(lively music) >> Narrator: Live from Las Vegas, it's the CUBE covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. >> Okay, welcome back everyone. We are here live in Las Vegas, the Mandalay Bay for Oracle's Modern CX conference, hashtag Modern CX. This is the CUBE. I'm John Furrier Silicone Angle. My cost, Peter Burch with us for two days. Our next guest is Ron Corbisier. Owner and CEO of Relationship One. Back again, from last year. It was one of my memorable interviews last year. Welcome back-- >> Ron: Thank you for having me. >> to the cube. We went down and dirty last year. I remember we were having a great conversation about ad tech. If you've taken that video, it's on YouTube and look at it, I guarantee you, it's going to play right into what happened this year. Again, we predicted it. We didn't say AI but we did say we're going to see data really driving. That's what Oracle ended up locking in on daily. >> Yeah, absolutely. Data is going to be the underlying conversation for the next few years. We spoke, a lot, last year about martech stack. Actually, martech and ad tech colliding, coming together. All of that is being fueled by the mass quantities of data that we have as sales and marketing folks out there, to leverage and how do you use it. It's never about, do I have enough data? A lot of times you feel you, almost, have too much. But it's, now how can you use it appropriately? >> We were talking, before we came on camera here about that dynamic of ad tech and marteh collision which we talked about last year. It's interesting. If you just say digital, end-to-end, as a fabric, then you can still talk about these pillars of solutions but they're not silos. If you look at the holistic data approach and say, hey, if we're going to have horizontally scalable data which we want, frictionless less than 150 milliseconds responses they want to promote. You can still do your pillars but be open to data sharing versus here's my silent stack. I do this, I do this, that's shifted and that's what Oracle's main news is here. >> Yeah, absolutely. I think what you're seeing, even in, not only Oracle, that organizational level, people are taking a more holistic view of data that they own and data that they can enrich with external information, right? How does that information, then, fuel all of these other areas within customer experience within the CX world? How do you use that to provide better service? How do you use that information to optimize your sales efforts and from a marketing standpoint, obviously, my background, it's how do we leverage that to optimize our spend, optimize our communication, our orchestration, all of those pieces. It all comes down to that common language of data that we have access to. >> Tell me about the real time aspect cause we teased on it last time and we did talk about how to leverage some of the advertising opportunities and the role of data in real time. That's been a message here from batch to real time. The consumer's in motion all the time depending upon their context. How does real time fit into this? >> Yeah, this is the evolution of what we're seeing in the technology, right? Historically, you've built a campaign. You've, maybe, created some type of journey or persona. You're building content around very specific elements within a life cycle structure. Life cycles are not linear any longer. They never really were but they're, definitely, not now and you have to adapt very quickly. Leverage technology, to say, one of my saying, communicating and what channel but in more in a real time thing. You have to look at what was the last thing that individual did, the activity, all of that. Historically, you haven't had that depth or degree of real time lists. It's been more of a structured candance. That doesn't exist, right? That's not going to exist going forward. That's where things like AI which I always hesitate to use that term because it's the buzz word now of today. But tools that are more of that augmentation of how we do things. Leveraging the power of technology. That's going to change how we orchestrate things and how we communicate. >> I'm just looking at your tweet here. I want to bring this up because you mentioned AI and we were talking about it. Thanks to all who stopped by my MME 17 Modern Marketing Experience 17. A little bit of a jab at the messaging that's cool like that. Session on artificial intelligence. Loved all the support from my fellow modern marketers. What do you mean by that? You make a bold statement. Did you have courage? Did you stand tall? Did you call out AI? What was the conversation there? >> Well I called out the silliness of the term AI. I picked on that the marketers but I picked on the term We, as marketers, I call them the squirell moments that, as marketers, we're on to the next thing. I reviewed the past eight some years of these conferences and what were the topics, right? There were some topics that were transformational topics like how does marketing automation or organizational change or those type of things. Those are things that stick with you. There is things that are more timely things. Like predictive scoring and their tactics. There more things that I use as a marketer or sales person. What I was picking on with AI is that it's the buzz word. It gets you funding. It gets you people in a room for a conference, that's great. But it doesn't do anything by itself. It's really an enabler. It's a pervasive thing that combines machine cycle and data but you have to teach it, you have to incorporate it into your applications. As marketers, ultimately, it's going to change our tool set to make it better. It's more poking fun at the term-- >> We always say AI. I've said it on the CUBE, AI's BS. Although, I'm a software guy. I love AI because it really promotes software that has been very nuance. So, IOT, machine learning, this is very geeky computer science stuff that's super cool. Anything that can take that mainstream in the software world, I'm a big fan of. That being said, I think the augmentation is the real message which is, you can use machine learning, you can use software, use some technical things, to make things better. You said it on our earlier segment this morning which is there's a variety of things that you can automate away. >> The thing that's, and you mentioned earlier, it's the ability that we now have the ability to collect an enormous amount of data, that's relevant and important. And we now have the technology to, actually, do something with that data. But we still have to apply it and there's a lot of change that has to happen. The way AI is different from other systems is that, historically, financial systems, software would deliver and answer. It was highly stylized. It was rarely, a clear correspondence with the real world. We closed the books. How much money did we make? There was an answer and it came from some data structures that were defined within the system. Now we're trying to bring in the real world and have these technologies focus on the real world. And they're giving ranges of possible options. That is new. It's good and it's useful but it does not take the requirement for discretion out of the system. That's why it's the augmentation. >> Ron and I were talking last year about this, Peter and I. I think you're getting a trajectory that, I've been saying for a while and this is developing in real time here on the CUBE and also some of our commentary is the role of software development and DevOps that we've seen in Cloud, is moving into the front lines of business. Meaning their techniques. You're seeing Agile, already, being talked about. You're seeing standing up campaigns. Language, you can go to the Cloud stack and say, building blocks, EC2, S3, Cooper Netties, containers, micro services and apply that to marketing because there's a lot of parallels going on to the characteristics of the infrastructure. Certainly critical infrastructure to enabling infrastructure. It's interesting that you're seeing marketers being more savvy and inadative. What's your thoughts on that, a reaction? >> Yeah, it's the evolution though. If you go back to, we as marketers have been using rules engines, we've been using tools like collaborative filtering. You go back to late 90's, early 2000's when we were building recommendation engines in simple. That's algorithmic stuff, right? No different than we're doing today with pricing rules and all that stuff. The difference it that you now have more power to do it. You have the ability to do it more real time and on the fly. You use far more data. More computing power and more data. Not only your data that you own but data that you leverage from third party to really understand people. You have a wider lens. Historically, you're making recommendations based on what you had in a cart or some other things that people have bought that also had that in the cart, that's different now, right? With this type of technology, this enabling kind of world, you an look at a lot more data points to give you that. The problem is that anything around AI requires a couple of things. It is a dumb system so AI. (laughs) >> Still a computer. >> It's still a computer. Everyone forgets that for it to work, it has to learn. I have some friends who have built marketing tools on top of Watson, for example. It takes hundreds and hundreds of hours for it to start doing something. You have to train it. You have to, not only, give it the data, you have to train it. >> Even the word learning and training is misleading in may respects. At the end of the day it's software but what is new is it's being applied in richer, more complex domains. The recommendation engine used to be just for recommendation. Now we're using those same models and we're combining them and applying them to richer more complex domains. Yet, ideally, the software's not getting more difficult to use. And I think what really makes this compelling, as a software engineer, is that we're doing all this more complexity but we're packing it and making it simpler. >> I think that's the point of where Oracle's going and why they don't call it AI. They're using it more the adaptive. Because they're thinking of it at the micro service level. They're thinking of how can they make these widgets of functionality to better the tools we have. To incorporate it into not make it so a jump forward in our tool set. It's just now, an augmented component of what we do today. >> It's, almost, a stack approach. You got foundational building blocks and at the top is high velocity, highly dynamic apps and you could argue, we were talking that the CMO's going to be an app shop, some day. This banks the question and I'd like to get both of you guys to weigh in on this. Because this is a question that I'd like to get on the record. What is modern marketing these days? Define modern marketing because what we're getting at here is, to your point of the evolution is we've seen this movie before. Is it a replatforming? Is it a building block approach? What is a modern marketer? What is a modern marketer mean? How do you execute that? >> I think it's quick and nimble and adaptive. The whole point of modern marketing is that you're always looking at how you can rethink, how you can optimize, how you can leverage technology to do things. It's not about replacing head count with a machine or a tool or a tech. It's really about how do you leverage that head count more effectively? How can you focus on optimization using those technologies. Modern marketing is, again, probably another buzz word but just like modern sales, modern commerce, all of that. It's really about how do you enable it with that stack do better. >> So, is it fashion or is it like hey, there's a modern marketer over there, look at what he or she is wearing. Or is it more technology based that's got some fundamental foundational shifts that are being worked on or both? >> It's leveraging technology and it's leveraging data more effectively and creatively. It's not being stuck with a prescriptive approach on campaign and orchestration and building. It still requires strategy and all of that but it's really how you approach it. >> So, how you think of it. What's your angle on this? >> That's a great question. And that's why I giggled about it. I think you gave a great answer. The three key precepts of Agile are, iterative, opportunistic and empirical and it's nimble quick and you change. But to me, I'll answer the question this way. Modern marketing focuses on delivering value to the customer not back into the business. It used to be that you would deliver into the business. He'd say, oh, we give you a whole bunch of new leads. We give you a whole bunch of this. If along the way, it created value for the customer, that's okay. But more often that not, it was annoying. As customer's can share their experience and share information about how (mumbles) engaged them, that's amplified. Annoying gets amplified. I think if you focus on are you creating value for the customer, you also end up with the derivative element that you're accelerating leads, they are in the process and where they are in the journey. The way I'd answer it. It's not distinct from yours but the idea of modern marketing focuses on creating value for the customer. The only way you, consisting do that is by being nimble and blah-blah-blah-blah-blah. >> I agree, in the same thing though. A core tenant, if you will, of modern marketing is absolutely. It is the value proposition. It's also making sure you understand the impact of the value of proposition The velocity of the pipeline, the impact on revenue, all of those things right? Because it's all about that value which it has to be, from a customers perspective but you're not doing all of the other pieces. You're not going to justify the spend. You're not going to get all of those together. >> Let me see if I can thread the two points together. Cause what I'm seeing, by listening is, you mentioned, the main thing in my mind was the data. That's different right? You're saying okay, thing differently, talk to the customer and the value to the enterprise value is being created through a different mechanism versus just serving it. >> Not really, not really. The fundamental focus, historically, of marketing has been what are we doing for the business? What are we doing for sales? Now, if we focus on, now you say well no. We have to created value for the customer in every thing we do, then we get permission to do things differently. We get more data out of the customer because the trust is there. We're allowed to bias the customer to the next, best option. >> I'm trying to answer my questions. I can see your point. My point is this, the modern marketer is defined by doing it. The business practices it a little bit differently to achieve the same thing. >> By focusing or creating value they have to do things differently and now they can because technology allows them to do it. >> We saw Time Warner, they weren't using data prior. That's a little different. If you go outward to go in, it's a great value while doing the table stake stuff. >> It's changing strategically thinking different of how you do it. Creating that value proposition's very different and also being able to measure and optimize are you doing it correctly? Is it having impact on the business? Most of my customers are not for profits They, actually, have to show, bottom line an impact. All of that requires data and speed and velocity in which we have to run requires tech. >> They got gestures in the market with customers. They have that touch point. They can leverage that. >> Here's (mumbles) modern marketing is not speeding up and increasing the rate and lowering the cost of doing bad marketing. >> No, no, I mean that's exactly. >> It was marketers point. >> That's right. (laughing) You can spend a lot of money to do bad marketing. >> Let's double down on our bad marketing. Ron, thanks so much for coming on the CUBE again. Thanks for sharing the insights. It's always a pleasure to get down and dirty and peel back the onion on some of these things. Final question for you. What do you expect for the evolution for this next year. >> I think AI's going to be with us for awhile just because it's the new buzz word. We've got a couple cycles on that. >> John: It reminds me of Web 2.0, what is it? >> And that lasted for a few years as well. I think over the next year or so, we're going to see the benefits of that augmentation. We're going to, actually, see some of these micro services as people start fueling some of the tools that we already have. You're also going to see some of that further collision of ad tech and mertech. Cause everything's digital and the impact of what that means for us as marketers. >> I can't wait of the hashtag, marketing native. Cause Cloud Native is coming. Someone's going to make it up, I hope not. >> Peter: You did. >> Ron: You just did. >> Okay, Marketing Native. What does that mean? We'll do a whole segment on that. We'll get Ron to come in. Hey, thanks for coming on the CUBE. >> Thanks for having me. >> Great to see you. I'm John Furrier. Peter Burris here inside the CUBE getting all the action. Straight from the data and sharing it with you. Thank you Ron, for coming on again twice in a row, two years in a row. This is the CUBE. We'll be back with more after this short break. (lively music) >> Narrator: Robert Herjavec. >> People, obviously, know you from Shark Tank. But the Herjavec group has been, really, laser folks in cyber security. >> Cause I, actually, helped bring a product called Check Point to Canada, firewalls, URI filtering, that kind of stuff. >> But you're also an entrepreneur? And you know the business. You've been in software, in the tech business. (mumbles) you get a lot of pitches as entertainment meets business. >> On our show, we're a bubble. We don't get to do a lot of tech.

(calm and uplifting music) (moves into soft and soothing music) >> Announcer: Live from Las Vegas, it's theCUBE. Covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. (chill and calm electronic music) >> Hey, welcome back everyone. We are live here at the Mandalay Bay in Las Vegas for theCUBE's special coverage of Oracle's marketing clouds event called Modern CX for Modern Customer Experience. I'm John Furrier, founder of SiliconANGLE, with Peter Burris, head of research at wikibon.com. This is our wrap up of day one. We've got day two coverage tomorrow. Peter, we saw some great news from Oracle on stage. I'll say modernizing their platform, the positioning, certainly, how they're packaging the offering of a platform with the focus of apps, with the additive concept of adaptive intelligence, which gives the notion of moving from batch to realtime, data in motion, and then a series of other enhancements going on. And the guests we talked to have been phenomenal, but what's coming out of this, at least in my mind, I would love to get your reaction to today, is data. Data is the key, and it's clear that Oracle is differentiating with their data. They have a database. They're now bringing their Cloud Suite concept to marketing and extending that out. Interesting. AI is in there, they got some chatbots, so some sizzle, but the steak is the data. So you got the sizzle and you got the steak. >> Well, we heard, you're absolutely right, John. We heard today a lot, and I think this is a terminology that we're going to hear more frequently, is this notion of first person data versus third person data. Where first person data is the data that's being generated by the business and the business's applications and third person data being data that's generated by kind of the noise that's happening in a lot of other people's first person data. And I think that's going to be one of the biggest challenges in the industry. And Oracle has an inside track on a lot of that first person data because a lot of people are big time Oracle customers for big time operational acts, applications that are today delivering big time revenue into the business. >> In the spirit of marketing speak at these events you hear things, "It's outcomes, digital transmissions. "It's all about the outcomes." Agreed, that's standard, we hear that. But here we're seeing something for the first time. You identified it in one of our interviews with Jack Horowitz, which had 150 milliseconds, it's a speeds and feeds game. So Oracle's premise, you pointed out, I'd like to get deeper on this, because this is about not moving the data around if you don't have to. >> Yeah, yeah. >> This is interesting. >> This is a centerpiece of Wikibon's research right now, is that if you start with a proposition that we increasingly through digital transformation are now talking about how we're going to use data to differentiate business, then we need to think about what does it mean to design business, design business activities, design customer promises around the availability of data or the desire to get more data. And data has a physical element. Moving data around takes time and it generates cost, and we have to be very, very careful about what that means, let alone some of the legal and privacy issues. So we think that there's two things that all businesses are going to have to think about, the relationship between data and time. Number one, Can I serve up the right response, the right business action, faster than my competitors, which is going to matter, and number two is can I refine and improve the quality of my models that I'm using to serve things up faster than my competitors. So it's a cycle time on what the customer needs right now, but it's also a strategic cycle time in how I improve the quality of the models that I'm using to run my business. >> What's also interesting is some things that, again that you're doing on the research side, that I think plays into the conversations and the content and conversations here at Oracle's Modern CX event is the notion of the business value of digital. And I think, and I want to get your reaction to this because this is some insight that I saw this morning through my interviews, is that there are jump in points for companies starting this transformation. Some are more advanced than others, some are at the beginning, some are in kindergarten, some are in college, some are graduated, and so on and so forth. But the key is, you're seeing an Agile mindset. That was a term that was here, we had the Agile Marketer, the author of The Agile Marketer, here on our-- Roland Smart, who wrote the book The Agile Marketer. But Agile can be applied because technology's now everywhere. But with data and now software, you now have the ability to not only instrument, but also get value models from existing and new applications. >> Well let's bring it back to the fundamental point that you made up front, because it's the right one. None of this changes if you don't recognize these new sources of data, typically and increasingly, the customer being a new source, and what we can do with it. So go back to this notion of Agile. Agile works when you are, as we talked about in the interview, when you have three things going on. First off, the business has to be empirical, it has to acknowledge that these new sources of information are useful. You have to be willing to iterate. Which means you have to sometimes recognize you're going to fail, and not kill people who fail as long as they do it quickly. And then you have to be opportunistic. When you find a new way of doing things, you got to go after it as hard as you possibly can. >> And verify it, understand it, and then double down on it. >> Absolutely, absolutely. Yeah, customer-centric and all the other stuff. But if you don't have those three things in place, you are not going to succeed in this new world. You have to be empirical, you have to be iterative, and you have to be opportunistic. Now take that, tie that back to some of the points that you were making. At the end of the day, we heard a lot of practitioners as well as a lot of Oracle executives, I don't want to say, be challenged to talk about the transformation or the transition, but sometimes they use different language. But when we push them, it all boiled down to, for the first time, our business acknowledged the value of data, and specifically customer data, in making better decisions. The roadmap always started with an acknowledgement of the role that data's going to play. >> And the pilots that we heard from Time Warner's CMO, Kristen O'Hara, pointed it out really brilliantly that she did pilots as a way to get started, but she had to show the proof. But not instant gratification, it was, "Okay, we'll give you some running room, "three feet and a cloud of dust, go see what happens. "Here's enough rope to hang yourself or be successful." But getting those proof points, to your point of iteration. You don't need to hit the home run right out of the gate. >> Absolutely not. In fact, typically you're not. But the idea is, you know, people talk about how frequently product launches fail. Products, you know, the old adage is it fails 80% of the time. We heard a couple of people talk about how other research firms have done research that suggests that 83 or 84% of leads are useless to salespeople. We're talking about very, very high failure rates here and just little changes, little improvements in the productivity of those activities, have enormous implications for the revenue that the business is able to generate and the cost that the business has to consume to generate those revenues. >> John: I want to get your reaction to-- Oh, go ahead, sorry. >> No, all I was going to say, it all starts with that fundamental observation that data is an asset that can be utilized differently within business. And that's what we believe is the essence of digital business. >> The other reaction I'd like to get your thoughts on is a word that we've been using on theCUBE that you had brought up here first in the conversation, empathy to users. And then we hear the word empowerment, they're calling about heroes is their theme, but it's really empowerment, right? Enabling people in the organization to leverage the data, identify new insights, be opportunistic as you said, and jump on these new ways of doing things. So that's a key piece. So with empathy for the users, which is the customer experience, and the empowerment for the people to make those things happen, you have the convergence of ad tech and mar-tech, marketing tech. Advertising tech and marketing tech, known as ad tech and mar-tech, coming together. One was very good at understanding collective intelligence for which best ad to serve where. Now the infrastructure's changing. Mar-tech is an ever-evolving and consolidating ecosystem, with winners and losers coming together and changing so the blender of ad tech and mar-tech is now becoming re-platformed for the enterprise. How does a practitioner who's looking at sources like Oracle and others grock this concept? Because they know about ads and that someone buys the ads, but also they have marketing systems in place and sales clouds. >> Well, I think, and again, it's this notion of hero and empowerment and enablement, all of them boil down to are we making our people better? And I think, in many respects, a way of thinking about this is the first thing we have to acknowledge is the data is really valuable. The second thing we have to acknowledge is that when we use data better, we make our people more successful. We make our people more valuable. We talk about the customer experience, well employee experience also matters because at the end of the day, those employees, and how we empower them and how we turn them into heroes, is going to have an enormous impact on the attitude that they take when they speak with customers, their facility at working with customers, the competency that they bring to the table, and the degree to which the customer sees them as a valuable resource. So in many respects, the way it all comes together is, we can look at all these systems, but are these systems, in fact, making the people that are really generating the value within the business more or less successful? And I think that's got to be a second touchstone that we have to keep coming back to. >> Some great interviews here this morning on day one. Got some great ones tomorrow, but two notables. I already mentioned the CMO, Kristen O'Hara, who was at Time Warner, great executive, made great change in how they're changing their business practices, as well as the financial outcome. But the other one was Jack Berkowitz. And we had an old school moment, we felt like a bunch of old dogs and historians, talking about the OSI, Open Systems Interconnect Model, seven layers of openness, of which it only went half way, stopped at TCPIP, but you can argue some other stuff was standardized. But, really, if you look at the historical perspective, it was really fun, because you can also learn, what you can learn about history as it relates to what's happening today. It's not always going to be the same, but you can learn from it. And that moment was this grocking of what happened with TCPIP as a standardization, coalescing moment. And it's not yet known in this industry what that will be. We sense it to be data. It's not clear yet how that's going to manifest itself. Or is it to you? >> Well here's what I'd say, John. I think you're right, kind of the history moment was geez, wasn't it interesting that TCPIP, the OSI stack, and they're related, they're not the same, obviously, but that it defined how a message, standards for moving messages around, now messages are data, but it's a specialized kind of a data. And then what we talked about is when we get to layer seven, it's going to be interesting to see what kind of standards are introduced, in other words, the presentation layer, or the application layer. What kind of standards are going to be introduced so that we can enfranchise multiple sources of cloud services together in new ways. Now Oracle appears to have an advantage here. Why? Because Oracle's one of those companies that can talk about end to end. And what Jack was saying, it goes back again to one of the first things we mentioned in this wrap, is that it's nice to have that end to end capability so you can look at it and say "When do we not have to move the data?" And a very powerful concept that Jack introduced is that Oracle's going to, you know, he threw the gauntlet down, and he said "We are going to help our customers "serve their customers within 150 milliseconds. "On a worldwide basis, "anywhere that customer is in the world, any device, "we're going to help our customers serve their customers "in 150 milliseconds." >> That means pulling data from any database, anywhere, first party, third party, all unified into one. >> But you can do it if and only if you don't have to move the data that much. And that's going to be one of the big challenges. Oracle's starting from an end to end perspective that may not be obviously cloud baked. Other people are starting with the cloud native perspective, but don't have that end to end capability. Who's going to win is going to be really interesting. And that 150 millisecond test is, I think, going to emerge as a crucial test in the industry about who's going to win. >> And we will be watching who will win because we're going to be covering it on SiliconANGLE.com and wikibon.com, which has got great research. Check out wikibon.com, it's subscription only. Join the membership there, it's really valuable data headed up by Peter. And, of course, theCUBE at siliconangle.tv is bringing you all the action. I'm John Furrier with Peter Burris, Day one here at the Mandalay Bay at the Oracle Modern CX, #ModernCX. Tweet us @theCUBE. Glad to chat with you. Stay tuned for tomorrow. Thanks for watching. (chill and calm electronic music) >> Announcer: Robert Herjavec >> Interviewer: People obviously know you from Shark Tank but the Herjavec group has been--

>> Voiceover: Live, from Las Vegas, It's theCUBE. Covering Oracle Modern Customer Experience 2017. Brought to you by Oracle. (techno beats) >> Hello everyone, welcome to SiliconANGLE's theCUBE, with flagship programming, we go out to the events, and extract the signal and noise. I'm John Furrier with Peter Burris, my cohost this week at Oracle Modern Customer Experience, in Las Vegas. A lot going on in Las Vegas, at the NAB Show, down the street where the Cube is, also we're here, for the second year in a row at the Oracle Modern Customer Experience, #ModernCX. Tweet at us @theCUBE. I'm John Furrier with Peter Barris. Kicking off two days of wall-to-wall coverage, we have some amazing guests. We have the top executives at Oracle Marketing Cloud, as well as some of their customers, as well as some other guests in the industry. Peter, we've been covering this marketing cloud kind of, as part of the bigger picture of the systems of engagement that is growing out of cloud infrastructure and big data. There's really a collision going on between accelerating applications with infrastructure, powered by the cloud, powered by hybrid cloud, and data's at the center of the value proposition, and literally is the key point in all this. So, I want to get your thoughts, and we talked about this last year, what's different from last year to this year, with Oracle Marketing Customer Experience, from your perspective? >> Well, I think there's three things that are different, John. The first thing that's different is that, the reality of how difficult it is to integrate technology into the marketing function is setting in, in a lot of marketers. So, we're not hearing anymore comments or promises about how marketing expenditure is going to exceed IT expenditures for technology. So, there's a reality set in about, what does it really mean to incorporate technology in the working market? The second thing that's happening is AI. We're going to hear a lot about AI, we're going to hear a lot about these new ways of taking big data and making them more useful to the business, and that's going to have an enormous impact on marketing, for a variety of different reasons. When you talk about next best action, predicting customer experience, prognosticating value propositions, all those other things, AI is going to have a role to play. How fast it gets adopted, we'll see, but we're going to hear a lot about it. >> John: It's interesting, we always talk on the cube here, if you follow the Cube you know, we always kind of, always pontificate on this notion of horizontally-scalable, and we talked about it last year, but there's an era of specialization, that you need to have vertically-oriented into some of these industries. But what's interesting, Pete, and I want to get your thoughts on this, because I was commenting last year at the show that, marketing was always a silo, and Oracle has had a integration strategy that's been kind of horizontal, and the trends in cloud computing and data is horizontal-scalability, with value propositions differentiate at the applications So, this begs the question, what does that mean for marketing in a digital business? If you go digital all the way, from the beginning of the journey to the moment of truth to the customer, sales or conversion, it's all digital, marketing's in every piece of the equation along the way, and that's what Mark Hurd was saying yesterday. >> Peter: Well, customer engagement's in every piece of the equation along the way and then the question is, is marketing going to evolve to become primary in customer engagement? It's not going to be just your direct sales force, customers are going to move amongst different channels. We've heard a lot about on the channel, so, to what role, to what degree will marketing become primary? And the third point I was going to make, John, is related to this, and that is, one of the big changes between this year and last year, is that Oracle has really thrown the tiller over, and tacked towards the cloud. And it's going to be interesting to see whether or not the cloud customer experience story, or the marketing cloud customer experience story, in the cloud, is lining up with the rest of Oracle's cloud story. >> John: It's as with, Don Clien, from our team, who last night in the hallway conversations here, in the Mandalay Bay with the convention, that the conference is happening, it's interesting, we were talking about the role of platforms, and you can't see in the news these days, anything from Facebook's relative to fake news, to some of the killings on Facebook Live, to YouTube and moderating comments, these emergence of platforms has been a very interesting dynamic, but at the end of the day, content needs to have an authentic piece to it. So, you now blending in a marketing and conversion, with customers, we're living in a content world. I'm wearing a wearable, my content is my interface to wherever I am in real time. My experience at the rental car dealership, or wherever I'm at, is going to be all about, the content is not some siloed, "Hey, hello, buy this." It's everything is content-driven. >> Well everything is value-driven, right? And the question is, is the content going to be valuable? And if there's a big, going back to that first point, what's the big issue about marketing? We thought that if we just through technology, we could automate the same ways that marketing is already, always done stuff, but the reality is marketing does a lot of stuff that is not valuable to customers. It may be valuable to the organization or their ways, but it's not valuable to customers. And often it's really annoying, and so marketing has to decide, if in fact they are going to take a primary role in engagement across channel over time, as customers move amongst organizations, then they're going to have to start dedicating themselves to creating content that's valuable to the customer, in the form that the customer needs, when the customer needs it, where the customer needs it. And that's a challenge. >> And the engagement piece is critical, I love that angle, but let's take it to the next level. Every example of marketing cloud or any kind of digital experience use case has data in it. It's data-driven. Even Mark Hurd, on his keynote, talked about his experience at the rental car place, that's data-driven. You got to know, that's the CEO of Oracle. So, this is again, the data is at the center of this. It's flowing through all the apps, and has to be available, has to be real time, this is fundamental. >> Peter: And digital assets are data as well, and applications, when you go back to what computer science says, applications themselves are data. So, increasingly, it's all data. Customers want to be engaged digitally. They want to be able to take their digital experience, whatever channel, the data has to follow them. You have to anticipate what data you're going to generate in the form of content. You have to be able to capture data without annoying them. So, in many respects, John, this all comes down, the challenge for marketing is, how do we capture data without being annoying? How do we provision data in a way that's valuable, so that we increase the view of the brand. >> John: I want to put you on the spot, because I know marketing's a lot of different components to it, but one of the things that everyone in the industry is talking about, is the role that salesforce.com has taken in its SaaS cloud platform, vis-a-vis an app, where you just put your contacts in, and you manage your relationships, and how that's grown and shifted over to being a SaaS platform. And here's the question I want to ask, and get your thoughts on, and just riff here in real time. Back in the old days, analog sales needed a system to provide automation for those sales guys. Boom. Salesforce.com is born. Marketing would provide email marketing and content, here's a package of content, if you're interested, click on it and we'll get you more information. Marketing department sends those leads to the analog sales team. The leads aren't good enough, the leads are crap. Glengarry, Glen Ross kind of thing going on there. Now that's shifted with the digital fabric, end to end, from initiation to moment of truth. Digital. That kind of goes away. So, sales cloud and marketing cloud are blurring, yes or no, what're your thoughts on the role of sales kind of thing, and the marketing piece? >> Well, it all comes down to, and again this is one of the precepts of the whole notion of customer experience, it all comes down to the customer is on a journey to solve a problem, to generate some utility out of the purchase that they're making, whether it's a product or service. They go through discovery process, they go through a buying process, they go through a utilization process. All of that requires engagement. And so the data, and they way you provision your resources, to that customer has to fit naturally in the way the customer does stuff. So one of the reasons why this is blurring is because customers themselves are demanding that they be treated digitally in some coherent manner. Now, institutionally and organizationally, there's still a lot of tensions, as you said, between sales and marketing, and it's not enough to just say we're going to do a marketing cloud because there's marketing budgets, and we're going to do a sales cloud, because the sales budgets, and a product cloud because of product budgets, etc. This has to come together. We have to render this coherently in front of customers, or in front of businesses because businesses have to render themselves coherently in front of customers as they go through their journey. >> Great observation, I would just add that this notion of a platform is an indicator of where the market's going. Certainly we're seeing in the mainstream some things are being tweaked, and Facebook admitted in the New York Times that they're working on it. They're going to work on these things. But let's bring that platform, if what you say is true, which I believe it is, everything has to come together, because it's not one or the other, there's not mutually exclusive. Now, sales guys had the data from the old days, but now it's all digital, so the question is, that shifts the scales, because in the old days, marketing was to provide value to the organization, the enterprise itself, the business value of the enterprise, and that comes from selling something. >> Peter: Right, right, right, right. >> John: And so, to your value point, which I think that this market shifts the value to the marketing team because they have a broader perspective in that journey. Or have more touch points in the engagement of the customer. >> Peter: And that's key. The question is can they be the orchestrator of a coherent and holistic engagement strategy with a customer. >> John: So, I'm a CIO, I'm looking at a complete replatforming. I think that's a better approach than trying to take Salesforce and make it work over here, and if you look at Salesforce, they've done a bunch of different acquisitions, not always kind of tightly coupled, a little bit of awkwardness here, chatter, all these components. Oracle's taking a different approach, they're saying we're going to integrate all this stuff, and you pick and choose. I think, if I'm a CIO, I might want to take a more holistic view from initiation, to moment of truth with the customer, and the lifecycle that journey. There's more marketing touch points in there, so I'm probably designed that way, your thoughts. >> Peter: Well, so, it's interesting John. The whole CRM industry went through an extremely challenging birth. One of the biggest challenges is that, as you said, we used to be analog. Sales people would go on a call, they'd write up a trip report, they'd hand it to and administrator, and the administrator would do the data entry, and we'd get it into the system someway. But the minute you start automating that, now the sales guys are doing data entry. And if you talk to sales organizations today, one of the biggest problems is how much time are my folks doing data entry, how much time are my folks generating content for customers, how much time are they doing all these other things, and not selling, and that's an issue. So, when we think about where this is going to go, at the end of the day, Salesforce has done the best job of presenting CRM to the marketplace, for a variety of different reasons. But it still is a let's capture sales activity kind of a platform. The question is, are we actually going to get to a platform that is truly able to provide coherent, holistic value at the moment that the customer wants it, and that includes delivery. And I think Oracle has an opportunity in all of this. It's to actually utilize their various clouds, to provide a way of engaging customers across the entire journey, because they can do the discovery piece, they can do the sales piece, and they can also do digital products, and digital capabilities anyway, the delivery piece. >> Well, Peter Burris from Wikibon.com, head of research over there. Check out some of the work they're doing with the digital, role of the digital business and assets, digital experiences, they're all assets, whether it's content, engagement, or an experience that someone has, it's all a data asset, it's a digital asset, and that needs to be harnessed and looked at holistically in a way. You got some great research over at Wikibon.com, check it out. I'm John Furrier, here for two days at Oracle Modern Customer Experience Show. Should be great, really cutting edge stuff, really as the world replatforms in the cloud, content and experiences will be fundamental, and data's at the center of it. We'll bring you all the coverage here. We'll be right back with more great coverage after this short break. (techno beats)

(upbeat music) >> Welcome back to Supercloud 2, which is an open industry collaboration between technologists, consultants, analysts, and of course, practitioners, to help shape the future of cloud. And at this event, one of the key areas we're exploring is the intersection of cloud and data, and how building value on top of hyperscale clouds and across clouds is evolving, a concept we call supercloud. And we're pleased to welcome Harvir Singh, who's the chief data architect and global head of data at Western Union. Harvir, it's good to see you again. Thanks for coming on the program. >> Thanks, David, it's always a pleasure to talk to you. >> So many things stand out from when we first met, and one of the most gripping for me was when you said to me, "When data moves, money moves." And that's the world we live in today, and really have for a long time. Money has moved as bits, and when it has to move, we want it to move quickly, securely, and in a governed manner. And the pressure to do so is only growing. So tell us how that trend is evolved over the past decade in the context of your industry generally, and Western Union, specifically. Look, I always say to people that we are probably the first ones to introduce digital currency around the world because, hey, somebody around the world needs money, we move data to make that happen. That trend has actually accelerated quite a bit. If you look at the last 10 years, and you look at all these payment companies, digital companies, credit card companies that have evolved, majority of them are working on the same principle. When data moves, money moves. When data is stale, the money goes away, right? I think that trend is continuing, and it's not just the trend is in this space, it's also continuing in other spaces, specifically around, you know, acquisition of customers, communication with customers. It's all becoming digital, and it's, at the end of the day, it's all data being moved from one place or another. At the end of the day, you're not seeing the customer, but you're looking at, you know, the data that he's consuming, and you're making actionable items on it, and be able to respond to what they need. So I think 10 years, it's really, really evolved. >> Hmm, you operate, Western Union operates in more than 200 countries, and you you have what I would call a pseudo federated organization. You're trying to standardize wherever possible on the infrastructure, and you're curating the tooling and doing the heavy lifting in the data stack, which of course lessens the burden on the developers and the line of business consumers, so my question is, in operating in 200 countries, how do you deal with all the diversity of laws and regulations across those regions? I know you're heavily involved in AWS, but AWS isn't everywhere, you still have some on-prem infrastructure. Can you paint a picture of, you know, what that looks like? >> Yeah, a few years ago , we were primarily mostly on-prem, and one of the biggest pain points has been managing that infrastructure around the world in those countries. Yes, we operate in 200 countries, but we don't have infrastructure in 200 countries, but we do have agent locations in 200 countries. United Nations says we only have like 183 are countries, but there are countries which, you know, declare themselves countries, and we are there as well because somebody wants to send money there, right? Somebody has an agent location down there as well. So that infrastructure is obviously very hard to manage and maintain. We have to comply by numerous laws, you know. And the last few years, specifically with GDPR, CCPA, data localization laws in different countries, it's been a challenge, right? And one of the things that we did a few years ago, we decided that we want to be in the business of helping our customers move money faster, security, and with complete trust in us. We don't want to be able to, we don't want to be in the business of managing infrastructure. And that's one of the reasons we started to, you know, migrate and move our journey to the cloud. AWS, obviously chosen first because of its, you know, first in the game, has more locations, and more data centers around the world where we operate. But we still have, you know, existing infrastructure, which is in some countries, which is still localized because AWS hasn't reached there, or we don't have a comparable provider there. We still manage those. And we have to comply by those laws. Our data privacy and our data localization tech stack is pretty good, I would say. We manage our data very well, we manage our customer data very well, but it comes with a lot of complexity. You know, we get a lot of requests from European Union, we get a lot of requests from Asia Pacific every pretty much on a weekly basis to explain, you know, how we are taking controls and putting measures in place to make sure that the data is secured and is in the right place. So it's a complex environment. We do have exposure to other clouds as well, like Google and Azure. And as much as we would love to be completely, you know, very, very hybrid kind of an organization, it's still at a stage where we are still very heavily focused on AWS yet, but at some point, you know, we would love to see a world which is not reliant on a single provider, but it's more a little bit more democratized, you know, as and when what I want to use, I should be able to use, and pay-per-use. And the concept started like that, but it's obviously it's now, again, there are like three big players in the market, and, you know, they're doing their own thing. Would love to see them come collaborate at some point. >> Yeah, wouldn't we all. I want to double-click on the whole multi-cloud strategy, but if I understand it correctly, and in a perfect world, everything on-premises would be in the cloud is, first of all, is that a correct statement? Is that nirvana for you or not necessarily? >> I would say it is nirvana for us, but I would also put a caveat, is it's very tricky because from a regulatory perspective, we are a regulated entity in many countries. The regulators would want to see some control if something happens with a relationship with AWS in one country, or with Google in another country, and it keeps happening, right? For example, Russia was a good example where we had to switch things off. We should be able to do that. But if let's say somewhere in Asia, this country decides that they don't want to partner with AWS, and majority of our stuff is on AWS, where do I go from there? So we have to have some level of confidence in our own infrastructure, so we do maintain some to be able to fail back into and move things it needs to be. So it's a tricky question. Yes, it's nirvana state that I don't have to manage infrastructure, but I think it's far less practical than it said. We will still own something that we call it our own where we have complete control, being a financial entity. >> And so do you try to, I'm sure you do, standardize between all the different on-premise, and in this case, the AWS cloud or maybe even other clouds. How do you do that? Do you work with, you know, different vendors at the various places of the stack to try to do that? Some of the vendors, you know, like a Snowflake is only in the cloud. You know, others, you know, whether it's whatever, analytics, or storage, or database, might be hybrid. What's your strategy with regard to creating as common an experience as possible between your on-prem and your clouds? >> You asked a question which I asked when I joined as well, right? Which question, this is one of the most important questions is how soon when I fail back, if I need to fail back? And how quickly can I, because not everything that is sitting on the cloud is comparable to on-prem or is backward compatible. And the reason I say backward compatible is, you know, there are, our on-prem cloud is obviously behind. We haven't taken enough time to kind of put it to a state where, because we started to migrate and now we have access to infrastructure on the cloud, most of the new things are being built there. But for critical application, I would say we have chronology that could be used to move back if need to be. So, you know, technologies like Couchbase, technologies like PostgreSQL, technologies like Db2, et cetera. We still have and maintain a fairly large portion of it on-prem where critical applications could potentially be serviced. We'll give you one example. We use Neo4j very heavily for our AML use cases. And that's an important one because if Neo4j on the cloud goes down, and it's happened in the past, again, even with three clusters, having all three clusters going down with a DR, we still need some accessibility of that because that's one of the biggest, you know, fraud and risk application it supports. So we do still maintain some comparable technology. Snowflake is an odd one. It's obviously there is none on-prem. But then, you know, Snowflake, I also feel it's more analytical based technology, not a transactional-based technology, at least in our ecosystem. So for me to replicate that, yes, it'll probably take time, but I can live with that. But my business will not stop because our transactional applications can potentially move over if need to. >> Yeah, and of course, you know, all these big market cap companies, so the Snowflake or Databricks, which is not public yet, but they've got big aspirations. And so, you know, we've seen things like Snowflake do a deal with Dell for on-prem object store. I think they do the same thing with Pure. And so over time, you see, Mongo, you know, extending its estate. And so over time all these things are coming together. I want to step out of this conversation for a second. I just ask you, given the current macroeconomic climate, what are the priorities? You know, obviously, people are, CIOs are tapping the breaks on spending, we've reported on that, but what is it? Is it security? Is it analytics? Is it modernization of the on-prem stack, which you were saying a little bit behind. Where are the priorities today given the economic headwinds? >> So the most important priority right now is growing the business, I would say. It's a different, I know this is more, this is not a very techy or a tech answer that, you know, you would expect, but it's growing the business. We want to acquire more customers and be able to service them as best needed. So the majority of our investment is going in the space where tech can support that initiative. During our earnings call, we released the new pillars of our organization where we will focus on, you know, omnichannel digital experience, and then one experience for customer, whether it's retail, whether it's digital. We want to open up our own experience stores, et cetera. So we are investing in technology where it's going to support those pillars. But the spend is in a way that we are obviously taking away from the things that do not support those. So it's, I would say it's flat for us. We are not like in heavily investing or aggressively increasing our tech budget, but it's more like, hey, switch this off because it doesn't make us money, but now switch this on because this is going to support what we can do with money, right? So that's kind of where we are heading towards. So it's not not driven by technology, but it's driven by business and how it supports our customers and our ability to compete in the market. >> You know, I think Harvir, that's consistent with what we heard in some other work that we've done, our ETR partner who does these types of surveys. We're hearing the same thing, is that, you know, we might not be spending on modernizing our on-prem stack. Yeah, we want to get to the cloud at some point and modernize that. But if it supports revenue, you know, we'll invest in that, and get the, you know, instant ROI. I want to ask you about, you know, this concept of supercloud, this abstracted layer of value on top of hyperscale infrastructure, and maybe on-prem. But we were talking about the integration, for instance, between Snowflake and Salesforce, where you got different data sources and you were explaining that you had great interest in being able to, you know, have a kind of, I'll say seamless, sorry, I know it's an overused word, but integration between the data sources and those two different platforms. Can you explain that and why that's attractive to you? >> Yeah, I'm a big supporter of action where the data is, right? Because the minute you start to move, things are already lost in translation. The time is lost, you can't get to it fast enough. So if, for example, for us, Snowflake, Salesforce, is our actionable platform where we action, we send marketing campaigns, we send customer communication via SMS, in app, as well as via email. Now, we would like to be able to interact with our customers pretty much on a, I would say near real time, but the concept of real time doesn't work well with me because I always feel that if you're observing something, it's not real time, it's already happened. But how soon can I react? That's the question. And given that I have to move that data all the way from our, let's say, engagement platforms like Adobe, and particles of the world into Snowflake first, and then do my modeling in some way, and be able to then put it back into Salesforce, it takes time. Yes, you know, I can do it in a few hours, but that few hours makes a lot of difference. Somebody sitting on my website, you know, couldn't find something, walked away, how soon do you think he will lose interest? Three hours, four hours, he'll probably gone, he will never come back. I think if I can react to that as fast as possible without too much data movement, I think that's a lot of good benefit that this kind of integration will bring. Yes, I can potentially take data directly into Salesforce, but I then now have two copies of data, which is, again, something that I'm not a big (indistinct) of. Let's keep the source of the data simple, clean, and a single source. I think this kind of integration will help a lot if the actions can be brought very close to where the data resides. >> Thank you for that. And so, you know, it's funny, we sometimes try to define real time as before you lose the customer, so that's kind of real time. But I want to come back to this idea of governed data sharing. You mentioned some other clouds, a little bit of Azure, a little bit of Google. In a world where, let's say you go more aggressively, and we know that for instance, if you want to use Google's AI tools, you got to use BigQuery. You know, today, anyway, they're not sort of so friendly with Snowflake, maybe different for the AWS, maybe Microsoft's going to be different as well. But in an ideal world, what I'm hearing is you want to keep the data in place. You don't want to move the data. Moving data is expensive, making copies is badness. It's expensive, and it's also, you know, changes the state, right? So you got governance issues. So this idea of supercloud is that you can leave the data in place and actually have a common experience across clouds. Let's just say, let's assume for a minute Google kind of wakes up, my words, not yours, and says, "Hey, maybe, you know what, partnering with a Snowflake or a Databricks is better for our business. It's better for the customers," how would that affect your business and the value that you can bring to your customers? >> Again, I would say that would be the nirvana state that, you know, we want to get to. Because I would say not everyone's perfect. They have great engineers and great products that they're developing, but that's where they compete as well, right? I would like to use the best of breed as much as possible. And I've been a person who has done this in the past as well. I've used, you know, tools to integrate. And the reason why this integration has worked is primarily because sometimes you do pick the best thing for that job. And Google's AI products are definitely doing really well, but, you know, that accessibility, if it's a problem, then I really can't depend on them, right? I would love to move some of that down there, but they have to make it possible for us. Azure is doing really, really good at investing, so I think they're a little bit more and more closer to getting to that state, and I know seeking our attention than Google at this point of time. But I think there will be a revelation moment because more and more people that I talk to like myself, they're also talking about the same thing. I'd like to be able to use Google's AdSense, I would like to be able to use Google's advertising platform, but you know what? I already have all this data, why do I need to move it? Can't they just go and access it? That question will keep haunting them (indistinct). >> You know, I think, obviously, Microsoft has always known, you know, understood ecosystems. I mean, AWS is nailing it, when you go to re:Invent, it's all about the ecosystem. And they think they realized they can make a lot more money, you know, together, than trying to have, and Google's got to figure that out. I think Google thinks, "All right, hey, we got to have the best tech." And that tech, they do have the great tech, and that's our competitive advantage. They got to wake up to the ecosystem and what's happening in the field and the go-to-market. I want to ask you about how you see data and cloud evolving in the future. You mentioned that things that are driving revenue are the priorities, and maybe you're already doing this today, but my question is, do you see a day when companies like yours are increasingly offering data and software services? You've been around for a long time as a company, you've got, you know, first party data, you've got proprietary knowledge, and maybe tooling that you've developed, and you're becoming more, you're already a technology company. Do you see someday pointing that at customers, or again, maybe you're doing it already, or is that not practical in your view? >> So data monetization has always been on the charts. The reason why it hasn't seen the light is regulatory pressure at this point of time. We are partnering up with certain agencies, again, you know, some pilots are happening to see the value of that and be able to offer that. But I think, you know, eventually, we'll get to a state where our, because we are trying to build accessible financial services, we will be in a state that we will be offering those to partners, which could then extended to their customers as well. So we are definitely exploring that. We are definitely exploring how to enrich our data with other data, and be able to complete a super set of data that can be used. Because frankly speaking, the data that we have is very interesting. We have trends of people migrating, we have trends of people migrating within the US, right? So if a new, let's say there's a new, like, I'll give you an example. Let's say New York City, I can tell you, at any given point of time, with my data, what is, you know, a dominant population in that area from migrant perspective. And if I see a change in that data, I can tell you where that is moving towards. I think it's going to be very interesting. We're a little bit, obviously, sometimes, you know, you're scared of sharing too much detail because there's too much data. So, but at the end of the day, I think at some point, we'll get to a state where we are confident that the data can be used for good. One simple example is, you know, pharmacies. They would love to get, you know, we've been talking to CVS and we are talking to Walgreens, and trying to figure out, if they would get access to this kind of data demographic information, what could they do be better? Because, you know, from a gene pool perspective, there are diseases and stuff that are very prevalent in one community versus the other. We could probably equip them with this information to be able to better, you know, let's say, staff their pharmacies or keep better inventory of products that could be used for the population in that area. Similarly, the likes of Walmarts and Krogers, they would like to have more, let's say, ethnic products in their aisles, right? How do you enable that? That data is primarily, I think we are the biggest source of that data. So we do take pride in it, but you know, with caution, we are obviously exploring that as well. >> My last question for you, Harvir, is I'm going to ask you to do a thought exercise. So in that vein, that whole monetization piece, imagine that now, Harvir, you are running a P&L that is going to monetize that data. And my question to you is a there's a business vector and a technology vector. So from a business standpoint, the more distribution channels you have, the better. So running on AWS cloud, partnering with Microsoft, partnering with Google, going to market with them, going to give you more revenue. Okay, so there's a motivation for multi-cloud or supercloud. That's indisputable. But from a technical standpoint, is there an advantage to running on multiple clouds or is that a disadvantage for you? >> It's, I would say it's a disadvantage because if my data is distributed, I have to combine it at some place. So the very first step that we had taken was obviously we brought in Snowflake. The reason, we wanted our analytical data and we want our historical data in the same place. So we are already there and ready to share. And we are actually participating in the data share, but in a private setting at the moment. So we are technically enabled to share, unless there is a significant, I would say, upside to moving that data to another cloud. I don't see any reason because I can enable anyone to come and get it from Snowflake. It's already enabled for us. >> Yeah, or if somehow, magically, several years down the road, some standard developed so you don't have to move the data. Maybe there's a new, Mogli is talking about a new data architecture, and, you know, that's probably years away, but, Harvir, you're an awesome guest. I love having you on, and really appreciate you participating in the program. >> I appreciate it. Thank you, and good luck (indistinct) >> Ah, thank you very much. This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more great coverage from Supercloud 2. (uplifting music)

(upbeat music) >> Hello, everybody. John Walls here, as we continue our coverage of AWS re:Invent here on theCUBE. And today we're going to go talk about the edge. What's out there on the edge, and how do we make sense of it? How do we use that data, and put it to work, and how do we keep it secure? Big questions, a lot of questions, and at the end of the day, what's the value prop for you, the customer, to make it all work? With me to talk about that is the Executive Vice President and GM of HPE GreenLake, Keith White. Keith, thanks for joining us here on theCUBE. >> John, thanks so much for having me. I really appreciate the opportunity, and excited to have a conversation today. >> Yeah, good. Well, let's just jump right in. First off, about the edge. There was a time, not so long ago, that it was kind of the Wild, Wild West out there, right? And we were trying to corral this fantastic reservoir of data that was streaming in from every which point, to the point now where we've realized how to refine that, how to develop that, how to reduce that complexity, to make that actionable. Talk about that journey a little bit, about where we were with edge technology maybe five, six years ago, and how we've migrated to the point we are now, where GreenLake is doing the great work that it is. >> You know, it's really a great question, John, cause I think there's a lot of different definitions of the edge, and what does "the edge" actually mean. And you're right, you know, there's been a pretty big transformation over the last few years, especially as we think about things like IoT, and just being able to engage with edge scenarios. But today what you're seeing is a lot of digital transformations happening with companies around three big megatrends. Cloud, meaning hybrid cloud, multi-cloud, data, and how you analyze that data to make decisions. And of course the edge, like we're talking through. And you know, frankly, with the edge, this is where we see the connectivity and security requirements really connect, because that edge information is so important, so critical to stay secure, but also it's creating that tremendous amount of data, as you mentioned. And so folks want to pull that into their cloud environment, and then make decisions and analyze that data, and plug it into the systems that they have overall. And you know, you're seeing companies like Auckland Transport, right? They basically do an AI-enhanced video feed to optimize their transport routes. And as you think about supply chain and the big challenges that we're seeing today, or you think about public transportation, and, you know, really providing information with respect to customers, but how do you take and get all that information pulled together, to then make decisions from these various edge points throughout? Or a company like ABB, who's been building the factory of the future, and doing, basically, you know, robotics-as-a-service, if you will, in order to really get that precision required at the edge in order to manufacture what they need to. So, massive uses around the edge, massive data getting created, and HPE GreenLake's a great spot for folks to help, you know, really take and leverage that data, to make those those decisions that are required. >> You know, one example in terms of case studies, or in terms of your client base that you talk about, you know, the automotive sector. >> Yeah. >> And I think about what's going on in terms of, with that technology, and I can't even imagine the kind of mechanics that are happening, right? In real time, at 60, 70 miles an hour, through all kinds of environmental conditions. So maybe just touch base, too, about what you're doing that's in terms of automotive, and what's going to be- >> No, it's great, John, yeah. >> (indistinct) then? >> Yeah, no, it's an awesome question, because, you know, we're working closely with a lot of the car manufacturers, as well as their sort of subsidiaries, if you will. So you look at autonomous driving, which is a great example. All that data has to come in and get analyzed. And if you look at a company like Volvo, they use a third party called Zenseact, who basically uses our high-performance compute to deliver it as a service through HPE GreenLake. They get all this massive parallel computing, modeling and simulations happening, with all this data coming in. And so what we've done with GreenLake is we give them that ability to easily scale up, to grow capacity, to get access to that hundreds of petabytes of data that you just mentioned. And then, you know, really basically take and make analytics and AI models and machine learning capabilities out of that, in order to really direct and fuel their mission to develop that next-generation software to support that autonomous driving capability. And so you're seeing that with a ton of different car manufacturers, as well as a lot of different other scenarios as well. So you're spot on. Automotive is a key place for that. >> You know, and too, the similarities here, the common thread, I think, threads, actually, plural, are very common. We think about access, right? We think about security, we think about control, we think about data, we think about analytics, so I mean, all these things are factoring in, in this extraordinarily dynamic environment. So is there a batting order, or a pecking order, in terms of addressing those areas of concern, or what kind of, I guess, learning curve have we had on that front? >> Well, I think you're, I think the key is, as I mentioned earlier, so you have this connectivity piece, and you've got to be able to connect and be available as required. That might be through SD-WAN, that might be Wi-Fi, that might be through a network access point, et cetera. But the key is that security piece of it as well. Customers need to know that that data and that edge device is very, very secure. And then you've got to have that connectivity back into your environment. And so what we've learned with HPE GreenLake, which, really what that does, is that brings that cloud experience, that public cloud experience, to customers in their data center, on-premise, in their colo, or at the edge, like we're talking about now, because there's a lot of need to keep that data secure, private, to make sure that it's not out in the public cloud and accessible, or those types of scenarios. So as I think about that piece of it, then it turns into, okay, how do we take all that data and do the analytics and the AI modeling that we talked about before? So it's a really interesting flow that has to happen. But what's happening is, people are really transforming their business, transforming their business models, as we just talked about. Factory of the future, you know, transportation needs. We're seeing it in different environments as well. Automotive, as you mentioned. But it's exciting, it's an exciting time, with all of this opportunity to really change not only how a business can run, but how we as consumers interact and engage with that. >> And then ultimately for the company, the value prop's got to be there. And you've already cited a number of areas. Is there one key metric that you look at, or one key deliverable that you look at here, in terms of what the ultimate value proposition is for a customer? >> You bet. I think the biggest thing is, you know, our customers and their satisfaction. And so, to date, you know, we have well over 60,000 customers on the platform. We have a retention rate of 96%, so a very, very small number that haven't stayed on the platform itself. And that means that they're satisfied. And what we're seeing also is a continued growth in usage for new environments, new workloads, new solutions that a customer is trying to drive as well. And so those are some of the key metrics we look at, with respect to our customer satisfaction, with their retention rate, with their usage capabilities, and then how we're growing that piece. And the interesting thing, John, is what we've learned is that HPE, as a company, traditionally was very hardware focused, it was a hardware vendor, transacting, responding to RFPs for compute, storage, and networking. With GreenLake now moving into the cloud services realm, we're now having conversations with customers as their partner. How do we solve this problem? How do we transform our business? How do we accelerate our growth? And that's been very exciting for us as a company, to really make that significant transformation and shift to being part of our customer's environments in a partnership type way. >> Yeah. And now you're talking about ecosystem, right? And what you're developing, not only in your partners, but also maybe what lessons you're learning in one respect you can apply to others. What's happening in that respect, in terms of the kind of universe that you're developing, and how applicable, maybe, one experience is to another client's needs? >> Yeah, no, it's a great question, because in essence, what happens is, we're sort of the tip of the spear, and we're partnering with customers to really go in deep, and understand how to utilize that. We can take that learning, and then push that out to our ecosystem, so that they can scale and they can work with more customers with respect to that piece of it. The second is, is that we're really driving into these more solution-oriented partners, right? The ISVs, the system integrators, the managed service providers, the colos, and even the hyperscalers, as we've talked about, and why we're here with our friends at AWS, is, customers are requiring a hybrid environment. They want to leverage tools up in the public cloud, but they also want the on-prem capabilities, and they need those to work together. And so this ecosystem becomes very dynamic with respect to, hey, what are we learning, and how do we solve our customer's problems together? I always talk about the ecosystem being 1 + 1 = 3 for our customers. It has to be that way, and frankly, our customers are expecting that. And that's why we're excited to be here today with our, as I said, our friends at AWS. >> And how does open play in all this too, right? Because, I mean, that provides, I assume, the kind of flexibility that people are looking for, you know, they, you know, having that open environment and making an opportunity available to them is a pretty big attractive element. >> It's huge, right? Yeah, as you know, people don't want to get locked in to a single technology. They don't want to get locked in to a single cloud. They don't want to have to, they want to be able to utilize the best of the best. And so maybe there's some tools in the public cloud that can really help from an analytics standpoint, but we can store and we can process it locally in our data center, at the edge, or in a colo. And so that best of both worlds is there, but it has to be an open platform. I have to be able to choose my container, my virtual machine, my AI tools, my, you know, capabilities, my ISV application, so that I have that flexibility. And so it's been fantastic for us to move into this open platform environment, to be able to have customers leverage the best and what's going to work best for them, and then partnering with those folks closely to, again, deliver those solutions that are required. >> You know, this is, I mean, it appears, as I'm hearing you talk about this, in terms of the partnerships you're creating, the ecosystem that you're developing, how that's evolving, lessons that you've learned, the attention you've paid to security and data analytics. I get the feeling that you've got a lot of momentum, right? A lot of things are happening here. You've got big mo on your side right now. (Keith laughs) Would you characterize it that way? >> Yeah, you know, there's a ton of momentum. I think what we're finding is, customers are requiring that cloud experience on-prem. You know, they're getting it from AWS and some of the other hyperscalers, but they want that same capability on-prem. And so what we've seen is just a dramatic increase with respect to usage, customers. We're adding hundreds of customers every quarter. We're growing in the triple digits, three of the last four quarters. And so, yeah, we're seeing tremendous momentum, but as I said, what's been most important is that relationship with the customer. We've really flipped it to becoming that partner with them. And again, bringing that ecosystem to bear, so that we can have the best of all worlds. And it's been fantastic to see, and frankly, the momentum's been tremendous. And we're in a quiet period right now, but you'll see what our earnings are here in the next couple weeks, and we can talk more details on that, but in the past, as we talked about, we've grown, you know, triple digits three of the last four quarters, and, you know, well over $3 billion, well over $8 billion of total contract value that we've implemented to date. And, you know, the momentum is there, but, again, most importantly is, we're solving our customers' problems together, and we're helping them accelerate their business and their transformation. >> I know you mentioned earnings, the report's a few weeks away. I saw your smile, that big old, you know, grin, so I have a feeling the news is pretty good from the HPE GreenLake side. >> It is. We're excited about it. And you know, again, this really is just a testament to the transformation we've made as a company in order to move towards those cloud services. And you know, you'll hear us talk about it as the core of what we're doing as a company, holistically, again, because this is what customers are requiring, this is what our ecosystem is moving towards. And it's been really fun, it's been a great, great ride. >> Excellent. Keith, appreciate the time, and keep up the good work, and I'm going to look for that earnings report here in a few weeks. >> Awesome. Thanks so much, John. Take good care. Appreciate it. >> You bet, you too. Keith White joining us here, talking about HPE GreenLake, and defining what they're doing in terms of bringing the edge back into the primary systems for a lot of companies. So, good work there. We'll continue our coverage here in theCUBE. You're watching theCUBE coverage of AWS re:Invent. And I'm John Walls. (lively music)

>>Hello. Welcome back to our super cloud 22 event. I'm John F host the cue with my co-host Dave ante. Extracting the signal from noise. We're proud to have two amazing cube alumnis here. We got Sanja Putin. Who's now the CEO of cohesive the emo Aaron who's the CTO. Co-founder also former CEO Cub alumni. The father of hyper-converged welcome back to the cube I endorsed the >>Cloud. Absolutely. Is the father. Great >>To see you guys. Thank thanks for coming on and perfect timing. The new job taking over that. The helm Mo it at cohesive big news, but part of super cloud, we wanna dig into it. Thanks for coming on. >>Thank you for having >>Us here. So first of all, we'll get into super before we get into the Supercloud. I want to just get the thoughts on the move Sanjay. We've been following your career since 2010. You've been a cube alumni from that point, we followed that your career. Why cohesive? Why now? >>Yeah, John David, thank you first and all for having us here, and it's great to be at your event. You know, when I left VMware last year, I took some time off just really primarily. I hadn't had a sabbatical in probably 18 years. I joined two boards, Phillips and sneak, and then, you know, started just invest and help entrepreneurs. Most of them were, you know, Indian Americans like me who were had great tech, were looking for the kind of go to market connections. And it was just a wonderful year to just de to unwind a bit. And along the, the way came CEO calls. And I'd asked myself, the question is the tech the best in the industry? Could you see value creation that was signi significant and you know, three, four months ago, Mohit and Carl Eschenbach and a few of the board members of cohesive called me and walk me through Mo's decision, which he'll talk about in a second. And we spent the last few months getting to know him, and he's everything you describe. He's not just the father of hyperconverge. And he wrote the Google file system, wicked smart, built a tech platform better than that second time. But we had to really kind of walk through the chemistry between us, which we did in long walks in, in, you know, discrete places so that people wouldn't find us in a Starbucks and start gossiping. So >>Why Sanjay? There you go. >>Actually, I should say it's a combination of two different decisions. The first one was to, for me to take a different role and I run the company as a CEO for, for nine years. And, you know, as a, as a technologist, I always like, you know, going deep into technology at the same time, the CEO duties require a lot of breadth, right? You're talking to customers, you're talking to partners, you're doing so much. And with the way we've been growing the with, you know, we've been fortunate, it was becoming hard to balance both. It's really also not fair to the company. Yeah. So I opted to do the depth job, you know, be the visionary, be the technologist. And that was the first decision to bring a CEO, a great CEO from outside. >>And I saw your video on the site. You said it was your decision. Yes. Go ahead. I have to ask you, cuz this is a real big transition for founders and you know, I have founder artists cuz everyone, you know, calls me that. But being the founder of a company, it's always hard to let go. I mean nine years as CEO, it's not like you had a, you had a great run. So this was it timing for you? Was it, was it a structural shift, like at super cloud, we're talking about a major shift that's happening right now in the industry. Was it a balance issue? Was it more if you wanted to get back in and in the tech >>Look, I, I also wanna answer, you know, why Sanja, but, but I'll address your question first. I always put the company first what's right for the company. Is it for me to start get stuck the co seat and try to juggle this depth and Brad simultaneously. I mean, I can stroke my ego a little bit there, but it's not good for the company. What's best for the company. You know, I'm a technologist. How about I oversee the technology part in partnership with so many great people I have in the company and I bring someone kick ass to be the CEO. And so then that was the second decision. Why Sanja when Sanjay, you know, is a very well known figure. He's managed billions of dollars of business in VMware. You know, been there, done that has, you know, some of the biggest, you know, people in the industry on his speed dial, you know, we were really fortunate to have someone like that, come in and accept the role of the CEO of cohesive. I think we can take the company to new Heights and I'm looking forward to my partnership with, with Sanja on this. >>It it's we, we called it the splash brothers and >>The, >>In the vernacular. It doesn't matter who gets the ball, whether it's step clay, we shoot. And I think if you look at some of the great partnerships, whether it was gates bomber, there, plenty of history of this, where a founder and a someone who was, it has to be complimentary skills. If I was a technologist myself and wanted to code we'd clash. Yeah. But I think this was really a match me in heaven because he, he can, I want him to keep innovating and building the best platform for today in the future. And our customers tell one customer told me, this is the best tech they've seen since VMware, 20 years ago, AWS, 10 years ago. And most recently this was a global 100 big customers. So I feel like this combination, now we have to show that it works. It's, you know, it's been three, four months. My getting to know him, you know, I'm day eight on the job, but I'm loving it. >>Well, it's a sluman model too. It's more modern example. You saw, he did it with Fred Ludy at service now. Yes. And, and of course at, at snowflake, yeah. And his book, you read his book. I dunno if you've read his book, amp it up, but app it up. And he says, I always you'll love this. Give great deference to the founder. Always show great respect. Right. And for good reason. So >>In fact, I mean you could talk to him, you actually met to >>Frank. I actually, you know, a month or so back, I actually had dinner with him in his ranch in Moana. And I posed the question. There was a number of CEOs that went there and I posed him the question. So Frank, you know, many of us, we grow being deaf guys, you know? And eventually when we take on the home of our CEO, we have to do breadth. How do you do it? And he's like, well, let me tell you, I was never a death guy. I'm a breath guy. >>I'm like, >>That's my answer. Yeah. >>So, so I >>Want the short story. So the day I got the job, I, I got a text from Frank and I said, what's your advice the first time CEO, three words, amp it up, >>Amp it up. Right? Yeah. >>And so you're always on brand, man. >>So you're an amazing operator. You've proven that time and time again at SAP, VMware, et cetera, you feel like now you, you, you wanna do both of those skills. You got the board and you got the operations cuz you look, you know, look at sloop when he's got Scarelli wherever he goes, he brings Scarelli with him as sort of the operator. How, how do you, how are you thinking >>About that? I mean it's early days, but yeah. Yeah. Small. I mean I've, you know, when I was, you know, it was 35,000 people at VMware, 80, 90,000 people at SAP, a really good run. The SAP run was 10 to 20 billion innovative products, especially in analytics and VMware six to 12 end user computing cloud. So I learned a lot. I think the company, you know, being about 2000 employees plus not to mayor tomorrow, but over the course next year I can meet everybody. Right? So first off the executive team, 10 of us, we're, we're building more and more cohesiveness if I could use that word between us, which is great, the next, you know, layers of VPs and every manager, I think that's possible. So I I'm a people person and a customer person. So I think when you take that sort of extroverted mindset, we'll bring energy to the workforce to, to retain the best and then recruit the best. >>And you know, even just the week we, we were announced that this announcement happened. Our website traffic went through the roof, the highest it's ever been, lots of resumes coming in. So, and then lots of customer engagement. So I think we'll take this, but I, I feel very good about the possibilities, because see, for me, I didn't wanna walk into the company to a company where the technology risk was high. Okay. I feel like that I can go to bed at night and the technology risk is low. This guy's gonna run a machine at the current and the future. And I'm hearing that from customers. Now, what I gotta do is get the, the amp it up part on the go to market. I know a little thing or too about >>That. You've got that down. I think the partnership is really key here. And again, nine use the CEO and then Sanja points to our super cloud trend that we've been looking at, which is there's another wave happening. There's a structural change in real time happening now, cloud one was done. We saw that transition, AWS cloud native now cloud native with an kind of operating system kind of vibe going on with on-premise hybrid edge. People say multi-cloud, but we're looking at this as an opportunity for companies like cohesive to go to the next level. So I gotta ask you guys, what do you see as structural change right now in the industry? That's disruptive. People are using cloud and scale and data to refactor their business models, change modern cases with cloud native. How are you guys looking at this next structural change that's happening right now? Yeah, >>I'll take that. So, so I'll start by saying that. Number one, data is the new oil and number two data is exploding, right? Every year data just grows like crazy managing data is becoming harder and harder. You mentioned some of those, right? There's so many cloud options available. Cloud one different vendors have different clouds. There is still on-prem there's edge infrastructure. And the number one problem that happens is our data is getting fragmented all over the place and managing so many fragments of data is getting harder and harder even within a cloud or within on-prem or within edge data is fragmented. Right? Number two, I think the hackers out there have realized that, you know, to make money, it's no longer necessary to Rob banks. They can actually see steal the data. So ransomware attacks on the rise it's become a boardroom level discussion. They say there's a ransomware attack happening every 11 seconds or so. Right? So protecting your data has become very important security data. Security has become very important. Compliance is important, right? So people are looking for data management solutions, the next gen data management platform that can really provide all this stuff. And that's what cohesive is about. >>What's the difference between data management and backup. Explain that >>Backup is just an entry point. That's one use case. I wanna draw an analogy. Let's draw an analogy to my former company, Google right? Google started by doing Google search, but is Google really just a search engine. They've built a platform that can do multiple things. You know, they might have started with search, but then they went down to roll out Google maps and Gmail and YouTube and so many other things on that platform. So similarly backups might be just the first use case, but it's really about that platform on which you can do more with the data that's next gen data management. >>But, but you am, I correct. You don't consider yourself a security company. One of your competitors is actually pivoting and in positioning themselves as a security company, I've always felt like data management, backup and recovery data protection is an adjacency to security, but those two worlds are coming together. How do you see >>It? Yeah. The way I see it is that security is part of data management. You start maybe by backing with data, but then you secure it and then you do more with that data. If you're only doing security, then you're just securing the data. You, you gotta do more with the data. So data management is much bigger. So >>It's a security is a subset of data. I mean, there you go. Big TA Sanjay. >>Well, I mean I've, and I, I, I I'd agree. And I actually, we don't get into that debate. You know, I've told the company, listen, we'll figure that out. Cuz who cares about the positioning at the bottom? My email, I say we are data management and data security company. Okay. Now what's the best word that describes three nouns, which I think we're gonna do management security and analytics. Okay. He showed me a beautiful diagram, went to his home in the course of one of these, you know, discrete conversations. And this was, I mean, he's done this before. Many, if you watch on YouTube, he showed me a picture of an ice big iceberg. And he said, listen, you know, if you look at companies like snowflake and data bricks, they're doing the management security and mostly analytics of data. That's the top of the iceberg, the stuff you see. >>But a lot of the stuff that's get backed archive is the bottom of the iceberg that you don't see. And you try to, if you try to ask a question on age data, the it guy will say, get a ticket. I'll come back with three days. I'll UNIV the data rehydrate and then you'll put it into a database. And you can think now imagine that you could do live searches analytics on, on age data that's analytics. So I think the management, the security, the analytics of, you know, if you wanna call it secondary data or backed up data or data, that's not hot and live warm, colder is a huge opportunity. Now, what do you wanna call one phrase that describes all of it. Do you call that superpower management security? Okay, whatever you wanna call it. I view it as saying, listen, let's build a platform. >>Some people call Google, a search company. People, some people call Google and information company and we just have to go and pursue every CIO and every CSO that has a management and a security and do course analytics problem. And that's what we're doing. And when I talk to the, you know, I didn't talk to all the 3000 customers, but the biggest customers and I was doing diligence. They're like this thing has got enormous potential. Okay. And we just have to now go focus, get every fortune 1000 company to pick us because this problem, even the first use case you talk back up is a little bit like, you know, razor blades and soap you've needed. You needed it 30 years ago and you'll need it for 30 years. It's just that the tools that were built in the last generation that were companies formed in 1990s, one of them I worked for years ago are aids are not built for the cloud. So I think this is a tremendous opportunity where many of those, those, those nos management security analytics will become part of what we do. And we'll come up with the right phrase for what the companies and do course >>Sanjay. So ma and Sanja. So given that given that's this Google transition, I like that example search was a data problem. They got sequenced to a broader market opportunity. What super cloud we trying to tease out is what does that change over from a data standpoint, cuz now the operating environments change has become more complex and the enterprises are savvy. Developers are savvy. Now they want, they want SAS solutions. They want freemium and expanding. They're gonna drive the operations agenda with DevOps. So what is the complexity that needs to be abstracted away? How do you see that moment? Because this is what people are talking about. They're saying security's built in, driven by developers. Developers are driving operations behavior. So what is the shift? Where do you guys see this new? Yeah. Expansive for cohesive. How do you fit into super cloud? >>So let me build up from that entry point. Maybe back up to what you're saying is the super cloud, right? Let me draw that journey. So let's say the legacy players are just doing backups. How, how sad is it that you have one silo sitting there just for peace of mind as an insurance policy and you do nothing with the data. If you have to do something with the data, you have to build another silo, you have to build another copy. You have to manage it separately. Right. So clearly that's a little bit brain damaged. Right. So, okay. So now you take a little bit of, you know, newer vendors who may take that backup platform and do a little bit more with that. Maybe they provide security, but your problem still remains. How do you do more with the data? How do you do some analytics? >>Like he's saying, right. How do you test development on that? How do you migrate the data to the cloud? How do you manage it? The data at scale? How do you do you provide a unified experience across, across multiple cloud, which you're calling the super cloud. That's where cohesive goes. So what we do, we provide a platform, right? We have tentacles in on-prem in each of the clouds. And on top of that, it looks like one platform that you manage. We have a single control plane, a UI. If you may, a single pin of glass, if, if you may, that our customers can use to manage all of it. And now it looks, starts looking like one platform. You mentioned Google, do you, when you go to, you know, kind Google search or a URL, do you really care? What happens behind the scenes mean behind the scenes? Google's built a platform that spans the whole world. No, >>But it's interesting. What's behind the scenes. It's a beautiful now. And I would say, listen, one other thing to pull on Dave, on the security part, I saw a lot of vendors this day in this space, white washing a security message on top of backup. Okay. And CSO, see through that, they'll offer warranties and guarantees or whatever, have you of X million dollars with a lot of caveats, which will never paid because it's like escape clause here. We won't pay it. Yeah. And, and what people really want is a scalable solution that works. And you know, we can match every warranty that's easy. And what I heard was this was the most scalable solution at scale. And that's why you have to approach this with a Google type mindset. I love the fact that every time you listen to sun pitch, I would, what, what I like about him, the most common word to use is scale. >>We do things at scale. So I found that him and AUR and some of the early Google people who come into the company had thought about scale. And, and even me it's like day eight. I found even the non-tech pieces of it. The processes that, you know, these guys are built for simple things in some cases were better than some of the things I saw are bigger companies I'd been used to. So we just have to continue, you know, building a scale platform with the enterprise. And then our cloud product is gonna be the simple solution for the masses. And my view of the world is there's 5,000 big companies and 5 million small companies we'll push the 5 million small companies as the cloud. Okay. Amazon's an investor in the company. AWS is a big partner. We'll talk about I'm sure knowing John's interest in that area, but that's a cloud play and that's gonna go to the cloud really fast. You not build you're in the marketplace, you're in the marketplace. I mean, maybe talk about the history of the Amazon relationship investing and all that. >>Yeah, absolutely. So in two years back late 2020, we, you know, in collaboration with AWS who also by the way is an investor now. And in cohesive, we rolled out what we call data management as a service. It's our SaaS service where we run our software in the cloud. And literally all customers have to do is just go there and sign on, right? They don't have to manage any infrastructure and stuff. What's nice is they can then combine that with, you know, software that they might have bought from cohesive. And it still looks like one platform. So what I'm trying to say is that they get a choice of the, of the way they wanna consume our software. They can consume it as a SAS service in the cloud. They can buy our software, manage it themselves, offload it to a partner on premises or what have you. But it still looks like that one platform, what you're calling a Supercloud >>Yeah. And developers are saying, they want the bag of Legos to compose their solutions. That's the Nirvana they want to get there. So that's, it has to look the same. >>Well, what is it? What we're calling a Superlo can we, can we test that for a second? So data management and service could span AWS and on-prem with the identical experience. So I guess I would call that a Supercloud I presume it's not gonna through AWS span multiple clouds, but, but >>Why not? >>Well, well interesting cuz we had this, I mean, so, okay. So we could in the future, it doesn't today. Well, >>David enough kind of pause for a second. Everything that we do there, if we do it will be customer driven. So there might be some customers I'll give you one Walmart that may want to store the data in a non AWS cloud risk cuz they're competitors. Right. So, but the control plane could still be in, in, in the way we built it, but the data might be stored somewhere else. >>What about, what about a on-prem customer? Who says, Hey, I, I like cohesive. I've now got multiple clouds. I want the identical experience across clouds. Yeah. Okay. So, so can you do that today? How do you do that today? Can we talk >>About that? Yeah. So basically think roughly about the split between the data plane and the control plane, the data plane is, you know, our cohesive clusters that could be sitting on premises that could be sitting in multiple data centers or you can run an instance of that cluster in the cloud, whichever cloud you choose. Right. That's what he was referring to as the data plane. So collectively all these clusters from the data plane, right? They stored the data, but it can all be managed using the control plane. So you still get that single image, the single experience across all clouds. And by the way, the, the, the, the cloud vendor does actually benefit because here's a customer. He mentioned a customer that may not wanna go to AWS, but when they get the data plane on a different cloud, whether it's Azure, whether it's the Google cloud, they then get data management services. Maybe they're able to replicate the data over to AWS. So AWS also gains. >>And your deployment model is you instantiate the cohesive stack on each of the regions and clouds, is that correct? And you building essentially, >>It all happens behind the scenes. That's right. You know, just like Google probably has their tentacles all over the world. We will instantiate and then make it all look like one platform. >>I mean, you should really think it's like a human body, right? The control planes, the head. Okay. And that controls everything. The data plane is large because it's a lot of the data, right? It's the rest of the body, that data plane could be wherever you want it to be. Traditionally, the part the old days was tape. Then you got disk. Now you got multiple clouds. So that's the way we think about it. And there on that piece of it will be neutral, right? We should be multi-cloud to the data plane being every single place. Cause it's customer demand. Where do you want your store data? Air gapped. On-prem no problem. We'll work with Dell. Okay. You wanna be in a particular cloud, AWS we'll work then optimized with S3 and glacier. So this is where I think the, the path to a multi-cloud or Supercloud is to be customer driven, but the control plane sits in Amazon. So >>We're blessed to have a number of, you know, technical geniuses in here. So earlier we were speaking to Ben wa deja VI, and what they do is different. They don't instantiate an individual, you know, regions. What they do is of a single global. Is there a, is there an advantage of doing it the way the cohesive does it in terms of simplicity or how do you see that? Is that a future direction for you from a technology standpoint? What are the trade offs there? >>So you want to be where the data is when you said single global, I take it that they run somewhere and the data has to go there. And in this day age, correct >>Said that. He said, you gotta move that in this >>Day and >>Age query that's, you know, across regions, look >>In this day and age with the way the data is growing, the way it is, it's hard to move around the data. It's much easier to move around the competition. And in these instances, what have you, so let the data be where it is and you manage it right there. >>So that's the advantage of instantiating in multiple regions. As you don't have to move the >>Data cost, we have the philosophy we call it. Let's bring the, the computation to the data rather than the data to >>The competition and the same security model, same governance model, same. How do you, how do you federate that? >>So it's all based on policies. You know, this overarching platform controlled by, by the control plane, you just, our customers just put in the policies and then the underlying nuts and bolts just take care >>Of, you know, it's when I first heard and start, I started watching some of his old videos, ACE really like hyperconverged brought to secondary storage. In fact, he said, oh yeah, that's great. You got it. Because I first called this idea, hyperconverged secondary storage, because the idea of him inventing hyperconverge was bringing compute to storage. It had never been done. I mean, you had the kind of big VC stuff, but these guys were the first to bring that hyperconverge at, at Nutanix. So I think this is that same idea of bringing computer storage, but now applied not to the warm data, but to the rest of the data, including a >>Lot of, what about developers? What's, what's your relationship with developers? >>Maybe you talk about the marketplace and everything >>He's yeah. And I'm, I'm curious as to do you have a PAs layer, what we call super PAs layer to create an identical developer experience across your Supercloud. I'm gonna my >>Term. So we want our customers not just to benefit from the software that we write. We also want them to benefit from, you know, software that's written by developers by third party people and so on and so forth. So we also support a marketplace on the platform where you can download apps from third party developers and run them on this platform. There's a, a number of successful apps. There's one, you know, look like I said, our entry point might be backups, but even when backups, we don't do everything. Look, for instance, we don't backup mainframes. There is a, a company we partner with, you know, and their software can run in our marketplace. And it's actually used by many, many of our financial customers. So our customers don't get, just get the benefit of what we build, but they also get the benefit of what third parties build. Another analogy I like to draw. You can tell. And front of analogy is I drew an analogy to hyperscale is like Google. Yeah. The second analogy I like to draw is that to a simple smartphone, right? A smartphone starts off by being a great phone. But beyond that, it's also a GPS player. It's a, it's a, it's a music player. It's a camera, it's a flashlight. And it also has a marketplace from where you can download apps and extend the power of that platform. >>Is that a, can we think of that as a PAs layer or no? Is it really not? You can, okay. You can say, is it purpose built for what you're the problem that you're trying to solve? >>So we, we just built APIs. Yeah. Right. We have an SDK that developers can use. And through those APIs, they get to leverage the underlying services that exist on the platform. And now developers can use that to take advantage of all that stuff. >>And it was, that was a key factor for me too. Cause I, what I, you know, I've studied all the six, seven players that sort of so-called leaders. Nobody had a developer ecosystem, nobody. Right? The old folks were built for the hardware era, but anyones were built for the cloud to it didn't have any partners were building on their platform. So I felt for me listen, and that the example of, you know, model nine rights, the name of the company that does back up. So there's, there's companies that are built on and there's a number of others. So our goal is to have a big tent, David, to everybody in the ecosystem to partner with us, to build on this platform. And, and that may take over time, but that's the way we're build >>It. And you have a metadata layer too, that has the intelligence >>To correct. It's all abstract. That that's right. So it's a combination of data and metadata. We have lots of metadata that keeps track of where the data is. You know, it allows you to index the data you can do quick searches. You can actually, you, we talking about the control plan from that >>Tracing, >>You can inject a search that'll through search throughout your multi-cloud environment, right? The super cloud that you call it. We have all that, all that goodness sounds >>Like a Supercloud John. >>Yeah. I mean, data tracing involved can trace the data lineage. >>You, you can trace the data lineage. So we, you know, provide, you know, compliance and stuff. So you can, >>All right. So my final question to wrap up, we guys, first of all, thanks for coming on. I know you're super busy, San Jose. We, we know what you're gonna do. You're gonna amp it up and, you know, knock all your numbers out. Think you always do. But what I'm interested in, what you're gonna jump into, cuz now you're gonna have the creative license to jump in to the product, the platform there has to be the next level in your mind. Can you share your thoughts on where this goes next? Love the control plane, separate out from the data plane. I think that plays well for super. How >>Much time do you have John? This guy's got, he's got a wealth. Ditis keep >>Going. Mark. Give us the most important thing you're gonna focus on. That kind of brings the super cloud and vision together. >>Yeah. Right away. I'm gonna, perhaps I, I can ion into two things. The first one is I like to call it building the, the machine, the system, right. Just to draw an analogy. Look, I draw an analogy to the us traffic system. People from all walks of life, rich, poor Democrats, Republicans, you know, different states. They all work in the, the traffic system and we drive well, right. It's a system that just works. Whereas in some other countries, you know, the system doesn't work. >>We know, >>We know a few of those. >>It's not about works. It's not about the people. It's the same people who would go from here to those countries and, and not dry. Well, so it's all about the system. So the first thing I, I have my sights on is to really strengthen the system that we have in our research development to make it a machine. I mean, it functions quite well even today, but wanna take it to the next level. Right. So that I wanna get to a point where innovation just happens in the grassroots. And it just, just like >>We automations scale optic brings all, >>Just happens without anyone overseeing it. Anyone there's no single point of bottleneck. I don't have to go take any diving catches or have you, there are people just working, you know, in a decentralized fashion and innovation just happens. Yeah. The second thing I work on of course is, you know, my heart and soul is in, you know, driving the vision, you know, the next level. And that of course is part of it. So those are the two things >>We heard from all day in our super cloud event that there's a need for an, an operating system. Yeah. Whether that's defacto standard or open. Correct. Do you see a consortium around the corner potentially to bring people together so that things could work together? Cuz there really isn't no stand there. Isn't a standards bodies. Now we have great hyperscale growth. We have on-prem we got the super cloud thing happening >>And it's a, it's kind of like what is an operating system? Operating system exposes some APIs that the applications can then use. And if you think about what we've been trying to do with the marketplace, right, we've built a huge platform and that platform is exposed through APIs. That third party developers can use. Right? And even we, when we, you know, built more and more services on top, you know, we rolled our D as we rolled out, backup as a service and a ready for thing security as a service governance, as a service, they're using those APIs. So we are building a distributor, putting systems of sorts. >>Well, congratulations on a great journey. Sanja. Congratulations on taking the hem. Thank you've got ball control. Now you're gonna be calling the ball cohesive as they say, it's, >>It's a team. It's, you know, I think I like that African phrase. If you want to go fast, you go alone. If you wanna go far, you go together. So I've always operated with the best deal. I'm so fortunate. This is to me like a dream come true because I always thought I wanted to work with a technologist that frees me up to do what I like. I mean, I started as an engineer, but that's not what I am today. Right? Yeah. So I do understand the product and this category I think is right for disruption. So I feel excited, you know, it's changing growing. Yeah. No. And it's a, it requires innovation with a cloud scale mindset and you guys have been great friends through the years. >>We'll be, we'll be watching you. >>I think it's not only disruption. It's creation. Yeah. There's a lot of white space that just hasn't been created yet. >>You're gonna have to, and you know, the proof, isn't the pudding. Yeah. You already have five of the biggest 10 financial institutions in the us and our customers. 25% of the fortune 500 users, us two of the biggest five pharmaceutical companies in the world use us. Probably, you know, some of the biggest companies, you know, the cars you have, you know, out there probably are customers. So it's already happening. >>I know you got an IPO filed confidentially. I know you can't talk numbers, but I can tell by your confidence, you're feeling good right now we are >>Feeling >>Good. Yeah. One day, one week, one month at a time. I mean, you just, you know, I like the, you know, Jeff Bezos, Andy jazzy expression, which is, it's always day one, you know, just because you've had success, even, you know, if, if a and when an IPO O makes sense, you just have to stay humble and hungry because you realize, okay, we've had a lot of success in the fortune 1000, but there's a lot of white space that hasn't picked USS yet. So let's go, yeah, there's lots of midmarket account >>Product opportunities are still, >>You know, I just stay humble and hungry and if you've got the team and then, you know, I'm really gonna be working also in the ecosystem. I think there's a lot of very good partners. So lots of ideas brew through >>The head. Okay. Well, thank you so much for coming on our super cloud event and, and, and also doubling up on the news of the new appointment and congratulations on the success guys. Coverage super cloud 22, I'm sure. Dave ante, thanks for watching. Stay tuned for more segments after this break.

>>Welcome back to Boston. Everybody watching the cubes coverage, OFS reinforce 22 from Boston, Atlanta chow lobster, the SOS a ruin in my summer, Andy and Smith is here is the CMO of laminar. Andy. Good to see you. Good >>To see you. Great to be >>Here. So laminar came outta stealth last year, 2021, sort of, as we were exiting the isolation economy. Yeah. Why was laminar started >>Really about there's there's two mega trends in the industry that, that created a problem that wasn't being addressed. Right? So the two mega trends was cloud transformation. Obviously that's been going on for a while, but what most people doesn't don't realize is it really accelerated with COVID right? Being all, everybody having to be remote, et cetera, various stats I've read like increased five times, right? So cloud transformation are now you are now problem, right? That's going on? And then the other next big mega trend is data democratization. So more data in the cloud than ever before. And this is, this is just going and going and going. And the result of those two things, more data in the cloud, how am I securing that data? You know, the, the, the breach culture we're in like every day, a new, a new data breach coming up, et cetera, just one Twitter, one yesterday, et cetera. The, those two things have caused a gap with data security teams and, and that's what he >>Heard at attract. Yeah. So, you know, to your point and we track this stuff pretty carefully quarterly, and you saw, it was really interesting trend. You actually saw AWS's growth rate accelerate during the pandemic. Absolutely. You know? Absolutely. So you're talking about, you know, a couple of hundred billion dollars for the big four clouds. If you, if you include Alibaba and it's still growing at 35, you know, 40% a year, which is astounding, so, okay. So more cloud, more data. Explain why that's a, a problem for practitioners. >>Yeah, exactly. The reality is in, in the security, what, what are we doing? What all the security it's about protecting your data in the end, right? Like, like we're here at this at, at reinforce all these security vendors here really it's about protecting your data, your sensitive data. And, but what, what had been happening is all the focus was on the infrastructure, the network, et cetera, et cetera, and not as much focus, particularly on the data and, and the move to the cloud gave the developers and the data scientists, way more power. They don't longer have to ask for permission. And so they can just do what they want. And it's actually wonderful for the business. The business is moving faster, you spin up applications sooner, you get new, new insights. So all those things are really great, but because the developer has so much power, they can just copy data over here, make a backup over here, new et cetera. And, and security has no idea about all these copies of the, of the data that are out there. And they're typically not as well protected as that main production source. And that's the gap that >>Exists. Okay. So there was this shift from sort of perimeter hardening the perimeter, hardening the infrastructure and, and now your premises, it's moving to the data we saw when, when there was during the pandemic, there was definitely a shift to end point security. There was a shift to cloud security rethinking the network, but it was still a lot of, you know, kind of cha chasing the whackamole and people have talked about this is a data problem for years. Yeah. But it was, it's taken a while for, for companies, for the technology industry to, to come at it. You guys are one of the first, if not the first. Yeah. Why do you think it took so long? Is this cuz it's really hard. >>Yeah. I mean, it, it's hard. You need to focus on it. The, the traditional security has been around the network and the box, right. And those are still necessary. It's important to, you know, your use identity to cover the edge, to, to make sure people can't get into the box, but you also have to have data. So what, what happens is there's really good solutions for enterprise data security, looking at database, you know, technology, et cetera. There are good solutions for cloud infrastructure security. So the CSP of the world and the CWPP are protecting containers, you know, protecting the infrastructure. But there really wasn't much for cloud everything you build and run in the cloud. So basically your custom application, your custom applications in the IAS and PAs environments, there really wasn't anything solving that. And that's really where laminar is focused. >>Okay. So you guys use this term shadow data. We talk about shadow. It what's shadow data. >>Yeah. So what we're finding at a hundred percent of our customer environments and our POVs and talking to CISOs out there is that they have these shadow data assets and shadow data elements that they have no clue that existed. So here's the example. Everybody knows the main RDS database that is in production. And this is where, you know, our, our data is taken from. But what people don't realize is there's a copy of that. You know, in a dev environment, somebody went to run a test and they was supposed to be there for two weeks. But then that developer left forgot, left it there. They left the company, oh, now it's been there for two years that there was an original SQL database left over from a lift and shift project. They got moved to RDS, but nobody deleted that thing there, you know, it's a database connected to an application, the application left, but that database, that abandoned database is still sitting. These are all real life customer examples of shadow data that we run into. And there's, and what the problem is that main production data store is secured pretty well. It's following all your policies, et cetera. But all these shadow data resources are typically less well protected unmonitored. And that is what the attackers are after. >>So you're, you know, the old, the, the Watergate follow the money, you're following the data, >>Following the data. >>How do you follow that data if there's so much of it, it, and it's, you know, sometimes, you know, not really well understood where it is. How do you know where >>It is? Yeah. It's the beauty of partnering with somebody like AWS, right? So with each of the cloud providers, we actually take a role in your cloud account and use the APIs from the cloud provider to see all the changes in all the instances are going on. Like it is, the problem is way more complicated in the cloud because I mean, AWS has over 200 services, dozens of ways to store data, right. It's wonderful for the developer, but it's very hard for the security practitioner. And so, because we have that visibility through the cloud provider's APIs, we can see all those changes that are happening. We can then say, ah, that's a data store. Let me go analyze, make a copy, have a snapshot of that and do the analyzing of that data right inside our customer's account without pulling the data out. And we have complete visibility to everything. And then we can give that data catalog over to the customer. >>All right. I gotta ask you a couple Colombo questions. So if you know, we talk about encryption, everything's encrypted everything. If, if the data is encrypted, why then would I need laminar? >>Because I mean, we'll make sure that the data's encrypted okay. Right. Often. So it's not supposed to be and not right. Two is, we're gonna tell you what type of data is inside there. Oh, is this, is this health information? Is it personal identify information? Is it credit cards? You know, et cetera, C so we'll classify the data for you. We will also, then there's things like retention, period. How long should we, I hold onto that data, all the things about what are, who has access, what's the exposure level for that data. And so when you, when you think about data security posture, what's the posture of that data you're looking at at those data policies. It's something that has been very well defined and written down. But in the past, there was just no way to go verify that those, that, that, that policy is actually being followed. And so we're doing that verification automatically. >>So without the context, you can't answer those other questions. So you make sure it's encrypted. If it's not, or you can at least notify me that it's not, you don't do the encryption. Right. Or do you, >>We don't do it ourselves, but we can give you here. Here's the command in and the Amazon to go encrypt it >>Right. Then I can automate that. And then the classification is key because now you're telling me the context. So I can say, okay, apply this policy to that data, retain it for this long, get rid of it after X number of years, or if it's work, process, get rid of it now. Yeah. And then who should have access to that data. And so you can help at least inform how to enforce those policies. >>Exactly. And so we, we, we call it guided remediation because what we're, you know, talking to a CISO, they're like, I need 400 more alerts, like a hole in the head like that. Doesn't do me any good. If you can't tell me how to resolve the, the, the, this security gap that I have or this, then it doesn't do any good. And, and the first, first it starts with who do I need to go talk to? Right. So they have hundreds, if not thousands of developers. Oh, great. You found this issue. I, I, I don't know who to go. Like, I can't just delete it myself, but I need to go talk to somebody really, should this be deleted? We need, do we really, really need to hold onto this? So we, we help guide who the data owner is. So we give you who to talk to. You, give you all the context. Here's the data, here's the data asset that it's in. Here's our suggestion. Here's the problem. Here's our suggestion for >>Solution. And you started the company on AWS >>Started on AWS. Absolutely. >>So what's of course it's best cloud and why not start there? So what's the relationship like, I mean, how'd you get started? You said, okay, Hey, we're we got an idea for a company. We're gonna build it on AWS. We're gonna become a customer. We're gonna, you know, >>We actually, so insight partners is our main investor. Yeah. And they were very helpful in giving us access to literally hundreds of CSOs, who we had conversations with before we actually launched the company. And so we did some shifting and to, to figure out our exact use case. But by the time we came to market, it was in February this year, we actually GAed the product that, where like product market fit nailed because we'd had so many conversations that we knew the problem in the market that we needed to solve. And we knew where we needed to solve it first. And, and the, the, the relationship we AWS is great. We just got on the marketplace, just became a, a partner. So really good. Good >>Start. So I gotta ask you, so I always ask this question. So how do you actually know when you have product market fit? >>You it's about those conversations. Right. You know, so like, I I've been to lots of startups and sometimes you're you're, you, you each have a conversation and then they, they saying, oh, well kind of want this. And we kind of like that. And so it, the more conversations you have, the more, you know, you're solving a real problem. Right. And, and, and, and, and you re react to what that, what that prospect is telling you back and, or that advisor or that whoever we're talking to. And, and every single one of the CISO conversations we had was I don't have a good inventory of my data in the cloud. >>The reason I asked that, cause I always ask the startups, like, when do you scale? Cause I think startups sometimes scale too fast. They try to scale too fast, they'll hire 50 sales people. And then they, you know, churn, you know, they, they got a 50% churn, but they're trying to optimize their go to market when they got 50% of their customers are gonna leave. So it's, it's gotta be the sequential thing. So, so you got product market fit. So are, are you in the scaling phase >>Now? We are. Yeah. Yeah, yeah. So now it's about how quickly can we deliver? We, we we're ramping customer base significantly. And, and you know, we've got a whole go to market team in, you know, sales and marketing in the us and, and often off to the races >>And you just run on AWS or you run another clouds. >>It's multi-cloud so AWS, Azure, GCP, et cetera. >>Okay. So then my least my next question is it sort of, you can do this within each of the individual clouds today. Do you see a day and maybe it's here today is where you can create a single experience across those clouds >>Today. It's a single experience across cloud. So our SaaS, we have our SaaS portion runs in AWS, but the actual data analysis runs in each cloud provider. So AWS, Azure, GCP and snowflake too, actually. >>Ah, okay. So I come through your whatever portal, like if I can use that term. Yep. And that's running on AWS. Yes. You're SAS, as you say, and then you go out to these other environments, GCP, Azure, AWS itself, and snowflake. Yep. And I see laminar, is that right? Or >>There's a piece running inside our customer's environment. Okay. So, so we have a customer, they, the, we have, we get a role inside of their cloud account or read only role inside of their cloud account. And we spin up serverless functions in that cloud account. That's where all the analysis happens. And that's why we don't take any data out of the environment. So it all stays there. And, and therefore we don't, we don't actually see the data outside of the environment. Like, I, I can tell you there's a metadata comes out. I can tell you, there are credit cards inside that data store, but I can't tell you exactly which credit card it is cuz I don't know. So all the important actions happens are there and just the metadata metadata comes out. So we can give you a cross cloud dashboard of all your sensitive data. >>And of course, so take the example of snowflake. They're going across clouds, they're building what we call super cloud sort of, of a layer that floats on top. You're just sort of going wherever that data goes. >>Yeah, exactly. So, so each of there's a component that lives in the customer's environment in the, in those multi-cloud environments and then a single view of the world dashboard that is our SaaS component that runs an AWS. So >>You guys are, is, am I correct? You're series a funded >>Series, a funded yeah, exactly. >>And, and already scaling to go to market. Yeah. Which is, which is early to scale. Right. I mean you've got startup experience. Right? >>Absolutely. >>How does it compare? >>Well, what was amazing here was access. I mean, really it was through the relationship with insight. It was access to the CISOs that I had never had at any of the other startups I was with. You're trying to get meetings, you're meeting with a lot of practitioners, you know, et cetera. But getting all those conversations with buyers was, was super valuable for us to say, ah, I know I'm solving a real problem that has value that they will pay for. Right. And, and, and so that, that was a year and a half probably still of all that work going on. We just, just waited to GA until we understood the market >>Better. Yeah. Insight. They're amazing. The way to talk about scaling. I mean, they've just the last 10 years that comp that, that PE firm has just gone wild in terms of just their, their philosophy, their approach, their cadence, their consistency. And now of course their portfolio. >>Yeah. And, and they started doing a little bit earlier and earlier stage. I mean, I, I always think of them as PE too, but you know, they, they did our seed round. Right. They did our a round and, and they're doing earlier stages, but particularly what they saw in Laar was exactly what we started this conversation with. They saw cloud transformation speeding up, they saw data democratization happening. They're like, we need to invest in this now because this is a now a problem to solve. >>Yeah. It's interesting. Cuz when you go back even pre 2010, you talk to, you know, look at insight, they would wait. They would invest in companies unless there was, you know, on the way to five plus million dollar ARR, they weren't doing seed deals. Totally. Like they saw, wow, these actually can be pretty lucrative and we can play and we have a point of view and yeah. So cool. Well, congratulations. I'll give you the final word. What, what should we be watching for from, from Laar as sort of, you know, milestones that you guys want to hit and, and indicators of success. >>Yeah. Now it's all about growth partnerships, you know, integrations with, with other of the players out here. Right. And so, you know, like scaling our AWS partnership is one of the key aspects for us. And so, you know, just look for, look for the name out there and, and you'll start, you'll start to see it a lot more. And, and if, if you have the need, you know, come look us up. Laar security.com. >>Awesome. Well thanks very much for coming to Cuban. Good luck. Appreciate it. All right. >>Wonderful. Thanks. You're >>Welcome. All right. Keep it right there, everybody. This is Dave ante. We'll be back right after this short break from AWS reinvent 2022 in Boston. You're watching the cue.

>> From the theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Welcome to this week's Wikibon, theCUBE's insights powered by ETR. As we exited the isolation economy last year, supercloud is a term that we introduced to describe something new that was happening in the world of cloud. In this Breaking Analysis, we address the 10 most frequently asked questions we get around supercloud. Okay, let's review these frequently asked questions on supercloud that we're going to try to answer today. Look at an industry that's full of hype and buzzwords. Why the hell does anyone need a new term? Aren't hyperscalers building out superclouds? We'll try to answer why the term supercloud connotes something different from hyperscale clouds. And we'll talk about the problems that superclouds solve specifically. And we'll further define the critical aspects of a supercloud architecture. We often get asked, isn't this just multi-cloud? Well, we don't think so, and we'll explain why in this Breaking Analysis. Now in an earlier episode, we introduced the notion of super PaaS. Well, isn't a plain vanilla PaaS already a super PaaS? Again, we don't think so, and we'll explain why. Who will actually build and who are the players currently building superclouds? What workloads and services will run on superclouds? And 8-A or number nine, what are some examples that we can share of supercloud? And finally, we'll answer what you can expect next from us on supercloud? Okay, let's get started. Why do we need another buzzword? Well, late last year, ahead of re:Invent, we were inspired by a post from Jerry Chen called "Castles in the Cloud." Now in that blog post, he introduced the idea that there were sub-markets emerging in cloud that presented opportunities for investors and entrepreneurs that the cloud wasn't going to suck the hyperscalers. Weren't going to suck all the value out of the industry. And so we introduced this notion of supercloud to describe what we saw as a value layer emerging above the hyperscalers CAPEX gift, we sometimes call it. Now it turns out, that we weren't the only ones using the term as both Cornell and MIT have used the phrase in somewhat similar, but different contexts. The point is something new was happening in the AWS and other ecosystems. It was more than IaaS and PaaS, and wasn't just SaaS running in the cloud. It was a new architecture that integrates infrastructure, platform and software as services to solve new problems that the cloud vendors in our view, weren't addressing by themselves. It seemed to us that the ecosystem was pursuing opportunities across clouds that went beyond conventional implementations of multi-cloud. And we felt there was a structural change going on at the industry level, the supercloud, metaphorically was highlighting. So that's the background on why we felt a new catch phrase was warranted, love it or hate it. It's memorable and it's what we chose. Now to that last point about structural industry transformation. Andy Rappaport is sometimes and often credited with identifying the shift from the vertically integrated IBM mainframe era to the fragmented PC microprocesor-based era in his HBR article in 1991. In fact, it was David Moschella, who at the time was an IDC Analyst who first introduced the concept in 1987, four years before Rappaport's article was published. Moschella saw that it was clear that Intel, Microsoft, Seagate and others would replace the system vendors, and put that forth in a graphic that looked similar to the first two on this chart. We don't have to review the shift from IBM as the center of the industry to Wintel, that's well understood. What isn't as well known or accepted is what Moschella put out in his 2018 book called "Seeing Digital" which introduced the idea of "The Matrix" that's shown on the right hand side of this chart. Moschella posited that new services were emerging built on top of the internet and hyperscale clouds that would integrate other innovations and would define the next era of computing. He used the term Matrix because the conceptual depiction included not only horizontal technology rose like the cloud and the internet, but for the first time included connected industry verticals, the columns in this chart. Moschella pointed out that whereas historically, industry verticals had a closed value chain or stack and ecosystem of R&D, and production, and manufacturing, and distribution. And if you were in that industry, the expertise within that vertical generally stayed within that vertical and was critical to success. But because of digital and data, for the first time, companies were able to traverse industries, jump across industries and compete because data enabled them to do that. Examples, Amazon and content, payments, groceries, Apple, and payments, and content, and so forth. There are many examples. Data was now this unifying enabler and this marked a change in the structure of the technology landscape. And supercloud is meant to imply more than running in hyperscale clouds, rather it's the combination of multiple technologies enabled by CloudScale with new industry participants from those verticals, financial services and healthcare, manufacturing, energy, media, and virtually all in any industry. Kind of an extension of every company is a software company. Basically, every company now has the opportunity to build their own cloud or supercloud. And we'll come back to that. Let's first address what's different about superclouds relative to hyperscale clouds? You know, this one's pretty straightforward and obvious, I think. Hyperscale clouds, they're walled gardens where they want your data in their cloud and they want to keep you there. Sure, every cloud player realizes that not all data will go to their particular cloud so they're meeting customers where their data lives with initiatives like Amazon Outposts and Azure Arc, and Google Anthos. But at the end of the day, the more homogeneous they can make their environments, the better control, security, cost, and performance they can deliver. The more complex the environment, the more difficult it is to deliver on their brand promises. And of course, the lesser margin that's left for them to capture. Will the hyperscalers get more serious about cross-cloud services? Maybe, but they have plenty of work to do within their own clouds and within enabling their own ecosystems. They had a long way to go a lot of runway. So let's talk about specifically, what problems superclouds solve? We've all seen the stats from IDC or Gartner, or whomever the customers on average use more than one cloud. You know, two clouds, three clouds, five clouds, 20 clouds. And we know these clouds operate in disconnected silos for the most part. And that's a problem because each cloud requires different skills because the development environment is different as is the operating environment. They have different APIs, different primitives, and different management tools that are optimized for each respective hyperscale cloud. Their functions and value props don't extend to their competitors' clouds for the most part. Why would they? As a result, there's friction when moving between different clouds. It's hard to share data, it's hard to move work. It's hard to secure and govern data. It's hard to enforce organizational edicts and policies across these clouds, and on-prem. Supercloud is an architecture designed to create a single environment that enables management of workloads and data across clouds in an effort to take out complexity, accelerate application development, streamline operations and share data safely, irrespective of location. It's pretty straightforward, but non-trivial, which is why I always ask a company's CEO and executives if stock buybacks and dividends will yield as much return as building out superclouds that solve really specific and hard problems, and create differential value. Okay, let's dig a bit more into the architectural aspects of supercloud. In other words, what are the salient attributes of supercloud? So first and foremost, a supercloud runs a set of specific services designed to solve a unique problem and it can do so in more than one cloud. Superclouds leverage the underlying cloud native tooling of a hyperscale cloud, but they're optimized for a specific objective that aligns with the problem that they're trying to solve. For example, supercloud might be optimized for lowest cost or lowest latency, or sharing data, or governing, or securing that data, or higher performance for networking, for example. But the point is, the collection of services that is being delivered is focused on a unique value proposition that is not being delivered by the hyperscalers across clouds. A supercloud abstracts the underlying and siloed primitives of the native PaaS layer from the hyperscale cloud and then using its own specific platform as a service tooling, creates a common experience across clouds for developers and users. And it does so in a most efficient manner, meaning it has the metadata knowledge and management capabilities that can optimize for latency, bandwidth, or recovery, or data sovereignty, or whatever unique value that supercloud is delivering for the specific use case in their domain. And a supercloud comprises a super PaaS capability that allows ecosystem partners through APIs to add incremental value on top of the supercloud platform to fill gaps, accelerate features, and of course innovate. The services can be infrastructure-related, they could be application services, they could be data services, security services, user services, et cetera, designed and packaged to bring unique value to customers. Again, that hyperscalers are not delivering across clouds or on-premises. Okay, so another common question we get is, isn't that just multi-cloud? And what we'd say to that is yes, but no. You can call it multi-cloud 2.0, if you want, if you want to use it, it's kind of a commonly used rubric. But as Dell's Chuck Whitten proclaimed at Dell Technologies World this year, multi-cloud by design, is different than multi-cloud by default. Meaning to date, multi-cloud has largely been a symptom of what we've called multi-vendor or of M&A, you buy a company and they happen to use Google Cloud, and so you bring it in. And when you look at most so-called, multi-cloud implementations, you see things like an on-prem stack, which is wrapped in a container and hosted on a specific cloud or increasingly a technology vendor has done the work of building a cloud native version of their stack and running it on a specific cloud. But historically, it's been a unique experience within each cloud with virtually no connection between the cloud silos. Supercloud sets out to build incremental value across clouds and above hyperscale CAPEX that goes beyond cloud compatibility within each cloud. So if you want to call it multi-cloud 2.0, that's fine, but we chose to call it supercloud. Okay, so at this point you may be asking, well isn't PaaS already a version of supercloud? And again, we would say no, that supercloud and its corresponding superPaaS layer which is a prerequisite, gives the freedom to store, process and manage, and secure, and connect islands of data across a continuum with a common experience across clouds. And the services offered are specific to that supercloud and will vary by each offering. Your OpenShift, for example, can be used to construct a superPaaS, but in and of itself, isn't a superPaaS, it's generic. A superPaaS might be developed to support, for instance, ultra low latency database work. It would unlikely again, taking the OpenShift example, it's unlikely that off-the-shelf OpenShift would be used to develop such a low latency superPaaS layer for ultra low latency database work. The point is supercloud and its inherent superPaaS will be optimized to solve specific problems like that low latency example for distributed databases or fast backup and recovery for data protection, and ransomware, or data sharing, or data governance. Highly specific use cases that the supercloud is designed to solve for. Okay, another question we often get is who has a supercloud today and who's building a supercloud, and who are the contenders? Well, most companies that consider themselves cloud players will, we believe, be building or are building superclouds. Here's a common ETR graphic that we like to show with Net Score or spending momentum on the Y axis and overlap or pervasiveness in the ETR surveys on the X axis. And we've randomly chosen a number of players that we think are in the supercloud mix, and we've included the hyperscalers because they are enablers. Now remember, this is a spectrum of maturity it's a maturity model and we've added some of those industry players that we see building superclouds like CapitalOne, Goldman Sachs, Walmart. This is in deference to Moschella's observation around The Matrix and the industry structural changes that are going on. This goes back to every company, being a software company and rather than pattern match an outdated SaaS model, we see new industry structures emerging where software and data, and tools, specific to an industry will lead the next wave of innovation and bring in new value that traditional technology companies aren't going to solve, and the hyperscalers aren't going to solve. You know, we've talked a lot about Snowflake's data cloud as an example of supercloud. After being at Snowflake Summit, we're more convinced than ever that they're headed in this direction. VMware is clearly going after cross-cloud services you know, perhaps creating a new category. Basically, every large company we see either pursuing supercloud initiatives or thinking about it. Dell showed project Alpine at Dell Tech World, that's a supercloud. Snowflake introducing a new application development capability based on their superPaaS, our term of course, they don't use the phrase. Mongo, Couchbase, Nutanix, Pure Storage, Veeam, CrowdStrike, Okta, Zscaler. Yeah, all of those guys. Yes, Cisco and HPE. Even though on theCUBE at HPE Discover, Fidelma Russo said on theCUBE, she wasn't a fan of cloaking mechanisms, but then we talked to HPE's Head of Storage Services, Omer Asad is clearly headed in the direction that we would consider supercloud. Again, those cross-cloud services, of course, their emphasis is connecting as well on-prem. That single experience, which traditionally has not existed with multi-cloud or hybrid. And we're seeing the emergence of companies, smaller companies like Aviatrix and Starburst, and Clumio and others that are building versions of superclouds that solve for a specific problem for their customers. Even ISVs like Adobe, ADP, we've talked to UiPath. They seem to be looking at new ways to go beyond the SaaS model and add value within their cloud ecosystem specifically, around data as part of their and their customers digital transformations. So yeah, pretty much every tech vendor with any size or momentum and new industry players are coming out of hiding, and competing. Building superclouds that look a lot like Moschella's Matrix, with machine intelligence and blockchains, and virtual realities, and gaming, all enabled by the internet and hyperscale cloud CAPEX. So it's moving fast and it's the future in our opinion. So don't get too caught up in the past or you'll be left behind. Okay, what about examples? We've given a number in the past, but let's try to be a little bit more specific. Here are a few we've selected and we're going to answer the two questions in one section here. What workloads and services will run in superclouds and what are some examples? Let's start with analytics. Our favorite example is Snowflake, it's one of the furthest along with its data cloud, in our view. It's a supercloud optimized for data sharing and governance, query performance, and security, and ecosystem enablement. When you do things inside of that data cloud, what we call a super data cloud. Again, our term, not theirs. You can do things that you could not do in a single cloud. You can't do this with Redshift, You can't do this with SQL server and they're bringing new data types now with merging analytics or at least accommodate analytics and transaction type data, and bringing open source tooling with things like Apache Iceberg. And so it ticks the boxes we laid out earlier. I would say that a company like Databricks is also in that mix doing it, coming at it from a data science perspective, trying to create that consistent experience for data scientists and data engineering across clouds. Converge databases, running transaction and analytic workloads is another example. Take a look at what Couchbase is doing with Capella and how it's enabling stretching the cloud to the edge with ARM-based platforms and optimizing for low latency across clouds, and even out to the edge. Document database workloads, look at MongoDB, a very developer-friendly platform that with the Atlas is moving toward a supercloud model running document databases very, very efficiently. How about general purpose workloads? This is where VMware comes into to play. Very clearly, there's a need to create a common operating environment across clouds and on-prem, and out to the edge. And I say VMware is hard at work on that. Managing and moving workloads, and balancing workloads, and being able to recover very quickly across clouds for everyday applications. Network routing, take a look at what Aviatrix is doing across clouds, industry workloads. We see CapitalOne, it announced its cost optimization platform for Snowflake, piggybacking on Snowflake supercloud or super data cloud. And in our view, it's very clearly going to go after other markets is going to test it out with Snowflake, running, optimizing on AWS and it's going to expand to other clouds as Snowflake's business and those other clouds grows. Walmart working with Microsoft to create an on-premed Azure experience that's seamless. Yes, that counts, on-prem counts. If you can create that seamless and continuous experience, identical experience from on-prem to a hyperscale cloud, we would include that as a supercloud. You know, we've written about what Goldman is doing. Again, connecting its on-prem data and software tooling, and other capabilities to AWS for scale. And we can bet dollars to donuts that Oracle will be building a supercloud in healthcare with its Cerner acquisition. Supercloud is everywhere you look. So I'm sorry, naysayers it's happening all around us. So what's next? Well, with all the industry buzz and debate about the future, John Furrier and I, have decided to host an event in Palo Alto, we're motivated and inspired to further this conversation. And we welcome all points of view, positive, negative, multi-cloud, supercloud, hypercloud, all welcome. So theCUBE on Supercloud is coming on August 9th, out of our Palo Alto studios, we'll be running a live program on the topic. We've reached out to a number of industry participants, VMware, Snowflake, Confluent, Sky High Security, Gee Rittenhouse's new company, HashiCorp, CloudFlare. We've hit up Red Hat and we expect many of these folks will be in our studios on August 9th. And we've invited a number of industry participants as well that we're excited to have on. From industry, from financial services, from healthcare, from retail, we're inviting analysts, thought leaders, investors. We're going to have more detail in the coming weeks, but for now, if you're interested, please reach out to me or John with how you think you can advance the discussion and we'll see if we can fit you in. So mark your calendars, stay tuned for more information. Okay, that's it for today. Thanks to Alex Myerson who handles production and manages the podcast for Breaking Analysis. And I want to thank Kristen Martin and Cheryl Knight, they help get the word out on social and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE, who does a lot of editing and appreciate you posting on SiliconANGLE, Rob. Thanks to all of you. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. It publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me @DVellante, or comment on my LinkedIn post. And please do check out ETR.ai for the best survey data. And the enterprise tech business will be at AWS NYC Summit next Tuesday, July 12th. So if you're there, please do stop by and say hello to theCUBE, it's at the Javits Center. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching. And we'll see you next time on "Breaking Analysis." (bright music)

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase. Topic is MarTech: Emerging Cloud-Scale Experience. This is season two, episode three of the ongoing series covering the exciting startups from the AWS ecosystem. Talk about their value proposition and their company and all the good stuff that's going on. I'm your host, John Furrier. And today we're excited to be joined by Manyam Mallela who's the co-founder and head of AI at Blueshift. Great to have you on here to talk about the Blueshift-Intelligent Customer Engagement, Made Simple. Thanks for joining us today. >> Thank you, John. Thank you for having me. >> So last time we did our intro video. We put it out in the web. Got great feedback. One of the things that we talked about, which is resonating out there in the viral Twitter sphere and in the thought leadership circles is this concept that you mentioned called 10X marketer. That idea that you have a solution that can provide 10X value. Kind of a riff on the 10X engineer in the DevOps cloud world. What does it mean? And how does someone get there? >> Yeah, fantastic. I think that's a great way to start our discussion. I think a lot of organizations, especially as of this current economic environment are looking to say, I have limited resources, limited budgets, how do I actually achieve digital and customer engagement that helps move the needle for my key metrics, whether it's average revenue per user, lifetime value of the user and frequent interactions. Above all, the more frequently a brand is able to interact with their customers, the better they understand them, the better they can actually engage them. And that usually leads to long term good outcomes for both customer and the brand and the organizations. So the way I see 10X marketer is that you need to have tools that give you that speed and agility without hindering your ability to activate any of the campaigns or experience that you want to create. And I see the roadblocks usually for many organizations, is that kind of threefold. One is your data silos. Usually data that is on your sites, does not talk to your app data, does not talk to your social data, does not talk to your CRM data and so forth. So how do I break those silos? The second is channel silos. I actually have customers who are only engaging on email or some are on email and mobile apps. Some are on email and mobile apps and maybe the OTT TV in a Roku or one of the connected TV experiences, or maybe in the future, another Web3 environments. How do I actually break those channel silos so that I get a comprehensive view of the customer and my marketing team can engage with all of them in respect to the channel? So break the channel silos. And the last part, what I call like some of the little talked about is I call the inside silo, which is that, not only do you need to have the data, but you also have to have a common language to share and talk about within your organizations. What are we learning from our customers? What do we translate our learning and insight on this common data platform or fabric into an action? And that requires the shared language of how do I actually know my customers and what do I do with them? Like either the inside silo as well. I think a lot of times organizations do get into this habit like each one speaks their own language, but they don't actually are talking the common language of what did we actually know about the real customer there. >> Yeah, and I think that's a great conversation because there's two, when you hear 10X marketer or 10X conversations, it implies a couple things. One is you're breaking an old way and bringing in something new. And the new is a force multiplier, in this case, 10X marketer. But this is the cloud scale so marketing executives, chiefs, staffs, chiefs of staffs of CMOs and their staffs. They want to get that scale. So marketing at scale is now the table stakes. Now budget constraints are there as well. So you're starting to see, okay, I need to do more with less. Now the big question comes up is ROI. So I want to have AI. I want to have all these force multipliers. What do I got to do with the old? How do I handle that? How do I bring the new in and operationalize it? And if that's the case, I'm making a change. So I have to ask you, what's your view on the ROI of AI marketing, because this is a key component 'cause you've got scale factor here. You've got to force multiplier opportunity. How do you get that ROI on the table? >> I think that as you rightly said, it's table stakes. And I think the ROI of AI marketing starts with one very key simple premise that today some of the tools allow you to do things one at a time. So I can actually say, "can I run this campaign today?" And you can scramble your team, hustle your way, get everybody involved and run that campaign. And then tomorrow I'd say like, Hey, I looked at the results. Can I do this again? And they're like, oh, we just asked for all of us to get that done. How do I do it tomorrow? How do I do it next week? How do I do it for every single week for the rest of the year? That's where I think the AI marketing is essentially taking your insight, taking your creativity, and creating a platform and a tool that allows you to run this every single day. And that's agility at scale. That is not only a scale of the customer base, but scale across time. And that AI-based automation is the key ROI piece for a lot of AI marketing practitioners. So Forrester, for example, did a comprehensive total economic impact study with our customers. And what they found out was actually the 781% ROI that they reported in that particular report is based on three key factors. One is being able to do experiences that are intelligent at scale, day in and day out. So do your targeting, do your recommendations. Not just one day, but do it every single day. And don't hold back yourself on being able to do that. >> I think they got to get the return. They got to get the sales too. This is the numbers. >> That's right. They actually have real dollars, real numbers attached to it. They have a calculator. You can actually go in and plug your own numbers and get what you might expect from your existing customer base. The second is that once you have a unified platform like ours, the 10X marketer that we're talking about is actually able to do more. It's sometimes actually, it's kind of counterintuitive to think that a smaller team does more. But in reality, what we have seen, that is the case. When you actually have the right tools, the smaller teams actually achieve more. And that's the redundant operations, conflicting insights that go away into something more coherent and comprehensive. And that's the second insight that they found. And the third is just having reporting and all of the things in one place means that you can amplify it. You can amplify it across your paid media channels. You can amplify it across your promotions programs and other partnerships that you're running. >> That's the key thing about platforms that people don't understand is that you have a platform and it enables a lot of value. In this case, force multiplier value. It enables more value than you pay for it. But the key is it enables customers to do things without a line of code, meaning it's a platform. They're innovating on top of it. And that's, I think, where the ROI comes in and this leads me where the next question is. I wanted to ask you is, not to throw a wet blanket on the MarTech industry, but I got to think of when I hear marketing automation, I kind of think old. I think old, inadequate antiquated technologies. I think email blasting and just some boring stuff that just gets siloed or it's bespoke from something else. Are marketing automation tools created equal? Does something like, what you guys are doing with SmartHub? Change that, and can you just talk about that 'cause it's not going to go away. It's just another level that's going to be abstracted away under the coverage. >> Yeah, great question. Certainly, email marketing has been practiced for two or three decades now and in some form or another. I think we went from essentially what people call list-based marketing. I have a list, let me keep blasting the same message to everybody and then hopefully something will come out of it. A little bit more of saying, then they can, okay, maybe now I have CRM database and can I do database marketing, which they will call like, "Hey, Hi John. Hi Manyam", which is the first name. And that's all they think will get the customer excited about because you'll call them by name, which is certainly helpful, but not enough. I think now what we call like, the new age that we live in is that we call it graph-based marketing. And the way we materialize that is that every single user is interacting with a brand with their offerings. So that this interaction graph that's happening across millions of customers, across thousands of content articles, videos, shows, products, items, and that graph actually has much richer knowledge of what the customer wants than the first names or list-based ones. So I think the next evolution of marketing automation, even though the industry has been there a while, there is a step change in what can actually be done at scale. And which is taking that interaction graph and making that a part of the experience for the customer, and that's what we enable. That's why we do think of that as a big step change from how people are being practicing list-based marketing. And within that, certainly there is a relation of curve as to how people approach AI marketing and they are in a different spectrum. Some people are still at list-based marketing. Some people are database marketing. And hopefully will move them to this new interaction graph-based marketing. >> Yeah and I think the context is key. I like how you bring up the graph angle on this because the graph databases imply there's a lot of different optionality around what's happened contextually both over time and currently and it adds to it. Makes it smarter. It's not just siloed, just one dimensional. It feels like it's got a lot there. This is clearly I'm a big fan of and I think this is the way to go. As you get more personalization, you get more data. Graphic database makes a lot of sense. So I have to ask you, this is a really cutting edge value proposition, who are the primary buyers and users in an organization that you guys are working with? >> Yeah, great question. So we typically have CMO organizations approaching us with this problem and they usually talk to their CIO organizations, their counterparts, and the chief information officers have been investing in data fabrics, data lakes, data warehouses for the better part of last decade or two, and have some very cutting edge technology that goes into organizing all this data. But that doesn't still solve the problem of how do I take this data and make a meaningful, relevant, authentic experience for the customer. That's the CMO problem. And CMO are now challenge with creating product level experience with every interaction and that's where we coming. So the CMO are the buyers of our SmartHub CDP platform. And we're looking for consolidating hundreds of tools that they had in the past and making that one or two channel marketers. Actually, the 10X marketer that we talk about. And you need the right tool on top of your data lakes and data warehouses to be able to do that. So CMO are also the real drivers of using this technology. >> I think that also place the ROI equation around ROI and having that unified platform. Great call out there. I got to ask you the question here 'cause this comes up a lot and when I hear you talking, I think, okay, all the great stuff you guys have there. But if I'm a company, I want to make my core competencies mine. I don't really want to outsource or buy something that's going to be core to my business. But at the same time as market shifts, the business changes. And sometimes people don't even know what business they're in at the end of the day. And as it gets more complicated too, by the way. So the question comes up with companies and I can see this clearly, do I buy it? Do I build it? When it comes to AI because that's a core competency. Wait a minute, AI. I'm going to maybe buy some chatbot technology. That's not really AI, but it feels like AI, but I'm a company, I want to buy it or build it. That's a choice. What do you see there? 'Cause you guys have a very comprehensive platform. It's hard to replicate, imitates, inimitable. So what's your customers doing with respect buy and build? And where do they get the core competency? What do they get to have as a core competency? >> Fantastic. I think certainly, AI as it applies to at the organization level, I've seen this at my previous organization that I was part of, and there will be product and financial applications that are using AI for the service of that organization. So we do see, depending upon the size of the organization having in-house AI and data science teams. They are focused on these long term problems that they are doing as part of their product itself. Adjacent to that, the CMO organization gets some resources, but not certainly a lot. I think the CMO organization is usually challenged with the task, but not given the hundred people data science and engineering team to be able to go solve that. So what we see among our customer base is that they need agile platform to do most of the things that they need to do on a day to day basis, but augmented with what our in-house data science they have. So we are an extensible platform. What we have seen is that half of our customers use us solely for the AI needs. The other half certainly uses both AI modules that we provide and are actually augmented with things that they've already built. And we do not have a fight in that ring. But we do acknowledge and we do provide the right hooks for getting the data out of our system and bringing their AI back into our system. And we think that at the end of the day, if you want agility for the CMO, there should not be any barriers. >> It's like they're in the data business and that's the focus. So I think with what I hear you saying is that with your technology and platform, you're enabling to get them to be in the data business as fast as possible. >> That's right. >> Versus algorithm business, which they could add to over time. >> Certainly they could add to. But I think the bulk of competencies for the CMO are on the creative side. And certainly wrangling with data pipelines day in and day out and wondering what actually happened to a pipeline in the middle of the night is not probably what they would want to focus on. >> Not their core confidence. Yeah, I got that. >> That's right. >> You can do all the heavy lifting. I love that. I got to ask you on the Blueshift side on customer experience consumption. how can someone experience the product before buying? Is there a trial or POC? What's the scale and scope of operationalizing and getting the Blueshift value proposition in them? >> Yeah, great. So we actually recently released a fantastic way to experience our product. So if you go to our website, there's only one call-to-action saying, explore Blueshift. And if you click on that, without asking, anything other than your business email address, you're shown the full product. You're given a guided tour of all the possibilities. So you can actually experience what your marketing team would be doing in the product. And they call it Project Rover. We launched it very recently and we are seeing fantastic reception to that. I think a lot of times, as you said, there is that question mark of like, I have a marketing team that is already doing X, Y, Z. Now you are asking me to implement Blueshift. How would they actually experience the product? And now they can go in and experience the product. It's a great way to get the gist of the product in 10 clicks. Much more than going through any number of videos or articles. I think people really want to say, let me do those 10 clicks. And I know what impression that I can get from platform. So we do think that's a great way to experience the product and it's easily available from the main website. >> It's in the value proposition. It isn't always a straight line. And you got that technology. And I got to ask from between your experience with the customers that you're talking to, prospects, and customers, where do you see yourself winning deals on Customer Engagement, Made Simple because the word customer engagement's been around for a while, and it's become, I won't say cliche, but there's been different generational evolutions of technology that made that possible. Obviously, we're living in an era of high velocity Omni-Channel, a lot of data, the graph databases you mentioned are in there, big part of it. Where are you winning deals? Where are customers pain points where you are solving that specifically? >> Yeah, great question. So the organizations that come to us usually have one of the dimensions of either they have offering complexity, which is what catalog of content or videos or items do they offer to the customers. And on the data complexity on the other side is to what the scale of customer base that I usually target. And that problem has not gone away. I think the customer engagement, even though has been around for a while, the problem of engaging those customers at scale hasn't gone away and it only is getting harder and harder and organizations that have, especially on what we call the business-to-consumer side where the bulk of what marketing organizations in a B2C segments are doing. I have tens to millions of customers and how do I engage them day in and day out. And I think that all that problem is only getting harder because consumer preferences keeps shifting all the time. >> And where's your sweet spot for your customer? What size? Can you just share the target organization? Is it medium enterprise, large B2C, B2B2C? What's the focus area? >> Yeah, great question. So we have seen like startups that are in Silicon Valley. I have now half a million monthly active users, how do I actually engage them to customers and clients like LendingTree and PayPal and Discovery and BBC who have been in the business for multiple decades, have tens of millions of customers that they're engaging with. So that's kind of our sweet spot. We are certainly not maybe for small shop with maybe a hundred plus customers. But as you reach the scale of tens of thousands of customers, you start seeing this problem. And then you start to look out for solutions that are beyond, especially list-based marketing and email blast. >> So as the scale, you can dial up and down, but you have to have some enough scale to get the data pattern. >> That's right. >> If I can connect the dots there. >> I would probably say, looking at a hundred thousand or more monthly active customer base, and then you're trying to ramp up your own growth based on what you're learning and to engage those customers. >> It's like a bulldozer. You need the heavy equipment. Great conversation. For the last minute we have here Manyam, give you a plug for the company. What's going on? What are you guys doing? What's new? Give some success stories, your latest achievements. Take a minute to give a plug for the company. >> Yeah, great. We have been recognized by Deloitte as the fastest growth startup two years in a row and continuing to be on that streak. We have released currently integrations with AWS partners and Snowflake partners and data lake partners that allow implementing Blueshift a much streamlined experience with bidirectional integrations. We have now hundred plus data connectors and data integrations in our system and that takes care of many of our needs. And now, I think organizations that have been budget constraint and are trying to achieve a lot with a small team are actually going to look at these solutions and say, "Can I get there?" and "Can I become that 10X marketing organization? And as you have said, agility at scale is very, very hard to achieve. Being able to take your marketing team and achieve 10X requires the right platform and the right solution. We are ready for it. >> And every company's in the data business that's the asset. You guys make that sing for them. It's good stuff. Love the 10X. Love the scale. Manyam Mallela, thanks for coming on. Co-founder, Head of AI at Blueshift. This is the AWS Startup Showcase season two, episode three of the ongoing series covering the exciting startups from the AWS ecosystem. I'm John Furrier, your host. Thanks for watching. >> Thank you, John. (upbeat music)

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's continuing coverage of HPE. Discover 22 live from Las Vegas, the Venetian expo center at Lisa Martin and Dave Velante have a very special guest. Next one of our esteemed alumni here on the cube, Antonio Neri, the president and CEO of HPE, Antonio. Thank you so much for joining us this morning. >>Well, thanks for free with us today. >>Great to be back here after three years away. Yeah. Sit on stage yesterday in front of a massive sea of people. The energy here is electric. Yeah. Must have felt great yesterday, but you, you stood on stage three years ago and said buy 20, 22. And here it is. Yeah. We're gonna deliver our entire portfolio as a service. What was it like to be on stage and say we've done that. Here's where we are. We are a new company. >>Well, first of all, as always, I love the cube to cover HP discover, as you said, has been many, many years, and I hope you saw a different company yesterday. I'm really proud of what happened yesterday, because it was a pivotable moment in our journey. If I reflect back in my four years as a CEO, we said the enterprise of the future will be edge centric, cloud enable and data driven in 2018. And I pledged to invest 4 billion over four years. And you see the momentum we have at the edge with our business. And then in 19, to your point, Lisa, we said, by the end of 2022, we will offer everything as a service. When you look at the floor behind us, everything is a as a service experience from the moment you log through IHP GreenLake platform to all the cloud services we offer. So for me, it is a proud moment because our team worked really hard to deliver on that province on the face of a lot of challenges, >>Tremendous challenges, the last couple years that nobody could have predicted or even forecast, how can we tolerate this? Talk to me about your customer conversations and how they have changed and evolved as every company today has to be a data company. >>Well, even this morning, up to this interview, I already met four customers in, in less than an hour and a half. And I will say all of them, first of all, really appreciated bringing HP discover back. And what they really appreciated was the fact that they had the opportunity to meet and greet and talk to people. The energy that comes from that engagement is second to none. And I think says something right about the moment we are at this time, where the return to work and everything else. I think this is a wake up call in many ways, but customers are telling us is that they want to engage with a partner that has a vision that can take them to their journey, whatever that journey is. And we know digital transformation is core to everything, but ultimately they are now more focused on delivering outcomes for the organization they're running in it. And that's why HP GreenLake is incredible well positioned to do so, you >>Know, just picking up on that. I, I, I counted Antonio. I think I've been to 14 HP and HPE discovers when you include Europe. Yeah. I mean, Frankford, London, Barcelona Madrid, of course, you know the us, and I've never seen why I've tweeted this out. I've never seen this type of energy. Right. People are excited to get back. That's part of it. The other big part of it is course the focus. Yeah. So that focus on as a service was a burn, the boat moment for HPV. >>I don't think it was a burn the boat moment. It was a moment that we have to decide how we think about the future and how we become even more relevant for customers. And we are very important to all the customers they buy from us. Right. But I think about the next 3, 5, 10 years, how we position the company, enter the future to be relevant to whatever they need to do. >>Well, what I mean by that is you're not turning back. No, the bridge is gone. You go, you're going forward. And so my question is, did the pandemic accelerate that move or did it, did it hinder it? And, and, and how so >>Actually it was an, a moment for us to think about how we go further and faster to what we call this journey to one, one platform, one experience. And, and we felt as a team, as an organization, this was a unique moment in time to go further, faster. So to us, it was a catalyst to accelerate that transformation. >>Yeah. Now I, I want to ask you a question in your keynote. I love this, cuz you say I'm often asked by customers, what workload should we move to the public cloud and what should stay on prem? I'm like, yeah, I get that question all the time. And I was waiting for the answer. You said, that's the wrong question. And I was like, wait, but that's the question everybody's asking. So it was really interesting that you said that. And I wonder if you could, you could comment. And I think you said basically the world's hybrid is your challenge with, with the customers in this initiative to actually get people to stop asking that question. Right. And not think about that. >>No, I think the challenge we all collectively have is that how we think about data and how we drive what I call a data first modernization, you know, strategy for our customers in an age to cloud architecture, which basically says you are living a hybrid world is not a question which workloads are put in the public cloud, which workloads are put OnPrem. You know, the, all the issues around data gravity and whatnot is a question of how I bring the cloud experience to all your workloads of data, wherever they live. And that's where, you know, the opportunity really exists. And as customers understand more and more about the new environments, how they work, how they enable these new experiences is all driven by that data. And that data has enormous value. So it's not about which cloud use is about how you bring the cloud experience to your data in workloads. >>When you're talking to CIOs, especially transformational CIOs, what's the value pro to those CIOs that wanna transform and need to transform with the power of HPE. >>More and more of them are becoming conscious about the fact that they need to go faster in everything they do. We have done some interesting analysis with the brands that have done a better job or have become way more proficient on extracting insight from the data. They are actually the brands that winning the marketplace, not just with customers driving the preference, but also in the market capitalization because they become where more sophisticated in driving better efficiency, which is a necessity today. Second is the fact that also they need to improve their security aspect of it, but they are creating new experiences and new revenue streams. And those transformational CIOs are transforming their business in the way they run it into more an innovation engine. And so that's why, you know, we love working with them because they are advanced and the push has to think differently in the way we think about the innovation. How >>Do you help customers go from data, rich insight, port to data, rich insight, rich actions, new revenue, streams, new services. >>Well, first of all, you have to deploy the right architecture, which starts with a network, obviously because digital transformation requires an on-ramp and the connectivity is the first step. Second is to make sure you have a true end to end visibility of that data. And that's why we announced yesterday with the data fabric, right? A, a revolutionary way to think about that age to cloud architecture from a data driven perspective. And then the third piece of this is, is the aspect of how we bring that intelligence to that data. And that's where, you know, we are enabling all these amazing services with AI machine learning with, with, you know, HP GreenLake, which is ultimately the way we are gonna enable them. >>What's your favorite announcement from this week? >>I think HP GreenLake, you know, I think I >>Mentioned a lot of GreenLake, >>36 times HP GreenLake. And to me, you know, as I think about what comes next, right, is about how we innovate now on the platform at the pace that customers are demanding. And so for me, there is a lot of things there, but obviously the private cloud enterprise edition was a big moment for us because that's the way we bring the cloud operating experience on-prem and at the edge, but also all the hybrid capabilities that Brian showed during the demo is something that I think customers now say, wow, I didn't know. We can do that. >>And thinking about your business, you know, despite some macro headwinds and, and like you, you reaffirmed your guidance on the, on the last earnings call. Does GreenLake give you better visibility or is it harder to predict? >>No, I think the more we get engaged with customers in running their workloads and data, the more visibility we get, you know, I said, you know, customers voted with the workloads and data. And in, in that context, you know, we already have 65,000 customers more than 120,000 users. And the one interesting stat, which I hope it didn't go lost during that transition was the, the fact that we now have under the GreenLake management over an next bite of data. And so to me, right, that's a unique, a unique opportunity for us to learn and improve the whole cycle. >>So obviously a big pillar of your strategy is the data. And I wanted, if you could talk more about that because I, I would observe, you know, we, the cube started in sort of as big data, you know, started to take off and you saw that had ecosystem and, and that ecosystem has dispersed now. Yeah. So gone into the cloud, it's got snowflakes pulling and some in Mongo. Now you have the opportunity with this ecosystem yeah. To have a data ecosystem. How do you look at the ecosystem and the value that your partners can build on top of GreenLake and specifically monetize? Well, >>If you walk through the floor, one of the things we changed this time is that the partners are actually in the flow of all our solutions, not sitting on a corner of the show floor, right? And, and, and that's because what we have done in the last three years has been together with our partners, but we conceive HP GreenLake with the partners in mind, at the core of everything we do in the platform. And that's why on Monday we announced the new partner one ready vantage program that actually opens the platform through our APIs for allowing them to add their own value on the platform, whether in their own services to the marketplace or the other way around they to use our capabilities in their own solutions. Because some of these cloud operating capabilities are hard to develop, whether it is, you know, metering and billing and all the other services, sometimes you don't don't have to build yourself. So that's why, what we love about our strategies, the partners can decide where to participate in this broad ecosystem and then grow with us and we can grow through them as well. >>So GreenLake as a service, the focus is, is very clear. Hybrid is very clear. What's less clear to me is, is that I'll and I'll ask you to comment, is this, we go a term called super cloud and super cloud is different than multi-cloud multi-cloud oh, I run in AWS or, and, or I run in Azure. I run in, in, in GCP, Supercloud builds a layer above that hides the underlying complexity of the primitives and the APIs, and then builds new value on top of that, out to the edge as well. You guys talk about the edge all the time. You have Aruba a as an asset, you got space space born. You're doing some pretty edge. Like, well, >>We have it here. Yeah. Yeah. We are connected to the ISS. So if you were to that show floor, you can actually see what's being processed today. >>I mean, that's, you don't get more edge than that. So my question is, is, is that part of the vision to actually build that I call super cloud layer? Or is it more to be focused on hybrid and connecting on-prem to the cloud? >>No, I, I don't like to call it super cloud because that means, unless you are a superpower, you can't do what you need to do. I, I think I call it a super straight okay. Right. That we are enabling to our H to cloud architecture. So the customers can build their own experiences and consume the services that they need to compete and win in today's market. So our H to cloud approach is to create that substrate with connectivity, obviously the cloud and the data capability that you need to operate in today's >>Environment. Okay. So they're fair enough. I would say that your customers are gonna build then the super cloud on top of that software. >>Well, actually we want to give it to them. They don't have to build anything. They just need to run the business. Well, they don't have the time to really build stuff. They just need to innovate that's our, our value proposition. So they don't have to waste cycles in doing so if it comes ready to go, why you want to build it? >>Well, when I say build it, I'm talking about building their business on top of it things you're not gonna, I agree with that, bringing their tools, financial services companies with their data, their tools, their ecosystem, connecting OnPrem to the clouds. Yeah. That above that substrate that's their as a digital. >>Yeah. And that's why I said yesterday with our approach, we're actually enabling customers to power the next generation business models that they need. We enable the substrate, they can innovate on the platform, these next gen business models, >>Tap your engineering mind. And I'd like you to talk about how architectures are changing you along with many, many other CEOs signed a letter to, to the us government, you know, urging them to, to, to pass the chips act. As I posted on LinkedIn, there were, there were a few notables missing apple wasn't on there, meta wasn't on there, Tesla wasn't on there. I'd like to see them step up and sign that. Yeah. And so why did you, you know, sign that? Why did you post that? And, and, and why is that important? >>Well, first of all, it's important to customers because obviously they need to get access to technologies in a more ubiquitous way. And second it's important for the United States. We live in a, in a global economy that today is going to a refactoring of sorts where supply chain disruption has caused a lot of consternation and disruption across many industries. And I think, you know, as we think about the next generation supply chains, which are built for resiliency and obviously inclusion, we need to make sure that the United States address this problem. Because once you fall behind, it takes a long time to catch up. Even if we sign the chips act, it's gonna take many years for us, but we need to start now. Otherwise we never get what we need to >>Get. I, I agree. We're late. I think pat Gelsinger has done a very good job laying out the mission, you know, to bring, I mean, to me, it's modest, bring 20% of the manufacturing back to the us by the end of the decade. I mean that that's not gonna be easy, but even so that's, >>That's, we need stuff somewhere. Absolutely. You know, we are great partners with Intel. I really support the vision that path has laid out. And its not just about Intel again, it's about our customers in the United States, >>HP and HPE now cuz H HP labs is part of, of HPE. I believe that's correct state. Well, >>We refocus HP labs as a part of our high performance. Yeah. And AI business. Yes. >>But H HP and, and now HPE possess custom Silicon expertise. We may, we always >>Had. >>Yeah, exactly. And, and you know, with the fabulous world, do you see, I mean, you probably do in some custom Silicon today that I don't really, you know, have visibility on, but do you see getting more into that? Is there a need for >>That? Yeah. Well we already design more than 60 different silicons that are included in our solution. More and more of that. Silicon is actually in support of our other service experience. That's truly programmable for this new way to deploy a cloud or a data fabric or a network in fabric of sorts. When you look our, our age portfolio as a part of green lake through our Aruba set of offerings, we actually have a lot of the Silicon building. Our switching portfolio that's program. Normally give us the ability to drive intelligent routing in the network at the application layer. But also as you know, many years ago, we introduced our own ILO, the lights out technology, the BMC type of support that allows us to provide security to the root of our systems. But now more implement a cloud operating security environment if you will, but there is many more in the analog space for AI at scale. And even the latest introduction with frontier. When you look at frontier that wonderful high performance exit scale system, the, the magic of that is in the Silicon we developed, which is the interconnect fabric. Plus the smart mix at massive massive scale for parallel computing. And then ultimately it's the software environment that we put on top of it. So we can process billion, billion, square transactions per second. >>And when you think about a lot of the AI today is modeling, that's done in the cloud. When you think about the edge actual real time in, you're not gonna send all that back to the cloud. When you have to make a left turn or a right turn, >>Stop sign. I think, you know, people need to realize that 70% of the data today is outside the public cloud and 50% is at the edge. And when you think about the real time use cases, actually 30% of that data will need to be processed real time. So which means you need to establish inference the rate at the edge and at the same time run, you know, the analytics at the edge, whether it's machine learnings or some sort of simulation they need to do at the edge. And so that's why, you know, we can provide inference. We can provide machine learning at the edge on top of the connectivity and the edge compute or cloud computing at the edge. But also we can provide on the other side, AI at scale for massive amount of data analytics. And >>Will that be part of the GreenLake? >>We already offered that experience. We already offered that as a HPC, as a service is one of the key services we provide at scale. And then you also have machine learning operations as a service. So we have both and with the data fabric, now we're gonna take it to one step forward so we can connect the data. And I think one of the most exciting services, I actually, I'm a true believer. That is the capability we develop through HP labs. Since you asked for that early on, which is called the swarm learning technology. Of >>Course. Yeah. I've talked to Dr. GU about there you >>Go. >>So, so he >>Will do a better job than me explaining, >>Hey, I don't know. You're pretty, pretty good at it, but he's awesome. I mean, I have to admit on your keynote, you specifically took the time to mention your support for women's rights. Yes. Will HPE pay for women to leave the state to have a medical procedure? >>Yeah. So what happened last week was a sad moment in a history. I believe we, as a company felt compelled to stand up and take a position on the rights of women to choose. And as a part of that, we already offer as a part of our benefits, the ability to travel and pay all the medical expenses related to their choice. >>Yeah. Well thank you for doing that. I appreciate it. As a, as a father of two daughters who have less rights than, than my wife did when she was their age, I applaud you for your bravery and standing up and, and thank you for doing that. How excited are you for Janet Jackson? >>I think is gonna be a phenomenal rap of the HP discover, I think is gonna be a great moment for people to celebrate the coming together. One of the feedback I got from the meetings early on from customers is that put aside the vision, the strategy, the solutions that they actually can experience themselves is the fact that the, the, the one thing that really appreciated it is that they can be together. They can talk to people, they can learn with each other from each other. That energy is obviously very palpable when you go through it. And I think, you know, the celebration tonight and I want to thank the sponsor for allowing us to do so, is, is the fact that, you know, it's gonna be a moment of reuniting ourselves and look at the Fu at the future with optimism, but have some fun. >>Well, that's great, Antonio, as I said, I've been to a lot of HP and HPE discovers. You've brought a new focus clearly to the company, outstanding job of, of getting people aligned. I mean, it's not easy. It's 60,000, you know, professionals a around the globe and the energy is like I've never seen before. So congratulations. Thank you so much for coming back in the queue. >>Thank you, Dave. And as always, we appreciate you covering the, the event. You, you share the news with all the audiences around the globe here and, and that's, that means us means a lot to us. Thank you. Thank you. >>And thank you for watching. This is Dave Velante for Lisa Martin and John furrier. We'll be right back with our next guest. Live from HPE. Discover 2022 in Las Vegas.

>> Narrator: TheCUBE presents HPE Discover 2022. Brought to you by HPE. >> Welcome back to Las Vegas. Lisa Martin and Dave Vellante here bringing you day one of theCUBE's coverage of HPE Discover 22. We've had a lot of great conversations so far. Just a few hours in. We have two of our alumni back with us. Powerhouses, two powerhouses from Nutanix. Two for the price of one. Mandy Dhaliwal joins us. The CMO of 90 days at Nutanix. It's great to see you. Congratulations on the gig. >> Thanks Lisa. It's great to be here and great to be at Nutanix. >> Isn't it? And Tarkan Maner, the Chief Commercial Officer at Nutanix. Welcome back Tarkan. >> Great to see you guys. >> So this is only day one of the the main show Tarkan. We've been hearing a lot about cloud as an operating model. We've heard your CEO Rajiv talking about it. Break that down from Nutanix's point of view. >> Yeah, look at the end, the tech conference we are talking a lot technology but at the end it is all about outcomes. I saw Keith was here earlier, you know, GreenLake's story. We were on a session earlier. Everything is about business outcomes for the customers. And obviously our partner Ecosystems, NBC all these double technologies come together and become an open model. And our customers are moving from a CAPEX model, old school model, what I call dinosaur model, into an OPEX model, subscription model. Which Nutanix basically the category creator for this, in a hybrid multi-cloud fashion. One platform, one experience, any app, any user, anytime, and make it count. Let the customers focus on business outcomes. Let us deal with infrastructure for you. >> What are some of the key outcomes that you're seeing customers achieve? We've seen so much change in the last couple of years. >> Tarkan: Right. >> A lot of acceleration. >> Tarkan: Right. >> Every company has to be a data company today to compete. >> Right. >> What are some of the outcomes that you're really proud of? >> So look, at the end of the, day's it's all about digital transformation and it's a big loaded word. But at the end of the day every company is trying to get digitized. And hybrid multicloud is the only way to get there in a cost effective way. So that cost is a big story. Highly secure. Manageable, available, reliable, total cost ownership definitely depressed and take the complexity out. Let us deal with the infrastructure for you. You focus on your time to market, and the best applications for the best users. >> So Mandy, I remember, you know you talked about your category creator Tarkan, and I remember Stu Miniman and I, were in the Wikibon offices. We were just getting started and he said, "Dave you got to come in here." And Dhiraj was on the phone. They were describing this new category and I was blown away. I'm like, wow, that's like the cloud but you know, for on-prem. So what does the, what does the cloud operating model mean to Nutanix Mandy? >> Really, what we're trying to do is become this common cloud platform across Core, Edge and Cloud. We're known for our strength in HCI on premise. We have capability across. So it's really important for us to share this story with the market. Now, also one of the reasons I joined. You know this story needs to be told in a bigger fashion. So I'm here to really help evolve this category. We've won HCI, right? What's next? So stay tuned. >> So we call that super cloud. I call it. >> Yes, I love that name. >> So it, but it needs has meaning, right? >> Right. >> It's a new layer. It's not just, oh, I run on Azure. I run an Aw or running green. >> Mandy: Right. >> It's actually a common infrastructure. >> Yes. >> Common experience across maybe and even out to the edge. >> Mandy: Right. >> Right. So is, is that, do you guys see that or do you think this is just a little buzzword that Dave made up? >> No, I think it has legs. And I think at the core of it, it's simplicity and elegance. And if you look, and, and again, I'm drinking the the champagne, right? We have that we architected for that. We've solved that problem. So we now can extend it to become ubiquitous in the market. So it's, it's an amazing place to be because we've got the the scale, frankly, and the breadth now of the technology platform to be able to go deliver that super cloud. >> And you have to do the work, right? You, you, you have to hide all the complexity- >> Mandy: Yeah. >> Of whether it's AWS, Azure, Google, GreenLake wherever you go on prem. >> Mandy: Right. >> And not only that, as you know Dave, many people think about cloud, they automatically think about public cloud. AWS, Azure, or Google. Guess what? We have customers. Some of the workloads and apps running on a local country. If you're in Singapore, on Singtel, and your, if you're in Switzerland on Swisscom, if you're in Japan on NTT, guess what? Our cloud runs also on those clouds. For those customers who want the data, gravity, local issues with the security and privacy laws in the local country then all this SI you have HCI, Emphasis VIDPro, Accenture, CAPS, JAM, and ITCS. They have also cloud services. What we build as Mandy said as the creator, make the platform run anywhere. So the customers can move data, apps, workloads from cloud to cloud. From private to public and within public, from public to public. From AWS to Singtel. From Singtel to Swisscom to Azure, doesn't matter. We want to make sure one platform one experience, any app, any user. >> And at least a lot of those guys are building on OpenStack. We don't talk about OpenStack anymore. But a lot of the local telcos they actually it's alive and well and actually growing. >> So you, you make it sound simple. So I got to ask you as the chief marketing officer how do you message that simplicity and actually make it tangible for customers? >> That's a great question. It's really about the customer story, right? How do we share that we're able to take something that took months to deploy and have it done in in days, minutes, right? So there's a lot of those kinds of stories that you'll see across the platform coming. We're getting a lot more messaging around that. We're also tightening up the message to be more easily conveyed. So that's a lot of the stuff that I'm working on right now and really super excited. You know, we've got leading retailers, financial services institutions, public sector agencies that are running on our platform. So we've got this amazing cadre of customers and their stories just need to be told. >> That voice of the customer is so powerful. >> Mandy: Yep. >> As you well know Tarkan. That's, that's the objective voice right? That is ideally articulating your value proposition. >> Yeah. >> Validating that helping other customers understand this, these are the outcomes we are achieving. >> Mandy: Right? >> You can do the same. >> Mandy: Right. >> And, and different personas. >> Mandy: Right. >> It's not one customer fits all right. You heard Home Depot, Daniel with Antonio on the keynote. The stores, the distribution center, the warehousing and their service department, their mobile app all that data has to move from place to place. And we want to make sure it's cost effective. It's secure. And not only for the system, people like Daniel but also for application developers. Dave, you talked about, you know, Open Source, OpenStack, a lot of new application development is all open source. >> Mandy: Yep. >> And we need to also gear toward them and give them a platform, a hybrid multicloud platform. So they can build applications and then run applications and manage lifecycle applications anywhere in simple ways securely. So this platform is not only for running applications but also build a new set of digital transformation driven applications. >> So what are you doing with GreenLake especially in that context, right? 'Cause that's what we're looking for. Is like are people going to build applications on top. Maybe it's the incumbents. It might not be startups, but what what are you doing there? >> Right. So look, I'll give you the highlights on this. I know you talked to Keith again we had the session earlier. We have about 2000 plus customers. Customers are moving from a CapEx model to an OPEX model. They like the subscription side of the business and basically our strategy and many is leading this globally making cloud on your terms. So you have the control, you move from CapEx to OPEX and we bring the data in cloud to you. So you can manage the data securely, privately build your applications, and then they're ready. You can move applications based on microservices capabilities we deliver to different cloud as, as you wish. >> So then what are you hearing from customers? What are they most excited about right now given the massive potential that you're about to unleash? >> It it's really about best in class, right? So you get these these amazing technologies to come together. We abstract the complexity away for the customer. So HP GreenLake brings economic benefit. Nutanix brings experience. So you couple those two. And all of a sudden they've got time to value. Like they've never had before. Add on top of that the skills gap that we've got in the market, right? The new breed of folks that are deploying and managing these applications just don't have an appetite for complexity like they did in the old world. So we've got elegance, that's underpinning our architecture and simplicity and ease of use that learn that really translates into customer delight. So that's our secret sauce. >> You talk about time to value. Sorry, Dave. Time to value is no joke as a marketer. Talk to me about what does that mean from a translation perspective? Because these days, one of the things we learned in the pandemic, other than everyone had no patience and still probably doesn't is that access to realtime data no longer a, oh, it's awesome. It's Fanta, it's, it's table stakes. It is it's, what's going to help companies succeed. And those not. So from a time to value perspective, talk a little bit more about that as really impactful to every industry. >> Right, And, and, and underpin underpinning, all of it is that simplicity and ease of use, right? So if I can pick up and have portability across all aspects of my platform, guess what? I've got a single pane of glass that's that I'm able to manage my entire infrastructure through. That's really powerful. So I don't have to waste time doing an undifferentiated heavy lifting, all of a sudden there's huge value there in simplicity and ease of use, right? So it translates for things that would take months and you know, hundreds of developers all of a sudden you can vend out infrastructure in a way that's performant, reliable, scalable and all of a sudden, right? Everybody's happy. People are not losing sleep anymore because they know they've got a reliable way of deploying and managing and running their infrastructure. >> Perfect example for you very quick. Just is very exciting. Mandy and I, were in the session, Texas Children's Hospital. >> Yeah. >> Theresa Montag. I mean, Tonthat, she's the head of infrastructure, with Keith, with us you should listen to the patient care Pediatric, you know, oncology, realtime data. Hip regulation, highly regulated industry data. Gravity is super important. State laws, city laws, healthcare laws. The data cannot go to a public cloud service but has to be cloud driven, cloud enabled and data driven and eccentric on the site. But cloud operating model, Nutanix again with GreenLake, delivers a subscription methodology, a you know, OPEX model and delivers desktop service cloud native applications, supporting all these tools like epic all happening in healthcare. >> You guys have a high net promoter score. What, what got you there? And what's going to keep you there in the future. >> It's underpinned by the technology itself and also our outstanding support team right. We hear our competitors' customers call us for support first, before they call our competitors. If you can't take that to the bank, what can you, right. It's crazy. They, our customers tell us this >> Dave: Really? >> Really. >> It's pretty validating. >> Yeah. >> Yeah, help us with has help us with this XYZ stuff. Yeah. >> And it becomes even more important with this new cloud era. >> Yes. >> As you're moving the data, the applications to different places, they want the same experience. And look as a company, we spent the investment. It's not free. >> Mandy: Yeah. >> It costs us a lot of money to make that happen. One of the best support organizations I've been in industry for 30 years, I've never seen this kind of a maniacal focus on customer service. And without that success will not come. >> Yeah, I mean, I've met a lot of Nutanix customers at the various shows over the years. Ridden in taxis bus rides, you know, cocktail parties. They're, they're an interesting bunch, right. They, they were kind of leading edge early on. They saw the light bulb went off, they adopted. >> Right. >> Right, so, and think about thinking about aligning with where they're going where are they going and how is Nutanix aligning with them? >> There's, there's so much complexity in the world, right? So we're abstracting away the complexity. Not all workloads are meant to run in an either or situation. >> Right. >> Right, and we're hearing from IDC as well in, in, by 2026, 75% of workloads are going to be misplaced. How do they have a strategic partner? That's going to help them run their organization effectively and efficiently. We become that open and neutral player in the market. That can be the trusted advisor for them to help with workload placement optimization. They're standardizing, they're consolidating they're modernizing, they're transforming. There's a lot going on right. And so how do they come to somebody? That's voice of reason that also is well networked across the ecosystem. And that interoperability is key and yes, I'm still drinking the Kool-Aid, but it, I see it. It's, it's a tremendous story. >> Switzerland with weapons. (everyone laughing) >> You said it, you said it, Dave. >> And also one other thing it's important competition makes us better not bitter. >> Yeah. >> We have the best best partner network, 10,000 plus partners but more than numbers, quality, constantly working theater. And some of our partners also are competitors. We compete with them and we deliver solutions this way. Customers don't have to forklift out forklift in Nutanix. We leverage their past investment, current investment so they can tie Nutanix in different ways for different workloads, not one size fits all. We have multiple solutions, multiple ways you know, small, medium, large, extra large D in terms of scale and different workloads from the, you know Edge to the Cloud. And to at the end of the day to data as a whole, as you heard from HP today, our strategy, our roadmaps super aligned. That's why we were having a lot of success with GreenLake as well. >> Mandy, can you talk a last question about the partner ecosystem that Tarkan mentioned? How were you leveraging that to, to modify the messaging that you talked about? You've only been here almost 90 days. >> Mandy: Right. >> How is the partner ecosystem going to be a facilitator of the Nutanix brand and messaging and the reach? >> They're, they're tremendous, right? Because we're able to now, like we're doing here, right. Be able to reach into their customer base, and showcase our stories in a purpose built way right. This is, this is reality and solutions that we're driving for the customers with like-minded problems, like-minded people so they can see that. And so we do that across the, the ecosystem and all of a sudden, we've got this rolling thunder if you will. So it's up to us to, to, to really hone in on the right narrative and hand it to them and have them run with it that there's going to be practices built on this, even in a deeper way, moving forward. I see it, you know, we've done, I've done this before in my career. And so I've got conviction that we're on the right track and, you know, watch the space. >> Dot, dot, dot, to be continued. Watch the space. You heard it here on theCUBE. Mandy, Tarkan, thank you so much for joining Dave and me talking about the power of Nutanix with HPE, what you're doing and what you're enabling customers to achieve. It's transformative and, and best of luck. You'll have to come back in the next 90 days so we can see some of those customer stories. >> Absolutely. Absolutely, would love to, thank you. >> Thanks guys. >> Mandy: Yeah. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from the show floor of HPE Discover 22. Day one coverage continues after a short break.

(upbeat music) >> Welcome back, everyone. It's theCUBE's coverage here in Las Vegas. I'm John Furrier, your host of theCUBE with re:MARS. Amazon re:MARS stands for machine learning, automation, robotics, and space. Lot of great content, accomplishment. AI meets meets robotics and space, industrial IoT, all things data. And we've got two great guests here to unpack the AI side of it. Manoj Suvarna, Managing Director at AI Ecosystem at Deloitte and Arte Merritt, Conversational AI Lead at AWS. Manoj, it's great to see you CUBE alumni. Art, welcome to theCUBE. >> Thanks for having me. I appreciate it. >> So AI's the big theme. Actually, the big disconnect in the industry has been the industrial OT versus IT, and that's happening. Now you've got space and robotics meets what we know is machine learning and AI which we've been covering. This is the confluence of the new IoT market. >> It absolutely is. >> What's your opinion on that? >> Yeah, so actually it's taking IoT beyond the art of possible. One area that we have been working very closely with AWS. We're strategic alliance with them. And for the past six years, we have been investing a lot in transformations. Transformation as it relate to the cloud, transformation as it relate to data modernization. The new edge is essentially on AI and machine learning. And just this week, we announced a new solution which is more focused around enhancing contact center intelligence. So think about the edge of the contact center, where we all have experiences around dealing with customer service and how to really take that to the next level, challenges that clients are facing in every part of that business. So clearly. >> Well, Conversational AI is a good topic. Talk about the relationship with Deloitte and Amazon for a second around AI because you guys have some great projects going on right now. That's well ahead of the curve on solving the scale problem 'cause there's a scale and problem, practical problem and then scale. What's the relationship with Amazon and Deloitte? >> We have a great alliance and relationship. Deloitte brings that expertise to help folks build high quality, highly effective conversational AI and enterprises are implementing these solutions to really try to improve the overall customer experience. So they want to help agents improve productivity, gain insights into the reasons why folks are calling but it's really to provide that better user experience being available 24/7 on channels users prefer to interact. And the solutions that Deloitte is building are highly advanced, super exciting. Like when we show demos of them to potential customers, the eyes light up and they want those solutions. >> John: Give an example when their eyes light up. What are you showing there? >> One solution, it's called multimodal interfaces. So what this is, is when you're call into like a voice IVR, Deloitte's solution will send the folks say a mobile app or a website. So the person can interact with both the phone touching on the screen and the voice and it's all kept in sync. So imagine you call the doctor's office or say I was calling a airline and I want to change my flight or sorry, change the seat. If they were to say, seat 20D is available. Well, I don't know what that means, but if you see the map while you're talking, you can say, oh, 20D is the aisle. I'm going to select that. So Deloitte's doing those kind of experiences. It's incredible. >> Manoj, this is where the magic comes into play when you bring data together and you have integration like this. Asynchronously or synchronously, it's all coming together. You have different platforms, phone, voice, silo databases potentially, the old way. Now, the new ways integrating. What makes it all work? What's the key to success? >> Yeah, it's certainly not a trivial feat. Bringing together all of these ecosystems of relationships, technologies all put together. We cannot do it alone. This is where we partner with AWS with some of our other partners like Salesforce and OneReach and really trying to bring a symphony of some of these solutions to bear. When you think about, going back to the example of contact center, the challenges that the pandemic posed in the last couple of years was the fact that who's a humongous rise in volume of number of calls. You can imagine people calling in asking for all kinds of different things, whether it's airlines whether it is doctor's office and retail. And then couple with that is the fact that there's the labor shortage. And how do you train agents to get them to be productive enough to be able to address hundreds or thousands of these calls? And so that's where we have been starting to, we have invested in those solutions bringing those technologies together to address real client problems, not just slideware but actual production environments. And that's where we launched this solution called TrueServe as of this week, which is really a multimodal solution that is built with preconceived notions of technologies and libraries where we can then be industry agnostic and be able to deliver those experiences to our clients based on whatever vertical or industry they're in. >> Take me through the client's engagement here because I can imagine they want to get a practical solution. They're going to want to have it up and running, not like a just a chatbot, but like they completely integrated system. What's the challenge and what's the outcome first set of milestones that you see that they do first? Do they just get the data together? Are they deploying a software solution? What's the use cases? >> There's a couple different use cases. We see there's the self-service component that we're talking about with the chatbots or voice IVR solutions. There's also use cases for helping the agents, so real-time agent assist. So you call into a contact center, it's transcribed in real time, run through some sort of knowledge base to give the agents possible answers to help the user out, tying in, say the Salesforce data, CRM data, to know more about the user. Like if I was to call the airline, it's going to say, "Are you calling about your flight to San Francisco tomorrow?" It knows who I am. It leverages that stuff. And then the key piece is the analytics knowing why folks are calling, not just your metrics around, length of calls or deflections, but what were the reasons people were calling in because you can use that data to improve your underlying products or services. These are the things that enterprise are looking for and this is where someone like Deloitte comes in, brings that expertise, speeds up the time to market and really helps the customers. >> Manoj, what was the solution you mentioned that you guys announced? >> Yeah, so this is called Deloitte TrueServe. And essentially, it's a combination of multiple different solutions combinations from AWS, from Salesforce, from OneReach. All put together with our joint engineering and really delivering that capability. Enhancing on that is the analytics component, which is really critical, especially because when you think about the average contact center, less than 10% of the data gets analyzed today, and how do you then extract value out of that data and be able to deliver business outcomes. >> I was just talking to some of the other day about Zoom. Everyone records their zoom meetings, and no one watches them. I mean, who's going to wade through that. Call center is even more high volume. We're talking about massive data. And so will you guys automate that? Do you go through every single piece of data, every call and bring it down? Is that how it works? >> Go ahead. >> There's just some of the things you can do. Analyze the calls for common themes, like figuring out like topic modeling, what are the reasons people are calling in. Summarizing that stuff so you can see what those underlying issues are. And so that could be, like I was mentioning, improving the product or service. It could also be for helping train the agents. So here's how to answer that question. And it could even be reinforcing positive experiences maybe an agent had a particular great call and that could be a reference for other folks. >> Yeah, and also during the conversation, when you think about within 60 to 90 seconds, how do you identify the intonation, the sentiments of the client customer calling in and be able to respond in real time for the challenges that they might be facing and the ability to authenticate the customer at the same time be able to respond to them. I think that is the advancements that we are seeing in the market. >> I think also your point about the data having residual values also excellent because this is a long tail of value in this data, like for predictions and stuff. So NASA was just on before you guys came on, talking about the Artemis project and all the missions and they have to run massive amounts of simulations. And this is where I've kind of seen the dots connect here. You can run with AI, run all the heavy lifting without human touching it to get that first ingestion or analysis, and then iterating on the data based upon what else happens. >> Manoj: Absolutely. >> This is now the new normal, right? Is this? >> It is. And it's transverse towards across multiple domains. So the example we gave you was around Conversational AI. We're now looking at that for doing predictive analytics. Those are some examples that we are doing jointly with AWS SageMaker. We are working on things like computer vision with some of the capabilities and what computer vision has to offer. And so when you think about the continuum of possibilities of what we can bring together from a tools, technology, services perspective, really the sky is the limit in terms of delivering these real experiences to our clients. >> So take me through a customer. Pretending I'm a customer, I get it. I got to do this. It's a competitive advantage. What are the outcomes that they are envisioning? What are some of the patterns you're seeing with customers? What outcomes are they expecting and what kind of high level upside you see them envisioning coming out of the data? >> So when you think about the CxOs today and the board, a lot of them are thinking about, okay, how do you build more efficiency in those system? How do you enable a technology or solution for them to not only increase their top line but as well as their bottom line? How do you enhance the customer experience, which in this case is spot on because when you think about, when customers go repeat to a vendor, it's based on quality, it's based on price. Customer experience is now topping that where your first experience, whether it's through a chat or a virtual assistant or a phone call is going to determine the longevity of that customer with you as a vendor. And so clearly, when you think about how clients are becoming AI fuel, this is where we are bringing in new technologies, new solutions to really push the art to the limit and the art of possible. >> You got a playbook too to do this? >> Yeah, yeah, absolutely. We have done that. And in fact, we are now taking that to the next level up. So something that I've mentioned about this before, which is how do you trust an AI system as it's building up. >> Hold on, I need to plug in. >> Yeah, absolutely. >> I put this here for a reason to remind me. No, but also trust is a big thing. Just put that trustworthy. This is an AI ethics question. >> Arte: It's a big. >> Let's get into it. This is huge. Data's data. Data can be biased from coming in >> Part of it, there are concerns you have to look at the bias in the data. It's also how you communicate through these automated channels, being empathetic, building trust with the customer, being concise in the answers and being accessible to all sorts of different folks and how they might communicate. So it's definitely a big area. >> I mean, you think about just normal life. We all lived situations where we got a text message from a friend or someone close to us where, what the hell, what are you saying? And they had no contextual bad feelings about it or, well, there's misunderstandings 'cause the context isn't there 'cause you're rapid fire them on the subway. I'm riding my bike. I stop and text, okay, I'm okay. Church response could mean I'm busy or I'm angry. Like this is now what you said about empathy. This is now a new dynamic in here. >> Oh, the empathy is huge, especially if you're say a financial institution or building that trust with folks and being empathetic. If someone's reaching out to a contact center, there's a good chance they're upset about something. So you have to take that. >> John: Calm them down first. >> Yeah, and not being like false like platitude kind of things, like really being empathetic, being inclusive in the language. Those are things that you have conversation designers and linguistics folks that really look into that. That's why having domain expertise from folks like Deloitte come in to help with that. 'Cause maybe if you're just building the chat on your own, you might not think of those things. But the folks with the domain expertise will say like, Hey, this is how you script it. It's the power of words, getting that message across clearly. >> The linguistics matter? >> Yeah, yeah. >> It does. >> By vertical too, I mean, you could pick any the tribe, whatever orientation and age, demographics, genders. >> All of those things that we take for granted as a human. When you think about trust, when you think about bias, when you think about ethics, it just gets amplified. Because now you're dealing with millions and millions of data points that may or may not be the right direction in terms of somebody's calling in depending on what age group they're in. Some questions might not be relevant for that age group. Now a human can determine that, but a bot cannot. And so how do you make sure that when you look at this data coming in, how do you build models that are ethically aware of the contextual algorithms and the alignment with it and also enabling that experience to be much enhanced than taking it backwards, and that's really. >> I can imagine it getting better with as people get scaled up a bit 'cause then you're going to have to start having AI to watch the AI at some point, as they say. Where are we in the progress in the industry right now? Because I know there's been a lot of news stories around, ethics and AI and bias and it's a moving train actually, but still problems are going to be solved. Are we at the tipping point yet? Are we still walking in before we crawl or crawling before we walk? I should say, I mean, where are we? >> I think we are in between a crawling or walk phase. And the reason for that is because it varies depending on whether you're regulated industry or unregulated. In the regulated industry, there are compliance regulations requirements, whether it's government whether it's banking, financial institutions where they have to meet Sarbanes-Oxley and all kinds of compliance requirements, whereas an unregulated industry like retail and consumer, it is anybody's gain. And so the reality of it is that there is more of an awareness now. And that's one of the reasons why we've been promoting this jointly with AWS. We have a framework that we have established where there are multiple pillars of trust, bias, privacy, and security that companies and organizations need to think about. Our data scientists, ML engineers need to be familiar with it, but because while they're super great in terms of model building and development, when it comes to the business, when it comes to the client or a customer, it is super important for them to trust this platform, this algorithm. And that is where we are trying to build that momentum, bring that awareness. One of my colleagues has written this book "Trustworthy AI". We're trying to take the message out to the market to say, there is a framework. We can help you get there. And certainly that's what we are doing. >> Just call Deloitte up and you're going to take care of them. >> Manoj: Yeah. >> On the Amazon side, Amazon Web Services. I always interview Swami every year at re:Invent and he always get the updates. He's been bullish on this for a long time on this Conversational AI. What's the update on the AWS side? Where are you guys at? What's the current trends that you're riding? What wave are you riding right now? >> So some of the trends we see in customer interest, there's a couple of things. One is the multimodal interfaces we we're just chatting about where the voice IVA is synced with like a web or mobile experience, so you take that full advantage of the device. The other is adding additional AI into the Conversational AI. So one example is a customer that included intelligent document processing as part of the chatbot. So instead of typing your name and address, take a photo of your driver's license. It was an insurance onboarding chatbot, so you could take a photo of your existing insurance policy. It'll extract that information to build the new insurance policy. So folks get excited about that. And the third area we see interest is what's called multi-bot orchestration. And this is where you can have one main chatbot. Marshall user across different sub-chatbots based on the use case persona or even language. So those things get people really excited and then AWS is launching all sorts of new features. I don't know which one is coming out. >> I know something's coming out tomorrow. He's right at corner. He's big smile on his face. He wouldn't tell me. It's good. >> We have for folks like the closer alliance relationships, we we're able to get previews. So there a preview of all the new stuff. And I don't know what I could, it's pretty exciting stuff. >> You get in trouble if you spill the beans here. Don't, be careful. I'll watch you. We'll talk off camera. All exciting stuff. >> Yeah, yeah. I think the orchestrator bot is interesting. Having the ability to orchestrate across different contextual datasets is interesting. >> One of the areas where it's particularly interesting is in financial services. Imagine a bank could have consumer accounts, merchant accounts, investment banking accounts. So if you were to chat with the chatbot and say I want to open account, well, which account do you mean? And so it's able to figure out that context to navigate folks to those sub-chatbots behind the scenes. And so it's pretty interesting style. >> Awesome. Manoj while we're here, take a minute to quickly give a plug for Deloitte. What your program's about? What customers should expect if they work with you guys on this project? Give a quick commercial for Deloitte. >> Yeah, no, absolutely. I mean, Deloitte has been continuing to lead the AI field organization effort across our client base. If you think about all the Fortune 100, Fortune 500, Fortune 2000 clients, we certainly have them where they are in advanced stages of multiple deployments for AI. And we look at it all the way from strategy to implementation to operational models. So clients don't have to do it alone. And we are continuing to build our ecosystem of relationships, partnerships like the alliances that we have with AWS, building the ecosystem of relationships with other emerging startups, to your point about how do you continue to innovate and bring those technologies to your clients in a trustworthy environment so that we can deliver it in production scale. That is essentially what we're driving. >> Well, Arte, there's a great conversation and the AI will take over from here as we end the segment. I see a a bot coming on theCUBE later and there might be CUBE be replaced with robots. >> Right, right, right, exactly. >> I'm John Furrier, calling from Palo Alto. >> Someday, CUBE bot. >> You can just say, Alexa do my demo for me or whatever it is. >> Or digital twin for John. >> We're going to have a robot on earlier do a CUBE interview and that's Dave Vellante. He'd just pipe his voice in and be fun. Well, thanks for coming on, great conversation. >> Thank you. Thanks for having us. >> CUBE coverage here at re:MARS in Las Vegas. Back to the event circle. We're back in the line. Got re:Inforce and don't forget re:Invent at the end of the year. CUBE coverage of this exciting show here. Machine learning, automation, robotics, space. That's MARS, it's re:MARS. I'm John Furrier. Thanks for watching. (gentle music)

(upbeat music) >> Hey, gang. Welcome back to theCUBE's coverage of Snowflake Summit '22 live on the show floor at Caesar's Forum in Las Vegas. Lisa Martin here with Dave Vellante. Couple of guests joining us to unpack more of what we've been talking about today. George Fraser joins us, the CEO of Fivetran, and Veronika Durgin, the head of data at Saks Fifth Avenue. Guys, welcome to the program. >> Thank you for having us. >> Hello. >> George, talk to us about Fivetran for the audience that may not be super familiar. Talk to us about the company, your vision, your mission, your differentiation, and then maybe the partnership with Snowflake. >> Well, a lot of people in the audience here at Snowflake Summit probably are familiar with Fivetran. We have almost 2000 shared customers with them. So a considerable amount of the data that we're all talking about here, flows through Fivetran. But in brief, what Fivetran is, is we're data pipeline. And that means that we go get all the data of your company in all the places that it lives. So all your tools and systems that you use to run your company. We go get that data and we bring it all together in one place like Snowflake. And that is the first step in doing anything with data is getting it all in one place. >> So you've been considerable amount of shared customers. I think I saw this morning on the slide over 5,900, but you're saying you're already at around 2000 shared customers. Lots of innovation I'm sure, with between both companies, but talk to us about some of the latest developments at Fivetran, in terms of product, in terms of company growth, what's going on? >> Well, one of the biggest things that happened recently with Fivetran is we acquired another data integration company called HVR. And HVR specialty has always been replicating the biggest, baddest enterprise databases like Oracle and SQL Server databases that are enormous, that are run within an inch of their capabilities by their DBAs. And HVR was always known as the best in the business at that scenario. And by bringing that together with Fivetran, we now really have the full spectrum of capabilities. We can replicate all types of data for all sizes of company. And so that's a really exciting development for us and for the industry. >> So Veronika, head of data at Saks, what does that entail? How do you spend your time? What's your purview? >> So the cool thing abouts Saks is a very old company. Saks is the premier luxury e-commerce platform. And we help our Saks Fifth Avenue customers just express themselves through fashion. So we're trying to modernize very old company and we do have the biggest, baddest databases of any flavor you can imagine. So my job is to modernize, to bring us to near real-time data, to make sure data is available to all of our users so they can actually take advantage of it. >> So let's talk about some of those biggest, baddest hair balls that you've, and how you deal with that. So lot of over time, you've built up a lot of data. You've got different data stores. So, what are you doing with that? And what role does Fivetran and Snowflake play in helping you modernize? >> Yeah, Fivetran helps us ingest data from all of those data sources into Snowflake near real-time. It's very important to us. And like one of the examples that I give is within a matter of maybe a few weeks, we were able to get data from over a dozen of different data sources into Snowflake in near real-time. And some of those data sources were not available to our users in the past, and everybody was so excited. And the reason they weren't available is because they require a lot of engineering effort to actually build those data pipelines to manage them and maintain them. >> Lisa: Whoa, sorry. >> That was just a follow up. So, Fivetran is the consolidator of all that data and- >> That's right. >> Snowflake plays that role also. >> We bring it all together, and the place that it is consolidated is Snowflake. And from there you can really do anything with it. And there's really three things you were touching on it that make data integration hard. One is volume, and that's the one that people tend to talk about, just size of data. And that is important, but it's not the only thing. It's also latency. How fresh is the data in the locus of consolidation? Before Fivetran, the state of the art was nightly snapshots, once a day was considered pretty good. And we consider now once a minute pretty good and we're trying to make it even better. And then the last challenge, which people tend not to talk about, it's the dark secret of our industry is just incidental complexity. All of these data sources have a lot of strange behaviors and rules and corner cases. Every data source is a little bit different. And so a lot of what we bring that to the table, is that we've done the work over 10 years. And in the case of HVR, since the 90s', to map out all of these little complexities of all these data sources, that as a user, you don't have to see it. You just connect source, connect destination, and that's it. >> So you don't have to do the M word migrate off of all those databases. You can maybe allow them to dial them down over time, then create new value with using Fivetran and Snowflake. Is that the right way to think about it? >> Well, Fivetran, it's incredibly simple. You just connect it to whatever source, And then the matter of minutes you have a pipeline. And for us, it's in the matter of minutes, for Fivetran, there's hundreds of engineers, we're extending our data engineering team to now Fivetran. And we can pick and choose which tables we want to replicate which fields. And once data lands in Snowflake, now we have data across different sources in one place, in central place. And now we can do all kinds of different things. We can integrate it data together, we can do validations, we can do reconciliations. We now have ability to do point in time historical journey, in the past in transactional system, you don't see that, you only see data that's right now, but now that we replicate everything to Snowflake and Snowflake being so powerful as an analytical platform, we can do, what did it look like two months ago? What did it look like two years ago? >> You've got all that time series data, okay. >> And to address that word you mentioned a moment ago, migrate, this is something people often get confused about. What we're talking about here is not a migration, these source systems are not going away. These databases are the systems powering saks.com and they're staying right there. They're the systems you interact with when you place an order on this site. The purpose of our tool and the whole stack that Veronika has put together, is to serve other workloads in Snowflake that need to have access to all of the data together. >> But if you didn't have Snowflake, you would have to push those other data stores, try to have them do things that they have sometimes a tough time doing. >> Yeah, and you can't run analytical workloads. You cannot do reporting on the transactional database. It's not meant for that. It's supporting capability of an application and it's configured to be optimized for that. So we always had to offload those specific analytical reporting functionality, or machine learning somewhere else, and Snowflake is excellent for that. It's meant for that, yeah. >> I was going to ask you what you were doing before, you just answered that. What was the aha moment for realizing you needed to work with the power of Fivetran and Snowflake? If we look at, you talked about Saks being a legacy history company that's obviously been very successful at transforming to the digital age, but what was that one thing, as the head of the data you felt this is it? >> Great question. I've worked with Fivetran in the past. This is my third company, same with Snowflake. I actually brought Fivetran into two companies at this point. So my first experience with both Fivetran and Snowflake, was this like, this is where I want to be, this is the stack and the tooling, and just the engineering behind it. So as I moved on the next company, that that was, I'm bringing tools with me. So that was part. And the other thing I wanted to mention, when we evaluate tools for a new platform, we look at things in like three dimensions, right? One with cloud first, we want to have cloud native tools, and they have to be modular, but we also don't want to have too many tools. So Fivetran's certainly checks that off. They're first cloud native, and they also have a very long list of connectors. The other thing is for us, it's very important that data engineering effort is spent on actually analyzing data, not building pipelines and supporting infrastructure. In Fivetran, reliable, it's secure, it has various connectors, so it checks off that box as well. And another thing is that we're looking for companies we can partner with. So companies that help us grow and grow with us, we'll look in a company culture, their maturity, how they treat their customers and how they innovate. And again, Fivetran checks off that box as well. >> And I imagine Snowflake does as well, Frank Lutman on stage this morning talked about mission alignment. And it seemed to me like, wow, one of the missions of Snowflake is to align with its customer's missions. It sounds like from the conversations that Dave and I have had today, that it's the same with partners, but it sounds like you have that cultural alignment with Fivetran and Snowflake. >> Oh, absolutely. >> And Fivetran has that, obviously with 2000 shared customers. >> Yeah, I think that, well, not quite there yet, but we're close, (laughs) I think that the most important way that we've always been aligned with our customers is that we've been very clear on what we do and don't do. And that our job is to get the data from here to there, that the data be accurately replicated, which means in practice often joke that it is exactly as messed up as it was in the source. No better and no worse, but we really will accomplish that task. You do not need to worry about that. You can well and fully delegate it to us, but then what you do with the data, we don't claim that we're going to solve that problem for you. That's up to you. And anyone who claims that they're going to solve that problem for you, you should be very skeptical. >> So how do you solve that problem? >> Well, that's where modeling comes in, right? You get data from point A to point B, and it's like bad in, bad out. Like, that's it, and that's where we do those reconciliations, and that's where we model our data. We actually try to understand what our businesses, how our users, how they talk about data, how they talk about business. And that's where data warehouse is important. And in our case, it's data evolve. >> Talk to me a little bit before we wrap here about the benefits to the end user, the consumer. Say I'm on saks.com, I'm looking for a particular item. What is it about this foundation that Saks has built with Fivetran and with Snowflake, that's empowering me as a consumer, to be able to get, find what I want, get the transaction done like that? >> So getting access to, our end goal is to help our customers, right? Make their experience beautiful, luxurious. We want to make sure that what we put in front of you is what you're looking for. So you can actually make that purchase, and you're happy with it. So having that data, having that data coming from various different sources into one place enables us to do that near real-time analytics so we can help you as a customer to find what you're looking for. >> Magic on the back end, delighting customers. >> So the world is still messed up, right? Airlines are out of whack. There's supply imbalances. You've got the situation in Ukraine with oil prices. The Fed missed the mark. So can data solve these problems? If you think about the context of the macro environment, and you bring it down to what you're seeing at Saks, with your relationship with Fivetran and with Snowflake, do you see the light at the end of that confusion tunnel? >> That's such a great question. Very philosophical. I don't think data can solve it. Is the people looking at data and working together that can solve it. >> I think data can help, data can't stop a war. Data can help you forecast supply chain misses and mitigate those problems. So data can help. >> Can be a facilitator. >> Sorry, what? >> Can be a facilitator. >> Yeah, it can be a facilitator of whatever you end up doing with it. Data can be used for good or evil. It's ultimately up to the user. >> It's a tool, right? Do you bring a hammer to a gunfight? No, but t's a tool in the right hands, for the right purpose, it can definitely help. >> So you have this great foundation, you're able to delight customers as especially from a luxury brand perspective. I imagine that luxury customers have high expectations. What's next for Saks from a data perspective? >> Well, we want to first and foremost to modernize our data platform. We want to make sure we actually bring that near real-time data to our customers. We want to make sure data's reliable. That well understood that we do the data engineering and the modeling behind the scenes so that people that are using our data can rely on it. Because it's like, there is bad data is bad data but we want to make sure it's very clear. And what's next? The sky's the limit. >> Can you describe your data teams? Is it highly centralized? What's your philosophy in terms of the architecture of the organization? >> So right now we are starting with a centralized team. It just works for us as we're trying to rebuild our platform, and modernize it. But as we become more mature, we establish our practices, our data governance, our definitions, then I see a future where we like decentralize a little bit and actually each team has their own analytical function, or potentially data engineering function as well. >> That'll be an interesting discussion when you get there. >> That's a hot topic. >> It's one of the hardest problems in building a data team is whether decentralized or decentralized. We're still centralized at Fivetran, but companies now over 1000 people, and we're starting to feel the strain of that. And inevitably, you eventually have to find a way to find scenes and create specialization. >> You just have to be fluid, right? And then go with the company as the company grows and things change. >> Yeah, I've worked with some companies. JPMC is here, they've got a little, I'll call it a skunk works. They're probably under states what they're doing, but they're testing that out. A company like HelloFresh is doing some things 'cause their Hadoop cluster just couldn't scale. So they have to begin to decentralize. It is a hot topic these days. And I'm not sure there's a right or wrong. It's really a situational. But I think in a lot of situations, it's maybe the trend. >> Yeah. >> Yeah, I think centralized versus decentralized technology is a different question than centralized versus decentralized teams. >> Yes. >> They're both valid, but they're very different. And sometimes people conflate them, and that's very dangerous. Because you might want one to be centralized and the other to be decentralized. >> Well, it's true. And I think a lot of folks look at a centralized team and say, "Hey, it's more efficient to have these specialized roles, but at the same time, what's the outcome?" If the outcome can be optimized and it's maybe a little bit more people expensive, or I don't know. And they're in the lines of business where there's data context, that might be a better solution for a company. >> So to truly understand the value of data, you have to specialize in that specific area. So I see people like deep diving into specific vertical or whatever that is, and truly understanding what data they have and how to taken advantage of it. >> Well, all this talk about monetization and building data products, you're there, right? >> Yeah. >> You're on the cusp of that. And so who's going to build those data products? It's going to be somebody in the business. Today they don't "Own the life cycle" of the data. They don't feel responsible for it, but they complain when it's not what they want. And so, I feel as though what Snowflake is doing is actually attacking some of those problems. Not 100% there obviously, but a lot of work to do. >> Great analysts are great navigators of organizations amongst other things. And one of the best things that's happened as part of this evolution from technology like Hadoop to technology like Snowflake is the new stack is a lot simpler. There's a lot less technical knowledge that you need. You still need technical knowledge, but not nearly what you used to. And that has made it accessible to more people. People who bring different skills to the table. And in many cases, those are the skills you really need to deliver value from data is not, do you know the inner workings of HDFS? But do you know how to extract from your constituents in the organization, a precise version of the question that they're trying to ask? >> We really want them spending their time, the technical infrastructure is an operational detail, so you can put your teams on those types of questions, not how do we make it work? And that's what Hadoop was, "Hey, we got it to work." >> And that's something we're obsessed with. We're always trying to hide the technical complexities of the problem of data centralization behind the scenes. Even if it's harder for us, even if it's more expensive for us, we will pay any costs so that you don't have to see it. Because that allows our customers to focus on more high impact. >> Well, this is a case where a technology vendor's R&D is making your life easier. >> Veronika: Easier, right. >> I would presume you'd rather spend money to save time, than spend your time, to save engineering time, to save money. >> That's true. And at the end of the day, hiring three data engineers to do custom work that a tool does, it's actually not saving money. It costs more in the end. But to your point, pulling business people into those data teams gives them ownership, and they feel like they're part of the solution. And it's such a great feeling so that they're excited to contribute, they're excited to help us. So I love where the industry's going like in that direction. >> And of course, that's the theme of the show, the world around data collaborations. Absolutely critical, guys. Thank you so much for joining Dave and me, talking about Fivetran, Snowflake together, what you're doing to empower Saks, to be a data company. I'm going to absolutely have a different perspective next time I shop there. Thanks for joining us. Thank you. >> Dave: Thank you, guys. >> Thank you. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22, from Vegas. Stick around, our next guest joins us momentarily. (upbeat music)

(upbeat music) >> The ascendancy of cloud and SAS has shown new light on how organizations think about, pay for, and value hardware. Once sought after skills for practitioners with expertise in hardware troubleshooting, configuring ports, tuning storage arrays, and maximizing server utilization has been superseded by demand for cloud architects, DevOps pros, developers with expertise in microservices, container, application development, and like. Even a company like Dell, the largest hardware company in enterprise tech touts that it has more software engineers than those working in hardware. Begs the question, is hardware going the way of Coball? Well, not likely. Software has to run on something, but the labor needed to deploy, and troubleshoot, and manage hardware infrastructure is shifting. At the same time, we've seen the value flow also shifting in hardware. Once a world dominated by X86 processors value is flowing to alternatives like Nvidia and arm based designs. Moreover, other componentry like NICs, accelerators, and storage controllers are becoming more advanced, integrated, and increasingly important. The question is, does it matter? And if so, why does it matter and to whom? What does it mean to customers, workloads, OEMs, and the broader society? Hello and welcome to this week's Wikibon theCUBE Insights powered by ETR. In this breaking analysis, we've organized a special power panel of industry analysts and experts to address the question, does hardware still matter? Allow me to introduce the panel. Bob O'Donnell is president and chief analyst at TECHnalysis Research. Zeus Kerravala is the founder and principal analyst at ZK Research. David Nicholson is a CTO and tech expert. Keith Townson is CEO and founder of CTO Advisor. And Marc Staimer is the chief dragon slayer at Dragon Slayer Consulting and oftentimes a Wikibon contributor. Guys, welcome to theCUBE. Thanks so much for spending some time here. >> Good to be here. >> Thanks. >> Thanks for having us. >> Okay before we get into it, I just want to bring up some data from ETR. This is a survey that ETR does every quarter. It's a survey of about 1200 to 1500 CIOs and IT buyers and I'm showing a subset of the taxonomy here. This XY axis and the vertical axis is something called net score. That's a measure of spending momentum. It's essentially the percentage of customers that are spending more on a particular area than those spending less. You subtract the lesses from the mores and you get a net score. Anything the horizontal axis is pervasion in the data set. Sometimes they call it market share. It's not like IDC market share. It's just the percentage of activity in the data set as a percentage of the total. That red 40% line, anything over that is considered highly elevated. And for the past, I don't know, eight to 12 quarters, the big four have been AI and machine learning, containers, RPA and cloud and cloud of course is very impressive because not only is it elevated in the vertical access, but you know it's very highly pervasive on the horizontal. So what I've done is highlighted in red that historical hardware sector. The server, the storage, the networking, and even PCs despite the work from home are depressed in relative terms. And of course, data center collocation services. Okay so you're seeing obviously hardware is not... People don't have the spending momentum today that they used to. They've got other priorities, et cetera, but I want to start and go kind of around the horn with each of you, what is the number one trend that each of you sees in hardware and why does it matter? Bob O'Donnell, can you please start us off? >> Sure Dave, so look, I mean, hardware is incredibly important and one comment first I'll make on that slide is let's not forget that hardware, even though it may not be growing, the amount of money spent on hardware continues to be very, very high. It's just a little bit more stable. It's not as subject to big jumps as we see certainly in other software areas. But look, the important thing that's happening in hardware is the diversification of the types of chip architectures we're seeing and how and where they're being deployed, right? You refer to this in your opening. We've moved from a world of x86 CPUs from Intel and AMD to things like obviously GPUs, DPUs. We've got VPU for, you know, computer vision processing. We've got AI-dedicated accelerators, we've got all kinds of other network acceleration tools and AI-powered tools. There's an incredible diversification of these chip architectures and that's been happening for a while but now we're seeing them more widely deployed and it's being done that way because workloads are evolving. The kinds of workloads that we're seeing in some of these software areas require different types of compute engines than traditionally we've had. The other thing is (coughs), excuse me, the power requirements based on where geographically that compute happens is also evolving. This whole notion of the edge, which I'm sure we'll get into a little bit more detail later is driven by the fact that where the compute actually sits closer to in theory the edge and where edge devices are, depending on your definition, changes the power requirements. It changes the kind of connectivity that connects the applications to those edge devices and those applications. So all of those things are being impacted by this growing diversity in chip architectures. And that's a very long-term trend that I think we're going to continue to see play out through this decade and well into the 2030s as well. >> Excellent, great, great points. Thank you, Bob. Zeus up next, please. >> Yeah, and I think the other thing when you look at this chart to remember too is, you know, through the pandemic and the work from home period a lot of companies did put their office modernization projects on hold and you heard that echoed, you know, from really all the network manufacturers anyways. They always had projects underway to upgrade networks. They put 'em on hold. Now that people are starting to come back to the office, they're looking at that now. So we might see some change there, but Bob's right. The size of those market are quite a bit different. I think the other big trend here is the hardware companies, at least in the areas that I look at networking are understanding now that it's a combination of hardware and software and silicon that works together that creates that optimum type of performance and experience, right? So some things are best done in silicon. Some like data forwarding and things like that. Historically when you look at the way network devices were built, you did everything in hardware. You configured in hardware, they did all the data for you, and did all the management. And that's been decoupled now. So more and more of the control element has been placed in software. A lot of the high-performance things, encryption, and as I mentioned, data forwarding, packet analysis, stuff like that is still done in hardware, but not everything is done in hardware. And so it's a combination of the two. I think, for the people that work with the equipment as well, there's been more shift to understanding how to work with software. And this is a mistake I think the industry made for a while is we had everybody convinced they had to become a programmer. It's really more a software power user. Can you pull things out of software? Can you through API calls and things like that. But I think the big frame here is, David, it's a combination of hardware, software working together that really make a difference. And you know how much you invest in hardware versus software kind of depends on the performance requirements you have. And I'll talk about that later but that's really the big shift that's happened here. It's the vendors that figured out how to optimize performance by leveraging the best of all of those. >> Excellent. You guys both brought up some really good themes that we can tap into Dave Nicholson, please. >> Yeah, so just kind of picking up where Bob started off. Not only are we seeing the rise of a variety of CPU designs, but I think increasingly the connectivity that's involved from a hardware perspective, from a kind of a server or service design perspective has become increasingly important. I think we'll get a chance to look at this in more depth a little bit later but when you look at what happens on the motherboard, you know we're not in so much a CPU-centric world anymore. Various application environments have various demands and you can meet them by using a variety of components. And it's extremely significant when you start looking down at the component level. It's really important that you optimize around those components. So I guess my summary would be, I think we are moving out of the CPU-centric hardware model into more of a connectivity-centric model. We can talk more about that later. >> Yeah, great. And thank you, David, and Keith Townsend I really interested in your perspectives on this. I mean, for years you worked in a data center surrounded by hardware. Now that we have the software defined data center, please chime in here. >> Well, you know, I'm going to dig deeper into that software-defined data center nature of what's happening with hardware. Hardware is meeting software infrastructure as code is a thing. What does that code look like? We're still trying to figure out but servicing up these capabilities that the previous analysts have brought up, how do I ensure that I can get the level of services needed for the applications that I need? Whether they're legacy, traditional data center, workloads, AI ML, workloads, workloads at the edge. How do I codify that and consume that as a service? And hardware vendors are figuring this out. HPE, the big push into GreenLake as a service. Dale now with Apex taking what we need, these bare bone components, moving it forward with DDR five, six CXL, et cetera, and surfacing that as cold or as services. This is a very tough problem. As we transition from consuming a hardware-based configuration to this infrastructure as cold paradigm shift. >> Yeah, programmable infrastructure, really attacking that sort of labor discussion that we were having earlier, okay. Last but not least Marc Staimer, please. >> Thanks, Dave. My peers raised really good points. I agree with most of them, but I'm going to disagree with the title of this session, which is, does hardware matter? It absolutely matters. You can't run software on the air. You can't run it in an ephemeral cloud, although there's the technical cloud and that's a different issue. The cloud is kind of changed everything. And from a market perspective in the 40 plus years I've been in this business, I've seen this perception that hardware has to go down in price every year. And part of that was driven by Moore's law. And we're coming to, let's say a lag or an end, depending on who you talk to Moore's law. So we're not doubling our transistors every 18 to 24 months in a chip and as a result of that, there's been a higher emphasis on software. From a market perception, there's no penalty. They don't put the same pressure on software from the market to reduce the cost every year that they do on hardware, which kind of bass ackwards when you think about it. Hardware costs are fixed. Software costs tend to be very low. It's kind of a weird thing that we do in the market. And what's changing is we're now starting to treat hardware like software from an OPEX versus CapEx perspective. So yes, hardware matters. And we'll talk about that more in length. >> You know, I want to follow up on that. And I wonder if you guys have a thought on this, Bob O'Donnell, you and I have talked about this a little bit. Marc, you just pointed out that Moore's laws could have waning. Pat Gelsinger recently at their investor meeting said that he promised that Moore's law is alive and well. And the point I made in breaking analysis was okay, great. You know, Pat said, doubling transistors every 18 to 24 months, let's say that Intel can do that. Even though we know it's waning somewhat. Look at the M1 Ultra from Apple (chuckles). In about 15 months increased transistor density on their package by 6X. So to your earlier point, Bob, we have this sort of these alternative processors that are really changing things. And to Dave Nicholson's point, there's a whole lot of supporting components as well. Do you have a comment on that, Bob? >> Yeah, I mean, it's a great point, Dave. And one thing to bear in mind as well, not only are we seeing a diversity of these different chip architectures and different types of components as a number of us have raised the other big point and I think it was Keith that mentioned it. CXL and interconnect on the chip itself is dramatically changing it. And a lot of the more interesting advances that are going to continue to drive Moore's law forward in terms of the way we think about performance, if perhaps not number of transistors per se, is the interconnects that become available. You're seeing the development of chiplets or tiles, people use different names, but the idea is you can have different components being put together eventually in sort of a Lego block style. And what that's also going to allow, not only is that going to give interesting performance possibilities 'cause of the faster interconnect. So you can share, have shared memory between things which for big workloads like AI, huge data sets can make a huge difference in terms of how you talk to memory over a network connection, for example, but not only that you're going to see more diversity in the types of solutions that can be built. So we're going to see even more choices in hardware from a silicon perspective because you'll be able to piece together different elements. And oh, by the way, the other benefit of that is we've reached a point in chip architectures where not everything benefits from being smaller. We've been so focused and so obsessed when it comes to Moore's law, to the size of each individual transistor and yes, for certain architecture types, CPUs and GPUs in particular, that's absolutely true, but we've already hit the point where things like RF for 5g and wifi and other wireless technologies and a whole bunch of other things actually don't get any better with a smaller transistor size. They actually get worse. So the beauty of these chiplet architectures is you could actually combine different chip manufacturing sizes. You know you hear about four nanometer and five nanometer along with 14 nanometer on a single chip, each one optimized for its specific application yet together, they can give you the best of all worlds. And so we're just at the very beginning of that era, which I think is going to drive a ton of innovation. Again, gets back to my comment about different types of devices located geographically different places at the edge, in the data center, you know, in a private cloud versus a public cloud. All of those things are going to be impacted and there'll be a lot more options because of this silicon diversity and this interconnect diversity that we're just starting to see. >> Yeah, David. David Nicholson's got a graphic on that. They're going to show later. Before we do that, I want to introduce some data. I actually want to ask Keith to comment on this before we, you know, go on. This next slide is some data from ETR that shows the percent of customers that cited difficulty procuring hardware. And you can see the red is they had significant issues and it's most pronounced in laptops and networking hardware on the far right-hand side, but virtually all categories, firewalls, peripheral servers, storage are having moderately difficult procurement issues. That's the sort of pinkish or significant challenges. So Keith, I mean, what are you seeing with your customers in the hardware supply chains and bottlenecks? And you know we're seeing it with automobiles and appliances but so it goes beyond IT. The semiconductor, you know, challenges. What's been the impact on the buyer community and society and do you have any sense as to when it will subside? >> You know, I was just asked this question yesterday and I'm feeling the pain. People question, kind of a side project within the CTO advisor, we built a hybrid infrastructure, traditional IT data center that we're walking with the traditional customer and modernizing that data center. So it was, you know, kind of a snapshot of time in 2016, 2017, 10 gigabit, ARISTA switches, some older Dell's 730 XD switches, you know, speeds and feeds. And we said we would modern that with the latest Intel stack and connected to the public cloud and then the pandemic hit and we are experiencing a lot of the same challenges. I thought we'd easily migrate from 10 gig networking to 25 gig networking path that customers are going on. The 10 gig network switches that I bought used are now double the price because you can't get legacy 10 gig network switches because all of the manufacturers are focusing on the more profitable 25 gig for capacity, even the 25 gig switches. And we're focused on networking right now. It's hard to procure. We're talking about nine to 12 months or more lead time. So we're seeing customers adjust by adopting cloud. But if you remember early on in the pandemic, Microsoft Azure kind of gated customers that didn't have a capacity agreement. So customers are keeping an eye on that. There's a desire to abstract away from the underlying vendor to be able to control or provision your IT services in a way that we do with VMware VP or some other virtualization technology where it doesn't matter who can get me the hardware, they can just get me the hardware because it's critically impacting projects and timelines. >> So that's a great setup Zeus for you with Keith mentioned the earlier the software-defined data center with software-defined networking and cloud. Do you see a day where networking hardware is monetized and it's all about the software, or are we there already? >> No, we're not there already. And I don't see that really happening any time in the near future. I do think it's changed though. And just to be clear, I mean, when you look at that data, this is saying customers have had problems procuring the equipment, right? And there's not a network vendor out there. I've talked to Norman Rice at Extreme, and I've talked to the folks at Cisco and ARISTA about this. They all said they could have had blowout quarters had they had the inventory to ship. So it's not like customers aren't buying this anymore. Right? I do think though, when it comes to networking network has certainly changed some because there's a lot more controls as I mentioned before that you can do in software. And I think the customers need to start thinking about the types of hardware they buy and you know, where they're going to use it and, you know, what its purpose is. Because I've talked to customers that have tried to run software and commodity hardware and where the performance requirements are very high and it's bogged down, right? It just doesn't have the horsepower to run it. And, you know, even when you do that, you have to start thinking of the components you use. The NICs you buy. And I've talked to customers that have simply just gone through the process replacing a NIC card and a commodity box and had some performance problems and, you know, things like that. So if agility is more important than performance, then by all means try running software on commodity hardware. I think that works in some cases. If performance though is more important, that's when you need that kind of turnkey hardware system. And I've actually seen more and more customers reverting back to that model. In fact, when you talk to even some startups I think today about when they come to market, they're delivering things more on appliances because that's what customers want. And so there's this kind of app pivot this pendulum of agility and performance. And if performance absolutely matters, that's when you do need to buy these kind of turnkey, prebuilt hardware systems. If agility matters more, that's when you can go more to software, but the underlying hardware still does matter. So I think, you know, will we ever have a day where you can just run it on whatever hardware? Maybe but I'll long be retired by that point. So I don't care. >> Well, you bring up a good point Zeus. And I remember the early days of cloud, the narrative was, oh, the cloud vendors. They don't use EMC storage, they just run on commodity storage. And then of course, low and behold, you know, they've trot out James Hamilton to talk about all the custom hardware that they were building. And you saw Google and Microsoft follow suit. >> Well, (indistinct) been falling for this forever. Right? And I mean, all the way back to the turn of the century, we were calling for the commodity of hardware. And it's never really happened because you can still drive. As long as you can drive innovation into it, customers will always lean towards the innovation cycles 'cause they get more features faster and things. And so the vendors have done a good job of keeping that cycle up but it'll be a long time before. >> Yeah, and that's why you see companies like Pure Storage. A storage company has 69% gross margins. All right. I want to go jump ahead. We're going to bring up the slide four. I want to go back to something that Bob O'Donnell was talking about, the sort of supporting act. The diversity of silicon and we've marched to the cadence of Moore's law for decades. You know, we asked, you know, is Moore's law dead? We say it's moderating. Dave Nicholson. You want to talk about those supporting components. And you shared with us a slide that shift. You call it a shift from a processor-centric world to a connect-centric world. What do you mean by that? And let's bring up slide four and you can talk to that. >> Yeah, yeah. So first, I want to echo this sentiment that the question does hardware matter is sort of the answer is of course it matters. Maybe the real question should be, should you care about it? And the answer to that is it depends who you are. If you're an end user using an application on your mobile device, maybe you don't care how the architecture is put together. You just care that the service is delivered but as you back away from that and you get closer and closer to the source, someone needs to care about the hardware and it should matter. Why? Because essentially what hardware is doing is it's consuming electricity and dollars and the more efficiently you can configure hardware, the more bang you're going to get for your buck. So it's not only a quantitative question in terms of how much can you deliver? But it also ends up being a qualitative change as capabilities allow for things we couldn't do before, because we just didn't have the aggregate horsepower to do it. So this chart actually comes out of some performance tests that were done. So it happens to be Dell servers with Broadcom components. And the point here was to peel back, you know, peel off the top of the server and look at what's in that server, starting with, you know, the PCI interconnect. So PCIE gen three, gen four, moving forward. What are the effects on from an interconnect versus on performance application performance, translating into new orders per minute, processed per dollar, et cetera, et cetera? If you look at the advances in CPU architecture mapped against the advances in interconnect and storage subsystem performance, you can see that CPU architecture is sort of lagging behind in a way. And Bob mentioned this idea of tiling and all of the different ways to get around that. When we do performance testing, we can actually peg CPUs, just running the performance tests without any actual database environments working. So right now we're at this sort of imbalance point where you have to make sure you design things properly to get the most bang per kilowatt hour of power per dollar input. So the key thing here what this is highlighting is just as a very specific example, you take a card that's designed as a gen three PCIE device, and you plug it into a gen four slot. Now the card is the bottleneck. You plug a gen four card into a gen four slot. Now the gen four slot is the bottleneck. So we're constantly chasing these bottlenecks. Someone has to be focused on that from an architectural perspective, it's critically important. So there's no question that it matters. But of course, various people in this food chain won't care where it comes from. I guess a good analogy might be, where does our food come from? If I get a steak, it's a pink thing wrapped in plastic, right? Well, there are a lot of inputs that a lot of people have to care about to get that to me. Do I care about all of those things? No. Are they important? They're critically important. >> So, okay. So all I want to get to the, okay. So what does this all mean to customers? And so what I'm hearing from you is to balance a system it's becoming, you know, more complicated. And I kind of been waiting for this day for a long time, because as we all know the bottleneck was always the spinning disc, the last mechanical. So people who wrote software knew that when they were doing it right, the disc had to go and do stuff. And so they were doing other things in the software. And now with all these new interconnects and flash and things like you could do atomic rights. And so that opens up new software possibilities and combine that with alternative processes. But what's the so what on this to the customer and the application impact? Can anybody address that? >> Yeah, let me address that for a moment. I want to leverage some of the things that Bob said, Keith said, Zeus said, and David said, yeah. So I'm a bit of a contrarian in some of this. For example, on the chip side. As the chips get smaller, 14 nanometer, 10 nanometer, five nanometer, soon three nanometer, we talk about more cores, but the biggest problem on the chip is the interconnect from the chip 'cause the wires get smaller. People don't realize in 2004 the latency on those wires in the chips was 80 picoseconds. Today it's 1300 picoseconds. That's on the chip. This is why they're not getting faster. So we maybe getting a little bit slowing down in Moore's law. But even as we kind of conquer that you still have the interconnect problem and the interconnect problem goes beyond the chip. It goes within the system, composable architectures. It goes to the point where Keith made, ultimately you need a hybrid because what we're seeing, what I'm seeing and I'm talking to customers, the biggest issue they have is moving data. Whether it be in a chip, in a system, in a data center, between data centers, moving data is now the biggest gating item in performance. So if you want to move it from, let's say your transactional database to your machine learning, it's the bottleneck, it's moving the data. And so when you look at it from a distributed environment, now you've got to move the compute to the data. The only way to get around these bottlenecks today is to spend less time in trying to move the data and more time in taking the compute, the software, running on hardware closer to the data. Go ahead. >> So is this what you mean when Nicholson was talking about a shift from a processor centric world to a connectivity centric world? You're talking about moving the bits across all the different components, not having the processor you're saying is essentially becoming the bottleneck or the memory, I guess. >> Well, that's one of them and there's a lot of different bottlenecks, but it's the data movement itself. It's moving away from, wait, why do we need to move the data? Can we move the compute, the processing closer to the data? Because if we keep them separate and this has been a trend now where people are moving processing away from it. It's like the edge. I think it was Zeus or David. You were talking about the edge earlier. As you look at the edge, who defines the edge, right? Is the edge a closet or is it a sensor? If it's a sensor, how do you do AI at the edge? When you don't have enough power, you don't have enough computable. People were inventing chips to do that. To do all that at the edge, to do AI within the sensor, instead of moving the data to a data center or a cloud to do the processing. Because the lag in latency is always limited by speed of light. How fast can you move the electrons? And all this interconnecting, all the processing, and all the improvement we're seeing in the PCIE bus from three, to four, to five, to CXL, to a higher bandwidth on the network. And that's all great but none of that deals with the speed of light latency. And that's an-- Go ahead. >> You know Marc, no, I just want to just because what you're referring to could be looked at at a macro level, which I think is what you're describing. You can also look at it at a more micro level from a systems design perspective, right? I'm going to be the resident knuckle dragging hardware guy on the panel today. But it's exactly right. You moving compute closer to data includes concepts like peripheral cards that have built in intelligence, right? So again, in some of this testing that I'm referring to, we saw dramatic improvements when you basically took the horsepower instead of using the CPU horsepower for the like IO. Now you have essentially offload engines in the form of storage controllers, rate controllers, of course, for ethernet NICs, smart NICs. And so when you can have these sort of offload engines and we've gone through these waves over time. People think, well, wait a minute, raid controller and NVMe? You know, flash storage devices. Does that make sense? It turns out it does. Why? Because you're actually at a micro level doing exactly what you're referring to. You're bringing compute closer to the data. Now, closer to the data meaning closer to the data storage subsystem. It doesn't solve the macro issue that you're referring to but it is important. Again, going back to this idea of system design optimization, always chasing the bottleneck, plugging the holes. Someone needs to do that in this value chain in order to get the best value for every kilowatt hour of power and every dollar. >> Yeah. >> Well this whole drive performance has created some really interesting architectural designs, right? Like Nickelson, the rise of the DPU right? Brings more processing power into systems that already had a lot of processing power. There's also been some really interesting, you know, kind of innovation in the area of systems architecture too. If you look at the way Nvidia goes to market, their drive kit is a prebuilt piece of hardware, you know, optimized for self-driving cars, right? They partnered with Pure Storage and ARISTA to build that AI-ready infrastructure. I remember when I talked to Charlie Giancarlo, the CEO of Pure about when the three companies rolled that out. He said, "Look, if you're going to do AI, "you need good store. "You need fast storage, fast processor and fast network." And so for customers to be able to put that together themselves was very, very difficult. There's a lot of software that needs tuning as well. So the three companies partner together to create a fully integrated turnkey hardware system with a bunch of optimized software that runs on it. And so in that case, in some ways the hardware was leading the software innovation. And so, the variety of different architectures we have today around hardware has really exploded. And I think it, part of the what Bob brought up at the beginning about the different chip design. >> Yeah, Bob talked about that earlier. Bob, I mean, most AI today is modeling, you know, and a lot of that's done in the cloud and it looks from my standpoint anyway that the future is going to be a lot of AI inferencing at the edge. And that's a radically different architecture, Bob, isn't it? >> It is, it's a completely different architecture. And just to follow up on a couple points, excellent conversation guys. Dave talked about system architecture and really this that's what this boils down to, right? But it's looking at architecture at every level. I was talking about the individual different components the new interconnect methods. There's this new thing called UCIE universal connection. I forget what it stands answer for, but it's a mechanism for doing chiplet architectures, but then again, you have to take it up to the system level, 'cause it's all fine and good. If you have this SOC that's tuned and optimized, but it has to talk to the rest of the system. And that's where you see other issues. And you've seen things like CXL and other interconnect standards, you know, and nobody likes to talk about interconnect 'cause it's really wonky and really technical and not that sexy, but at the end of the day it's incredibly important exactly. To the other points that were being raised like mark raised, for example, about getting that compute closer to where the data is and that's where again, a diversity of chip architectures help and exactly to your last comment there Dave, putting that ability in an edge device is really at the cutting edge of what we're seeing on a semiconductor design and the ability to, for example, maybe it's an FPGA, maybe it's a dedicated AI chip. It's another kind of chip architecture that's being created to do that inferencing on the edge. Because again, it's that the cost and the challenges of moving lots of data, whether it be from say a smartphone to a cloud-based application or whether it be from a private network to a cloud or any other kinds of permutations we can think of really matters. And the other thing is we're tackling bigger problems. So architecturally, not even just architecturally within a system, but when we think about DPUs and the sort of the east west data center movement conversation that we hear Nvidia and others talk about, it's about combining multiple sets of these systems to function together more efficiently again with even bigger sets of data. So really is about tackling where the processing is needed, having the interconnect and the ability to get where the data you need to the right place at the right time. And because those needs are diversifying, we're just going to continue to see an explosion of different choices and options, which is going to make hardware even more essential I would argue than it is today. And so I think what we're going to see not only does hardware matter, it's going to matter even more in the future than it does now. >> Great, yeah. Great discussion, guys. I want to bring Keith back into the conversation here. Keith, if your main expertise in tech is provisioning LUNs, you probably you want to look for another job. So maybe clearly hardware matters, but with software defined everything, do people with hardware expertise matter outside of for instance, component manufacturers or cloud companies? I mean, VMware certainly changed the dynamic in servers. Dell just spun off its most profitable asset and VMware. So it obviously thinks hardware can stand alone. How does an enterprise architect view the shift to software defined hyperscale cloud and how do you see the shifting demand for skills in enterprise IT? >> So I love the question and I'll take a different view of it. If you're a data analyst and your primary value add is that you do ETL transformation, talk to a CDO, a chief data officer over midsize bank a little bit ago. He said 80% of his data scientists' time is done on ETL. Super not value ad. He wants his data scientists to do data science work. Chances are if your only value is that you do LUN provisioning, then you probably don't have a job now. The technologies have gotten much more intelligent. As infrastructure pros, we want to give infrastructure pros the opportunities to shine and I think the software defined nature and the automation that we're seeing vendors undertake, whether it's Dell, HP, Lenovo take your pick that Pure Storage, NetApp that are doing the automation and the ML needed so that these practitioners don't spend 80% of their time doing LUN provisioning and focusing on their true expertise, which is ensuring that data is stored. Data is retrievable, data's protected, et cetera. I think the shift is to focus on that part of the job that you're ensuring no matter where the data's at, because as my data is spread across the enterprise hybrid different types, you know, Dave, you talk about the super cloud a lot. If my data is in the super cloud, protecting that data and securing that data becomes much more complicated when than when it was me just procuring or provisioning LUNs. So when you say, where should the shift be, or look be, you know, focusing on the real value, which is making sure that customers can access data, can recover data, can get data at performance levels that they need within the price point. They need to get at those datasets and where they need it. We talked a lot about where they need out. One last point about this interconnecting. I have this vision and I think we all do of composable infrastructure. This idea that scaled out does not solve every problem. The cloud can give me infinite scale out. Sometimes I just need a single OS with 64 terabytes of RAM and 204 GPUs or GPU instances that single OS does not exist today. And the opportunity is to create composable infrastructure so that we solve a lot of these problems that just simply don't scale out. >> You know, wow. So many interesting points there. I had just interviewed Zhamak Dehghani, who's the founder of Data Mesh last week. And she made a really interesting point. She said, "Think about, we have separate stacks. "We have an application stack and we have "a data pipeline stack and the transaction systems, "the transaction database, we extract data from that," to your point, "We ETL it in, you know, it takes forever. "And then we have this separate sort of data stack." If we're going to inject more intelligence and data and AI into applications, those two stacks, her contention is they have to come together. And when you think about, you know, super cloud bringing compute to data, that was what Haduck was supposed to be. It ended up all sort of going into a central location, but it's almost a rhetorical question. I mean, it seems that that necessitates new thinking around hardware architectures as it kind of everything's the edge. And the other point is to your point, Keith, it's really hard to secure that. So when you can think about offloads, right, you've heard the stats, you know, Nvidia talks about it. Broadcom talks about it that, you know, that 30%, 25 to 30% of the CPU cycles are wasted on doing things like storage offloads, or networking or security. It seems like maybe Zeus you have a comment on this. It seems like new architectures need to come other to support, you know, all of that stuff that Keith and I just dispute. >> Yeah, and by the way, I do want to Keith, the question you just asked. Keith, it's the point I made at the beginning too about engineers do need to be more software-centric, right? They do need to have better software skills. In fact, I remember talking to Cisco about this last year when they surveyed their engineer base, only about a third of 'em had ever made an API call, which you know that that kind of shows this big skillset change, you know, that has to come. But on the point of architectures, I think the big change here is edge because it brings in distributed compute models. Historically, when you think about compute, even with multi-cloud, we never really had multi-cloud. We'd use multiple centralized clouds, but compute was always centralized, right? It was in a branch office, in a data center, in a cloud. With edge what we creates is the rise of distributed computing where we'll have an application that actually accesses different resources and at different edge locations. And I think Marc, you were talking about this, like the edge could be in your IoT device. It could be your campus edge. It could be cellular edge, it could be your car, right? And so we need to start thinkin' about how our applications interact with all those different parts of that edge ecosystem, you know, to create a single experience. The consumer apps, a lot of consumer apps largely works that way. If you think of like app like Uber, right? It pulls in information from all kinds of different edge application, edge services. And, you know, it creates pretty cool experience. We're just starting to get to that point in the business world now. There's a lot of security implications and things like that, but I do think it drives more architectural decisions to be made about how I deploy what data where and where I do my processing, where I do my AI and things like that. It actually makes the world more complicated. In some ways we can do so much more with it, but I think it does drive us more towards turnkey systems, at least initially in order to, you know, ensure performance and security. >> Right. Marc, I wanted to go to you. You had indicated to me that you wanted to chat about this a little bit. You've written quite a bit about the integration of hardware and software. You know, we've watched Oracle's move from, you know, buying Sun and then basically using that in a highly differentiated approach. Engineered systems. What's your take on all that? I know you also have some thoughts on the shift from CapEx to OPEX chime in on that. >> Sure. When you look at it, there are advantages to having one vendor who has the software and hardware. They can synergistically make them work together that you can't do in a commodity basis. If you own the software and somebody else has the hardware, I'll give you an example would be Oracle. As you talked about with their exit data platform, they literally are leveraging microcode in the Intel chips. And now in AMD chips and all the way down to Optane, they make basically AMD database servers work with Optane memory PMM in their storage systems, not MVME, SSD PMM. I'm talking about the cards itself. So there are advantages you can take advantage of if you own the stack, as you were putting out earlier, Dave, of both the software and the hardware. Okay, that's great. But on the other side of that, that tends to give you better performance, but it tends to cost a little more. On the commodity side it costs less but you get less performance. What Zeus had said earlier, it depends where you're running your application. How much performance do you need? What kind of performance do you need? One of the things about moving to the edge and I'll get to the OPEX CapEx in a second. One of the issues about moving to the edge is what kind of processing do you need? If you're running in a CCTV camera on top of a traffic light, how much power do you have? How much cooling do you have that you can run this? And more importantly, do you have to take the data you're getting and move it somewhere else and get processed and the information is sent back? I mean, there are companies out there like Brain Chip that have developed AI chips that can run on the sensor without a CPU. Without any additional memory. So, I mean, there's innovation going on to deal with this question of data movement. There's companies out there like Tachyon that are combining GPUs, CPUs, and DPUs in a single chip. Think of it as super composable architecture. They're looking at being able to do more in less. On the OPEX and CapEx issue. >> Hold that thought, hold that thought on the OPEX CapEx, 'cause we're running out of time and maybe you can wrap on that. I just wanted to pick up on something you said about the integrated hardware software. I mean, other than the fact that, you know, Michael Dell unlocked whatever $40 billion for himself and Silverlake, I was always a fan of a spin in with VMware basically become the Oracle of hardware. Now I know it would've been a nightmare for the ecosystem and culturally, they probably would've had a VMware brain drain, but what does anybody have any thoughts on that as a sort of a thought exercise? I was always a fan of that on paper. >> I got to eat a little crow. I did not like the Dale VMware acquisition for the industry in general. And I think it hurt the industry in general, HPE, Cisco walked away a little bit from that VMware relationship. But when I talked to customers, they loved it. You know, I got to be honest. They absolutely loved the integration. The VxRail, VxRack solution exploded. Nutanix became kind of a afterthought when it came to competing. So that spin in, when we talk about the ability to innovate and the ability to create solutions that you just simply can't create because you don't have the full stack. Dell was well positioned to do that with a potential span in of VMware. >> Yeah, we're going to be-- Go ahead please. >> Yeah, in fact, I think you're right, Keith, it was terrible for the industry. Great for Dell. And I remember talking to Chad Sakac when he was running, you know, VCE, which became Rack and Rail, their ability to stay in lockstep with what VMware was doing. What was the number one workload running on hyperconverged forever? It was VMware. So their ability to remain in lockstep with VMware gave them a huge competitive advantage. And Dell came out of nowhere in, you know, the hyper-converged market and just started taking share because of that relationship. So, you know, this sort I guess it's, you know, from a Dell perspective I thought it gave them a pretty big advantage that they didn't really exploit across their other properties, right? Networking and service and things like they could have given the dominance that VMware had. From an industry perspective though, I do think it's better to have them be coupled. So. >> I agree. I mean, they could. I think they could have dominated in super cloud and maybe they would become the next Oracle where everybody hates 'em, but they kick ass. But guys. We got to wrap up here. And so what I'm going to ask you is I'm going to go and reverse the order this time, you know, big takeaways from this conversation today, which guys by the way, I can't thank you enough phenomenal insights, but big takeaways, any final thoughts, any research that you're working on that you want highlight or you know, what you look for in the future? Try to keep it brief. We'll go in reverse order. Maybe Marc, you could start us off please. >> Sure, on the research front, I'm working on a total cost of ownership of an integrated database analytics machine learning versus separate services. On the other aspect that I would wanted to chat about real quickly, OPEX versus CapEx, the cloud changed the market perception of hardware in the sense that you can use hardware or buy hardware like you do software. As you use it, pay for what you use in arrears. The good thing about that is you're only paying for what you use, period. You're not for what you don't use. I mean, it's compute time, everything else. The bad side about that is you have no predictability in your bill. It's elastic, but every user I've talked to says every month it's different. And from a budgeting perspective, it's very hard to set up your budget year to year and it's causing a lot of nightmares. So it's just something to be aware of. From a CapEx perspective, you have no more CapEx if you're using that kind of base system but you lose a certain amount of control as well. So ultimately that's some of the issues. But my biggest point, my biggest takeaway from this is the biggest issue right now that everybody I talk to in some shape or form it comes down to data movement whether it be ETLs that you talked about Keith or other aspects moving it between hybrid locations, moving it within a system, moving it within a chip. All those are key issues. >> Great, thank you. Okay, CTO advisor, give us your final thoughts. >> All right. Really, really great commentary. Again, I'm going to point back to us taking the walk that our customers are taking, which is trying to do this conversion of all primary data center to a hybrid of which I have this hard earned philosophy that enterprise IT is additive. When we add a service, we rarely subtract a service. So the landscape and service area what we support has to grow. So our research focuses on taking that walk. We are taking a monolithic application, decomposing that to containers, and putting that in a public cloud, and connecting that back private data center and telling that story and walking that walk with our customers. This has been a super enlightening panel. >> Yeah, thank you. Real, real different world coming. David Nicholson, please. >> You know, it really hearkens back to the beginning of the conversation. You talked about momentum in the direction of cloud. I'm sort of spending my time under the hood, getting grease under my fingernails, focusing on where still the lions share of spend will be in coming years, which is OnPrem. And then of course, obviously data center infrastructure for cloud but really diving under the covers and helping folks understand the ramifications of movement between generations of CPU architecture. I know we all know Sapphire Rapids pushed into the future. When's the next Intel release coming? Who knows? We think, you know, in 2023. There have been a lot of people standing by from a practitioner's standpoint asking, well, what do I do between now and then? Does it make sense to upgrade bits and pieces of hardware or go from a last generation to a current generation when we know the next generation is coming? And so I've been very, very focused on looking at how these connectivity components like rate controllers and NICs. I know it's not as sexy as talking about cloud but just how these opponents completely change the game and actually can justify movement from say a 14th-generation architecture to a 15th-generation architecture today, even though gen 16 is coming, let's say 12 months from now. So that's where I am. Keep my phone number in the Rolodex. I literally reference Rolodex intentionally because like I said, I'm in there under the hood and it's not as sexy. But yeah, so that's what I'm focused on Dave. >> Well, you know, to paraphrase it, maybe derivative paraphrase of, you know, Larry Ellison's rant on what is cloud? It's operating systems and databases, et cetera. Rate controllers and NICs live inside of clouds. All right. You know, one of the reasons I love working with you guys is 'cause have such a wide observation space and Zeus Kerravala you, of all people, you know you have your fingers in a lot of pies. So give us your final thoughts. >> Yeah, I'm not a propeller heady as my chip counterparts here. (all laugh) So, you know, I look at the world a little differently and a lot of my research I'm doing now is the impact that distributed computing has on customer employee experiences, right? You talk to every business and how the experiences they deliver to their customers is really differentiating how they go to market. And so they're looking at these different ways of feeding up data and analytics and things like that in different places. And I think this is going to have a really profound impact on enterprise IT architecture. We're putting more data, more compute in more places all the way down to like little micro edges and retailers and things like that. And so we need the variety. Historically, if you think back to when I was in IT you know, pre-Y2K, we didn't have a lot of choice in things, right? We had a server that was rack mount or standup, right? And there wasn't a whole lot of, you know, differences in choice. But today we can deploy, you know, these really high-performance compute systems on little blades inside servers or inside, you know, autonomous vehicles and things. I think the world from here gets... You know, just the choice of what we have and the way hardware and software works together is really going to, I think, change the world the way we do things. We're already seeing that, like I said, in the consumer world, right? There's so many things you can do from, you know, smart home perspective, you know, natural language processing, stuff like that. And it's starting to hit businesses now. So just wait and watch the next five years. >> Yeah, totally. The computing power at the edge is just going to be mind blowing. >> It's unbelievable what you can do at the edge. >> Yeah, yeah. Hey Z, I just want to say that we know you're not a propeller head and I for one would like to thank you for having your master's thesis hanging on the wall behind you 'cause we know that you studied basket weaving. >> I was actually a physics math major, so. >> Good man. Another math major. All right, Bob O'Donnell, you're going to bring us home. I mean, we've seen the importance of semiconductors and silicon in our everyday lives, but your last thoughts please. >> Sure and just to clarify, by the way I was a great books major and this was actually for my final paper. And so I was like philosophy and all that kind of stuff and literature but I still somehow got into tech. Look, it's been a great conversation and I want to pick up a little bit on a comment Zeus made, which is this it's the combination of the hardware and the software and coming together and the manner with which that needs to happen, I think is critically important. And the other thing is because of the diversity of the chip architectures and all those different pieces and elements, it's going to be how software tools evolve to adapt to that new world. So I look at things like what Intel's trying to do with oneAPI. You know, what Nvidia has done with CUDA. What other platform companies are trying to create tools that allow them to leverage the hardware, but also embrace the variety of hardware that is there. And so as those software development environments and software development tools evolve to take advantage of these new capabilities, that's going to open up a lot of interesting opportunities that can leverage all these new chip architectures. That can leverage all these new interconnects. That can leverage all these new system architectures and figure out ways to make that all happen, I think is going to be critically important. And then finally, I'll mention the research I'm actually currently working on is on private 5g and how companies are thinking about deploying private 5g and the potential for edge applications for that. So I'm doing a survey of several hundred us companies as we speak and really looking forward to getting that done in the next couple of weeks. >> Yeah, look forward to that. Guys, again, thank you so much. Outstanding conversation. Anybody going to be at Dell tech world in a couple of weeks? Bob's going to be there. Dave Nicholson. Well drinks on me and guys I really can't thank you enough for the insights and your participation today. Really appreciate it. Okay, and thank you for watching this special power panel episode of theCube Insights powered by ETR. Remember we publish each week on Siliconangle.com and wikibon.com. All these episodes they're available as podcasts. DM me or any of these guys. I'm at DVellante. You can email me at David.Vellante@siliconangle.com. Check out etr.ai for all the data. This is Dave Vellante. We'll see you next time. (upbeat music)

(upbeat music) >> Welcome to this Cube conversation sponsored by Citrix. This is the third and final installment in the Citrix launchpad series. We're going to be talking about the launchpad series for work. Lisa Martin here with Dion Hinchcliffe, VP and principal analyst at Constellation research. Dion, welcome to the program. >> No, thanks Lisa. Great to be here. >> So we have seen a tremendous amount of change in the last 18, 19 months. You know, we saw this massive scatter to work from home a year and a half ago. Now we're in this sort of distributed environment. That's been persisting for a long time. Talk to me about, we're going to be talking about some of the things that Citrix is seeing and some of the things that they're doing to help individuals and teams, but give me your lens from Constellation's perspective. What are some of the major challenges with this distributed environment that you've seen? >> Sure. Well, so we've gone from this, you know, the world of work, the way that it was now, we're all very decentralized, you know, work from anywhere. Remote work is really dominating, you know, white collar types of activities in the workplace and workplaces that in our homes for most of us even today. But that started to change. Some people are going back. Although I just recently spoke to a panel of CIOs that says they have no plans anytime soon, but they're very aware that they need to have workable plans for when we start sending people back to the office and there's this big divide. How are we going to make sure that we have one common culture? We have a collaborative organization when, you know, a good percentage of our workers are in the office, but also maybe as much as half the organization is at home. And so, how to make processes seamless, how to make people collaborate and make sure there's equity and inclusion so that the people at home aren't left out and then people in the office, maybe you don't have an unfair advantage. So those are all the conversations. And of course, because this is a technology revolution, remote work was enabled by technology. We're literally looking at it again for this hybrid work, this, you know, this divided organization that we're going to have. >> You mentioned culture that's incredibly important, but also challenging to do with this distribution. I was looking at some research that Citrix provided, asking individuals from a productivity perspective, and two thirds said, hey, for our organizations that have given us more tools for collaboration and communication, yes, we are absolutely more productive. But the kicker is, the same amount of people, about two thirds that answered the survey said, we've now got about ten tools. So complexity is more challenging. It's harder to work individually. It's harder to work in teams. And so Citrix is really coming to the table here with the launchpad series for work, saying let's help these individuals and these teams, because as we, we think, and I'm sure you have insight Dion on this as well, this hybrid model that we're starting to see emerge is going to be persistent for a while. >> Yeah. For the foreseeable future. Cause we don't know what the future holds. So we'll have to hold the hybrid model as the primary model. And we may eventually go back to the way that we were. But for the next several years, there's going to be that. And so we're trying to wrap our arms around that. And I think that we're seeing with things like the Citrix announcements, a wave of responses saying, all right, let's really design properly for these changes. You know, we kind of just adapted quickly when everyone went to remote last year and now we're actually adding features to streamline, to reduce the friction, to simplify remote work, which does use, you have to use more applications. You have to switch between different things. You have to, you know, your employee experience in the digital world is just more cluttered and complicated, but it doesn't have to be. And so I, you know, we can look to some of these announcements for last year, I think address some of that. >> Let's break some of that down because to your point, it doesn't have to be complex complicated. It shouldn't be. Initially this scatter was, let's do everything we can to ensure that our teams and our people can be productive, can communicate, can collaborate. And now, since this is going to be persistent for quite some time, to your point, let's design for this distributed environment, this hybrid workforce of the future. Talk to me about the, one of the things that Citrix is doing with Citrix workspace, the app personalization, I can imagine as an individual contributor, but also as a team leader, the ability to customize this to the way that I work best is critical. >> And it really is, especially when you know, you have workers, you know, 18 or 19 months worth of new hires that you've never met. They don't really feel like, you know, this is maybe their organization. But if you allow them to shape it a little bit, make it contextual for them. So they don't just come into this cookie cutter digital experience that actually is kind of more meaningful for them. It makes it easier for them to get their job done and things are the way that they want them and where they want them. I think that makes a lot of sense. And so the app personalization announcements is important for remote workers in particular, but all workers to say, hey, can I start tailoring, you know, parts of my employee experience? So they make more sense for me. And I feel like I belong a little bit more. I think it's significant. >> It is. Let's talk about it from a security perspective though. We've seen massive changes in the security landscape in the last year and a half. We've seen some Citrix data that I was looking at, said between 2019 and 2020, ransomware up 435%, malware up 358%. And of course the weakest link being humans. Talk to me from a Citrix workspace perspective about some of the things that they've done to ensure that those security policies can be applied. >> Well, and the part that I really liked about the launchpad announcements around work in terms of security was this much more intelligent analysis. You know, one of the most frustrating things is you're trying to get work done remotely and maybe you're you're in crunch mode and all of a sudden the security system clamps down because they think you're doing something that, you know, you might be sharing information you shouldn't be and now you can't, get your deadline met. I really liked how the analytics inside the new security features really try to make sure they're applying intelligent analysis of behavior. And only when it's clear that a bad actor is in there doing something, then they can restrict access, protect information. And so I have no doubt they'll continue to evolve the product so that it's even even more effective in terms of how it can include or exclude bad actors from doing things inside your system. And so this is the kind of intelligence security increasingly based on AI type technologies that I think that will keep our workers productive, but clamp down on the much higher rate of that activity we see out there. Because we do have so many more endpoints there's a thousand or more times more endpoints in today's organizations because of remote work. >> Right. And one of the things that we've seen with ransomware, I mentioned those numbers that Citrix was sharing. It's gotten so much more personalized, so it's harder and harder to catch these things. One of the things that I found interesting, Dion, that from a secure collaboration perspective, that Citrix is saying is that, you know, we need to go, security needs to go beyond the devices and the endpoints and the apps that an employee is using, which of which we said, there are at least 10 apps that are being used today and it needs to actually be applied at a content level, the content creation level. Talk to me about your thoughts about that. >> I think that's exactly right. So if you know the profile of that worker and the types of things they normally do, and you see unusual behavior that is uncharacteristic to that worker, because you know their patterns, the types of content, the locations of that content that they might normally have access to. And if they're just accessing things, you know, periodically, that's usually not a problem. When they suddenly access a large volume of information and appear to be downloading it, those are the types of issues and especially of content they don't normally use for their work. Then you can intervene and take more intelligent actions as opposed to just trying to limit all content for example. So that knowledge workers can actually get access to all that great information in your IT systems. You can now give them access to it, but when clearly something, something bad is happening, the system automatically does it and steps in. >> I was looking at some of the data with respect to updates to Citrix analytics that it can now auto change permissions on shared files to read only, I think you alluded to this earlier, when it detects that excess sharing is going on. >> And, inappropriate access sharing. So sometimes it's okay for a worker to access, you know, documents. But the big fear is that a bad actor gets access. They get a USB key and they download a bunch of files and they get a whole bunch of IP or important knowledge. Well, when you have a system that's continually monitoring and you know, the unblinking gaze of Citrix security capabilities are looking at the patterns, not just the content alone or just the device alone, but at the, at the usage patterns and saying, I can make this read only because that's clearly the, you know, we don't want them to be able to download this because this activity is completely out of bounds or very unusual. >> Right. One of the things also that Citrix is doing is integrating with Microsoft teams. I was listening to a fun quiz show the other day that said, what were the top two apps downloaded in 2020? And I guessed one of them correctly, Tiktok though. I still don't know how to use it. And the second one was Zoom, and I'm sure Microsoft teams is way up there. I was looking at some stats that said, I think as of the spring of 2020, there were 145 million daily users of Microsoft teams. So that, from a collaboration perspective, something that a lot of folks are dependent on during the pandemic. And now within Teams, I can access Microsoft workspace? Citrix workspace. >> Yes. Well, and it's more significant than it sounds because there's a real hunger to find a center of gravity for the employee experience. What do I put that? Where should they be spending most of their time? Where should I be training them to focus most of their attention? And obviously workers collaborate a lot and Teams as part of Office 365, is a juggernaut? You know, the rise of it during the pandemic has been incredible. And just to show this, I have a digital workplace advisory board. Its companies who are heading, are the farthest along in designing digital employee experiences, and 31% of them said, this January, they're planning on centralizing the employee experience in Teams. Now, if you're a Citrix customer, you have workspace you go, how do I, I don't want to be left out. This announcement allows you to say, you can have the goodness of teams and its capabilities and the power of Citrix workspace, and you have them in one place and really creating a true center of gravity and simplifying and streamlining the employee experience. You don't have this fragmented pieces. Everything's right there in one place, in one pane of glass. And so I like this announcement. It brings Citrix up to parody with a lot of their competitors and actually eclipses several of them as well. So I really like to see this. >> So then from within teams, I can access Citrix workspace. I can share documents with team members and collaborate as well as that kind of the idea. >> Yes. That is the idea, and of course, they'll continue to evolve that, but now you can do your work in Citrix workspace and when documents are involved and you want to bring your team in, they're already right there inside that experience. >> That ability to streamline things, so critical, given the fact that we're still in this distributed environment, I'm sure families are still dealing with some, some amount of remote learning, or there's still distractions from the, do I live at work, do I work from home environment? One of the grips I really felt for when this happened, Dion, was the contact center. I thought these poor people, more people now with shorter and shorter fuses trying to get updates on whatever it was that they were, if they had something ordered and of course all the shipping delays. And the contact center of course went (blowing sound) scattered as well. And we've got people working from home, trying to do their jobs. Talk to me about some of those things that Citrix is doing to enable with Google, those contact center workers to have a good experience so that ultimately the employee experience is good, so is the customer experience? >> The contact center worker has the toughest of all of the different employee profiles I've seen, they have the most they have to learn, the most number of applications. They're typically not highly skilled workers. So they might only just have a, you know, high school education. Yet, they're being asked to cram all of these technologies, each one with a different employee experience, and they don't stay very long as a result of that. You might train them for two months before they're effective and they only stay for six months on average. And so, both businesses really want to be able to streamline onboarding and provisioning a and getting them set up and effective. And they want it too, if you want happy contact center workers making your customers happy and staying around. And so this announcements really allows you to deploy pre-configured Citrix workspaces on, on Chrome OS so that, you know, if you need to field a whole bunch of workers or you have a big dose say you're a relief company and you have a lot of disaster care workers. You can certainly this issue that these devices very easily, they're ready to go with their employee experience and all the right things in place so they can be effective with the least amount of effort. So I guess, it's a big step forward for a worker that is often neglected and underserved. >> Right. Definitely often neglected. And you, you brought up a good point there. And one of the things that, that peaked in my mind, as you talked about, you know, the onboarding experience, the retention, well, these contact center folks are the front lines to the customer. So from a brand reputation perspective, that's on the line, for companies in every industry where people with short fuses are dealing with contact center folks. So the ability to onboard them to give them a much more seamless experience is critical for the brand reputation, customer retention for every industry, I would imagine. >> Absolutely. Especially when you're setting up a contact center or you have a new product launching and you want, you know, you've got to bring, onboard all these new workers, you can do it, and they are going to have the least challenges. They're going to be ready to go right out of the box, be able to receive their package, with their device and their Citrix employee experience, ready to go. You know, just turn the machine on and they're off to the races. And that's the vision and that's the right one. So I was glad to see that as well. >> Yeah. Fantastic. One of the things also that Citrix did, the Citrix workspace app builder, so that Citrix workspace can now be a system of record for certain things like collaboration, surveys, maybe even COVID-19 information, that system of record. Talk to me about why that's so critical for the distributed worker. >> So we've had this, this longstanding challenge in that we've had our systems of record, you know, these are CRM systems, ERP, things like that, which we use to run our business. And then we've had our collaboration tools and they're separate, even though we're collaborating on sales deals and we're collaborating on our supply chain. And so like, the team's announcement was in the same game. We can say, let's close that gap between our systems of record and our collaboration tools. Well, this announcement says, all right, well, we still have these isolated systems of record. How can we streamline them to build and start connecting together a little bit so that we have processes that might cross all of those things, right? It's still going to order comes in from the CRM system. Then you can complete it in the, in the ERP system, you know, ordering that product for them. So they actually get it. You know, and that's probably overkill, that scenario for this particular example. But for example, collecting data from workers saying, let's build some forms and collect some data and then feed it to this process, or this system record. You can do it much more easily than before, before you would have to hire a development team or a contractor to develop another system that would integrate, you know, CRM or ERP or whatever. Now you can do it very quickly inside that builder. First simple, basic applications, and get a lot of the low hanging fruit off your plate and more automated inside of your Citrix workspace. >> And automation has been one of the keys that we've seen to streamlining worker productivity in the last 18 months. Another thing that I was looking at is, you know, the fact that we have so many different apps and we're constantly switching apps, context is constantly changing. Is this sort of system of record going to allow or reduce the amount of context switching that employees have to do? >> Yep. Almost all of these announcements have some flavor to that saying, can we start bringing more systems together in one place? So you're not switching between applications. You don't have different and disconnected sets of data that if you need to, and if they are disconnected, you can connect them, right. That's what the app builder announcement again is about saying, all right, if you're already, always using these three applications to do something, and you're switching between them, maybe you can just build something that connect them into one experience and, you know, maybe a low level of IT person, or even a business user can do that. That's the big trend right now. >> That's so important for that continued productivity, as things will continue to be a little bit unstable, I guess, for awhile. One more thing that I saw that Citrix is announcing is integrations with, Wrike I've been a Wrike user myself. I like to have program project management tools that I can utilize to keep track of projects, but they've done a number of integrations, one of them with Wrike Signature, which I thought was really cool. So for, to secure e-signature within Wrike, based on a program or a project that you're working on. Talk to me about some of the boosts to Wrike that they've done and how you think that's going to be influential in the employee experience. >> Well, first let's just say that the Wrike acquisition was a really important one for Citrix to go above just the basic digital workplace and simple systems of record. This is a really a mass collaboration tool for managing work itself. And so they're, this is taking Citrix up the stack in the more sophisticated work scenarios. And, and when you, we are in more sophisticated work scenarios, you want to be able to pull in different data sets. So, you know, they have the Citrix ShareFile support. You want to be able to bring in really important things like, you know, signing contracts or signing sales deals or mortgage applications, or all sorts of exciting things that actually run in your business. And so, Wrike Signatures, support's really important so that when you have key processes that involve people putting signatures on documents, you can just build collaborative work management flows that, that take all that into account without having to leave the experience. Everything's in one place as much as possible. And this is the big push and we need to have all these different systems. We don't have too many apps. What we have is too many touchpoints, so lets start combining some of these. And so the Wrike integrations, really help you do that. >> Well, and ultimately it seems like what Citrix is doing with the work launchpad series. All the announcements here is really helping workers to work how and where they want to work. Which is very similar to what we say when we're talking about the end user customer experience. When tech companies like Citrix say, we have to meet our customers where they are, it sounds like that's the same thing that's happening here. >> It is. And I would just add on top of that and to make it all safe. So you can bring all these systems together, work from anywhere, and you can feel confident that you're going to do so securely and safely. And it's that whole package I think that's really critical here. >> You're right, I'm glad you brought up that security. All right, Dion take out your crystal ball for me. As we wrap things up, you're saying, you know, going into the future, we're going to be moving from this distributed workforce to this hybrid. What are some of the things that you see as really critical happening in the next six to nine months? >> Well, there's a real push to say, we need to bring in all the workers that we've hired over the last year. Maybe not bringing them in, in person, but can we use these collaborative tools and technologies to bring them, hold them closer so they get to know us. And so, you know, things like, having Microsoft teams integrated right into your Citrix workspace makes it easier for you to collaborate with remote workers and inside any process wherever you are. So whether you're in the office or not, it should bring workers closer, especially those remote ones that are at risk of being left out as they move to hybrid work. And then it's really important. And so the things like the app builder are going to also allow building those connections. And I think that workers and businesses are really going to try and build those bridges, because the number one thing I'm hearing from business leaders and IT leaders is, is it, you know, we're worried about splitting into two different organizations, the ones that are remote and the ones that are in the office and any way that we can bring all of them together in an easy way, in a natural way, situate the digital employee experience so that we really back or back to one company, one common culture, everybody has equal access and equity to the employee experience. That's going to be really important. And I think that Citrix launchpad announcements around work really are a step, a major step in the right direction for that. There's still more things that have to be done and all, all vendors are working on that. But it's nice to see. I really liked what Citrix is doing here to move the ball forward towards where we're all going. >> It is nice to see, and those connections are critically important. I happen to be at an in-person event last week, and several folks had just had been hired during the pandemic and just got to meet some of their teams. So in terms of, of getting that cultural alignment, once again, this is a great step towards that. Dion thank you for joining me on the program, talking about the Citrix launchpad series for work, all the great new things that they're announcing and sharing with us as some of the things that you see coming down the pike. We appreciate your time. >> Thanks Lisa, for having me. >> For Dion Hinchcliffe. I'm Lisa Martin. You're watching this Cube conversation. (upbeat music)