies in India, I want to say it's one of the top five S. A. P. Implementations in the world has chosen. Green Lake is their opportunity as well, huge retailers like wal wars. Uh so worldwide we're seeing tremendous momentum. >>That's great. Congratulations on the momentum. I know you're not done uh Michelle new role for you. Awesome. Um when we covered uh discover this year in the cube, we talked about sort of new workload solutions that you guys had. Uh S. A. P. As keith was just mentioning Ml Ops V. D. I. A number of of those workloads that you were really focused on the solution side. How's that going? Give us the update there? >>No, it's coming along really well. I mean you highlighted some of the big ones there. I mean the way we are thinking about Green Lake. Right? I mean, you know, we talked about the great momentum that we've had. The question is why are we having that right? Why are missing that momentum in the market? And I think I'll kind of call out a few features of the green platform that's really making it attractive to customers. Right? What is the experience? What we're trying to do is make it a very, very seamless experience for them? Right. Quick provisioning, easy to manage, easy to monitor, kind of an automated solution. Right? So that's kind of a key element of what we're trying to offer performances. Another one. Right? I mean, the end of the day, what we're doing is we are building out our infrastructure stack and the software stack in such a way that is optimized for the performance. Right? I mean, if you take data for example, it's called the right elements to make sure that the analytics can be done in a machine learning algorithms can be run. So those are like, you know, some of the performance, I think it's a great experience is a big factor. Tco right? I mean customers are very, very focused on their cost base. Right? Especially as they are starting to run up the bills in public cloud. They're like, man, this is expensive, I need to start thinking about costs here because costs catch up pretty fast. So that's kind of another element that people are really focused on and I would say the last one being choice. Right? I mean we provide this platform which is open. Alright. So customers can use it if they want to migrate off it, they can migrate off it. We're not locking them in. So those are some of the value propositions that are really resonating in the marketplace and you're seeing that in the numbers that we just talked about. >>So keep speaking of transformation you guys are undergoing obviously a transformation your your cloud company now. Okay, so part of that is the ecosystem. The partners talk about your strategy in that regard, why you're so excited about welcoming the partners into this old Green Lake world, >>you bet and you know I'm a big fan of one plus one equals three. My seven year old daughter tells me that doesn't actually add up correctly but at the same time it's so true with what we're doing and as official just said an open platform that allows partners to really plug in so that we can leverage the power of S. A. P. Or the power of Nutanix. So the power of Citrix at the same time, all of these are solutions that require, you know deep system integration and capabilities to really be customized for that customers environment. So whether that's infosys or accenture or we pro you know that we need we need those partners as well along with our own advisory and professional services to help customers. But at the same time, you know we talked about the fact that this is really about bringing that cloud experience to the on prem world might be a data center but we're seeing a lot of customers get out of the data center management business and move into a Coehlo. And so the fact that we can partner with the ECU annexes and the Cyrus ones of the world really enable a whole new environment so that customers again can run their business and not get caught up with keeping the lights on and managing power and those types of things. And then finally I'll say, look, the channel itself is actually migrating to offer more services to their customers managed service providers, telcos, distance and resellers and now what we're providing them is that platform with which to offer their own manage services to customers in a much more cost effective cloud experience way with all the benefits of being on prem secure latency app integration and that sort of thing. So it's exciting to see the ecosystem really gate Gardner the momentum and really partner with us closely >>follow up on the partner question if I could. So partner services are part of Green Lake. It's a journey, not everything all at once. Uh but so it's essentially as simple as saying, okay, I want that service, that's my choice. Uh you've given them optionality and it's ideally as seamless as it is in HP services, that the direction that you're >>going. That's right, yeah. So the set that api set that Stalin team are building are basically saying, hey, leverage our cost analytics capabilities, leverage our capacity management, leverage the interface so that you can plug into that single control plane. And so they're making it super simple for our partner ecosystem to do that. And what I think is really important is that if you are a partner, you want to basically offer choice to the customer and if the customer decides, hey, I want to use um red hats open shift for the container platform versus rs morale offering, then they can get just as good of a first class offering with respect to that. Someone wants to use Citrix or Nutanix or VM ware for their video solution. They have that choice. And so we want to make sure we're offering customer choice for what's best for their situation, but also making sure that it's fully integrated with what we do. God thank >>You. So we see more software content of the show. I wonder if you could. I mean certainly as morale is a big piece of that. I talked earlier about margins hit record for HPE. Almost 35% gross margins. This course of software is gonna obviously push that further along um, Lighthouses, another one. How should we think about the direction that you're going >>software. Absolutely. So if you think about what we are building out here is a solution, right? This is solution that's very tightly integrated between the infrastructure stack and the soft and this software that enables it. So really there three or four components to the solution day. Right. So think about Lighthouse, which is an infrastructure stack that is optimized for what's going to run on that. Right? If it's a general purpose compute it will the infrastructure will look different. If it's a storage intensive workload, it will look different. If it's a machine learning workloads will look different. Right? So that's kind of the first component and just optimizing it for what's going to run on it. Second is, um, what we call the Green light platform, which is all about managing and orchestrating it. And what we want to do is we want to have a completely automated experience right from from the way you provisioned it to the way you run the workloads to the way you manage it, to the way you monitor it to the way partners link into it. Right to the way in the software vendors kind of sit on top of that. Right. And then we talked about escrow as well as the engine that runs it right from a container platform perspective or we spend some time talking about unified analytics today. Those are the types of data integration that power Green Lake and the last piece of software I would say is as we kind of think about the ecosystem that runs on top of Green Lake, whether it's our software or third party software. Right? They all have a place equal place on top of the green light platform. And we are very focused on building on the ecosystem. Right? So as a customer or an enterprise who wants to use you should have the choice to run you know 40 50 102 105 100 different software packages on top of Green Lake. And it should be all an automated fashion. But we have tested that in advance. There's there's commercials behind that. It becomes a very very self service provision, seamless experience from the customer's perspective. >>Great. Thank you. So keep 2020 was sort of like sometimes called the force marched to digital right? And some some customers they were already there. Uh so there's a majority now that we've been through this awful year and change, customers are kind of rethinking their digital strategies and their transformations that there can be a little bit more planned fel now you know the world didn't end and and you know I. T. Budgets kind of stabilized a bit actually, you know did better than perhaps we thought. So where are we in terms of transformations? What's the business angle? What are you seeing out there? >>Yeah. I mean customers found a lot of holes that they had in their environment because of the pandemic. I think customers are also seeing opportunities to grow pretty aggressively. You know we just announced Patrick terminals, one of the largest shipping companies in south pack and you know that whole shipping craziness that's going on right now they needed a new digital transformation in order to really make sure they could orchestrate their container ships effectively. Even we talked about Woolworth's there now, changing how they deal with their suppliers because of the Green Lake platform that they have. And so what you're seeing is, hey, you know, first phase of digital transformation public cloud was an interesting scenario. Now they're being able to be planned for like you said and say, where's the best place for me to run this for the latency required with that data, for the choice that we have from an I. S. V. Standpoint, you know, for the on prem capabilities of what we're trying to do from a security standpoint etcetera. So the nice thing is we've seen it move from, you know, hey, we're just trying to get the basic things modernized into truly modernizing data centers, monetizing the data that I have and continuing to transform that environment for their customers, partners, employees and products >>kind of a left field question a bit off topic, but certainly related edge. You guys talk about edge a lot. Hybrid is clear. I think in people's minds you've got an on prem you're connecting to a cloud maybe across clouds? Is edge an extension of hybrid or is it today sort of a bespoke opportunity that maybe we'll come back to this new version of cloud, What's happening at the edge >>that you see? Yeah. So let me just uh I mean think of the edge as it's a continuum. Right? The way at least we think about it, it's not data center or the edge. Right. Think of it as, you know, there's a data center, uh there's a hyper scale data center, there's a data center, there's a closet somewhere, right? There's a cola opportunity, Right? And then you're running something in the store. Right? So let's take the example of a retailer. They're running something in the store and what are they running? They're running? Point of service applications or they're running IOT devices. Right. And at some point they have to connect back into the cloud. Right. So we actually have, you know, something to find van capabilities that connect, you know, uh you know, the Edge devices or edge analytics back into the cloud, we actually have a small form factor kubernetes um operating system that runs on the edge. Right. So we think of all of that as kind of a distributed environment in which Edge is one place where the application runs and where the data sites but it needs to be connected back and so we provide the connectivity back, we provide the mechanism by which we run it and then there's a security model, especially around sassy that is emerging on securing that. So that's kind of how we think about it as part of the overall distributed architecture that we are building and that's where the world will be >>another node in the cloud. >>Another note in the distributed world. Exactly >>yeah. I think the other thing to think about with the edges that this is where the majority of your data is actually getting created. Right? You talked about IOT devices, you know, you'll hear from Zen's Act and what they're doing with respect to autonomous driving with vehicles. You know, we talk about folks like ab that are building the factory of the future and robotics as a service in order to be able to really make sure that that precision happens at that at that point. So a ton of data is coming from that. And so again, how do you analyze that? How do you monetize that? How do you make decisions off of it? And it's it's an exciting place for us. So it's great to have all the connectivity we talked >>about last question, maybe both could address it. Uh we've we we used to see this cadence of of products often times in the form of boxes come out from HP and HP. Now we're seeing a cadence of services, we're seeing more capabilities across this, this this this green lake uh state that you guys are building out. What should we expect in the future? What are the kinds of things that we should evaluate you on? >>Well, I'll start and then maybe you can jump in but you know, the reality is we are becoming much deeper partners with our customers right there looking to us to say help me run my data center, help me improve my data and analytics. Help me at the edge so that I can have the most effective scenario. So what you're seeing from us is this flip from hardware provider into deep partnerships with that with the open platform. I'd say the second thing that we're doing is we're helping them fuel that digital transformation because again, they're looking for that hybrid solution. And so now they're saying, hey HP come and showcase all the experience you have from point next from your advisor and professional services and help me understand what other customers are doing so that I can implement that faster, better, cheaper, easier, etcetera. And then from a product standpoint, kind of a ton of great things. >>That's exactly right. I mean uh we are taking a very, very focused customer back view as we are looking at the future of Green Lake. Right. And exactly the way kids said, right, I mean it's all about solving customer problems for us. Some customer problems are still in the data center, some of them are in close, some customer problems are in the edge. So they're all uh fair game for us as we think about, you know, what we are going to be building out and do your point earlier. Dave it's not about, you know, a server or storage is the institutions right. And the solutions have to have integrated hardware, integrated software, staff, integrated services. Right. There are partners who sell that, who service that and all that entire experience from a customer perspective has to be a seamless. Right? And it's just in our cloud platform, we kind of help the customer run it and manage it and we give them kind of the best performance at the lowest cost, which is what they're looking for. So that's kind of what you'll see us. You'll see more of a cadence of these services can come out, but it's all going in that direction in helping customers with new solutions. >>A lot of customer problems out there, which your opportunities and you know, generally the hyper scale as they are good at solutions. They don't, you know, there's not a lot of solution folks like that. That's a that's a wonderful opportunity for you to build on on top of that huge gift, that Capex gift >>at the hyper scholars have given us all. That's right. And we're seeing the momentum happen. So it's exciting. That's cool guys. Hey, thanks a lot for coming to the cube. Yeah, Yeah. All right, >>okay. And thank you for watching keep it right there more action from HP. Es Green Lake announcements, you're watching the cube. Mm. Mm

>> Okay, we continue now with the theme of turning ideas into insights so ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only. And a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real-time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal we heard, or at least semi normal as we begin to better understand and forecast demand and supply imbalances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processings, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz, who's a Cube alum and VP and principal analyst at Forrester, and doin' some groundbreaking work in this area. And Cindy Maike who is the vice president of industry solutions and value management at Cloudera. Welcome to both of you. >> Welcome, thank you. >> Thanks Dave. >> All right Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >> It's really about democratization. If you can't make your data accessible, it's not usable. Nobody's able to understand what's happening in the business and they don't understand what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships with their customers due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and peck around within your ecosystem to find what it is that's important. >> Great thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >> Yeah, there's quite a few. And especially as we look across all the industries that were actually working with customers in. A few that stand out in top of mind for me is one is IQVIA. And what they're doing with real-world evidence and bringing together data across the entire healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making it accessible by both internally, as well as for the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, they're are a European car manufacturer and how do they make sure that they have efficient and effective processes when it comes to fixing equipment and so forth. And then also there's an Indonesian based telecommunications company, Techsomel, who's bringing together over the last five years, all their data about their customers and how do they enhance a customer experience, how do they make information accessible, especially in these pandemic and post pandemic times. Just getting better insights into what customers need and when do they need it? >> Cindy, platform is another core principle. How should we be thinking about data platforms in this day and age? Where do things like hybrid fit in? What's Cloudera's point of view here? >> Platforms are truly an enabler. And data needs to be accessible in many different fashions, and also what's right for the business. When I want it in a cost and efficient and effective manner. So, data resides everywhere, data is developed and it's brought together. So you need to be able to balance both real time, our batch, historical information. It all depends upon what your analytical workloads are and what types of analytical methods you're going to use to drive those business insights. So putting in placing data, landing it, making it accessible, analyzing it, needs to be done in any accessible platform, whether it be a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing being the most successful. >> Great, thank you. Michelle let's move on a little bit and talk about practices and processes, the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >> Yeah, it's a really great question 'cause it's pretty complex when you have to start to connect your data to your business. The first thing to really gravitate towards is what are you trying to do. And what Cindy was describing with those customer examples is that they're all based off of business goals, off of very specific use cases. That helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in near time or real time, or later on, as you're doing your strategic planning. What that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, "Well, can I also measure the outcomes from those processes and using data and using insight? Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my analytic capabilities that are allowing me to be effective in those environments?" But everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it? But coming in more from that business perspective, to then start to be data driven, recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions. And ultimately getting to the point of being insight driven, where you're able to both describe what you want your business to be with your data, using analytics to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize and you can innovate. Because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >> I like how you said near time or real time, because it is a spectrum. And at one end of the spectrum, autonomous vehicles. You've got to make a decision in real time but near real-time, or real-time, it's in the eyes of the beholder if you will. It might be before you lose the customer or before the market changes. So it's really defined on a case by case basis. I wonder Michelle, if you could talk about in working with a number of organizations I see folks, they sometimes get twisted up in understanding the dependencies that technology generally, and the technologies around data specifically can sometimes have on critical business processes. Can you maybe give some guidance as to where customers should start? Where can we find some of the quick wins and high returns? >> It comes first down to how does your business operate? So you're going yo take a look at the business processes and value stream itself. And if you can understand how people, and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process? Or are you collecting information, asking for information that is going to help satisfy a downstream process step or a downstream decision? So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize do you need that real time, near real time, or do you want to start creating greater consistency by bringing all of those signals together in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process, and the decision points, and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >> Got it. Let's, bring Cindy back into the conversation here. Cindy, we often talk about people, process, and technology and the roles they play in creating a data strategy that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? >> Yeah. And that's kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but the fuel behind the process and how do you actually become insight-driven. And you look at the capabilities that you're needing to enable from that business process, that insight process. Your extended ecosystem on how do I make that happen? Partners and picking the right partner is important because a partner is one that actually helps you implement what your decisions are. So looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data within your process that if you need to do it in a real-time fashion, that they can actually meet those needs of the business. And enabling on those process activities. So the ecosystem looking at how you look at your vendors, and fundamentally they need to be that trusted partner. Do they bring those same principles of value, of being insight driven? So they have to have those core values themselves in order to help you as a business person enable those capabilities. >> So Cindy I'm cool with fuel, but it's like super fuel when you talk about data. 'Cause it's not scarce, right? You're never going to run out. (Dave chuckling) So Michelle, let's talk about leadership. Who leads? What does so-called leadership look like in an organization that's insight driven? >> So I think the really interesting thing that is starting to evolve as late is that organizations, enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be. Data driving into the insight or the raw data itself has the ability to set in motion what's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, your CRO coming back and saying, I need better data. I need information that's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come. Not just one month, two months, three months, or a year from now, but in a week or tomorrow. And so that is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity. You have your chief data officer that is shaping the experiences with data and with insight and reconciling what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities. And either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data-driven, but ultimately to be insight driven, you're seeing way more participation and leadership at that C-suite level and just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >> Great, thank you. Let's wrap, and I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. A lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a maturity model. I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on an insight driven organization, Cindy what do you see as the major characteristics that define the differences between sort of the early beginners sort of fat middle, if you will, and then the more advanced constituents? >> Yeah, I'm going to build upon what Michelle was talking about is data as an asset. And I think also being data aware and trying to actually become insight driven. Companies can also have data, and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, you're not going to be insight-driven. So you've got to move beyond that data awareness, where you're looking at data just from an operational reporting. But data's fundamentally driving the decisions that you make as a business. You're using data in real time. You're leveraging data to actually help you make and drive those decisions. So when we use the term you're data-driven, you can't just use the term tongue-in-cheek. It actually means that I'm using the recent, the relevant, and the accuracy of data to actually make the decisions for me, because we're all advancing upon, we're talking about artificial intelligence and so forth being able to do that. If you're just data aware, I would not be embracing on leveraging artificial intelligence. Because that means I probably haven't embedded data into my processes. Yes, data could very well still be a liability in your organization, so how do you actually make it an asset? >> Yeah I think data aware it's like cable ready. (Dave chuckling) So Michelle, maybe you could add to what Cindy just said and maybe add as well any advice that you have around creating and defining a data strategy. >> So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? Bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing it and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset, it has value. But you may not necessarily know what that value is yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action, for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away the gap between when you see it, know it, and then get the most value and really exploit what that is at the time when it's right, so in the moment. We talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. So are we just introducing it as data-driven organizations where we could see spreadsheets and PowerPoint presentations and lots of mapping to help make longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder if I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight, there is none, it's all coming together for the best outcomes. >> Right, it's like people are acting on perfect or near perfect information. Or machines are doing so with a high degree of confidence. Great advice and insights, and thank you both for sharing your thoughts with our audience today, it was great to have you. >> Thank you. >> Thank you. >> Okay, now we're going to go into our industry deep dives. There are six industry breakouts. Financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments. Now each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session of choice. Or for more information, click on the agenda page and take a look to see which session is the best fit for you and then dive in. Join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community, and enjoy the rest of the day. (upbeat music)

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

>>Speed is everything in a hyper competitive climate. The faster we get insights from data and get data products to market. The faster we grow and the more competitive we become, this is Dave Volante from the cutie inviting you to join us on Thursday, August 5th, for cloud areas, industry insights. We'll look at the biggest challenges facing businesses today, especially the need to access and leverage data at an accelerated velocity. You'll hear from industry leaders like Nick Collison, whose cloud era's president, Rob Bearden, the CEO of Cloudera, Michelle Goetz from Forrester. You'll hear from Nvidia and industry experts in insurance, manufacturing, retail, and public sector. Who can address your biggest concerns? Like how do I remove constraints and put data at the core of my business, streaming begins at 9:00 AM Pacific on the Q3 65. You're a leader in global enterprise tech coverage.

>> From around the globe, it's theCUBE, covering Data Citizens '21. Brought to you by Collibra. >> For the past decade organizations have been effecting very deliberate data strategies and investing quite heavily in people, processes and technology, specifically designed to gain insights from data, better serve customers, drive new revenue streams we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing. Data is increasingly distributed making collaboration and governance more challenging, especially where operational use cases are a priority. Hello, everyone. My name is Dave Vellante and you're watching theCUBE coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz who's the vice president and principal analyst at Forrester Research. Hello, Michele. Welcome to theCUBE. >> Hi, Dave. Thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, analyst, the analyst, et cetera, and many data-related functions. And my first question is what are they thinking about today? What's on their minds, these data experts? >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it, and secure, and protect that data? And then how do you ensure that it's hyper contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right. The data and I sort of refer to this as well is sort of out there, it's distributed at the edge. But generally our data organizations are actually quite centralized and as well you talk about the need to trust the data obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business, we're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typical enterprise architects for data, a data engineering team that's managing warehouses as in data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data, and analytics, and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge the goal is that you're actually driving an experience and an application. And so in that case we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that? And there's a team over there to help. So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that. You took a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and my inference is that's changing and I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation, whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary. And you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development. And still incorporating the business expertise from those stakeholders, so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well. Hubris out context in, I mean, that's kind of what's been lacking. And I'm glad you said you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this. I come back to organization because I think a lot of organizations look the most cost effective way for us to serve the business is to have a single data team with hyper specialized roles. That'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business, which as you pointed out has the context is frustrated. They can't get to data. So there's this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. There the use cases where a lot of the manufacturing industrial device. Any sort of IOT based use case really recognized that without applying data and intelligence to whatever process was going to be executed. It was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IOT or edge component to though. We live on our smart phones, we live on our smart watches, we've got our laptops. All of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics but how do you feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate but ensure that there sort of an activation and alignment where the value is going to come out, and still being trusted and governed. >> I got kind of a weird question, but I'm going. I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, hey, we're going to pay up for the price of a vaccine. The cost per dose out, 28 bucks or whatever it was. And he pointed out that the EU haggled big time and they don't want to pay $19. And as a result they're not as far along. Israel understood that the real value was opening up the economy. And so there's an analogy here which I want to come back to my organization and it relates to the DataOps. Is if the real metric is, hey, I have an idea for a data product. How long does it take to go from idea to monetization? That seems to me to be a better KPI than how much storage I have, or how much geometry petabytes I'm managing. So my question is, and it relates to DataOps. Can that DataOps, should that DataOps individual maybe live, and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that and maybe talk a little bit more about this DataOps role. Is it. >> Yeah. >> Fungible. >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data, and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping. A lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter, really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset this DataOps team aligned to solution development, aligned to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately scope the data products that they're delivering to incremental value-based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michelle. I want to come back to this idea of collaboration because over the last decade we've seen attempts, I've seen software come out to try to help the various roles collaborate and some of it's been okay, but you have these hyper specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process. That's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations for the data science teams as well or even if there's machine learning engineers incorporated. And then certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So processes is number one. And number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science-oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage, and almost force you to collaborate and manage through sharing. >> Thank you for that. I want to ask you about the role data governance. You've mentioned trust and that's data quality, and you've got teams that are focused on and specialists focused on data quality. There's the data catalog. Here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is are a lot of value, I would say most is modeling. And in the future, you mentioned edge it's going to be a lot of influencing in real time. And people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate. We talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor intensive. What are you seeing the trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective and governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point, it was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum. You really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules, and business logic, and policies into services that are composable components and bring those into the solutions that you're defining. And in that way what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally driven on executing on governance rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge and context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and we synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging, and glossaries, and governance of data than we had before. And that goes back into what we talked about earlier of like, this is the new wave of DataOps. This is how you bring data products to fruition now. Everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team they know when the data is crap within my CRM, but our data systems are context agnostic generally. And you obviously understand that problem well. But so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run is as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain-centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this, because as you're working within your CRM systems, like a Salesforce, for example you're using an iPaaS MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources. And what's happening there is that the data is being modeled and personalized to whatever view insight our task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift, both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying centralizing data and creating an over-weighted business application and an over, both in terms of the data foundation but also in terms of the types of business services, and status updates, and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michelle, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share, that you're super pumped about? >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation. Really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value and tying it very specifically to processes and metrics, which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights. And as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed. And doing so not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things together is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCUBE. Really awesome perspectives. You're in an exciting space, so appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCUBE. My name is Dave Vellante. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hello. Welcome to today's session of the AWS Startup Showcase, The Next Big Thing in AI, Security & Life Sciences. Today featuring TetraScience for the life sciences track. I'm your host Natalie Erlich, and now we are joined by our special guests, Michelle Bradbury, VP of Product at TetraScience, as well as Mike Tarselli, the Chief Scientific Officer at TetraScience. We're going to talk about the R&D Data Cloud movement in life sciences, unlocking experimental data to accelerate discovery. Thank you both very much for joining us today. >> Thank you for having us. >> Yeah, thank you. Great to be here. >> Well, while traditionally slower to adopt cloud technology in R&D, global pharmas are now launching digital lab initiatives to improve time to market for therapeutics. Now, can you discuss some of the key challenges still facing big pharma in terms of digital transformation? >> Sure. I guess I'll start in. The big pharma sort of organization that we have today happens to work very well in its particular way, i.e., they have some architecture they've installed, usually on-premises. They are sort of tentatively sticking their foot into the cloud. They're learning how to move forward into that, and in order to process and automate their data streams. However, we would argue they haven't done enough fast enough and that they need to get there faster in order to deliver patient value and efficiencies to their businesses. >> Well, how specifically, now for Michelle, can R&D Data Cloud help big pharma in this digital transformation? >> So the big thing that large pharmas face is a couple different things. So the ecosystem within large pharma is a lot of diverse data types, a lot of diverse file types. So that's one thing that the data cloud handles very well to be able to parse through, harmonize, and bring together your data so that it can be leveraged for things like AI and machine learning at large-scale, which is sort of the other part where I think one of the large sort of challenges that pharma faces is sort of a proliferation of data. And what cloud offers, specifically, is a better way to store, more scalable storage, better ability to even tier your storage while still making it searchable, maintainable, and offer a lot of flexibility to the actual pharma companies. >> And what about security and compliance, or even governance? What are those implications? >> Sure. I'll jump into that one. So security and compliance, every large pharma is a regulated industry. Everyone watching this probably is aware of that. And so we therefore have to abide by the same tenets that they would. So 21 CFR Part 11 compliance, getting ready for GXP ready systems, And in fact, doing extra certifications around a SOC 2 Type 2, ISO 9001, really every single regulation that would allow our cloud solution to be quality, ready, inspectable, and really performant for what needs to be done for an eventual FDA submission. >> And can you also speak about some of the advances that we're seeing in machine learning and artificial intelligence, and how that will impact pharma, and what your role is in that at TetraScience? >> Sure. I'll pass this one to Michelle first. >> I was going to say I can take that one. So one of the things that we're seeing in terms of where AI and ML will go with large pharma is their ability to not only search and build models against the data that they have access to right now, which is very limited in the way they search, but the ability to go through the historical amount of data, the ability to leverage mass parallel compute on top of these giant data clusters, and what that means in terms of not only faster time to market for drugs, but also, I think, more accurate and precise testing coming in the future. So I think there's so much opportunity for this really data-rich vertical and industry to leverage in a lot of the modern tooling that it hasn't been able to leverage so far. >> And Mike, what would you say are the benefits that a fully automated lab could bring with increased fairness and data liquidity? >> Yeah, sure. Let's go five years into the future. I am a bench chemist, and I'm trying to get some results in, and it's amazing because I can look up everything the rest of my colleagues have ever done on this particular project with a single click of a button in a simple term set in natural language. I can then find and retrieve those results, easily visualize them in our platform or in any other platform I choose to use. And then I can inspect those, interrogate those, and say, "Actually, I'm going to be able to set up this automation cascade." I'll probably have it ready by the afternoon. All the data that's returned to me through this is going to be easily integratable, harmonized, and you're going to be able to find it, obviously. You're going to interoperate it with any system, so if I suddenly decide that I need to send a report over to another division in their preferred vis tool or data system of choice, great! I click three buttons, configure it. Boom. There goes that report to them. This should be a simple vision to achieve even faster than five years. And that data liquidity that enables you to sort of pass results around outside of your division, and outside of even your sort of company or division, to other who are able to see it should be fairly easy to achieve if all that data is ingested the right way. >> Well, I'd love to ask this next question to both of you. What is your defining contribution to the future of cloud scale? >> Mike, you want to go first? >> (chuckles) I would love to. So right now the pharmaceutical and life sciences companies, they aren't seeing data increase linearly. They're seeing it increase exponentially, right? We are living in the exabyte era, and really have on the internet since about 2016. It's only going to get bigger, and it's going to get bigger in a power law, right? So you're going to see, as sequencing comes on, as larger form microscopy comes on, and as more and more companies are taking on more and more data about each individual sample, retaining that data for longer, doing more analytics of that data, and also doing personalized medicine, right, more data about a specific patient, or animal, or cell line. You're just going to see this absolute data explosion. And because of that, the only thing you can really do to keep up with that is be in the cloud. On-prem, you will be buying disk drives and out of physical materials before you're going to outstrip the data. Michelle? >> Yeah. And, I think, to go along with not just the data storage scale, I think the compute scale. Mike is absolutely right. We're seeing personalized drugs. We're seeing customers that want to, within a matter of three, four hours, get to a personalized drug for patients. And that kind of scale on a compute basis not just requires a ton of data, but requires mass compute ability to be able to get it right, right? And so it really becomes this marriage of getting a huge amount of data, and getting the mass compute to be able to really leverage that per patient. And then the one thing that... Sort of enabling that ecosystem to come centrally together across such a diverse dataset is sort of that driving force. If you can get the data together but you can't compute it, if you can compute it but you can't get it together, it all needs to come together. Otherwise it just doesn't work. >> Yeah. Well, on your website you have all these great case studies, and I'd love it if you could outline some of your success stories for us, some specific, concrete examples. >> Sure. I'll take one first, and then they'll pass to Michelle. One really great concrete example is we were able to take data format processing for a biotech that had basically previously had instruments sitting off in a corner that they could not connect, were integratable for a high throughput screening cascade. We were able to bring them online. We were able to get the datasets interpretable, and get literally their processing time for these screens from the order of weeks to the order of minutes. So they could basically be doing probably a couple hundred more screens per year than they could have otherwise. Michelle? >> We have one customer that is in the process of automating their entire lab, even using robotics arms. So it's a huge mix of being able to ingest IoT data, send experiment data to them, understand sampling, getting the results back, and really automating that whole process, which when they even walked me through it, I was like, "Wow," and I'm like, "so cool." (chuckles) And there's a lot of... I think a lot of pharma companies want, and life science companies, want to move forward in innovation and do really creative and cool things for patients. But at the end of it, you sort of have to also realize it's like their core competency is focusing on drugs, and getting that to market, and making patients better. And we're just one part of that, really helping to enable that process and that ecosystem come to life, so it's really cool to watch. >> Right, right. And I mean, in this last year we've seen how critical the healthcare sector is to people all over the world. Now, looking forward, what do you anticipate some of the big innovations in the sector will be in the next five years, and where do you see TetraScience's role in that? >> So I think some of the larger innovations are... Mike mentioned one of them already. It's going to be sort of the personalized drugs the personalized health care. I think it is absolutely going to go to full lab automation to some degree, because who knows when the next pandemic will hit, right? And we're all going to have to go home, right? I think the days of trying to move around data manually and trying to work through that is just... If we don't plan for that to be a thing of the past, I think we're all going to do ourselves a disservice. So I think you'll see more automation. I think you'll see more personalization, and you'll see more things that leverage larger amounts of data. I think where we hope to sit is really at the ecosystem enablement part of that. We want to remain open. That's one of the cornerstones. We're not a single partner platform. We're not tied to any vendors. We really want to become that central aid and the ecosystem enabler for the labs. >> Yeah, to that point- >> And I'd also love to get your insight. >> Oh! Sorry. (chuckles) Thank you. To that point, we're really trying to unlock discovery, right? Many other horizontal cloud players will do something like you can upload files, or you can do some massive compute, but they won't have the vertical expertise that we do, right? They won't have the actual deep life sciences dedication. We have several PhDs, postdocs, et cetera, on staff who have done this for a living and can do this going forward. So you're going to see the realization of something that was really exciting in sort of 2005, 2006, that is fully automated experimentation. So get a robot to about an experiment, design it, have a human operator assist with putting together all the automation, and then run that over and over again cyclically until you get the result you want. I don't think that the compute was ready for that at the time. I don't think that the resources were up to snuff, but now you can do it, and you can do it with any tool, instrument, technique you want, because to Michelle's point, we're a vendor-agnostic partner networked platform. So you can actually assemble this learning automation cascade and have it run in the background while you go home and sleep. >> Yeah, and we often hear about automation, but tell us a little bit more specifically what is the harmonizing effect of TetraScience? I mean, that's not something that we usually hear, so what's unique about that? >> You want to take that, or you want me to go? >> You go, please. (chuckles) >> All right. So, really, it's about... It's about normalizing and harmonizing the data. And what does that... What that means is that whether you're a chromatography machine from, let's say Waters, or another vendor, ideally you'd like to be able to leverage all of your chromatography data and do research across all of it. Most of our customers have machinery that is of same sort from different customers, or sorry, from different vendors. And so it's really the ability to bring that data together, and sometimes it's even diverse instrumentation. So if I track a molecule, or a project, or a sample through one piece, one set of instrumentation, and I want to see how it got impacted in another set of instrumentation, or what the results were, I'm able to quickly and easily be able to sort of leverage that harmonized data and come to those results quickly. Mike, I'm sure you have a- >> May I offer a metaphor from something outside of science? Hopefully that's not off par for this, but let's say you had a parking lot, right, filled with different kinds of cars. And let's say you said at the beginning of that parking lot, "No, I'm sorry. We only have space right here for a Ford Fusion 2019 black with leather interior and this kind of tires." That would be crazy. You would never put that kind of limitation on who could park in a parking lot. So why do specific proprietary data systems put that kind of limitation on how data can be processed? We want to make it so that any car, any kind of data, can be processed and considered together in that same parking lot. >> Fascinating. Well, thank you both so much for your insights. Really appreciate it. Wonderful to hear about R&D Data Cloud movement in big pharma, and that of course is Michelle Bradbury, VP of Product at TetraScience, as well as Mike Tarselli, the Chief Scientific Officer at TetraScience. Thanks again very much for your insights. I'm your host for theCUBE, Natalie Erlich. Catch us again for the next session of the AWS Startup Session. Thank you. (smooth music)

(upbeat music) >> From theCUBE studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is braking analysis with Dave Vellante. >> Database is the heart of enterprise computing. The market is both exploding and it's evolving. The major force is transforming the space include Cloud and data, of course, but also new workloads, advanced memory and IO capabilities, new processor types, a massive push towards simplicity, new data sharing and governance models, and a spate of venture investment. Snowflake stands out as the gold standard for operational excellence and go to market execution. The company has attracted the attention of customers, investors, and competitors and everyone from entrenched players to upstarts once in the act. Hello everyone and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we'll share our most current thinking on the database marketplace and dig into Snowflake's execution. Some of its challenges and we'll take a look at how others are making moves to solve customer problems and try to get a piece of the growing database pie. Let's look at some of the factors that are driving market momentum. First, customers want lower license costs. They want simplicity. They want to avoid database sprawl. They want to run anywhere and manage new data types. These needs often are divergent and they pull vendors and technologies in different direction. It's really hard for any one platform to accommodate every customer need. The market is large and it's growing. Gardner has it at around 60 to 65 billion with a CAGR of somewhere around 20% over the next five years. But the market, as we know it is being redefined. Traditionally, databases have served two broad use cases, OLTP or transactions and reporting like data warehouses. But a diversity of workloads and new architectures and innovations have given rise to a number of new types of databases to accommodate all these diverse customer needs. Many billions have been spent over the last several years in venture money and it continues to pour in. Let me just give you some examples. Snowflake prior to its IPO, raised around 1.4 billion. Redis Labs has raised more than 1/2 billion dollars so far, Cockroach Labs, more than 350 million, Couchbase, 250 million, SingleStore formerly MemSQL, 238 million, Yellowbrick Data, 173 million. And if you stretch the definition of database a little bit to including low-code or no-code, Airtable has raised more than 600 million. And that's by no means a complete list. Now, why is all this investment happening? Well, in a large part, it's due to the TAM. The TAM is huge and it's growing and it's being redefined. Just how big is this market? Let's take a look at a chart that we've shown previously. We use this chart to Snowflakes TAM, and it focuses mainly on the analytics piece, but we'll use it here to really underscore the market potential. So the actual database TAM is larger than this, we think. Cloud and Cloud-native technologies have changed the way we think about databases. Virtually 100% of the database players that they're are in the market have pivoted to a Cloud first strategy. And many like Snowflake, they're pretty dogmatic and have a Cloud only strategy. Databases has historically been very difficult to manage, they're really sensitive to latency. So that means they require a lot of tuning. Cloud allows you to throw virtually infinite resources on demand and attack performance problems and scale very quickly, minimizing the complexity and tuning nuances. This idea, this layer of data as a service we think of it as a staple of digital transformation. Is this layer that's forming to support things like data sharing across ecosystems and the ability to build data products or data services. It's a fundamental value proposition of Snowflake and one of the most important aspects of its offering. Snowflake tracks a metric called edges, which are external connections in its data Cloud. And it claims that 15% of its total shared connections are edges and that's growing at 33% quarter on quarter. This notion of data sharing is changing the way people think about data. We use terms like data as an asset. This is the language of the 2010s. We don't share our assets with others, do we? No, we protect them, we secure or them, we even hide them. But we absolutely don't want to share those assets but we do want to share our data. I had a conversation recently with Forrester analyst, Michelle Goetz. And we both agreed we're going to scrub data as an asset from our phrasiology. Increasingly, people are looking at sharing as a way to create, as I said, data products or data services, which can be monetized. This is an underpinning of Zhamak Dehghani's concept of a data mesh, make data discoverable, shareable and securely governed so that we can build data products and data services that can be monetized. This is where the TAM just explodes and the market is redefining. And we think is in the hundreds of billions of dollars. Let's talk a little bit about the diversity of offerings in the marketplace. Again, databases used to be either transactional or analytic. The bottom lines and top lines. And this chart here describe those two but the types of databases, you can see the middle of mushrooms, just looking at this list, blockchain is of course a specialized type of database and it's also finding its way into other database platforms. Oracle is notable here. Document databases that support JSON and graph data stores that assist in visualizing data, inference from multiple different sources. That's is one of the ways in which adtech has taken off and been so effective. Key Value stores, log databases that are purpose-built, machine learning to enhance insights, spatial databases to help build the next generation of products, the next automobile, streaming databases to manage real time data flows and time series databases. We might've missed a few, let us know if you think we have, but this is a kind of pretty comprehensive list that is somewhat mind boggling when you think about it. And these unique requirements, they've spawned tons of innovation and companies. Here's a small subset on this logo slide. And this is by no means an exhaustive list, but you have these companies here which have been around forever like Oracle and IBM and Teradata and Microsoft, these are the kind of the tier one relational databases that have matured over the years. And they've got properties like atomicity, consistency, isolation, durability, what's known as ACID properties, ACID compliance. Some others that you may or may not be familiar with, Yellowbrick Data, we talked about them earlier. It's going after the best price, performance and analytics and optimizing to take advantage of both hybrid installations and the latest hardware innovations. SingleStore, as I said, formerly known as MemSQL is a very high end analytics and transaction database, supports mixed workloads, extremely high speeds. We're talking about trillions of rows per second that could be ingested in query. Couchbase with hybrid transactions and analytics, Redis Labs, open source, no SQL doing very well, as is Cockroach with distributed SQL, MariaDB with its managed MySQL, Mongo and document database has a lot of momentum, EDB, which supports open source Postgres. And if you stretch the definition a bit, Splunk, for log database, why not? ChaosSearch, really interesting startup that leaves data in S-3 and is going after simplifying the ELK stack, New Relic, they have a purpose-built database for application performance management and we probably could have even put Workday in the mix as it developed a specialized database for its apps. Of course, we can't forget about SAP with how not trying to pry customers off of Oracle. And then the big three Cloud players, AWS, Microsoft and Google with extremely large portfolios of database offerings. The spectrum of products in this space is very wide, with you've got AWS, which I think we're up to like 16 database offerings, all the way to Oracle, which has like one database to do everything not withstanding MySQL because it owns MySQL got that through the Sun Acquisition. And it recently, it made some innovations there around the heat wave announcement. But essentially Oracle is investing to make its database, Oracle database run any workload. While AWS takes the approach of the right tool for the right job and really focuses on the primitives for each database. A lot of ways to skin a cat in this enormous and strategic market. So let's take a look at the spending data for the names that make it into the ETR survey. Not everybody we just mentioned will be represented because they may not have quite the market presence of the ends in the survey, but ETR that capture a pretty nice mix of players. So this chart here, it's one of the favorite views that we like to share quite often. It shows the database players across the 1500 respondents in the ETR survey this past quarter and it measures their net score. That's spending momentum and is shown on the vertical axis and market share, which is the pervasiveness in the data set is on the horizontal axis. The Snowflake is notable because it's been hovering around 80% net score since the survey started picking them up. Anything above 40%, that red line there, is considered by us to be elevated. Microsoft and AWS, they also stand out because they have both market presence and they have spending velocity with their platforms. Oracle is very large but it doesn't have the spending momentum in the survey because nearly 30% of Oracle installations are spending less, whereas only 22% are spending more. Now as a caution, this survey doesn't measure dollar spent and Oracle will be skewed toward the big customers with big budgets. So you got to consider that caveat when evaluating this data. IBM is in a similar position although its market share is not keeping up with Oracle's. Google, they've got great tech especially with BigQuery and it has elevated momentum. So not a bad spot to be in although I'm sure it would like to be closer to AWS and Microsoft on the horizontal axis, so it's got some work to do there. And some of the others we mentioned earlier, like MemSQL, Couchbase. As shown MemSQL here, they're now SingleStore. Couchbase, Reddis, Mongo, MariaDB, all very solid scores on the vertical axis. Cloudera just announced that it was selling to private equity and that will hopefully give it some time to invest in this platform and get off the quarterly shot clock. MapR was acquired by HPE and it's part of HPE's Ezmeral platform, their data platform which doesn't yet have the market presence in the survey. Now, something that is interesting in looking at in Snowflakes earnings last quarter, is this laser focused on large customers. This is a hallmark of Frank Slootman and Mike Scarpelli who I know they don't have a playbook but they certainly know how to go whale hunting. So this chart isolates the data that we just showed you to the global 1000. Note that both AWS and Snowflake go up higher on the X-axis meaning large customers are spending at a faster rate for these two companies. The previous chart had an end of 161 for Snowflake, and a 77% net score. This chart shows the global 1000, in the end there for Snowflake is 48 accounts and the net score jumps to 85%. We're not going to show it here but when you isolate the ETR data, nice you can just cut it, when you isolate it on the fortune 1000, the end for Snowflake goes to 59 accounts in the data set and Snowflake jumps another 100 basis points in net score. When you cut the data by the fortune 500, the Snowflake N goes to 40 accounts and the net score jumps another 200 basis points to 88%. And when you isolate on the fortune 100 accounts is only 18 there but it's still 18, their net score jumps to 89%, almost 90%. So it's very strong confirmation that there's a proportional relationship between larger accounts and spending momentum in the ETR data set. So Snowflakes large account strategy appears to be working. And because we think Snowflake is sticky, this probably is a good sign for the future. Now we've been talking about net score, it's a key measure in the ETR data set, so we'd like to just quickly remind you what that is and use Snowflake as an example. This wheel chart shows the components of net score, that lime green is new adoptions. 29% of the customers in the ETR dataset that are new to Snowflake. That's pretty impressive. 50% of the customers are spending more, that's the forest green, 20% are flat, that's the gray, and only 1%, the pink, are spending less. And 0% zero or replacing Snowflake, no defections. What you do here to get net scores, you subtract the red from the green and you get a net score of 78%. Which is pretty sick and has been sick as in good sick and has been steady for many, many quarters. So that's how the net score methodology works. And remember, it typically takes Snowflake customers many months like six to nine months to start consuming it's services at the contracted rate. So those 29% new adoptions, they're not going to kick into high gear until next year, so that bodes well for future revenue. Now, it's worth taking a quick snapshot at Snowflakes most recent quarter, there's plenty of stuff out there that you can you can google and get a summary but let's just do a quick rundown. The company's product revenue run rate is now at 856 million they'll surpass $1 billion on a run rate basis this year. The growth is off the charts very high net revenue retention. We've explained that before with Snowflakes consumption pricing model, they have to account for retention differently than what a SaaS company. Snowflake added 27 net new $1 million accounts in the quarter and claims to have more than a hundred now. It also is just getting its act together overseas. Slootman says he's personally going to spend more time in Europe, given his belief, that the market is huge and they can disrupt it and of course he's from the continent. He was born there and lived there and gross margins expanded, do in a large part to renegotiation of its Cloud costs. Welcome back to that in a moment. Snowflake it's also moving from a product led growth company to one that's more focused on core industries. Interestingly media and entertainment is one of the largest along with financial services and it's several others. To me, this is really interesting because Disney's example that Snowflake often puts in front of its customers as a reference. And it seems to me to be a perfect example of using data and analytics to both target customers and also build so-called data products through data sharing. Snowflake has to grow its ecosystem to live up to its lofty expectations and indications are that large SIS are leaning in big time. Deloitte cross the $100 million in deal flow in the quarter. And the balance sheet's looking good. Thank you very much with $5 billion in cash. The snarks are going to focus on the losses, but this is all about growth. This is a growth story. It's about customer acquisition, it's about adoption, it's about loyalty and it's about lifetime value. Now, as I said at the IPO, and I always say this to young people, don't buy a stock at the IPO. There's probably almost always going to be better buying opportunities ahead. I'm not always right about that, but I often am. Here's a chart of Snowflake's performance since IPO. And I have to say, it's held up pretty well. It's trading above its first day close and as predicted there were better opportunities than day one but if you have to make a call from here. I mean, don't take my stock advice, do your research. Snowflake they're priced to perfection. So any disappointment is going to be met with selling. You saw that the day after they beat their earnings last quarter because their guidance in revenue growth,. Wasn't in the triple digits, it sort of moderated down to the 80% range. And they pointed, they pointed to a new storage compression feature that will lower customer costs and consequently, it's going to lower their revenue. I swear, I think that that before earnings calls, Scarpelli sits back he's okay, what kind of creative way can I introduce the dampen enthusiasm for the guidance. Now I'm not saying lower storage costs will translate into lower revenue for a period of time. But look at dropping storage prices, customers are always going to buy more, that's the way the storage market works. And stuff like did allude to that in all fairness. Let me introduce something that people in Silicon Valley are talking about, and that is the Cloud paradox for SaaS companies. And what is that? I was a clubhouse room with Martin Casado of Andreessen when I first heard about this. He wrote an article with Sarah Wang, calling it to question the merits of SaaS companies sticking with Cloud at scale. Now the basic premise is that for startups in early stages of growth, the Cloud is a no brainer for SaaS companies, but at scale, the cost of Cloud, the Cloud bill approaches 50% of the cost of revenue, it becomes an albatross that stifles operating leverage. Their conclusion ended up saying that as much as perhaps as much as the back of the napkin, they admitted that, but perhaps as much as 1/2 a trillion dollars in market cap is being vacuumed away by the hyperscalers that could go to the SaaS providers as cost savings from repatriation. And that Cloud repatriation is an inevitable path for large SaaS companies at scale. I was particularly interested in this as I had recently put on a post on the Cloud repatriation myth. I think in this instance, there's some merit to their conclusions. But I don't think it necessarily bleeds into traditional enterprise settings. But for SaaS companies, maybe service now has it right running their own data centers or maybe a hybrid approach to hedge bets and save money down the road is prudent. What caught my attention in reading through some of the Snowflake docs, like the S-1 in its most recent 10-K were comments regarding long-term purchase commitments and non-cancelable contracts with Cloud companies. And the companies S-1, for example, there was disclosure of $247 million in purchase commitments over a five plus year period. And the company's latest 10-K report, that same line item jumped to 1.8 billion. Now Snowflake is clearly managing these costs as it alluded to when its earnings call. But one has to wonder, at some point, will Snowflake follow the example of say Dropbox which Andreessen used in his blog and start managing its own IT? Or will it stick with the Cloud and negotiate hard? Snowflake certainly has the leverage. It has to be one of Amazon's best partners and customers even though it competes aggressively with Redshift but on the earnings call, CFO Scarpelli said, that Snowflake was working on a new chip technology to dramatically increase performance. What the heck does that mean? Is this Snowflake is not becoming a hardware company? So I going to have to dig into that a little bit and find out what that it means. I'm guessing, it means that it's taking advantage of ARM-based processes like graviton, which many ISVs ar allowing their software to run on that lower cost platform. Or maybe there's some deep dark in the weeds secret going on inside Snowflake, but I doubt it. We're going to leave all that for there for now and keep following this trend. So it's clear just in summary that Snowflake they're the pace setter in this new exciting world of data but there's plenty of room for others. And they still have a lot to prove. For instance, one customer in ETR, CTO round table express skepticism that Snowflake will live up to its hype because its success is going to lead to more competition from well-established established players. This is a common theme you hear it all the time. It's pretty easy to reach that conclusion. But my guess is this the exact type of narrative that fuels Slootman and sucked him back into this game of Thrones. That's it for now, everybody. Remember, these episodes they're all available as podcasts, wherever you listen. All you got to do is search braking analysis podcast and please subscribe to series. Check out ETR his website at etr.plus. We also publish a full report every week on wikinbon.com and siliconangle.com. You can get in touch with me, Email is David.vellante@siliconangle.com. You can DM me at DVelante on Twitter or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week everybody, be well and we'll see you next time. (upbeat music)