>> We're here with Jeff Boudreau and Travis Vigil, and we're going to dig into the details about Dell's big data protection announcement. Guys, good to see you. Thanks for coming in. >> Good to see you. Thank you for having us. >> You're very welcome. Let's start off, Jeff, with a high level, you know I'd like to talk about the customer, what challenges they're facing. You're talking to customers all the time, what are they telling you? >> Sure. As you know, we spend a lot of time with our customers, specifically listening, learning understanding their use cases their pain points within their specific environments. They tell us a lot, to no surprise to any of us that data is a key theme that they talk about. It's one of their most important assets. They need to extract more value from that data to fuel their business models their innovation engines, their competitive edge. So they need to make sure that that data is accessible it's secure, and it's recoverable, especially in today's world with the increased cyber attacks. >> Okay. So maybe we could get into some of those challenges. I mean, when you talk about things like data sprawl what do you mean by that? What should people know? >> Sure, so for those big three themes, I'd say, you know you have data sprawl, which is the big one which is all about the massive amounts of data. It's the growth of that data which is growing at an unprecedented rates. It's the gravity of that data and the reality of the multi-cloud sprawl. So stuff is just everywhere, right? Which increases that service, attack space for cyber criminals. >> And by gravity you mean the data's there and people don't want to move it. >> It's everywhere, right? And so when it lands someplace, think Edge, Core or Cloud, it's there. And it's something we have to help our customers with. >> Okay. So it's nuanced cause complexity has other layers. What are those layers? >> Sure. When we talk to our customers they tell us complexity is one of their big themes. And specifically it's around data complexity. We talked about that growth and gravity of the data. We talk about multi-cloud complexity and we talk about multi-cloud sprawl. So multiple vendors, multiple contracts multiple tool chains, and none of those work together in this, you know, multi-cloud world. Then that drives their security complexity. So we talk about that increased attack surface. But this really drives a lot of operational complexity for their teams. Think about, we're lack consistency through everything. So people, process, tools, all that stuff which is really wasting time and money for our customers. >> So how does that affect the cyber strategies and the, I've often said the CISO, now they have this shared responsibility model they have to do that across multiple clouds. Every cloud has its own security policies and frameworks and syntax. So maybe you could double click on your perspective on that. >> Sure. I'd say the big challenge customers have seen, it's really inadequate cyber resiliency. And specifically they're feeling very exposed. And today as the world with cyber attacks being more and more sophisticated, if something goes wrong it is a real challenge for them to get back up and running quickly. And that's why this is such a big topic for CEOs and businesses around the world. >> You know, it's funny, I said this in my open, I think that prior to the pandemic businesses were optimized for efficiency and now they're like, wow, we have to actually put some headroom into the system to be more resilient, you know? Are you hearing that? >> Yeah, we absolutely are. I mean, the customers really they're asking us for help, right? It's one of the big things we're learning and hearing from them. And it's really about three things one's about simplifying IT. Two, it's really helping them to extract more value from their data. And then the third big piece is ensuring their data is protected and recoverable regardless of where it is going back to that data gravity and that very, you know the multi-cloud world. Just recently, I don't know if you've seen it, but the global data protected, excuse me the global data protection index. >> GDPI. >> Yes. Jesus! >> Not to be confused with GDPR. >> Actually that was released today and confirms everything we just talked about around customer challenges but also it highlights an importance of having a very cyber, a robust cyber resilient data protection strategy. >> Yeah, I haven't seen the latest, but I want to dig into it. I think this, you've done this many, many years in a row. I like to look at the time series and see how things have changed. All right. At a high level, Jeff, can you kind of address why Dell and from your point of view is best suited? >> Sure. So we believe there's a better way or a better approach on how to handle this. We think Dell is uniquely positioned to help our customers as a one stop shop, if you will, for that cyber resilient multi-cloud data protection solution and needs. We take a modern, a simple and resilient approach. >> Well what does that mean? What do you mean by modern? >> Sure. So modern, we talk about our software defined architecture, right? It's really designed to meet the needs not only of today but really into the future. And we protect data across any cloud and any workload. So we have a proven track record doing this today. We have more than 1700 customers that trust us to protect more than 14 exabytes of their data in the cloud today. >> Okay. So you said modern, simple and resilient. What, what do you mean by simple? >> Sure. We want to provide simplicity everywhere, going back to helping with the complexity challenge, and that's from deployment to consumption to management and support. So our offers will deploy in minutes. They are easy to operate and use and we support flexible consumption models for whatever customer may desire. So traditional, subscription, or as a service. >> And when you talk about resilient, I mean I put forth that premise, but it's hard because people say, well, that's going to going to cost us more. Well, it may, but you're going to also reduce your risk. So what's your point of view on resilience? >> Yeah, I think it's something all customers need. So we're going to be providing a comprehensive and resilient portfolio of cyber solutions that are secured by design. We have some some unique capabilities in a combination of things like built in immuneability, physical and logical isolation. We have intelligence built in with AI parred recovery and just one, I guess fun fact for everybody is we have our cyber vault is the only solution in the industry that is endorsed by Sheltered Harbor that meets all the needs of the financial sector. >> So it's interesting when you think about the NIST framework for cybersecurity, it's all about layers. You're sort of bringing that now to data protection. >> Correct. >> Yeah. All right. In a minute we're going to come back with Travis and dig into the news. We're going to take a short break, keep it right there. (calming piano music)

>>Good afternoon, everyone. And welcome back to the VMware Explorer. 2022 live from San Francisco. Lisa Martin, here with Dave. Valante good to be sitting next to you, sir. >>Yeah. Yeah. The big set >>And we're very excited to be welcoming buck. One of our esteemed alumni Sanja poin joins us, the CEO and president of cohesive. Nice to see >>You. Thank you, Lisa. Thank you, Dave. It's great to meet with you all the time and the new sort of setting here, but first >>Time, first time we've been in west, is that right? We've been in north. We've been in south. We've been in Las Vegas, right. But west, >>I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or hiatus. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being here with people. So >>You've also got some adrenaline, sorry, Dave. Yeah, you're good because you are new in the role at cohesive. You wrote a great blog that you are identified. The four reasons I came to cohesive. Tell the audience, just give 'em a little bit of a teaser about that. >>Yeah, I think you should all read it. You can Google and, and Google find that article. I talked about the people Mohi is a fantastic founder. You know, he was the, you know, the architect of the Google file system. And you know, one of the senior Google executives was on my board. Bill Corrin said one of the smartest engineers. He was the true father of hyperconverge infrastructure. A lot of the code of Nutanix. He wrote, I consider him really the father of that technology, which brought computer storage. And when he took that same idea of bringing compute to secondary storage, which is really what made the scale out architect unique. And we were at your super cloud event talking about that, Dave. Yeah. Right. So it's a people I really got to respect his smarts, his integrity and the genius, what he is done. I think the customer base, I called a couple of customers. One of them, a fortune 100 customer. I, I can't tell you who it was, but a very important customer. I've known him. He said, I haven't seen tech like this since VMware, 20 years ago, Amazon 10 years ago and now Ko. So that's special league. We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, Cisco as investors. Amazon's an investors. So, you know, and then finally the opportunity, I think this whole area of data management and data security now with threats, like ransomware big opportunity. >>Okay. So when you were number two at VMware, you would come on and say, we'd love all our partners and of course, okay. So you know, a little bit about how to work with, with VMware. So, so when you now think about the partnership between cohesive and VMware, what are the things that you're gonna stress to your constituents on the VMware side to convince them that Hey, partnering with cohesive is gonna gonna drive more value for customers, you know, put your thumb on the scale a little bit. You know, you gotta, you gotta unfair advantage somewhat, but you should use it. So what's the narrative gonna be like? >>Yeah, I think listen with VMware and Amazon, that probably their top two partners, Dave, you know, like one of the first calls I made was to Raghu and he knew about this decision before. That's the level of trust I have in him. I even called Michael Dell, you know, before I made the decision, there's a little bit of overlap with Dell, but it's really small compared to the overlap, the potential with Dell hardware that we could compliment. And then I called four CEOs. I was, as I was making this decision, Andy Jassey at Amazon, he was formerly AWS CEO sat Nadela at Microsoft Thomas cor at Google and Arvin Christian, IBM to say, I'm thinking about this making decision. They are many of the mentors and friends to me. So I believe in an ecosystem. And you know, even Chuck Robbins, who the CEO of Cisco is an investor, I texted him and said, Hey, finally, we can be friends. >>It was harder to us to be friends with Cisco, given the overlap of NSX. So I have a big tent towards everybody in our ecosystem with VMware. I think the simple answer is there's no overlap okay. With, with the kind of the primary storage capabilities with VSAN. And by the same thing with Nutanix, we will be friends and, and extend that to be the best data protection solution. But given also what we could do with security, I think this is gonna go a lot further. And then it's all about meet the field. We have common partners. I think, you know, sort of the narrative I talked about in that blog is just like snowflake was replacing Terada and ServiceNow replace remedy and CrowdStrike, replacing Symantec, we're replacing legacy vendors. We are viewed as the modern solution cloud optimized for private and public cloud. We can help you and make VMware and vs a and VCF very relevant to that part of the data management and data security continuum, which I think could end VMware. And by the way, the same thing into the public cloud. So most of the places where we're being successful is clearly withs, but increasingly there's this discussion also about playing into the cloud. So I think both with VMware and Amazon, and of course the other partners in the hyperscaler service, storage, networking place and security, we have some big plans. >>How, how much do you see this? How do you see this multi-cloud narrative that we're hearing here from, from VMware evolving? How much of an opportunity is it? How are customers, you know, we heard about cloud chaos yesterday at the keynote, are customers, do they, do they admit that there's cloud chaos? Some probably do some probably don't how much of an opportunity is that for cohesive, >>It's tremendous opportunity. And I think that's why you need a Switzerland type player in this space to be successful. And you know, and you can't explicitly rule out the fact that the big guys get into this space, but I think it's, if you're gonna back up office 365 or what they call now, Microsoft 365 into AWS or Google workspace into Azure or Salesforce into one of those clouds, you need a Switzerland player. It's gonna be hard. And in many cases, if you're gonna back up data or you protect that data into AWS banks need a second copy of that either on premise or Azure. So it's very hard, even if they have their own native data protection for them to be dual cloud. So I think a multi-cloud story and the fact that there's at least three big vendors of cloud in, in the us, you know, one in China, if include Alibaba creates a Switzerland opportunity for us, that could be fairly big. >>And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. Our control plane runs there. We can't take an all in AWS stack with the control plane and the data planes at AWS to Walmart. So what I've explained to both Microsoft and AWS is that data plane will need to be multi-cloud. So I can go to an, a Walmart and say, I can back up your data into Azure if you choose to, but the control plane's still gonna be an AWS, same thing with Google. Maybe they have another account. That's very Google centric. So that's how we're gonna believe the, the control plane will be in AWS. We'll optimize it there, but the data plane will be multicloud. >>Yeah. And that's what Mo had explained at Supercloud. You know, and I talked to him, he really helped me hone in on the deployment models. Yes. Where, where, where the cohesive deployment model is instantiating that technology stack into each cloud region and each cloud, which gives you latency advantages and other advantages >>And single code based same platform. >>And then bringing it, tying it together with a unified, you know, interface. That was he, he was, he was key. In fact, I, I wrote about it recently and, and gave him and the other 29 >>Quite a bit in that session, he went deep with you. I >>Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically say, okay, this is technically correct or no, Dave, your way off be. So I that's why I had to >>Go. I, I thought you did a great job in that interview because you probed him pretty deep. And I'm glad we could do that together with him next time. Well, maybe do that together here too, but it was really helpful. He's the, he's the, he's the key reason I'm here. >>So you say data management is ripe for disrupt disruption. Talk about that. You talked about this Switzerland effect. That sounds to me like a massive differentiator for cohesive. Why is data management right for disruption and why is cohesive the right partner to do it? >>Yeah, I think, listen, everyone in this sort of data protection backup from years ago have been saying the S Switzerland argument 18 years ago, I was a at Veras an executive there. We used the Switzerland argument, but what's changed is the cloud. And what's changed as a threat vector in security. That's, what's changed. And in that the proposition of a, a Switzerland player has just become more magnified because you didn't have a sales force or Workday service now then, but now you do, you didn't have multi-cloud. You had hardware vendors, you know, Dell, HPE sun at the time. IBM, it's now Lenovo. So that heterogeneity of, of on-premise service, storage, networking, HyperCloud, and, and the apps world has gotten more and more diverse. And I think you really need scale out architectures. Every one of the legacy players were not built with scale out architectures. >>If you take that fundamental notion of bringing compute to storage, you could almost paralyze. Imagine you could paralyze backup recovery and bring so much scale and speed that, and that's what Mo invented. So he took that idea of how he had invented and built Nutanix and applied that to secondary storage. So now everything gets faster and cheaper at scale. And that's a disruptive technology ally. What snowflake did to ator? I mean, the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since Ralph Kimball and bill Inman and the people who are fathers of data warehousing, they took that to Webscale. And in that came a disruptive force toter data, right on snowflake. And then of course now data bricks and big query, similar things. So we're doing the same thing. We just have to showcase the customers, which we do. And when large customers see that they're replacing the legacy solutions, I have a lot of respect for legacy solutions, but at some point in time of a solution was invented in 1995 or 2000, 2005. It's right. For change. >>So you use snowflake as an example, Frank SL doesn't like when I say playbook, cuz I says, Dave, I'm a situational CEO, no playbook, but there are patterns here. And one of the things he did is to your point go after, you know, Terra data with a better data warehouse, simplify scale, et cetera. And now he's, he's a constructing a Tam expansion strategy, same way he did at ServiceNow. And I see you guys following a similar pattern. Okay. You get your foot in the door. Let's face it. I mean, a lot of this started with, you know, just straight back. Okay, great. Now it's extending into data management now extending to multi-cloud that's like concentric circles in a Tam expansion strategy. How, how do you, as, as a CEO, that's part of your job is Tam expansion. >>So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart in size, Dave and Lisa number one, I estimate there's probably about 10 to 20 exabytes of data managed by these legacy players of on-prem stores that they back up to. Okay. So you add them all up in the market shares that they respectively are. And by the way, at the peak, the biggest of these companies got to 2 billion and then shrunk. That was Verto when I was there in 2004, 2 billion, every one of them is small and they stopped growing. You look at the IDC charts. Many of them are shrinking. We are the fastest growing in the last two years, but I estimate there's about 20 exabytes of data that collectively among the legacy players, that's either gonna stay on prem or move to the cloud. Okay. So the opportunity as they replace one of those legacy tools with us is first off to manage that 20 X by cheaper, faster with the Webscale glass offer the cloud guys, we could tip that into the cloud. Okay. >>But you can't stop there. >>Okay. No, we are not doing just backup recovery. We have a platform that can do files. We can do test dev analytics and now security. Okay. That data is potentially at a risk, not so much in the past, but for ransomware, right? How do we classify that? How do we govern that data? How do we run potential? You know, the same way you did antivirus some kind of XDR algorithms on the data to potentially not just catch the recovery process, which is after fact, but maybe the predictive act of before to know, Hey, there's somebody loitering around this data. So if I'm basically managing in the exabytes of data and I can proactively tell you what, this is, one CIO described this very simply to me a few weeks ago that I, and she said, I have 3000 applications, okay. I wanna be prepared for a black Swan event, except it's not a nine 11 planes getting the, the buildings. >>It is an extortion event. And I want to know when that happens, which of my 3000 apps I recover within one hour within one day within one week, no later than one month. Okay. And I don't wanna pay the bad guys at penny. That's what we do. So that's security discussions. We didn't have that discussion in 2004 when I was at another company, because we were talking about flood floods and earthquakes as a disaster recovery. Now you have a lot more security opportunity to be able to describe that. And that's a boardroom discussion. She needs to have that >>Digital risk. O O okay, go ahead please. I >>Was just gonna say, ransomware attack happens every what? One, every 11, 9, 11 seconds. >>And the dollar amount are going up, you know, dollar are going up. Yep. >>And, and when you pay the ransom, you don't always get your data back. So you that's not. >>And listen, there's always an ethical component. Should you do it or not do it? If you, if you don't do it and you're threatened, they may have left an Easter egg there. Listen, I, I feel very fortunate that I've been doing a lot in security, right? I mean, I built the business at, at, at VMware. We got it to over a billion I'm on the board of sneak. I've been doing security and then at SAP ran. So I know a lot about security. So what we do in security and the ecosystem that supports us in security, we will have a very carefully crafted stay tuned. Next three weeks months, you'll see us really rolling out a very kind of disciplined aspect, but we're not gonna pivot this company and become a cyber security company. Some others in our space have done that. I think that's not who we are. We are a data management and a data security company. We're not just a pure security company. We're doing both. And we do it well, intelligently, thoughtfully security is gonna be built into our platform, not voted on. Okay. And there'll be certain security things that we do organically. There's gonna be a lot that we do through partnerships, this >>Security market that's coming to you. You don't have to go claim that you're now a security vendor, right? The market very naturally saying, wow, a comprehensive security strategy has to incorporate a data protection strategy and a recovery, you know, and the things that we've talking about Mount ransomware, I want to ask you, you I've been around a long time, longer than you actually Sanjay. So, but you you've, you've seen a lot. You look, >>Thank you. That's all good. Oh, >>Shucks. So the market, I've never seen a market like this, right? I okay. After the.com crash, we said, and I know you can't talk about IPO. That's not what I'm talking about, but everything was bad after that. Right. 2008, 2000, everything was bad. I've never seen a market. That's half full, half empty, you know, snowflake beats and raises the stock, goes through the roof. Dev if it, if the area announced today, Mongo, DB, beat and Ray, that things getting crushed and, and after market never seen anything like this. It's so fed, driven and, and hard to protect. And, and of course, I know it's a marathon, you know, it's not a sprint, but have you ever seen anything like this? >>Listen, I walk worked through 18 quarters as COO of VMware. You've seen where I've seen public quarters there and you know, was very fortunate. Thanks to the team. I don't think I missed my numbers in 18 quarters except maybe once close. But we, it was, it's tough. Being a public company of the company is tough. I did that also at SAP. So the journey from 10 to 20 billion at SAP, the journey from six to 12 at VMware, that I was able to be fortunate. It's humbling because you, you really, you know, we used to have this, we do the earnings call and then we kind of ask ourselves, what, what do you think the stock price was gonna be a day and a half later? And we'd all take bets as to where this, I think you just basically, as a, as a sea level executive, you try to build a culture of beaten, raise, beaten, raise, beaten, raise, and you wanna set expectations in a way that you're not setting them up for failure. >>And you know, it's you, there's, Dave's a wonderful CEO as is Frank Salman. So it's hard for me to dissect. And sometimes the market are fickle on some small piece of it. But I think also the, when I, I encourage people say, take the long term view. When you take the long term view, you're not bothered about the ups and downs. If you're building a great company over the length of time, now it will be very clear over the arc of many, many quarters that you're business is trouble. If you're starting to see a decay in growth. And like, for example, when you start to see a growth, start to decay significantly by five, 10 percentage points, okay, there's something macro going on at this company. And that's what you won't avoid. But these, you know, ups and downs, my view is like, if you've got both Mongo D and snowflake are fantastic companies, they're CEOs of people I respect. They've actually kind of an, a, you know, advisor to us as a company, you knows moat very well. So we respect him, respect Frank, and you, there have been other quarters where Frank's, you know, the Snowflake's had a down result after that. So you build a long term and they are on the right side of history, snowflake, and both of them in terms of being a modern cloud relevant in the case of MongoDB, open source, two data technology, that's, you know, winning, I, I, we would like to be like them one day >>As, as the new CEO of cohesive, what are you most ask? What are you most anxious about and what are you most excited about? >>I think, listen, you know, you know, everything starts with the employee. You, I always believe I wrote my first memo to all employees. There was an article in Harvard business review called service profit chains that had a seminal impact on my leadership, which is when they studied companies who had been consistently profitable over a long period of time. They found that not just did those companies serve their customers well, but behind happy engaged customers were happy, engaged employees. So I always believe you start with the employee and you ensure that they're engaged, not just recruiting new employees. You know, I put on a tweet today, we're hiring reps and engineers. That's okay. But retaining. So I wanna start with ensuring that everybody, sometimes we have to make some unfortunate decisions with employees. We've, we've got a part company with, but if we can keep the best and brightest retained first, then of course, you know, recruiting machine, I'm trying to recruit the best and brightest to this company, people all over the place. >>I want to get them here. It's been, so I mean, heartwarming to come Tom world and just see people from all walks, kind of giving me hugs. I feel incredibly blessed. And then, you know, after employees, it's customers and partners, I feel like the tech is in really good hands. I don't have to worry about that. Cuz Mo it's in charge. He's got this thing. I can go to bed knowing that he's gonna keep innovating the future. Maybe in some of the companies I've worried about the tech innovation piece, but most doing a great job there. I can kind of leave that in his cap of hands, but employees, customers, partners, that's kind of what I'm focused on. None of them are for me, like a keep up at night, but there are are opportunities, right? And sometimes there's somebody you're trying to salvage to make sure or somebody you're trying to convince to join. >>But you know, customers, I love pursuing customers. I love the win. I hate to lose. So fortune 1000 global, 2000 companies, small companies, big companies, I wanna win every one of them. And it's not, it's not like, I mean, I know all these CEOs in my competitors. I texted him the day I joined and said, listen, I'll compete, honorably, whatever have you, but it's like Kobe and LeBron Kobe's passed away now. So maybe it's Steph Curry. LeBron, whoever your favorite athlete is you put your best on the court and you win. And that's how I am. That's nothing I've known no other gear than to put my best on the court and win, but do it honorably. It should not be the one that you're doing it. Unethically. You're doing it personally. You're not calling people's names. You're competing honorably. And when you win the team celebrates, it's not a victory for me. It's a victory for the team. >>I always think I'm glad that you brought up the employee experience and we're almost out of time, but I always think the employee experience and the customer experience are inextricably linked. This employees have to be empowered. They have to have the data that they need to do their job so that they can deliver to the customer. You can't do one without the other. >>That's so true. I mean, I, it's my belief. And I've talked also on this show and others about servant leadership. You know, one of my favorite poems is Brenda Naor. I went to bed in life. I dreamt that life was joy. I woke up and realized life was service. I acted in service was joy. So when you have a leadership model, which is it's about, I mean, there's lots of layers between me and the individual contributor, but I really care about that sales rep and the engineer. That's the leaf level of the organization. What can I get obstacle outta their way? I love skipping levels of going right. That sales rep let's go and crack this deal. You know? So you have that mindset. Yeah. I mean, you, you empower, you invert the pyramid and you realize the power is at the leaf level of an organization. >>So that's what I'm trying to do. It's a little easier to do it with 2000 people than I dunno, either 20, 20, 2000 people or 35,000 reported me at VMware. And I mean a similar number at SAP, which was even bigger, but you can shape this. Now we are, we're not a startup anymore. We're a midsize company. We'll see. Maybe along the way, there's an IP on the path. We'll wait for that. When it comes, it's a milestone. It's not the destination. So we do that and we are, we, I told people we are gonna build this green company. Cohesive is gonna be a great company like VMware one day, like Amazon. And there's always a day of early beginnings, but we have to work harder. This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of the kid. And you gotta work a little harder. So I love it. Yeah. >>Good luck. Awesome. Thank you. Best of luck. Congratulations. On the role, it sounds like there's a tremendous amount of adrenaline, a momentum carrying you forward Sanjay. We always appreciate having you. Thank >>You for having in your show. >>Thank you. Our pleasure, Lisa. Thank you for Sanja poin and Dave ante. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 2022, stick around our next guest. Join us momentarily.

>> Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, is modernize now. Yes, let's talk about that. And with me is Ravi mayor him, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >> Thank you so much. I'm so glad to be here with you. >> I want to ask you what the new requirements are around modern applications. I've seen some of your comments, you got to be flexible, distributed, multimodal, mobile, edge. Those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >> Yeah, I think what has basically happened is that so far it's been a transition of sorts. And now we are come to a point where that tipping point and that tipping point has been more because of COVID and there are COVID has pushed us to a world where we are living in a in a sort of occasionally connected manner where our digital interactions precede, our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than in a digital manner, as opposed to sort of making a more specific human contact. That does really been the sort of accelerant to this modernize Now, as a team. In this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. They're all sitting behind. They used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, but they are all centralized still, but where our engagement happens with the data is at the edge at your point of convenience, at your point of consumption, not where the data is actually sitting. So this has led to, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? But it just basically comes down to the fact that the data needs to be there, if you are engaging with it. And that means if you are doing it on your mobile phone, or if you're sitting, but doing something in your while you're traveling, or whether you're in a subway, whether you're in a plane or a ship, wherever the data needs to come to you and be available, as opposed to every time you going to the data, which is centrally sitting in some place. And that is the fundamental shift in terms of how the modern architecture needs to think when they, when it comes to digital transformation and, transitioning their old applications to the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Otherwise, people are basically waiting for that circle of death that we all know, and blaming the networks and other pieces. The problem was actually, the data is not where you are engaging with it. It's got to be fetched, you know, seven sea's away. And that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >> I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this because date data by its very nature is distributed. It's always been distributed, but with the distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, of, of a super rock solid database that can handle, you know, distributed data? >> Yes. So there are two issues that you alluded little too over there. The first is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data. In one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, when you have the data, you can first look at it to perform. Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five milliseconds, this is, this is a class of problem that we solve that same data. Now, eventually, without you ever having to sort of do a casting it to a different database, you can now do solid queries. Our classic SQL queries, which is our next magic. We are a no SQL database, but we have a full functional SQL. The SQL has been the language that has talked to data for 40 odd years successfully. Every other database has come and tried to implement their own QL query language, but they've all failed only SQL has stood the test of time of 40 odd years. Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is basically a look at the data and any common editorial, any, any which way you look at the data, all it will come, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries select star from where, kind of stuff, because it's at an English level becomes easy to so the same day that you didn't have to go move it to another database, do your sort of transformation of the data and all the stuff, same day that you do this. Now that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, what Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the, your ability to query the operational data in a different way. And talk querying, what was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and apply different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, the database management system. And that's where the distributed platform that we have built enables us to get it to where you need the data to be, you know, in the classic way we call it CDN'ing the data as in like content delivery networks. So far do static, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >> And on the first part of, of the, the, the answer to my question, are you saying you could do this without scheme with a no schema on, right? And then you can apply those techniques. >> Fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read thing. So, because there is no schema, it is just a Json document that is sitting inside. And Json is the lingua franca of the web, as you very well know by now. So it just Json that we manage, you can do key value look ups of the Json. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and other sophisticated pieces of technology behind it. You can do searching on it, using the, the full textual analysis pipeline. You can do ad hoc webbing on the analytics side, and you can write your own custom logic on it using or inventing capabilities. So that's, that's what it allows because we keep the data in the native form of Json. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, bring, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing in the last 40 years, because we developed various database systems and data processing systems at various points in time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. We had queuing systems, all these systems, if you want to use any one of them are answered. It always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this, it's not going to fly instead, bring the logic to the data, right? So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this. >> But as you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >> Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data recasting because it required you to have it in seven schema in one sense at, at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably related, but it not really, how do you say keep to the promise that it actually meant to be? So that's why it was a swamp I mean, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it. And you create different types of indexes to manage it. You distribute the index, you distribute the data you have, like we were discussing, you have ACID semantics on top of, and when you, when you put all these things together, it's, it's, it's a tough proposition, but we have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >> So you predicted the trend around multimodal and converged databases. You kind of led Couchbase through that. I, I want, I always ask this question because it's clearly a trend in the industry and it, and it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, where you have have a little teeny scissors and a knife, that's not that sharp. How, how do you respond to that? >> A great one. My answer is always, I use another analogy to tackle that, and is that, have you ever accused a smartphone of being a Swiss army knife? - No. No. >> Nobody does. That because it actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Right? As in a moment, it could be a TomTom, telling you all the directions, the next one, it's your PDA. Third one. It's a fantastic phone. Four. It's a beautiful camera which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment, it's the video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just thought that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, he missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app based economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get an alert saying that today you got to leave home at >> 8: 15 for your nine o'clock meeting. And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's got this notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place. Without that, you couldn't even do this simple function in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build. Because half the time you're running sideline to sideline, just, you know, integrating data from one system to the other. >> So I love the analogy with the smartphone. I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? So, so, but, but is there, is that a fair and where, in other words, those specialized databases, they say there still is a place for them, but they're getting. >> Absolutely, absolutely great analogy and a great extension to the question. That's like, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of my music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they, I mean, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. Yes, it's 90% there or 80% there. It depends on your audio file-ness of your, I mean, your experience super specialized ones do not go away. You know, there are, there are places where the specialized use cases will demand a separate system to exist. But even there that has got to be very closed. How do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that all, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car, walk into my living room, that same songs should continue and play in my living room speakers. Then it's a connected world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >> I love, I love that example too. When I was a kid, we used to go to Tweeter, et cetera. And we used to play around with three, take home, big four foot speakers. Those stores are out of business too. Absolutely. And now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi? >> I believe so, because I think what had happened was relational systems. I've mean where the norm, they rule the roost, if you will, for the last 40 odd years and then gain this no SQL movement, which was almost as though a rebellion from the relational world, we all inhabited because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee. They required your DBA and your data architect. And you had to call them just to add one column and stuff like that. And the world had moved on. This was a world of blogs and tweets and, you know, mashups and a different generation of digital behavior, There are digital, native people now who are operating in these and the, the applications, the, the consumer facing applications. We are living in this world. And yet the enterprise ones were still living in the, in the other, the other side of the divide. So out came this solution to say that we don't need SQL. Actually the problem was never SQL. No SQL was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations and the inability for these, the system to scale, the relational systems were built like airplanes, which is that if a San Francisco, Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the allowance that you'll somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. These are all vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world is make the system horizontally scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guest. I'll add one more coach to it, one more car to it. And the better part of the way we have done this here is that, and we are super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have, I need only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. You can attach the kind of coaches we call this multidimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it, right? So that's the beauty of this architecture. Now, why is that architecture important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because, you would say that I cannot run this analytical query because then my operational workload will suffer. Then my front end, then we'll slow down millions of customers that impacted that problem. They'll solve the same data once again, do analytical query, an operational query because they're separated by these cars, right? As in like we, we, we fence the, the, the resources so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or a query. And then yet you can run this analytical query, which will take a couple of minutes to them. One, not impeding the other. So that's in one sense, sort of the part of the problems that we have solved it here is that relational versus the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same query language on top. Why? It's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, the internal combustion engine the gas, you says, these were the issues we really wanted to solve. So solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, over there your gear shifters. Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So even when you feed people, the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blow harder to go fast. And they lean back for, for it to, you know, to apply a break that's, that's how we seem to define design software. Instead, we shouldn't be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, and the gas pedal and the, and the gear shifters by putting SQL back on underneath the surface, we have completely solved the relational limitations of schema, as well as scalability. So in, in, in that way, and by bringing back the classic ACID capabilities, which is what relational systems we accounted on, and being able to do that with the SQL programming language, we call it like multi-statement SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up to solve in the modern times, rather than get sort of pedantic about whether it's we have no SQL or SQL or new SQL, or, you know, any of that sort of jargon oriented debate. This is, these are the debates of computer science that they are actually, and they were the solve, and they have solved them with the latest release of 7.0, which we released a few months ago. >> Right, right. Last July, Ravi, we got got to leave it there. I love the examples and the analogies. I can't wait to be face-to-face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >> Fantastic. Thanks for the time. And the opportunity I was, I mean, very insightful questions really appreciate it. - Thank you. >> Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

(upbeat music) >> Welcome back to HPE Discover 2021, theCube's virtual coverage, continuous coverage of HPE's annual customer event. My name is Dave Vellante and we're going to dive into the intersection of high-performance computing, data and AI with Dr. Eng Lim Goh who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Dr. Goh, great to see you again. Welcome back to theCube. >> Hey, hello, Dave. Great to talk to you again. >> You might remember last year we talked a lot about swarm intelligence and how AI is evolving. Of course you hosted the Day 2 keynotes here at Discover. And you talked about thriving in the age of insights and how to craft a data-centric strategy and you addressed some of the biggest problems I think organizations face with data. And that's, you got to look, data is plentiful, but insights, they're harder to come by and you really dug into some great examples in retail, banking, and medicine and healthcare and media. But stepping back a little bit we'll zoom out on Discover '21, you know, what do you make of the events so far and some of your big takeaways? >> Hmm, well, you started with the insightful question. Data is everywhere then but we lack the insight. That's also part of the reason why that's a main reason why, Antonio on Day 1 focused and talked about that, the fact that we are in the now in the age of insight and how to thrive in this new age. What I then did on the Day 2 keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights when you know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Very pertinent question, Dave. You know, the two challenges I spoke about how to, that we need to overcome in order to thrive in this new age, the first one is the current challenge. And that current challenge is, you know state of this, you know, barriers to insight, when we are awash with data. So that's a statement. How to overcome those barriers. One of the barriers to insight when we are awash in data, in the Day 2 keynote, I spoke about three main things, three main areas that receive from customers. The first one, the first barrier is with many of our customers, data is siloed. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing, and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a Federation layer above all those silos so that when you build applications above they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know, barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And it's tough to get to value out of them. In that case I use the example of the May 6, 2010 event where the stock market dropped a trillion dollars in tens of minutes. We all know those who are financially attuned with, know about this incident. But that this is not the only incident. There are many of them out there. And for that particular May 6, event, you know it took a long time to get insight, months, yeah, before we, for months we had no insight as to what happened, why it happened. And there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road to go with the tough data. Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road and analyze that data took a long time to assemble. And he discovered that there was quote stuffing. That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees had the rule in there that says all trades less than 100 shares don't need to report in there. And so what people did was sending a lot of less than 100 shares trades to fly under the radar to do this manipulation. So here is, here the second barrier. Data could be raw and disperse. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah. Here we have three quick examples of customers. One was a great example where they were trying to build a language translator a machine language translator between two languages. But in order to do that they need to get hundreds of millions of word pairs of one language compare with the corresponding other hundreds of millions of them. They say, "Where I'm going to get all these word pairs?" Someone creative thought of a willing source and huge source, it was a United Nations. You see, so sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data. The second one has to do with, there was the, sometimes you may just have to generate that data. Interesting one. We had an autonomous car customer that collects all these data from their cars. Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hail because that's a rare occurrence. So instead of waiting for a time where the car can drive in hail, they build a simulation by having the car collected in snow and simulated hail. So these are some of the examples where we have customers working to overcome barriers. You have barriers that is associated with the fact, that data silo, if federated barriers associated with data that's tough to get at. They just took the hard road. And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow, I tell you, I have about 100 questions based on what you just said. And as a great example, the flash crash in fact Michael Lewis wrote about this in his book, the "Flash Boys" and essentially. It was high frequency traders trying to front run the market and sending in small block trades trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. Can I guess my question is can technology help us get get out of the problem? And that maybe is where AI fits in. >> Yes. Yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, assemble them to see if you can find a material trend. You can see lots of trends. Like, no, we, if humans at things we tend to see patterns in clouds. So sometimes you need to apply statistical analysis, math to be sure that what the model is seeing is real. And that required work. That's one area. The second area is, you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. And in this case before the change in the rules. By the way, after the discovery, the authorities changed the rules and all shares all trades of different, any sizes it has to be reported. Not, yeah. But the rule was applied to to say earlier that shares under 100, trades under 100 shares need not be reported. So sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't, wanted for various reasons not to put everything in there so that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such there are times we just need to go back to the raw data. >> I want to ask you-- Or be it that it's going to be tough there. >> Yeah, so I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about and I'm going to make a statement. You tell me if it's on point or off point. Seems that most of the AI going on in the enterprise is modeling data science applied to troves of data. But there's also a lot of AI going on in consumer, whether it's fingerprint technology or facial recognition or natural language processing. Will, to two-part question, will the consumer market, let's say as it has so often in the enterprise sort of inform us is sort of first part. And then will there be a shift from sort of modeling, if you will, to more, you mentioned autonomous vehicles more AI inferencing in real-time, especially with the Edge. I think you can help us understand that better. >> Yeah, this is a great question. There are three stages to just simplify, I mean, you know, it's probably more sophisticated than that, but let's just simplify there're three stages to building an AI system that ultimately can predict, make a prediction. Or to assist you in decision-making, have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data. And the machine starts to evolve a model based on all the data is seeing it starts to evolve. To a point that using a test set of data that you have separately kept a site that you know the answer for. Then you test the model, you know after you're trained it with all that data to see whether his prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision and that's the inference. So a lot of times depending on what we are focusing on. We in data science are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you wanted to make. You pick the right models and then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that a robust, it is good, but then it is not consistent. Now what you do is you try another model. So sometimes you just keep trying different models until you get the right kind, yeah, that gives you a good robust decision-making and prediction. Now, after which, if it's tested well, Q8 you will then take that model and deploy it at the Edge, yeah. And then at the Edge is essentially just looking at new data applying it to the model that you have trained and then that model will give you a prediction or a decision. So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful, can you also do learning at the Edge? That's the reason why we spoke about swarm learning the last time, learning at the Edge as a swarm. Because maybe individually they may not have enough power to do so, but as a swarm, they may. >> Is that learning from the Edge or learning at the Edge. In other words, is it-- >> Yes. >> Yeah, you don't understand my question, yeah. >> That's a great question. That's a great question. So answer is learning at the Edge, and also from the Edge, but the main goal, the goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. So that you don't need to have to send all that data back and assemble it back from all the different Edge devices assemble it back to the Cloud side to do the learning. With swarm learning, you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send the autonomous vehicle example you gave is great 'cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front and then maybe they do that and then they send that smaller data set back and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming to, let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah, well, today full of these insightful questions that actually touches on the second challenge. How do we, to in order to thrive in this new age of insight. The second challenge is our future challenge. What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talk about what to collect, and when to organize it when you collect, and then where will your data be going forward that you are collecting from? So what, when, and where. For the what data, for what data to collect that was the question you asked. It's a question that different industries have to ask themselves because it will vary. Let me give you the, you use the autonomous car example. Let me use that and you have this customer collecting massive amounts of data. You know, we talking about 10 petabytes a day from a fleet of their cars and these are not production autonomous cars. These are training autonomous cars, collecting data so they can train and eventually deploy a commercial cars. Also these data collection cars, they collect 10 as a fleet of them collect 10 petabytes a day. And then when it came to us, building a storage system to store all of that data they realize they don't want to afford to store all of it. Now here comes the dilemma. What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma. Now in working with them on this process of trimming down what they collected. I'm constantly reminded of the 60s and 70s. To remind myself 60s and 70s, we call a large part of our DNA, junk DNA. Today we realized that a large part of that, what we call junk has function has valuable function. They are not genes but they regulate the function of genes. So what's junk in yesterday could be valuable today, or what's junk today could be valuable tomorrow. So there's this tension going on between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you know you worry, you ignore the wrong ones. You can see this tension in our customers. And then it depends on industry here. In healthcare they say, I have no choice. I want it all, why? One very insightful point brought up by one healthcare provider that really touched me was you know, we are not, we don't only care. Of course we care a lot. We care a lot about the people we are caring for. But we also care for the people we are not caring for. How do we find them? And therefore, they did not just need to collect data that they have with, from their patients they also need to reach out to outside data so that they can figure out who they are not caring for. So they want it all. So I asked them, "So what do you do with funding if you want it all?" They say they have no choice but they'll figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us, rightfully that you know, we have to then work out a way to to help them build a system. So that healthcare. And if you go to other industries like banking, they say they can afford to keep them all. But they are regulated same like healthcare. They are regulated as to privacy and such like. So many examples, different industries having different needs but different approaches to how, what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can store. But on the other hand you know, if you kind of don't want to afford it and decide not to store some, maybe those some become highly valuable in the future. You worry. >> Well, we can make some assumptions about the future, can't we? I mean we know there's going to be a lot more data than we've ever seen before, we know that. We know, well not withstanding supply constraints and things like NAND. We know the price of storage is going to continue to decline. We also know and not a lot of people are really talking about this but the processing power, everybody says, Moore's Law is dead. Okay, it's waning but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth, actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again another insightful question that we touched on, on our keynote and that goes up to the why, I'll do the where. Where will your data be? We have one estimate that says that by next year, there will be 55 billion connected devices out there. 55 billion. What's the population of the world? Well, off the order of 10 billion, but this thing is 55 billion. And many of them, most of them can collect data. So what do you do? So the amount of data that's going to come in is going to way exceed our drop in storage costs our increasing compute power. So what's the answer? The answer must be knowing that we don't and even a drop in price and increase in bandwidth, it will overwhelm the 5G, it'll will overwhelm 5G, given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all that data from the 55 billion devices of the data back out to a central, as a bunch of central cost because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you still be too expensive given the number of devices out there. You know given storage costs dropping it'll still be too expensive to try and install them all. So the answer must be to start at least to mitigate the problem to some leave most a lot of the data out there. And only send back the pertinent ones, as you said before. But then if you did that then, how are we going to do machine learning at the core and the Cloud side, if you don't have all the data you want rich data to train with. Sometimes you want to a mix of the positive type data, and the negative type data. So you can train the machine in a more balanced way. So the answer must be you eventually, as we move forward with these huge number of devices are at the Edge to do machine learning at the Edge. Today we don't even have power. The Edge typically is characterized by a lower energy capability and therefore, lower compute power. But soon, you know, even with low energy, they can do more with compute power, improving in energy efficiency. So learning at the Edge today we do inference at the Edge. So we data, model, deploy and you do inference at age. That's what we do today. But more and more, I believe given a massive amount of data at the Edge you have to have to start doing machine learning at the Edge. And if when you don't have enough power then you aggregate multiple devices' compute power into a swarm and learn as a swarm. >> Oh, interesting, so now of course, if I were sitting in a flyer flying the wall on HPE Board meeting I said, "Okay, HPE is a leading provider of compute." How do you take advantage that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products, but there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for your customers? >> The wall will have to have a balance. Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud side. And it has to be hybrid. And then we need capability on the Edge side. Yeah that we need to build systems that on one hand is Edge-adapted. Meaning they environmentally-adapted because the Edge differently are on it. A lot of times on the outside, they need to be packaging-adapted and also power-adapted. Because typically many of these devices are battery-powered. So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. They must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insightful for that. Antonio announced in 2018 for the next four years from 2018, $4 billion invested to strengthen our Edge portfolio our Edge product lines, Edge solutions. >> Dr. Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers. The diversity of customers and the diversity in the way they have approached their different problems with data strategy. So the excitement is around data strategy. Just like, you know, the statement made for us was so, was profound. And Antonio said we are in the age of insight powered by data. That's the first line. The line that comes after that is as such we are becoming more and more data-centric with data the currency. Now the next step is even more profound. That is, you know, we are going as far as saying that data should not be treated as cost anymore, no. But instead, as an investment in a new asset class called data with value on our balance sheet. This is a step change in thinking that is going to change the way we look at data, the way we value it. So that's a statement. So this is the exciting thing, because for me a CTO of AI, a machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. So that's why when the people start to value data and say that it is an investment when we collect it it is very positive for AI because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. So it'd be great if the community values data. >> Well, are you certainly see it in the valuations of many companies these days? And I think increasingly you see it on the income statement, you know data products and people monetizing data services, and yeah, maybe eventually you'll see it in the balance sheet, I know. Doug Laney when he was at Gartner Group wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? Dr. Goh. >> Yeah, yeah, yeah. Your question is the process and methods in valuation. But I believe we'll get there. We need to get started and then we'll get there, I believe, yeah. >> Dr. Goh it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh yeah, no doubt. People will better understand how to align some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCube. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (both chuckling) >> Well, excellent, we'll leave it there. Thank you for spending some time with us so keep it right there for more great interviews from HPE Discover '21. This is Dave Vellante for theCube, the leader in enterprise tech coverage. We'll be right back (upbeat music)

(upbeat music) >> Welcome back to HPE Discover 2021, theCUBE's virtual coverage, continuous coverage of HPE's Annual Customer Event. My name is Dave Vellante, and we're going to dive into the intersection of high-performance computing, data and AI with Doctor Eng Lim Goh, who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Doctor Goh, great to see you again. Welcome back to theCUBE. >> Hello, Dave, great to talk to you again. >> You might remember last year we talked a lot about Swarm intelligence and how AI is evolving. Of course, you hosted the Day 2 Keynotes here at Discover. And you talked about thriving in the age of insights, and how to craft a data-centric strategy. And you addressed some of the biggest problems, I think organizations face with data. That's, you've got a, data is plentiful, but insights, they're harder to come by. >> Yeah. >> And you really dug into some great examples in retail, banking, in medicine, healthcare and media. But stepping back a little bit we zoomed out on Discover '21. What do you make of the events so far and some of your big takeaways? >> Hmm, well, we started with the insightful question, right, yeah? Data is everywhere then, but we lack the insight. That's also part of the reason why, that's a main reason why Antonio on day one focused and talked about the fact that we are in the now in the age of insight, right? And how to try thrive in that age, in this new age? What I then did on a Day 2 Keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So, maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights. You know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Oh, very pertinent question, Dave. You know the two challenges I spoke about, that we need to overcome in order to thrive in this new age. The first one is the current challenge. And that current challenge is, you know, stated is now barriers to insight, when we are awash with data. So that's a statement on how do you overcome those barriers? What are the barriers to insight when we are awash in data? In the Day 2 Keynote, I spoke about three main things. Three main areas that we receive from customers. The first one, the first barrier is in many, with many of our customers, data is siloed, all right. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above, they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know? Barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And you know, it's tough to get at, to tough to get a value out of them, right? And in that case, I use the example of, you know, the May 6, 2010 event where the stock market dropped a trillion dollars in terms of minutes. We all know those who are financially attuned with know about this incident but that this is not the only incident. There are many of them out there. And for that particular May 6 event, you know, it took a long time to get insight. Months, yeah, before we, for months we had no insight as to what happened. Why it happened? Right, and there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road they go with the tough data, right? Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road. And analyze that data took a long time to assemble. And they discovered that there was caught stuffing, right? That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees, the rule in there that says, all trades less than a hundred shares don't need to report in there. And so what people did was sending a lot of less than a hundred shares trades to fly under the radar to do this manipulation. So here is the second barrier, right? Data could be raw and dispersed. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah? Here we have three quick examples of customers. One was a great example, right? Where they were trying to build a language translator or machine language translator between two languages, right? By not do that, they need to get hundreds of millions of word pairs. You know of one language compare with the corresponding other. Hundreds of millions of them. They say, well, I'm going to get all these word pairs. Someone creative thought of a willing source and a huge, it was a United Nations. You see? So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data, right? The second one has to do with, there was the sometimes you may just have to generate that data. Interesting one, we had an autonomous car customer that collects all these data from their their cars, right? Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can drive in hill, they build a simulation by having the car collected in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated. In fact, that data silo, they federated it. Virus associated with data, that's tough to get at. They just took the hard road, right? And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow! I tell you, I have about a hundred questions based on what you just said, you know? (Dave chuckles) And as a great example, the Flash Crash. In fact, Michael Lewis, wrote about this in his book, the Flash Boys. And essentially, right, it was high frequency traders trying to front run the market and sending into small block trades (Dave chuckles) trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. (Dave chuckles) I guess my question is can technology help us get out of the problem? And that maybe is where AI fits in? >> Yes, yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, right? Assembled them to see if you can find a material trend, right? You can see lots of trends, right? Like, no, we, if humans look at things that we tend to see patterns in Clouds, right? So sometimes you need to apply statistical analysis math to be sure that what the model is seeing is real, right? And that required, well, that's one area. The second area is you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. Now, in this case, before the change in the rules, right? But by the way, after the discovery, the authorities changed the rules and all shares, all trades of different any sizes it has to be reported. >> Right. >> Right, yeah? But the rule was applied, you know, I say earlier that shares under a hundred, trades under a hundred shares need not be reported. So, sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't wanted a various reasons not to put everything in there. So that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such, there are times we just need to go back to the raw data. >> I want to ask you... >> Oh, it could be, that it's going to be tough, yeah. >> Yeah, I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about but. And I'm going to make a statement, you tell me if it's on point or off point. So seems that most of the AI going on in the enterprise is modeling data science applied to, you know, troves of data. But there's also a lot of AI going on in consumer. Whether it's, you know, fingerprint technology or facial recognition or natural language processing. Well, two part question will the consumer market, as it has so often in the enterprise sort of inform us is sort of first part. And then, there'll be a shift from sort of modeling if you will to more, you mentioned the autonomous vehicles, more AI inferencing in real time, especially with the Edge. Could you help us understand that better? >> Yeah, this is a great question, right? There are three stages to just simplify. I mean, you know, it's probably more sophisticated than that. But let's just simplify that three stages, right? To building an AI system that ultimately can predict, make a prediction, right? Or to assist you in decision-making. I have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data, and the machine starts to evolve a model based on all the data it's seeing. It starts to evolve, right? To a point that using a test set of data that you have separately kept aside that you know the answer for. Then you test the model, you know? After you've trained it with all that data to see whether its prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision. And that's the inference, right? So a lot of times, depending on what we are focusing on, we in data science are, are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you need to make. You pick the right models. And then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that robust. It is good, but then it is not consistent, right? Now what you do is you try another model. So sometimes it gets keep trying different models until you get the right kind, yeah? That gives you a good robust decision-making and prediction. Now, after which, if it's tested well, QA, you will then take that model and deploy it at the Edge. Yeah, and then at the Edge is essentially just looking at new data, applying it to the model that you have trained. And then that model will give you a prediction or a decision, right? So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful. Can you also do learning at the Edge? >> Right. >> That's the reason why we spoke about Swarm Learning the last time. Learning at the Edge as a Swarm, right? Because maybe individually, they may not have enough power to do so. But as a Swarm, they may. >> Is that learning from the Edge or learning at the Edge? In other words, is that... >> Yes. >> Yeah. You do understand my question. >> Yes. >> Yeah. (Dave chuckles) >> That's a great question. That's a great question, right? So the quick answer is learning at the Edge, right? And also from the Edge, but the main goal, right? The goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the Call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. Right? So that you don't need to have to send all that data back and assemble it back from all the different Edge devices. Assemble it back to the Cloud Site to do the learning, right? Some on you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send. >> Yeah. >> The autonomous vehicle, example you gave is great. 'Cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front. And then maybe they do that and then they send that smaller data setback and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming through. Let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah. Well, today full of these insightful questions. (Dr. Eng chuckles) That actually touches on the the second challenge, right? How do we, in order to thrive in this new age of insight? The second challenge is our future challenge, right? What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talked about what to collect, right? When to organize it when you collect? And then where will your data be going forward that you are collecting from? So what, when, and where? For what data to collect? That was the question you asked, it's a question that different industries have to ask themselves because it will vary, right? Let me give you the, you use the autonomous car example. Let me use that. And we do have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from a fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars, right? Also this data collection cars, they collect 10, as a fleet of them collect 10 petabytes a day. And then when they came to us, building a storage system you know, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma, right? Now in working with them on this process of trimming down what they collected, you know, I'm constantly reminded of the 60s and 70s, right? To remind myself 60s and 70s, we called a large part of our DNA, junk DNA. >> Yeah. (Dave chuckles) >> Ah! Today, we realized that a large part of that what we call junk has function as valuable function. They are not genes but they regulate the function of genes. You know? So what's junk in yesterday could be valuable today. Or what's junk today could be valuable tomorrow, right? So, there's this tension going on, right? Between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you worry, you ignore the wrong ones, right? You can see this tension in our customers, right? And then it depends on industry here, right? In healthcare they say, I have no choice. I want it all, right? Oh, one very insightful point brought up by one healthcare provider that really touched me was you know, we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But who also care for the people we are not caring for? How do we find them? >> Uh-huh. >> Right, and that definitely, they did not just need to collect data that they have with from their patients. They also need to reach out, right? To outside data so that they can figure out who they are not caring for, right? So they want it all. So I asked them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us rightfully, that you know we have to then work out a way to help them build a system, you know? So that's healthcare, right? And if you go to other industries like banking, they say they can afford to keep them all. >> Yeah. >> But they are regulated, seemed like healthcare, they are regulated as to privacy and such like. So many examples different industries having different needs but different approaches to what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can install, right? But on the other hand, you know if you kind of don't want to afford it and decide not to start some. Maybe those some become highly valuable in the future, right? (Dr. Eng chuckles) You worry. >> Well, we can make some assumptions about the future. Can't we? I mean, we know there's going to be a lot more data than we've ever seen before. We know that. We know, well, not withstanding supply constraints and things like NAND. We know the prices of storage is going to continue to decline. We also know and not a lot of people are really talking about this, but the processing power, but the says, Moore's law is dead. Okay, it's waning, but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again, another insightful question that we touched on our Keynote. And that goes up to the why, uh, to the where? Where will your data be? Right? We have one estimate that says that by next year there will be 55 billion connected devices out there, right? 55 billion, right? What's the population of the world? Well, of the other 10 billion? But this thing is 55 billion. (Dave chuckles) Right? And many of them, most of them can collect data. So what do you do? Right? So the amount of data that's going to come in, it's going to way exceed, right? Drop in storage costs are increasing compute power. >> Right. >> Right. So what's the answer, right? So the answer must be knowing that we don't, and even a drop in price and increase in bandwidth, it will overwhelm the, 5G, it will overwhelm 5G, right? Given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all of that data from the 55 billion devices of the data back to a central, as a bunch of central cost. Because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you'll still be too expensive given the number of devices out there. You know given storage costs dropping is still be too expensive to try and install them all. So the answer must be to start, at least to mitigate from to, some leave most a lot of the data out there, right? And only send back the pertinent ones, as you said before. But then if you did that then how are we going to do machine learning at the Core and the Cloud Site, if you don't have all the data? You want rich data to train with, right? Sometimes you want to mix up the positive type data and the negative type data. So you can train the machine in a more balanced way. So the answer must be eventually, right? As we move forward with these huge number of devices all at the Edge to do machine learning at the Edge. Today we don't even have power, right? The Edge typically is characterized by a lower energy capability and therefore lower compute power. But soon, you know? Even with low energy, they can do more with compute power improving in energy efficiency, right? So learning at the Edge, today we do inference at the Edge. So we data, model, deploy and you do inference there is. That's what we do today. But more and more, I believe given a massive amount of data at the Edge, you have to start doing machine learning at the Edge. And when you don't have enough power then you aggregate multiple devices, compute power into a Swarm and learn as a Swarm, yeah. >> Oh, interesting. So now of course, if I were sitting and fly on the wall and the HPE board meeting I said, okay, HPE is a leading provider of compute. How do you take advantage of that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products. But there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for the customers? >> Hmm, the wall will have to have a balance, right? Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud Site, right? And it has to be hybrid. And then we need capability on the Edge side that we need to build systems that on one hand is an Edge adapter, right? Meaning they environmentally adapted because the Edge differently are on it, a lot of times on the outside. They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. It must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insight for that Antonio announced in 2018. For the next four years from 2018, right? $4 billion invested to strengthen our Edge portfolio. >> Uh-huh. >> Edge product lines. >> Right. >> Uh-huh, Edge solutions. >> I could, Doctor Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of, certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers, right? The diversity of customers and the diversity in the way they have approached different problems of data strategy. So the excitement is around data strategy, right? Just like, you know, the statement made for us was so was profound, right? And Antonio said, we are in the age of insight powered by data. That's the first line, right? The line that comes after that is as such we are becoming more and more data centric with data that currency. Now the next step is even more profound. That is, you know, we are going as far as saying that, you know, data should not be treated as cost anymore. No, right? But instead as an investment in a new asset class called data with value on our balance sheet. This is a step change, right? Right, in thinking that is going to change the way we look at data, the way we value it. So that's a statement. (Dr. Eng chuckles) This is the exciting thing, because for me a CTO of AI, right? A machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. Right? (Dr. Eng chuckles) So, that's why when the people start to value data, right? And say that it is an investment when we collect it it is very positive for AI. Because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. >> Yeah. >> So it'd be great, if the community values data. >> Well, you certainly see it in the valuations of many companies these days. And I think increasingly you see it on the income statement. You know data products and people monetizing data services. And yeah, maybe eventually you'll see it in the balance sheet. I know Doug Laney, when he was at Gartner Group, wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? >> Yeah, yeah. >> Dr. Goh... (Dave chuckles) >> The question is the process and methods in valuation. Right? >> Yeah, right. >> But I believe we will get there. We need to get started. And then we'll get there. I believe, yeah. >> Doctor Goh, it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh, yeah, no doubt. People will better understand how to align, you know some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCUBE. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (Dave chuckles) (Dr. Eng laughs) >> Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HPE Discover 21. This is Dave Vellante for theCUBE, the leader in Enterprise Tech Coverage. We'll be right back. (upbeat music)

(upbeat music) >> Welcome back to HPEDiscover 2021. My name is Dave Vellante and you're watching the Cube's virtual coverage of Discover, and we're super excited to have Jason Newton back in the cube. He's part of the HPE mastermind alliance behind its messaging and marketing. And he's been instrumental in up leveling the conversation over the last several years from ports and LUNs and gigahertz to topics that resonate with business technology executives, which is basically every executive on the planet. Jason, great to see you, welcome back to the program. >> Hey, I'm thrilled to be here. >> Okay, we're going to talk about the future of enterprise tech and the evolution of cloud, hybrid cloud, it's expansion to the edge, where we are today, where we're headed and how we're going to get there. And I'm excited to start this off. We're living in an era where value and competition, we talk about this all the time, it's defined by data and the insights that organizations can extract from that data, the products and services that they can build, that are data centric, what do you think this means to HPE and what does it mean for your customers? >> Well, I think we're at the right moment of the right time and I think for the customer, it just what's happening now, what's possible to create value from data is just a tremendous opportunity to accelerate the transformation they were already driving for their business. We're seeing our customers do amazing things with data, not just monetizing data, but like world-changing types of things around in healthcare, in finance, transforming experiences for their customers and all of this is being driven by data. >> Well, I'm excited to see how you guys approach that. I mean, you're talking about this the cloud-to-edge strategy and I've been having discussions with various execs at Discover, obviously, remotely about how far HPE goes and certainly you're going to have compute everywhere. And Aruba seems to me to be a really interesting part of that platform. You're going to go to the deep edge. So, you got a lot of assets in the arsenal, how are you thinking about that? >> Well, it really all needs to come together into one experience. And you mentioned Aruba, I mean, that's where it all starts, with secure connectivity. The more that we connect things up in a secure way, the more data that we're going to be able to create, analyze and act upon. So, it really plays a critical role. But if you look at HPE, we really have an embarrassment of riches of assets and expertise and partnerships at global scale and there's not a part of our business that isn't focused on some part of the data challenges that customers have. From edge computing to super computing, to storage, what we're doing with the SRL software, it's all focused on helping customers take in that data and then create insights from it, Create new innovations from it. >> Talk a little bit more about the customer challenges that you're specifically solving at HPE. What do you see there? How are you thinking about that? >> I think one of the biggest ones the conversation always starts with is "I have a lot of data, but it's all in silos. Even within my organization or in some cases, I know there's data out there, but it's in another silo. How do I get access to it?" I hear that word a lot when we talk to customers "I need to get access for my teams to that data." So, first step is just, how do I bring it all together? How do I federate all of that data in one place? That's one area that we're helping customers solve. The second is in order to bring those pieces together, the different data owners have to have a trust to share the data 'cause often there's not an incentive for them to do that. Like I own the data, I don't want to share it. So, we have to establish different parameters or capabilities in order to enable that type of trust and sharing and there has to be some mutual benefit as part of that and we see that with inside of companies and we see it with multiple different organizations. Once you can overcome those, those are really hard challenges. Once you overcome those things, everything becomes astronomically more easy to deal with and everything starts to go faster. And that's where we're trying to get people on that modern data maturity curve up to that point where they do have Federation, they do have curation, they are able to share, they know what they're going to benefit from it and then we can get onto the task of enabling the teams to do analytics at speed and scale. >> Yeah, you talk about Federation. And so there's an interesting challenge that you're describing and you and I have had some good conversations about this because you want to tame that data, if you will, put it in a place that you can actually get to it, share it, make it discoverable. And of course at the same time, it's all over the place. So, you've got these pods that could talk to each other and facilitate that data sharing and then what I call building data products, building data services, and technology is at the point now it's evolving to enable us to do that. Look back at the last 10 years, it was just far too complex. >> Yeah, we heard Antonio earlier today talk about building, not private clouds, but private data spaces. And it's really that idea of how do I bring an experience to the data that is agile and fast and cloud-like? Or cloud, in the case of what we're actually doing now, building a cloud platform. That's exactly where customers are trying to get to. And we look at these data spaces as the advantage by going, bringing that to the data. Obviously there's the the physics of it, the performance and that kind of thing. But we can pay more attention to like-data sovereignty laws, we can address things like data ownership within these spaces so that teams can come together and freely collaborate and act on that data together. >> You know, I've been watching you guys for now several years and you've taken this messaging and marketing thing pretty seriously. Even a lot of times we see it all. A lot of times it's gimmicks and I don't mean that necessarily in a bad way. There are actually some really good gimmicky marketing that gets a lot of attention, but your approach is different. It's very thoughtful, it's cultural, I'll say. You're trying to get and acculturate what you say with what you do. And so I want to ask you, how are you going about changing the way in which you provide solutions? I alluded that to that at the top, versus how you've done it in the past and how you're helping customers redefine their business for success? >> Well, the way that we're thinking about that is, and I think you heard it very clearly and succinctly from Antonio earlier today, we're transforming into an edge-to-cloud company. We are building an edge-to-cloud platform that is GreenLake. That platform is the way that we'll deliver cloud services to our customers, for their workloads, to their data sets, wherever that needs to be. We're committed to a truly hybrid model. Edge, Onprem, Cloud together. And so those elements, it starts to crystallize, I think a lot more about who this company is and the type of challenges that we need to solve. Talking about the things is not interesting to customers. They want to know what problems can you help me solve, how fast can you do it, what outcome can you help me achieve? And that's the way that we've, we've talked about this a lot, Dave, that we continue to transform and have those more meaningful conversations. And like I said, every time we get to the data challenges, they know the opportunities there, they have a dream and a vision of what they want to go do. They just need a partner like HPE to help them get there. >> So, we talk a lot about GreenLake and as a service, you guys threw the gauntlet down first, I got to give you props because you're all in on it. You're not a halfway house, I'll give you that much. But now we've seen, at least, I could count, at least four other large competitors follow suit. How should we think about your strategy and specifically your advantage relative to the competitors? Let's talk first in terms of as a service in GreenLake and then maybe overall. >> Yeah, I mean, I think you see a lot of people following GreenLake's lead. I mean, we've been out in front for a while. We were the first to say the world will be hybrid and it is, we were the first to make the big bet at the edge, we were the first to see that not all the data's going to go into one unified location, it's going to continue to be distributed and therefore cloud experience has to travel to that data. We created the GreenLake brand years before anybody else did. And now, they're just now trying to figure out, "Well, how do I do hardware as a service or a better way to sell my products?" We're moving on. We're focused on the workloads and the workflows and the data sets. GreenLake is much, much more mature and now that we have everybody onboard across the company, we're moving much faster as well. And that's more of a statement for the traditional competitors, the traditional spaces, they're still just stuck on like hardware as a service, infrastructure as a service. We're at the workload level and much higher. And I think what you're seeing from the public cloud players is, wow, Data Center and On-prem and Edge is hard. A lot harder than I think they really anticipated. And they're reassessing. So, I feel like we're in the place where the world is moving to. And we're really writing the first chapter of the new HPE, not the last. >> Has it changed, the way this as a service mentality, has it changed the way or how has it changed the way in which your product groups are behaving? >> Quite a bit. It is a mindset shift and I think we have the culture that will successfully enable that 'cause we've always been so customer centric. I think as you move to an as a service, it becomes much more about, "How do I ensure customer success?" How do I put an environment in place and then use that as an opportunity to solve more problems across our customer's environments?" I think that aspect is what, really is driving our thinking now is what new services can I land on the GreenLake Edge-to-Cloud platform to solve different data-centric challenges? >> You talked about lead and where you are in the maturity model, what was the hardest part about making that change? Was it the leadership? Was it the sales compensation? Was it to get the product guys out of the widgets? What was the hardest thing? >> Yeah, I think, I think go to market is as big a challenge as anything, I think in marketing, it's our job to show the art of the possible in the future, even if it's uncomfortable for the organization. And I think that helps articulate Antonio's vision and give him a true north. And he's a fabulous leader in a culture that they believe in trust in him. And so they're following, but the challenges are not so much the technology. In many cases, it is the people and the skills and building those new relationships within accounts and those aspects, those intangible things. So we're doing a lot around enablement, sales enablement, and of course, and most importantly with our partners who are out there selling for us. It is a new approach, but it's a good approach 'cause it's so customer centric, it's not product centric. >> So, how are the customers and partners reacting? Of course, you're going to say great, but how do you know? Like what metrics do you look at? What things that are important to you to track that give you confidence that you're on the right track? >> They're buying more stuff. >> Yeah, okay, that's a good metric. >> Yeah, yeah, no, I mean, like, I think there was some skepticism at first, because we had been doing some of that infrastructure as a service type of thing for a while before we ever had a GreenLake brand. And they're like, "This is just the same thing." Like, no, we're truly, cloudifying this platform. We are building a cloud-native platform, you saw it in the announcements today. With cloud native security, just like you get in the public cloud, but you can deploy and run these workloads in your choice of location. And the more that we can show evidence of our messaging in the experience that we actually deliver, that's when customers start to lean in. So, we look at a ton of metrics. I mean, it's not one data point. We listen to Gartner, we have our own internal research that we do. We're constantly getting feedback from our field. In fact, last week, was it two weeks ago, we had a board of advisors meeting, brought in some of our top, top customers just to hear from them. "What are we doing good, what are we not doing good?" So, it's a lot of different pieces that go into, how are we doing with the customer and how are they into this? We're only doing what they told us they wanted. "Bring the cloud to me and my data. I can't move at all, but I don't want different operating models. I want a consistent experience. I want to be able to focus and innovate. I don't want to deal with the underlying pieces of the infrastructure." Yeah, we're doing what they ask. >> Okay, that sounds good, but then it's hard to do that. I mean, you got to put real, that's a lot of elbow grease, a lot of investment, a lot of innovation, like you say, you got to align the organizations. That's not a trivial task. I mean, I tell you, Jason, I've been hearing this early days, even 10 years ago, I think we're finally at the point now where the industry is responding to what those customers really want. And of course, it's like Steve jobs with the iPhone, ask them what they want, they're not going to tell you an iPhone. Maybe they didn't know 10 years ago, but I think it really came into focus in the last several years and investment is the key there. >> Yeah, I think the last decade was, the digital transformation was all about how do I bring speed to code and take advantage of public cloud and I think that took us further, it took us, but now, okay, the next chapter is a very data centric, how do I bring speed and agility to data and data analytics and especially at the edge and where things are need to live, how do I make a consistent experience? That's going to be our focus for the next 10 years. And like I said, I feel like we're at the right moment in history as a company with the right assets, expertise, partnerships to go in and help customers take advantage of that. >> Well, it's interesting. The last decade we talked about big data, we don't use that term much anymore, but like many things like the internet, for example, it was all of a sudden, maybe it's over-hyped at the beginning, but it's always under hyped when you actually see the force it can be. I feel like we actually are now entering the true data era. So, you're excited about a lot of things, obviously as a service, but I got a sense there's more that you're not sharing with us. So, what are you most excited about for HPE in the future? >> Well, like I said becoming that edge-to-cloud company, watching GreenLake blossom as it is, I mean, tremendous innovations that we announced today and yes, there's things I can't share that I know are coming later this year. I've seen the roadmaps, it's really compelling, very compelling and impressive. The things that we're doing with Azmeril, combine that together with GreenLake and that experience, the types of data and analytic platform environments that we can build to unify those data silos, to accelerate the machine learning and analytics teams, it's really all coming together. And those are the things that I'm excited about. You know, changing that perception of HPE as infrastructure, as a service and hardware as a service and that kind of thing. As a service it's the experience, right? The value is in the data and watching us be able to help customers solve those data challenges and seize those data opportunities is what I'm most excited about. >> Well, the other thing too, is the world has some big challenges, population and energy, we can just make the huge list and I feel like tech companies not only are in a position to help, but I think they have a responsibility. And I got to say, I think most tech companies, large tech companies are stepping up and have great leadership around that and what are your thoughts on that? >> Well, yeah, we talked about value from data. It's all about the insights is where the value comes from, but value is not always about profit and monetization. I mean, data truly does have the opportunity to solve some of the world's biggest challenges. I was just reading this morning about, was it CGAIR? And the things that they're doing in agriculture with these, they've got a big data-set platform that I think could be literally the thing that ends up helping solve world hunger, the thing that everyone jokes about, I'm like, "No, seriously now with the data, that could be possible." >> Yeah, I think you're right. I think we are going to solve world hunger and world nutrition, maybe a different story, but we'll tackle that next. Last question, what else should we be focused on at Discover, how can folks learn more? >> Well this is a three-day event. So, today was really about the news and the excitement and clarifying our position as an edge-to-cloud company and that GreenLake is our edge-to-cloud platform, the way that we deliver the cloud to you. Tomorrow is really about how all of that vision strategy manifests itself into the experience and the products and the solutions that you can consume. They'll also be a lot of sharing of the keynote, is what I'm looking forward to with Dr. Ingram Gore, he's our head of AI, and he's going to be sharing all the lessons and learnings from hundreds of engagements that he's been driving with customers showing exactly how to overcome the data silo problem, the trust problem, how to bring agility to analytics and then Thursday is the geek-out day, we get to talk to Hewlett Packard labs, we get to go and touch the technology, meet the technologists, interact with them and understand what are those technologies that are going to be crucial for the next 10 years of data-driven transformation. >> Some really exciting stuff there, Jason. Thank you so much for spending some time on the Cube again. Really great to see you. >> I appreciate the invite every time is a pleasure. Thank you. >> All right and thanks for being with us for our ongoing coverage of HPEDiscover '21. This is Dave Vellante, you're watching the Cube, the leader in digital tech coverage. We'll be right back. (upbeat music)

>> We continue with cube on cloud. We here with Mai-Lan Tomsen Bukovec who's the vice president of block and object storage at AWS which comprises elastic block storage, AWS S3 and Amazon glacier. Mai-Lan Great to see you again. Thanks so much for coming on the program. >> Nice to be here. Thanks for having me, Dave. >> You're very welcome. So here we're unpacking the future of cloud and we'd love to get your perspectives on how customers should think about the future of infrastructure things like applying machine intelligence to their data but just to set the stage, when we look back at the history of storage and the cloud has obviously started with S3 and then a couple of years later AWS introduced EBS for block storage and those are the most well-known services in the portfolio but there's more of this cold storage and new capabilities that you announced recently at reinvent around, you know, super-duper block storage and in tiering is another example. But it looks like AWS is really starting to accelerate and pick up the pace of customer options in storage. So my first question is how should we think about this expanding portfolio? >> Well, I think you have to go all the way back to what customers are trying to do with their data Dave. The path to innovation is paved by data. If you don't have data, you don't have machine learning. You don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications, it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> Awesome, so thank you. So I want to make sure we're hitting on the big trends that you're seeing in the market that kind of are informing your strategy around the portfolio, and what you're seeing with customers. Instant usability, you know, you bring in machine learning into the equation. I think people have really started to understand the benefits of cloud storage as a service and the pay by the drink. and that whole model. Obviously COVID has accelerated that, you know, cloud migration is accelerated. Anything else we're missing there? What are the other big trends that you see? If any. >> Well, Dave, you did a good job of capturing a lot of the drivers. The one thing I would say that just sits underneath all of it is the massive growth of digital data year over year. IDC says digital data is growing at a rate of 40% year over year. And that has been true for a while and it's not going to stop. It's going to keep on growing because the sources of that data acquisition keeps on expanding and whether it's IOT devices whether it is a content created by users, that data is going to grow and everything you're talking about depends on the ability to not just capture it and store it. But as you say, use it. >> Well, you know, and we talk about data growth a lot and sometimes it can, it becomes bromide. But I think the interesting thing that I've observed over the last couple of decades really is that the growth is non-linear and it's really the curve is starting to shape exponentially. You guys always talk about that flywheel effect it's really hard to believe, you know people say trees don't grow to the moon. It seems like data does. >> It does and what's interesting about working in a world of AWS storage Dave is that it's counter-intuitive but our goal with a data growth is to make it cost effective. And so year over year how can we make it cheaper and cheaper? It is have customers store more and more data so they can use it. But it's also to think about the definition of usage and what kind of data is being tapped by businesses for their insights and make that easier than it's ever been before. >> Let me ask you a follow up question on that Mai-Lan. Cause I get asked this a lot, or I hear comments a lot that yes AWS continuously and rigorously reduces pricing but it's just kind of following the natural curve of Moore's law or whatever. How do you respond to that? Are there other factors involved? Obviously labor is another, you know, cost reducing factor, but what's the trend line say? >> Well, cost efficiency is in our DNA, Dave we come to work every day in AWS across all of our services and we ask ourselves, how can we lower our costs and be able to pass that along to customers. As you say, there are many different aspects to costs. There's a cost to the storage itself There's a cost to the data center. And that's really what we've seen impact a lot of customers that were slower or just getting started with a move to the cloud, is they entered 2020 and then they found out exactly how expensive that data center was to maintain because they had to put in safety equipment and they had to do all the things that you have to do in a pandemic, in a data center. And so sometimes that cost is a little bit hidden or it won't show up until you really don't need to have it land. But the costs of managing that explosive growth of data is very real. And when we're thinking about costs, we're thinking about costs in terms of how can I lower it on a per gigabyte per month basis, but we're also building into the product itself, adaptive discounts. Like we have a storage class in S3 that's called intelligent tiering. And in intelligent tiering we have built-in monitoring where if particular objects aren't frequently accessed in a given month, a customer will automatically get a discounted price for that storage or a customer can, you know, as of late last year say that they want to automatically move storage in the storage class that has been stored for example longer than 180 days and saves 95% by moving it into deep archive storage. And so it's not just, you know relentlessly going after and lowering the cost of storage. It's also building into the products these new ways where we can adaptively discount storage based on what a customer's storage is actually doing. >> Right, and I would add to already is the other thing Gatos has done is it's really forced transparency almost the same way that Amazon has done on retail. And now Mai-Lan when we talked last I mentioned that S3 was an object store. And of course that's technically correct but your comment to me was Dave, it's more than that. And you started to talk about SageMaker and AI and bringing in machine learning. And I wonder if you could talk a little bit about the future of how storage is going to be leveraged in the cloud. That's maybe different than what we've been used to in the early days of S3. And how your customers should be thinking about infrastructure, not as bespoke services, but as a suite of capabilities and maybe some of those adjacent services that you see as most leverageable for customers and why? >> Well, to tell this story, Dave, we're going to have to go a little bit back in time, all the way back to the 1990s or before then. When all you had was a set of hardware appliance vendors that sold you appliances that you put in your data center and inherently created a data silo because those hardware appliances were hardwired to your application. And so an individual application that was dealing with auditing as an example wouldn't really be able to access the storage for another application, because you know, the architecture of that legacy world is tied to a data silo and S3 came out launched in 2006 and introduced very low cost storage. That is an object. And I'll tell you, Dave, you know, over the last 10 plus years we have seen all kinds of data coming to S3. Whereas before it might've been backups or it might've been images and videos. Now a pretty substantial data set is our parquet files and work files. These files are there for business analytics for more real-time type of processing. And that has really been the trend of the future, is taking these different files putting them in a shared file layer, so any application today or in the future can tap into that data. And so this idea of the shared file layer is a major trend that has been taking off for the last I would say five or six years. And I expect that to not only keep on going but to really open up the type of services that you can then do on that shared file layer. And whether that's Sage maker or some of the machine learning introduced by our connect service, it's bringing together the data as a starting point and then the applications can evolve very rapidly on top of that. >> I want to ask your opinion about big data architectures. One of our guests Chamakh Tigani, she's amazing data architect. And she's put forth this notion of a distributed global mesh. And picking up on some of the comments, Andy Jassy made it at re-invent how essentially, "Hey we're bringing AWS to the edge. "We see the data center is just another edge node." So you're seeing this massive distributed system evolving. You guys have talked about that for a while and data by its very nature is distributed but we've had this tendency to put it into a monolithic data Lake or a data warehouse and it's sort of antithetical to that distributed nature. So how do you see that playing out? What do you see customers in the future doing in terms of their big data architectures and what does that mean for storage? >> It comes down to the nature of the data and again the usage and Dave that's where I see the biggest difference in these modern data architectures from the legacy of 20 years ago, is the idea that the data need drives the data storage. So let's take an example of the type of data that you always want to have on the edge. We have customers today that need to have storage in the field and whether the field of scientific research or oftentimes it's content creation in the film industry, or if it's for military operations there's a lot of data that needs to be captured and analyzed in the field. And for us, what that means is that, you know we have a suite of products called snow ball and whether it's snow ball or snow cone, take your pick. That whole portfolio of AWS services is targeted at customers that need to do work with storage at the edge. And so, you know, if you think about the need for multiple applications acting on the same data set that's when you keep it in an AWS region. And what we've done in AWS storage is we've recognized that depending on the need of usage where you put your data and how you interact with it may vary. But we've built a whole set of services like data transfer to help make sure that we can connect data from, for example that new snow cone into a region automatically. And so our goal Dave is to make sure that when customers are operating at the edge or they're operating in the region they have the same quality of storage service and they have easy ways to go between them. You shouldn't have to pick, you should be able to do it all. >> So in the spirit of do it all there's this sort of age old dynamic in the tech business where you've got the friction between the best of breed and the integrated suite. And my question is around what you're optimizing for customers. And can you have your cake and eat it too? In other words, why AWS storage? What makes it compelling? Is it because it's kind of a best of breed storage service or is it because it's integrated with AWS? Would you ever sub optimize one in order to get an advantage to the other? Or can you actually, you know have your cake and eat it too? >> The way that we build storage is to focus on being both the breadth of capabilities and the depth of capabilities. And so where we identify a particular need where we think that it takes a whole new service to deliver we'll go build that service. And an example for that as FTP our AWS SFTP service, which, you know, there's a lot of SFTP usage out there and there will be for a while because of the, you know, the legacy B2B type of architectures that still live in the business world today. And so we looked at that problem. We said, how are we going to build that in the best depth way, in the best focus? And we launched a separate service for that. And so our goal is to take the individual building blocks of EBS and glacier and S3 and make the best of class and the most comprehensive in the capabilities of what we can do and where we identify a very specific need. We'll go build a service for it. But Dave, you know as an example for that idea of both depth and breadth, S3 Storage Lens is a great example of that. S3 Storage Lens is a new capability that we launched late last year. And what it does is it lets you look across all your regions and all your accounts and get a summary view of all your S3 storage and whether that's buckets or the most active prefixes that you have and be able to drill down from that. And that is built in to the S3 service and available for any customer that wants to turn it on in the AWS management console. >> Right, and we saw just recently made, I called it super-duper block storage but you can make some improvements in really addressing the highest performance. I want to ask you, so we've all learned about an experience that benefits of cloud over the last several years and especially in the last 10 months during the pandemic but one of the challenges and it's particularly acute with IO is of course latency and moving data around and accessing data remotely. It's a challenge for customers, you know, due to speed of light, et cetera. So my question is how was AWS thinking about all that data that's still resides on premises? I think we heard at reinvent, that's still on 90% of the opportunity is, or the the workloads are still on prem that live inside a customer's data centers. So how do you tap into those and help customers innovate with on-prem data, particularly from a storage angle? >> Well, we always want to provide the best of class solution for those little latency workloads. And that's why we launched Block Express just late last year at reinvent. And Block Express has a new capability in preview on top of our IO to provisioned IOPS volume type. And what's really interesting about block express Dave is that the way that we're able to deliver the performance of Block Express, which is sound performance with cloud elasticity is that we went all the way down to the network layer and we customize the hardware software. And at the network layer we built Block Express on something called SRD which stands for a scalable reliable diagrams. And basically what it's letting us do is offload all of our EBS operations for Block Express on the nitrile card on hardware. And so that type of innovation where we're able to, you know, take advantage of modern cop commodity, multi-tenant data center networks, where we're sending in this new network protocol across a large number of network paths. And that type of innovation all the way down to that protocol level helps us innovate in a way that's hard. In fact, I would say impossible for other sound providers to kind of really catch up and keep up. And so we feel that the amount of innovation that we have for delivering those low latency workloads in our AWS cloud storage is unlimited really because of that ability to customize software hardware and network protocols as we go along without requiring upgrades from a customer it just gets better. And the customer benefits. Now, if you want to stay in your data center that's why we build outposts. And for outposts, we have UVS and we have S3 for outposts and our goal there is that some customers will have workloads where they want to keep them resident in the data center. And for those customers we want to give them that AWS storage opportunities as well. >> So thank you for coming back to Block Express. So you call it, you know, sand in the cloud. So is that essentially it comprises a custom built essentially storage network. Is that right? What you just described SRD? I think you called it. >> Yeah, it's a SRD is used by other AWS services as well but it is a custom network protocol that we designed to deliver the lowest latency experience and we're taking advantage of it with Block Express. >> So sticking with traditional data centers for a moment I'm interested in your thoughts on the importance of the cloud pricing approach, I.e the consumption model to pay by the drink. Obviously it's one of the most attractive features, and I asked that because we're seeing what Andy Jassy refers to as the old guard Institute, flexible pricing models two of the biggest storage companies, HP with GreenLake and Dell has this thing called apex. They've announced such models for on-prem and presumably cross cloud. How do you think this is going to impact your customers leverage of AWS cloud storage? Is it something that you have an opinion on? >> Yeah, I think it all comes down to, again that usage of the storage, and this is where I think there's an inherent advantage for our cloud storage. So there might be an attempt by the old guard to lower prices or add flexibility but at the end of the day it comes down to what the customer actually needs to tune. And if you think about gp3 which is the new EBS volume. The idea with gp3 is we're going to pass a long savings to the customer by making the storage 20% cheaper than gp2. And we're going to make the product better by giving a great, reliable baseline performance. But we're also going to let customers who want to run workloads like Cassandra on EBS tune their throughput separately, for example from their capacity. So if you're running Cassandra sometimes you don't need to change your capacity. Your storage capacity works just fine. But what happens with, for example Cassandra workload is that you may need more throughput. And if you're buying hardware appliance you just have to buy for your peak. You have to buy for the max of what you think your throughput and the max of what your storage is. And this inherent flexibility that we have for AWS storage and being able to tune throughput separate from up separate from capacity like you do for gp3 that is really where the future is for customers having control over costs and control over customer experience without compromising or trading off either one. >> Awesome, thank you for that. So in the time we have remaining Mai-Lan, I want to talk about the topic of diversity social impact, and as a woman leader, women executive, and I really want to get your perspectives on this. And I've shared with the audience previously, one of my breaking analysis segments, your boxing video which is awesome. And so, you've got a lot of unique non-traditional aspects to your life and I love it, but I want to ask you this. So it's obviously, you know, certainly politically and socially correct to talk about diversity, the importance of diversity, there's data that suggests that diversity is good both economically, not just socially, and of course it's the right thing to do. But there are those, you know, Peter teal is probably the most prominent but there are others that say, "You know what? "Forget that, just hire people, just like you'll be able "to go faster, ramp up more quickly, hit escape "velocity it's natural." And that's what you should do. Why is that not the right approach? Why is diversity both, of course, socially, you know responsible, but also, you know, good for business >> For Amazon we think about diversity as something that is essential to how we think about innovation. And so, Dave, as you know, from listening to some of the announcements at reinvent, we launch a lot of new ideas, like new concepts and new services in AWS. And just bringing that lens down to storage. Astri has been reinventing itself every year since we launched in 2006. EBS introduced the first sun on the cloud late last year, and continues to reinvent how customers think about block storage. We would not be able to look at a product in a different way and think to ourselves, not just what is the legacy system do in a data center today but how do we want to build this new distributed system in a way that helps customers achieve not just what they're doing today, but what they want to do in five and 10 years. You can't get that innovative mindset without bringing different perspectives to the table. And so we strongly believe in hiring people who are from under represented groups and whether that's gender or it's related to racial equality or if it's geographic diversity and bringing them in to have the conversation because those diverse viewpoints inform how we can innovate at all levels in AWS. >> Right, and so I really appreciate their perspectives on that. And we've had, as you probably know the cube has been, you know a very big advocate of diversity, you know, generally but women in tech specifically, we participated a lot. And I often ask this question is, you know, as a smaller company, I, and some of my other colleagues in small business, sometimes we struggle. And so my question is how do you go beyond what's your advice for going beyond, you know the good old boys network? I think it's large companies like AWS and, you know, the big players, you've got responsibility too that you can put somebody in charge and make it their full-time job. How should smaller companies that are largely white male dominated, how should they become more diverse? What should they do to increase that diversity? >> I think the place to start is voice. A lot of what we try to do is make sure that the under represented voice is heard. And so Dave, any small business owner of any industry can encourage voice for your under represented or your unheard populations. And honestly, it is as simple as being in a meeting and looking around that table or on your screen, as it were and asking yourself, who hasn't talked? Who hasn't weighed in? Particularly if the debate is contentious or even animated. And you will see, particularly if you note this over time you will see that there may be somebody and whether it's an under represented group or it's a woman who's early career, or it's not it's just a member of your team who happens to be a white male too, who's not being heard. And you can ask that person for their perspective. And that is a step that every one of us can and should do which is ask to have everyone's voice at the table to listen and to weigh in on it. So I think that is something everyone should do. I think if you are a member of an under represented group as for example, I'm Vietnamese American and I'm a female in tech, I think, it's something to think about how you can make sure that you're always taking that bold step forward. And it's one of the topics that we covered at re-invent. We had a great discussion with a group of women CEOs and a lot of it we talked about is being bold taking the challenge of being bold in tough situations. And that is an important thing, I think for anybody to keep in mind, but especially for members of under represented groups, because sometimes Dave that bold step that you kind of think of as like, "Oh I don't know if I should ask for that promotion." or "I don't know if I should volunteer for that project." It's not a big ask, but it's big in your head. And so if you can internalize as a member of some, you know, a group that maybe isn't heard as or seen as much how you can take those bold challenges and step forward and learn, maybe fail also cause that's how you learn. Then that is a way to also have people learn and develop and become leaders in whatever industry it is. >> That's great advice. It reminds me of, I think most of us can relate to that Mai-Lan, because when we started in the industry, we may be timid. You didn't want to necessarily speak up. And I think it's incumbent upon those in a position of power. And by the way power might just be running a meeting agenda to maybe call on those folks that are, maybe it's not diversity of gender or, you know, or race. Maybe it's just the under represented. Maybe that's a good way to start building muscle memory. So that's unique advice that I hadn't heard before. So thank you very much for that. I appreciate it. And Hey, listen. Thanks so much for coming on the Cube On Cloud. We're out of time and really always appreciate your perspectives and you're doing a great job. And thank you. >> Great, thank you Dave. Thanks for having me and have a great day. >> All right, and Keep it right there buddy. You're watching the Cube On Cloud. Right back. (gentle upbeat music)

>>Hello, everyone. And welcome back to our wall to wall coverage of the data Cloud Summit. This is Dave a lot. And we're seeing the emergence of a next generation workload in the cloud were more facile access and governed. Sharing of data is accelerating. Time to insights and action. All right, allow me to introduce our next guest. Amy Irwin is here. She's the vice president of strategy for experience. And Matt Glickman is VP customer product strategy it snowflake with an emphasis on financial services. Folks, welcome to the Cube. Thanks so much for coming on. >>Thanks for >>having us >>nice to be here. Hey, >>So, Amy, I mean, obviously 2020 has been pretty unique and crazy and challenging time for a lot of people. I don't know why I've been checking my credit score a lot more for some reason. On the app I love the app I got hacked. I had a lock it the other day I locked my credit. Somebody tried to dio on and it worked. I was so happy. So thank you for that. But so we know experience, but there's a ton of data behind what you do. I wonder if you could share kind of where you sit in the data space and how you've seen organizations leverage data up to this point. And really, if you could address maybe some of the changes that you're seeing as a result of the pandemic, that would be great. >>Sure, sure. Well, Azaz, you mentioned experience Eyes best known as a credit bureau. Uh, I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions and better understand and connect with consumers. Eso we offer markers products around data identity activation measurement. We have a consumer view data file that's based on off line P I and contains demographic interest, transaction data and other attributes on about 300 million people in the U. S. Uh, and on the identity side, we've always been known for our safe haven or privacy friendly matching that allows marketers to connect their first party data to experience or other third parties. Uh, but in today's world, with the growth and importance of digital advertising and consumer behavior shifting to digital, uh, experience also is working to connect that offline data to the digital world for a complete view of the customer you mentioned co vid, um, we actually we serve many different verticals. And what we're seeing from our clients during co vid is that there's a bearing impact of the pandemic. The common theme is that those that have successfully pivoted their businesses to digital are doing much better. Uh, as we all know, Kobe accelerated very strong trends to digital both in the commerce and immediate viewing habits. We work with a lot of retailers. Retail is a tale of two cities with big box and grocery growing and apparel retail really struggling. We've helped our clients leveraging our data to better understand the shifts in these consumer behaviors and better segment their customers during this really challenging time. Eso think about there's there's a group of customers that is still staying home that is sheltered in place. There's a group of customers starting that significantly varied their consumer behavior, but it's starting to venture out a little. And then there's a group of customers that's doing largely what they did before and a somewhat modified fashion. So we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups. And we're also helping them with at risk audiences. Eso That's more on the financial side. Which of your customers air really struggling? Do the endemic And how do you respond? >>It's awesome, thank you. You know, it's it's funny. I mean somebody I saw Twitter poll today asking if we measure our screen time and I said, Oh my no eso Matt, let me ask you. You spend a ton of time in financial services. You really kind of cut your teeth there, and it's always been very data oriented. You've seen a lot of changes tell us about how your customers are bringing together data, the skills that people obviously a big part of the equation and applications to really put data at the center of their universe. What's new and different that these companies were getting out of the investments in data and skills. >>That's a great question. Um, the acceleration that Amy mentioned Israel, Um, we're seeing it particularly this year, but I think even in the past few years, the reluctance of customers to embrace the cloud is behind us. And now there's this massive acceleration to be able to go faster on, and in some ways the new entrance into this category. Have an advantage versus, you know, the companies that have been in the space within its financial services or beyond. Um, and in a lot of ways they are are seeing the cloud and services like snowflake as a way toe not only catch up but leapfrog your competitors and really deliver a differentiated experience to your customers to your business, internally or externally. Um, and this past, you know, however long this crisis has been going on, has really only accelerated that, because now there's a new demand. Understand your customer better your your business better with with your traditional data sources and also new alternative data sources, Um, and also be able to take a pulse. One of things that we learned which was you know, I opening experience was as the crisis unfolded, one of our data partners decided to take the data sets about where the cases where were happening from the Johns Hopkins and World Health Organization and put that on our platform, and it became a runaway hit where now with thousands of our customers overnight, we're using this data to understand how their business was doing versus how the crisis was unfolding in real time. On this has been a game changer, and I think it's only it's only scratching the surface of what now the world will be able to do when data is really at their fingertips. You're not hindered by your legacy platforms. >>I wrote about that back in the early days of the pandemic when you guys did that and talked about some of the changes that you guys enabled and and, you know you're right about Cloud. I mean, financial services. Cloud used to be an evil word, and now it's almost become a mandate. Amy, I >>wonder if you >>could tell us a little bit more about what? What, you know your customers they're having to work through in order to achieve some of these outcomes. I mean, I'm interested in the starting point. I've been talking a lot and writing a lot on talking to practitioners about what I call the data lifecycle. Sometimes people call it the data pipeline. It za complicated matter, but those customers and companies that can put data at the center and really treat that pipeline is the heart of their organization, If you will, really succeeding. What are you seeing and what really is the starting point there? >>Yes, yes, that's a good question. And as you mentioned, first party, I mean, we start with first party data. Right? First party data is critical to understanding consumers on been in different verticals, different companies. Different brands have varying levels of first party data. So retailers gonna have a lot more first party data financial services company, then say an auto manufacturer. Uh, while many marketers have that first party data to really have a 3 60 view of the customer, they need third party data as well. And that's where experience comes in. We help brands connect those disparate data sets both 1st and 3rd party baked data to better understand consumers and create a single customer view, which has a number of applications. I think the last that I heard was that there's about eight devices on average per person. I always joke that we're gonna have these enormous. I mean, that that number is growing. We're gonna have these enormous charging stations in our house, and I think we're because all the different devices and way seamlessly move from device to device along our customer journey. And, um, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience and way site that about 95% of companies are actually that they are looking to achieve that single customer view. They recognize, um, that they need that. And they've aligned various teams from e commerce to marketing to sales toe at a minimum in just their first party data and then connect that data to better understand, uh, consumers so consumers can interact with the brand through website and mobile app in store visits, um, by the phone, TV ads, etcetera. And a brand needs to use all of those touchpoints often collected by different parts of the organization and then adding that third party data to really understand the consumers in terms of specific use cases, Um, there's there's about three that come to mind, so there's first. There's relevant advertising and reaching the right customer. There's measurement s or being able to evaluate your advertising efforts. Uh, if you see an ad on the if I see it out of my mobile and then I by by visiting a desktop website understanding or get a direct mail piece, understanding that those connect those interactions are all connected to the same person is critical for measurement. And then there's, uh, there's personalization, um, which includes encourage customer experience amongst your own, um, touch points with that consumer personalized marketing communication and then, of course, um, analytics. So those are the use cases we're seeing? Great. >>Thank you, Amy. I'm out. You can't really talk about data without talking about, >>you know, >>governance and and and compliance. And I remember back in 2006, when the Federal Rules of Civil Procedure went in, it was easy. The lawyers just said, No, nobody can have access, but that's changed. One of things I like about what snowflakes doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic, what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >>No, it's another great question. Um, this is where I think the major disruption is happening. Um, and what Amy described being able to join together 1st and 3rd party data sets. Um, being able to do this was always a challenge because data had to be moved around, had a ship, my first party data to the other side. The third party data had to be shipped to me on being able to join those data sets together, um was problematic at best. And now, with the focus on privacy and protecting P, I, um, this is this is something that has to change. And the good news is with the data cloud data does not have to move. Data can stay where it belongs. Experiencing keep its data experience. Customers can hold on to their data. Yet the data can be joined together on this universal global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are gonna prevent data from being collected on either a mobile device or in wet warren as cookies and Web browsers, new approaches. And we're seeing this a lot in our space, both in financials and in media is to set up these data clean rooms where both sides can give access to one another, but not have to reveal any P i i to do that joint. Um, this is gonna be huge right now. You actually can protect your your customers, private your consumers, private identities, but still accomplish that. Join that Amy mentioned to be able to thio relate the cause and effect of these campaigns and really understand the signals. Um, that these data sets are trying to say about one another again without having to move data without having to reveal P. I We're seeing this happening now. This is this is the next big thing that we're gonna see explode over the next months and years to come. >>I totally agree. Massive changes coming in public policy in this area, and I wanted we only have a few minutes left. I wonder if for our audience members that you know, looking for some advice, what's the what's the one thing you'd recommend? They start doing differently or consider putting in place. That's going to set them up for success over the next decade. >>Yeah, that's a good question. Um, you know, I think e always say, you know, first harness all of your first party data across all touchpoints. Get that first party data in one place and working together Second back that data with trusted third parties and in mats, just in some ways to do that and then third, always with the customer first speak their language. Uh, where and when they want to be, uh, reached out thio on and use the information. You have to really create a better a better customer experience for your customers. >>Matt. What would you add to that? Bring us home if you would >>applications. Um, the idea that data can now be your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications again without data moving these personalized applications experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're gonna build? If you had all of your consumers, consumers had access to their data in the app and not having to think about things you know from scratch. Leverage the data cloud leverage these, you know, service providers like experience and build the applications of tomorrow. >>I'm super excited when I talked to practitioners like yourselves about the future of data Guys, Thanks so much for coming on. The Cube was really a pleasure having you and hope we can continue this conversation in the future. >>Thank you. >>All right. Thank you for watching. Keep it right there. We've got great content. Tons of content coming at the Snowflake Data Cloud Summit. This is Dave Volonte for the Cube. Keep it right there.

(lively music) >> Live from San Francisco, it's theCUBE covering Micron Insight 2018. Brought to you by Micron. >> Welcome back to San Francisco Bay everybody, we're here covering Micron Insight 2018. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with my cohost David Floyer. Sanjay Mehrotra is here, he's the president and CEO of Micron. Sanjay, thanks very much for coming on theCUBE. >> Great to be on the show. >> So quite an event here! First of all beautiful venue. >> Lovely venue. >> Got the Golden Gate that way, we got Nob Hill over there. So tell us about this event. It's not just about hardcore tech and memory. You guys are talking about AI for good, healthcare, changing the world. What's behind that? >> Yeah, our focus is on AI technologies and how AI is really changing the world. In terms of life, in terms of business, in terms of health. This is a showcase of how these technologies are in very very early innings, they've just barely begun. And what's happened is that AI algorithms have been around for a long time but now the compute capability and the memory and storage capability have advanced to the levels that you can really mine through a lot of data real-time, derive lot of insights and translate those insights into intelligence. And Micron plays a pivotal role here because our memory, our storage is where all this data resides, where all this data is processed. So we are very excited to bring together many industry figures, industry luminaries, park leaders, researchers, engineers all here today to engage in a dialogue on where technology is going, where AI is going, how it's shaping the world. And for the realization that hardware is absolutely central to this trend. And memory and storage is key. And we are very excited about what it means for the future. >> So a lot of thought leaders here today. Well first of all you guys have some hard news, which is relevant to what we're talking about. Talk about the hundred million dollar fund and how you've deployed it even just today you've made some sub-announcements. >> So, one of the things we announced today is we are launching a hundred million dollar fund to support, to fund start-ups in AI. Because we really think AI is going to transform the world. We want to be in the front row. With not only the large existing players that are driving this change but also the start-ups that will drive innovation. Having the front row seat with those start-ups, through our investment fund, will really help us accelerate intelligence, accelerate time to market of various AI applications. So a hundred million dollar fund is targeted toward supporting start-ups that are developing AI technologies. And what I'm really excited to talk about here is that 20% of that fund will go to start-ups that have leadership that is represented by women or under-represented groups. Under-represented--those groups that are under-represented in tech today. This demonstrates Micron's commitment to diversity and inclusion in the technologies phase. >> Well that's, well first of all congratulations on that we're big supporters >> Absolutely >> Of women and tech and diversity, it's something that we cover on the theCUBE extensively. And now you've announced two grants just today, a half a million dollars each. One with Stanford, one with Berkeley that we heard. We heard Amazon up on stage talking about Alexa AI, Microsoft was onstage we had NVIDIA on theCUBE earlier. So bringing together an ecosystem that involves academia, your partners, your customers, talk about that a little bit. >> So the two grants that you talked about, those are from Micron Foundation that is again supporting advancement of AI and AI research as well as teaching of AI to kids so that we can build the pipeline of strong engineers and technologists of the future. So the two grants that we have announced today are one to Stanford Precision Health and Integrated Diagnostics Center, 200,000 grant to Stanford, pioneers in AI applications to precision management of your health. Very exciting field that will really truly enrich life and prolong life in the future as well as advance detection of diseases. Second $200,000 grant that we are giving is to Berkeley. Artificial Intelligence Research Center, absolutely cutting-edge that will be applicable to many industries and many walks of life. These are intended to support advancement of AI research. In addition to this advanced curiosity grant to these two institutions later today you'll hear there will be announcing a $100,000 grant to AI4ALL. And this is an institution that is encouraging women and under-represented minorities at high school level, 9th grade to 11th grade to pursue STEM careers. So Micron is really promoting study of advanced research and supporting the pipeline. In addition to this of course our focus today is on bringing together industry luminaries just like you mentioned, NVIDIA, Qualcomm, autonomous driving of the future, automotive partners, BMW, Visteon, really to engage in a dialogue of how AI is advancing in these various applications. We just heard great talk from vice president at Amazon, on Alexa devices really really exciting how those devices are truly making your life so easy and so intelligent. We heard from Microsoft Corporate Vice-President of AI research. So you see we really are as leaders in our industry, we are really bringing together industry experts to engage in a thought-provoking and inspiring dialogue on AI so that when we leave here today we leave with insights into what is coming next but even more importantly what do we all need to do to get there faster, and this is all about technology. >> So Sanjay and David too, Micron is one of the few companies that was here when I started in the business and is still around. At the time you were just a component manufacturer doin' memories and wow to watch the diversification of Micron over the years but also recently, I mean it's incredibly well-run company so congratulations on the recent success. At the analyst event in New York City this year, you talked about not only that diversification in your investments and innovation but you talked about the cyclicality of this business the historical cyclicality of this business you've dampened that down a little bit, for a variety of reasons. The capital requirements in this business are enormous, there's been consolidation. So how is that going, talk about sort of the trends in your business both in terms of diversification and your ability to make this business more predictable. >> So Dave you are very right to know that Micron is 40 year old company, we actually just turned 40, very proud of it. Really a company founded on the principles of innovation and tenacity. In fact the company has contributed to the industry to the world over the course of 40 years, 40,000 patents, just imagine that's a thousand patents a year, three patents a day over the course of 40 years. We are really a prolific inventor and we absolutely through our innovations in memory and storage have shaped the world here. As technology advances it really unleashes more applications and this is what has brought about the change in our industry. Today memory is not just in your PC. Of course it is in this PC but it is also in your data center it is going to be in the autonomous records of the future you going to have as much memory as what you had in the server just a few years ago. It's inside your mobile phone Artificial Intelligence, facial recognition is only possible because of the data and memory that you have in there. You have NAND Flash that is in these devices and with technology advancing that's bringing down the price points of NAND Flash really bringing more SSD's into these notebook computers, making these notebook computers lighter, longer battery life, more powerful. And of course Flash drives are also replacing hard test drives in data centers and cloud computing. So many applications, these diverse applications really have brought greater stability in our industry. And of course technology complexity has over time moderated the supply growth. And that's what we mean that the cyclicality of our industry, yes one or two quarters here or there you can have demand and supply mismatches but overall when you look at the demand trends and combine them with the moderating supply trends the long-term trajectory for our industry is very healthy. In fact we just completed a record year. >> Our fiscal year '18 was a record 30 billion dollar year for us with profitability that puts us at the very top of the most companies with 50% operating margin and with 30 billion in revenue we are actually number two largest semiconductor company in the U.S. And a lot of opportunity ahead given the demand drivers in the industry. >> Massive free cash flow, you've said publicly the stock is undervalued which is ya know, I don't know any CEO that says it's overvalued but nonetheless the performance that you've had suggests that you very well might be right. Go ahead David please. >> Yeah I just wanted to ask your opinion on, you are leading in this area now, very very clearly you're growing faster than the industry, you've had a magnificent year and the whole area is grown both the NAND and the DRAM. How are you judging how much to invest in this for the future? What's the balance between giving money back to the stockholders by buying stock back or versus investing in this what seems to me a very very exciting area. >> Do you have an AI algorithm for that? (laughing) >> We are in a great position where we are extremely disciplined about investing in CapEx to reduce cost of production and to deploy new technologies into production. We are very ROI focused in terms of any CapEx investments we make. We of course invest in R and D. I mentioned earlier 40,000 patents over the course of 40 years that only comes in investment in R and D. Investments in R and D are essential because we are today the most comprehensive technology solutions provider in memory and storage in the world. >> Yeah. >> In the world. With our DRAM, our Flash, our 3D crosspoint technologies, as well as future emerging technologies really position us as the only company in the world that have all of these memory and storage technologies under one company roof. So we do invest very thoughtfully and we manage our expenses very carefully but we do invest in R and D and of course we are committed to driving shareholder value as well. And we had announced earlier in the year ten billion dollar share buy back program with at least 50% of a fee cash flow. Every quarter on an annual basis actually, 50% of our fee cash flow on an annual basis going, at least 50% going toward share buy back. So we are managing the business, all aspects of it, excitedly looking forward to the opportunities. At the same time prudently in an otherwise driven fashion, building shareholder value through investments in R and D and manufacturing. >> Well of course the great Warren Buffett, David, says when asked if stock buy backs are a good investment says if your stock's undervalued it's a good investment, so. Obviously you believe that Sanjay, so. >> Absolutely! >> So thanks, thanks very much for coming on the theCUBE it was great to have you. >> Thank you. >> I hope we can have you back again. >> Thank you. >> We could talk to you for a long long time. >> Thank you very much. >> Alright, keep it right there buddy, >> Thank you. >> We'll be back with our next guest. We're live from San Francisco Bay Micron Insight 2018. You're watching theCUBE. (upbeat music).

continues coverage of vmworld live 2010 on the scene at the Moscone Center South with special guest Pat Cal singer from EMC the president former Intel 30-year veteran welcome back to the cube back with the bloggers will upgrade from our last gig VMworld rockin here in VMware so Pat back back at the cube we'll go and we know you're really busy so so you've been at EMC now for almost a year what's it like there and you just acquired greenplum you've been busy since we last met so tell us quick update what you're doing the one-year is coming up and you got green Flint under your belt what does all this mean for EMC and then we'll talk about the keynote okay so well you know overall I think things are going on schedule if you think about it that way you know we said and coming into it we had certain agendas we did product announcements last week that we'll talk to you know that I'll cover in my super session today we've said we're gonna be acquisitive right we did art early in the year we just a green plum VMware's continued their quiz in nature they announced two acquisitions this morning you're personally I said we were going to be a disruptive entity in the industry well I think service model right he laid out key strategic directions for things like security right and how that starts to implement through the V shield directions also this model of IT as a service for IT as well as for service providers the key partnerships there so you know a major delivery through the Redwood technology that they did and and then here following up with you know some of the substance - you know showing the clear tangible progress against the directions that Paul was describing very consistent with a lot of the things you've been talking about sort of in preview fashion for shocking yeah we taught that UNC Rose storage is sexy and that what that went over real well since then M&A has been off the charts sizzling hot storage now we're here and and what we're seeing is proof points and you've done some things with green plum talk about what it all means in terms of proof points what proof points do you see that absolutely established the reality of cloud and that this is a mandate going forward as a future architecture whether it's developers mobility and talk about those proof points yeah and I think you know let's be careful I don't want to be too what while I am gonna answer your question I don't want to get too far ahead of my skis in the sense that there's still a lot more cloud washing than there is cloud substance and you know if you go back to the theme right to us you know virtual you know virtual roads actual clouds are trying to say there is some substance to it but still there's a lot of visionary directions here no that's said right as part of the be plowed a partner program that Paul described today you know these customers these part are putting up real cloud offerings today and those are becoming very real things like vCloud director real tools to implement those in place real customers like the Levi's example there were they're implementing this and what they do yeah we had Tom Peck on down at sapphire our CIO Levi's great story there yeah and you know I met with customers like Telstra yesterday right who is absolutely implementing services and delivering them to enterprise clients so I think that we've clearly in the hype cycle where you know the height great it's often well in excess of the reality and I think that's been the case for the last two years and now we're seeing in that hype cycle that the reality is starting to build where our real customers real services real applications are being deployed against this cloud model and sort of the mantra that we've been laying and we're seeing increasing industry momentum saying yes indeed we all need to rationalize our products our services against that cloud strategy so you've seen a lot of inflection points in your day as have we where do you see this one rating based on in the context of what you just said the whole cloud computing inflection point is it bigger than all the previous ones in your opinion or still remains to be seen well any anybody making such a prediction right you should think twice about right you know the validity of their claim that says but I think there's two aspects to it that I think indicate that it could be bigger than anything before yeah the first one is just the industry is bigger right IT and as the economy has grown an IT has grown as an percent of the economy we're just big now and IT truly is just a huge sector of the economy particularly for United States right Silicon Valley area you know this is our agenda for the world so as the economy's bigger and secondly this is disruptive in multiple dimensions of the industry the changes the infrastructure it changes the application model it changes the service model it affects service providers that affect system integrators many of the prior changes were not as disruptive across all of the strata of the industry so because it's bigger because it is more in across all dimensions of the industry I believe and certainly you know as Joe has talked about Joe touchier CEO has said this is the biggest how big I don't know but as this one feels like you know this is sooo not Amica last wave if I was a surfer yeah Joe's famous wave slot we had Microsoft on yesterday who's actually here but they can't really show anything and we talked about them about their hypervisor I asked a specific question about as the PC era reached the Stu glass ceiling the bloated PC chained to the desk the PC centric view you've lived that generation it's not so much that it's irrelevant it's just that it's changing and and we're in a new era so what VMware is putting forth with this architecture and some of the things you've been working on you have a platform and you have agnostic devices that really changes the game on this PC centric I mean what do you see on them on the on the user centric side the key variables in the industry well I think you know number one any of these waves you know I predicted in 1990 the end of the mainframe varied 20 years later it still hasn't quite gone away right and that's that's it's not like these waves become the death of all prior you did some damage but it doesn't like immediately eliminate those prior technologies but the Nexus of innovation the foci of the industries new capabilities productivity applications is shifted and I think all of us today would say the PC right isn't that foci of innovation hey lots of apps are using it use it yeah hey I am much more productive on my laptop than animun iPad or iPhone or Android I mean you know I just you know just much more productive in that sense but you know I can't carry my laptop around in my pocket right clearly we're seeing the shift of innovation new application models new consumer centric usage models both the devices and the applications and I think as Steve Harrods akino talked about very much hey I want an app store like thing for my enterprise applications right where you see much of that consumerization coming to corporate IT as well so it has shifted right applications usage models will be device independent right they will become more sumer focus and essentially let's just say they're gonna be more iPhone like greater than how we'd get them and consume that at at to Orlando we sat down and we chatted about at a great chat and at the Cuban Orlando I asked you a question about apps and infrastructure and I asked you specifically our apps leading the way and showing the infrastructure and you answered no infrastructure as always was a leading indicator to apps but apps seem to have more momentum what is the VMware announcement today how does that shape that that thesis that you mentioned I mean obviously it looks like more enablement at the software level what can you share with it cuz that's real as a great point and what I want to bring that out again yeah yeah and the point I made there was hey you know we know a simple structural hardware guys right we create capabilities and then the apps guys come in and use them inefficiently and poorly but to enable new things a sort of that you know that gap right of capability in the infrastructure that then gets filled in by application vendors and I still believe fundamentally that's the case you don't write apps for infrastructure that doesn't exist yet you can't run an applications business that way so our job is the infrastructure guys is always to create these new gaps these new vacuums for the app guys that come and fill in and I think everything we're seeing is very much that case where all of a sudden there's lots of performance that's easily readily available you know think about how easy it is to deploy a VM today right literally you know if I would have had to go provision things buy some new servers get the port's alakay to get a network reroute you'll build up a new storage infrastructure it might be months for me to allow a major new application to occur literally now a few clicks we what do you see in the VMware announcement today given what you guys are doing in at the app level as the core enabler the disruptive enabler that's gonna really tip that over in terms of the innovation yeah they're anything new there well I think there are two aspects to it you know if you take the redwood the vCloud director and I think it really is this idea being able to rapidly with essentially no cost be able to create new virtual data center infrastructure be able to do it with the security model with policy based capabilities with the provisioning environment in the manager let's go with that is way profound right the second thing is all of all the things about spring right is not just being able to do infrastructure more rapidly it's not just encapsulating existing applications it's also enabling a new developer model for tomorrow's applications as well and that is truly right thrilling to anybody who's doing enterprise application development some of the CIOs we had on we're saying that they actually benched themselves against the cloud service providers do you see those two worlds the cloud service providers and big IT coming together or do you see the cloud service providers always having an edge over a big IT well I think there's an aspect to that that you it's not our job is to make the infrastructure as efficient on both sides of that equation as possible so that an IT guy isn't making the decision based on cost he's making the decision based on business relevant factors you know this is something hey you know I need to guarantee compliance in this application this is a business critical infrastructure element for me I'm gonna run into my infrastructure but I know the cost of doing so is still highly efficient I might Feder a tit with an external service right I might do tests and dev externally and when that's done I might bring it internally I might use Federation of outside compute capacity so that I don't need to build for peak I build for average and I spill over to rent VMs at quarter close or month close I might say boy I want to actually be able to take advantage and federated some of my key customers or channel partners or business partners I'm gonna have and I'd actually be able to them to be able to view me as their service provider right so hey just operate and utilize my applications right as one of my business partners as well and that's really the power of the vision that we're laying out it's not public versus private it's public and private and allow them to be federated together into hybrid services that give you the best of both worlds in a seamless agile manner Pat the M&A activities been hot you did a greenplum acquisition DMC bought cream plum which is a nice acquisition and rate act was very nice come on this is a game-changing acquisition by EMC to the cube we treat our guests nicely so it's hot so the horses are on the track I tweeted that and said hey everyone's out in the track right now the big guys Oracle EMC net AB so talk about what's happening why is the M&A activity so hot is it an indicated the fact that people have to retool faster is it an activity that they're behind is an activity that they need to move faster all of the above what is your view on that and you know what's next in M&A well I think it's a couple of different factors are at work and one is if you show up at my super session later today you know one of my slides is the three views of the cloud right the uber cloud model the Google's Amazon's I'll call it the vertical cloud model HP IBM and then I'll call it the virtual cloud model even seen anywhere right you know and of course you're right we contrast those three and what we're seeing I would say is those business models those industry structures those strategic frameworks are driving the consolidation of all of the medium and small players into one of those pictures so I think you can look at that lens and say everybody is taking M&A acquisitions to better implement and solidify their view of that strategy of these three different views of the cloud so right one is its industry structure that's going on second is you know after the downturn everybody's coming out of it cash rich right you know yeah yo people got money to spend there's a good time to do it right you know in that sense and you know so right there is a clear right earnestness to people saying okay right I can pay dividends I could buy back shares boy that's pretty innovative strategy isn't it right or let's go start you know taking more aggressive steps I think it also indicates that there are only so many exciting assets available right you know good assets that people could take actions on so you know and any buyer and seller market right prices get crazy when there's more buyers than there are three party people who followed your career know that you had Intel you were very Pro ecosystem and we just had some VCS on yesterday top DC's and cloud talking about how hot it is and they're looking for startups not the angel stuff but like real money real real technology talk about the ecosystem that's emerging in the startup community because so there are guys developing new cool stuff that very cloud centric that's yeah new so talk about your view there what you see and what your general opinion is well I think like any these waves there isn't just the wave of what goes on in terms of what the big guys do right there's new university research that's going on as some of that's exciting there's also than the adventure community great and with this wave you know it is so disruptive it changes consumer computing new consumer devices new consumer applications new enterprise infrastructure new enterprise applications and you know all of a sudden we're seeing a new round of dramatic VC activity again and they're not going into you know startups that are building a six and semiconductor sort of I grew up sort of saying let's go build new infrastructure components new applications that live on this new model and virtualization yeah absolutely it's all riding this cloud virtualization trend and you got just a stunning you know 17,000 people at VMworld I have one final question I know you I know you want to get a final question actually let me get my final question first in a new close I'll give you less word so you've been spend a lot of time in New England lately do you like it there you're gonna get a movie stir well we cry you out of here we've taken a second home Oh faster half time and my wife is actually loving this bicoastal a couple weeks a couple weeks there back and forth excellent ball season starting absolutely maybe the Red Sox so we had some readers point out on the blog that the Pat gal singer has the same exact track that Joe Tucci had CEO president CEO anything you want a nice they want you I want to say anything do I have no announcement that's great thanks so much I know you're super busy and coming on the show always thank you guys thanks thanks we right back

(gentle upbeat music) >> Hello, everyone, welcome to theCUBE's coverage of International Women's Day. I'm John Furrier host here in Palo Alto, California. theCUBE's second year covering International Women's Day. It's been a great celebration of all the smart leaders in the world who are making a difference from all kinds of backgrounds, from technology to business and everything in between. Today we've got a great guest, Dominique Bastos, who's the senior Vice President of Cloud at Persistent Systems, formerly with AWS. That's where we first met at re:Invent. Dominique, great to have you on the program here for International Women's Day. Thanks for coming on. >> Thank you John, for having me back on theCUBE. This is an honor, especially given the theme. >> Well, I'm excited to have you on, I consider you one of those typecast personas where you've kind of done a lot of things. You're powerful, you've got great business acumen you're technical, and we're in a world where, you know the world's coming completely digital and 50% of the world is women, 51%, some say. So you got mostly male dominated industry and you have a dual engineering background and that's super impressive as well. Again, technical world, male dominated you're in there in the mix. What inspires you to get these engineering degrees? >> I think even it was more so shifted towards males. When I had the inspiration to go to engineering school I was accused as a young girl of being a tomboy and fiddling around with all my brother's toys versus focusing on my dolls and other kind of stereotypical toys that you would give a girl. I really had a curiosity for building, a curiosity for just breaking things apart and putting them back together. I was very lucky in that my I guess you call it primary school, maybe middle school, had a program for, it was like electronics, that was the class electronics. So building circuit boards and things like that. And I really enjoyed that aspect of building. I think it was more actually going into engineering school. Picking that as a discipline was a little bit, my mom's reaction to when I announced that I wanted to do engineering which was, "No, that's for boys." >> Really. >> And that really, you know, I think she, it came from a good place in trying to protect me from what she has experienced herself in terms of how women are received in those spaces. So I kind of shrugged it off and thought "Okay, well I'm definitely now going to do this." >> (laughs) If I was told not to, you're going to do it. >> I was told not to, that's all I needed to hear. And also, I think my passion was to design cars and I figured if I enroll in an industrial engineering program I could focus on ergonomic design and ultimately, you know have a career doing something that I'm passionate about. So yeah, so my inspiration was kind of a little bit of don't do this, a lot of curiosity. I'm also a very analytical person. I've been, and I don't know what the science is around left right brain to be honest, but been told that I'm a very much a logical person versus a feeler. So I don't know if that's good or bad. >> Straight shooter. What were your engineering degrees if you don't mind sharing? >> So I did industrial engineering and so I did a dual degree, industrial engineering and robotics. At the time it was like a manufacturing robotics program. It was very, very cool because we got to, I mean now looking back, the evolution of robotics is just insane. But you, you know, programmed a robotic arm to pick things up. I actually crashed the Civil Engineering School's Concrete Canoe Building Competition where you literally have to design a concrete canoe and do all the load testing and the strength testing of the materials and basically then, you know you go against other universities to race the canoe in a body of water. We did that at, in Alabama and in Georgia. So I was lucky to experience that two times. It was a lot of fun. >> But you knew, so you knew, deep down, you were technical you had a nerd vibe you were geeking out on math, tech, robotics. What happened next? I mean, what were some of the challenges you faced? How did you progress forward? Did you have any blockers and roadblocks in front of you and how did you handle those? >> Yeah, I mean I had, I had a very eye-opening experience with, in my freshman year of engineering school. I kind of went in gung-ho with zero hesitation, all the confidence in the world, 'cause I was always a very big nerd academically, I hate admitting this but myself and somebody else got most intellectual, voted by the students in high school. It's like, you don't want to be voted most intellectual when you're in high school. >> Now it's a big deal. (laughs) >> Yeah, you want to be voted like popular or anything like that? No, I was a nerd, but in engineering school, it's a, it was very humbling. That whole confidence that I had. I experienced prof, ooh, I don't want to name the school. Everybody can google it though, but, so anyway so I had experience with some professors that actually looked at me and said, "You're in the wrong program. This is difficult." I, and I think I've shared this before in other forums where, you know, my thermodynamic teacher basically told me "Cheerleading's down the hall," and it it was a very shocking thing to hear because it really made me wonder like, what am I up against here? Is this what it's going to be like going forward? And I decided not to pay attention to that. I think at the moment when you hear something like that you just, you absorb it and you also don't know how to react. And I decided immediately to just walk right past him and sit down front center in the class. In my head I was cursing him, of course, 'cause I mean, let's be real. And I was like, I'm going to show this bleep bleep. And proceeded to basically set the curve class crushed it and was back to be the teacher's assistant. So I think that was one. >> But you became his teacher assistant after, or another one? >> Yeah, I gave him a mini speech. I said, do not do this. You, you could, you could have broken me and if you would've done this to somebody who wasn't as steadfast in her goals or whatever, I was really focused like I'm doing this, I would've backed out potentially and said, you know this isn't something I want to experience on the daily. So I think that was actually a good experience because it gave me an opportunity to understand what I was up against but also double down in how I was going to deal with it. >> Nice to slay the misogynistic teachers who typecast people. Now you had a very technical career but also you had a great career at AWS on the business side you've handled 'em all of the big accounts, I won't say the names, but like we're talking about monster accounts, sales and now basically it's not really selling, you're managing a big account, it's like a big business. It's a business development thing. Technical to business transition, how do you handle that? Was that something you were natural for? Obviously you, you stared down the naysayers out of the gate in college and then in business, did that continue and how did you drive through that? >> So I think even when I was coming out of university I knew that I wanted to have a balance between the engineering program and business. A lot of my colleagues went on to do their PEs so continue to get their masters basically in engineering or their PhDs in engineering. I didn't really have an interest for that. I did international business and finance as my MBA because I wanted to explore the ability of taking what I had learned in engineering school and applying it to building businesses. I mean, at the time I didn't have it in my head that I would want to do startups but I definitely knew that I wanted to get a feel for what are they learning in business school that I missed out in engineering school. So I think that helped me when I transitioned, well when I applied, I was asked to come apply at AWS and I kind of went, no I'm going to, the DNA is going to be rejected. >> You thought, you thought you'd be rejected from AWS. >> I thought I'd be, yeah, because I have very much a startup founder kind of disruptive personality. And to me, when I first saw AWS at the stage early 2016 I saw it as a corporation. Even though from a techie standpoint, I was like, these people are insane. This is amazing what they're building. But I didn't know what the cultural vibe would feel like. I had been with GE at the beginning of my career for almost three years. So I kind of equated AWS Amazon to GE given the size because in between, I had done startups. So when I went to AWS I think initially, and I do have to kind of shout out, you know Todd Weatherby basically was the worldwide leader for ProServe and it was being built, he built it and I went into ProServe to help from that standpoint. >> John: ProServe, Professional services >> Professional services, right. To help these big enterprise customers. And specifically my first customer was an amazing experience in taking, basically the company revolves around strategic selling, right? It's not like you take a salesperson with a conventional schooling that salespeople would have and plug them into AWS in 2016. It was very much a consultative strategic approach. And for me, having a technical background and loving to solve problems for customers, working with the team, I would say, it was a dream team that I joined. And also the ability to come to the table with a technical background, knowing how to interact with senior executives to help them envision where they want to go, and then to bring a team along with you to make that happen. I mean, that was like magical for me. I loved that experience. >> So you like the culture, I mean, Andy Jassy, I've interviewed many times, always talked about builders and been a builder mentality. You mentioned that earlier at the top of this interview you've always building things, curious and you mentioned potentially your confidence might have been shaken. So you, you had the confidence. So being a builder, you know, being curious and having confidence seems to be what your superpower is. A lot of people talk about the confidence angle. How important is that and how important is that for encouraging more women to get into tech? Because I still hear that all the time. Not that they don't have confidence, but there's so many signals that potentially could shake confidence in industry >> Yeah, that's actually a really good point that you're making. A lot of signals that women get could shake their confidence and that needs to be, I mean, it's easy to say that it should be innate. I mean that's kind of like textbook, "Oh it has to come from within." Of course it does. But also, you know, we need to understand that in a population where 50% of the population is women but only 7% of the positions in tech, and I don't know the most current number in tech leadership, is women, and probably a smaller percentage in the C-suite. When you're looking at a woman who's wanting to go up the trajectory in a tech company and then there's a subconscious understanding that there's a limit to how far you'll go, your confidence, you know, in even subconsciously gets shaken a little bit because despite your best efforts, you're already seeing the cap. I would say that we need to coach girls to speak confidently to navigate conflict versus running away from it, to own your own success and be secure in what you bring to the table. And then I think a very important thing is to celebrate each other and the wins that we see for women in tech, in the industry. >> That's awesome. What's, the, in your opinion, the, you look at that, the challenges for this next generation women, and women in general, what are some of the challenges for them and that they need to overcome today? I mean, obviously the world's changed for the better. Still not there. I mean the numbers one in four women, Rachel Thornton came on, former CMO of AWS, she's at MessageBird now. They had a study where only one in four women go to the executive board level. And so there's still, still numbers are bad and then the numbers still got to get up, up big time. That's, and the industry's working on that, but it's changed. But today, what are some of the challenges for this current generation and the next generation of women and how can we and the industry meet, we being us, women in the industry, be strong role models for them? >> Well, I think the challenge is one of how many women are there in the pipeline and what are we doing to retain them and how are we offering up the opportunities to fill. As you know, as Rachel said and I haven't had an opportunity to see her, in how are we giving them this opportunity to take up those seats in the C-suite right, in these leadership roles. And I think this is a little bit exacerbated with the pandemic in that, you know when everything shut down when people were going back to deal with family and work at the same time, for better or for worse the brunt of it fell on probably, you know the maternal type caregiver within the family unit. You know, I've been, I raised my daughter alone and for me, even without the pandemic it was a struggle constantly to balance the risk that I was willing to take to show up for those positions versus investing even more of that time raising a child, right? Nevermind the unconscious bias or cultural kind of expectations that you get from the male counterparts where there's zero understanding of what a mom might go through at home to then show up to a meeting, you know fully fresh and ready to kind of spit out some wisdom. It's like, you know, your kid just freaking lost their whatever and you know, they, so you have to sort a bunch of things out. I think the challenge that women are still facing and will we have to keep working at it is making sure that there's a good pipeline. A good amount of young ladies of people taking interest in tech. And then as they're, you know, going through the funnel at stages in their career, we're providing the mentoring we're, there's representation, right? To what they're aspiring to. We're celebrating their interest in the field, right? And, and I think also we're doing things to retain them, because again, the pandemic affected everybody. I think women specifically and I don't know the statistics but I was reading something about this were the ones to tend to kind of pull it back and say well now I need to be home with, you know you name how many kids and pets and the aging parents, people that got sick to take on that position. In addition to the career aspirations that they might have. We need to make it easier basically. >> I think that's a great call out and I appreciate you bringing that up about family and being a single mom. And by the way, you're savage warrior to doing that. It's amazing. You got to, I know you have a daughter in computer science at Stanford, I want to get to that in a second. But that empathy and I mentioned Rachel Thornton, who's the CMO MessageBird and former CMO of AWS. Her thing right now to your point is mentoring and sponsorship is very key. And her company and the video that's on the site here people should look at that and reference that. They talk a lot about that empathy of people's situation whether it's a single mom, family life, men and women but mainly women because they're the ones who people aren't having a lot of empathy for in that situation, as you called it out. This is huge. And I think remote work has opened up this whole aperture of everyone has to have a view into how people are coming to the table at work. So, you know, props are bringing that up, and I recommend everyone look at check out Rachel Thornton. So how do you balance that, that home life and talk about your daughter's journey because sounds like she's nerding out at Stanford 'cause you know Stanford's called Nerd Nation, that's their motto, so you must be proud. >> I am so proud, I'm so proud. And I will say, I have to admit, because I did encounter so many obstacles and so many hurdles in my journey, it's almost like I forgot that I should set that aside and not worry about my daughter. My hope for her was for her to kind of be artistic and a painter or go into something more lighthearted and fun because I just wanted to think, I guess my mom had the same idea, right? She, always been very driven. She, I want to say that I got very lucky that she picked me to be her mom. Biologically I'm her mom, but I told her she was like a little star that fell from the sky and I, and ended up with me. I think for me, balancing being a single mom and a career where I'm leading and mentoring and making big decisions that affect people's lives as well. You have to take the best of everything you get from each of those roles. And I think that the best way is play to your strengths, right? So having been kind of a nerd and very organized person and all about, you know, systems for effectiveness, I mean, industrial engineering, parenting for me was, I'm going to make it sound super annoying and horrible, but (laughs) >> It's funny, you know, Dave Vellante and I when we started SiliconANGLE and theCUBE years ago, one of the things we were all like sports lovers. So we liked sports and we are like we looked at the people in tech as tech athletes and except there's no men and women teams, it's one team. It's all one thing. So, you know, I consider you a tech athlete you're hard charging strong and professional and smart and beautiful and brilliant, all those good things. >> Thank you. >> Now this game is changing and okay, and you've done startups, and you've done corporate jobs, now you're in a new role. What's the current tech landscape from a, you know I won't say athletic per standpoint but as people who are smart. You have all kinds of different skill sets. You have the startup warriors, you have the folks who like to be in the middle of the corporate world grow up through corporate, climb the corporate ladder. You have investors, you have, you know, creatives. What have you enjoyed most and where do you see all the action? >> I mean, I think what I've enjoyed the most has been being able to bring all of the things that I feel I'm strong at and bring it together to apply that to whatever the problem is at hand, right? So kind of like, you know if you look at a renaissance man who can kind of pop in anywhere and, oh, he's good at, you know sports and he's good at reading and, or she's good at this or, take all of those strengths and somehow bring them together to deal with the issue at hand, versus breaking up your mindset into this is textbook what I learned and this is how business should be done and I'm going to draw these hard lines between personal life and work life, or between how you do selling and how you do engineering. So I think my, the thing that I loved, really loved about AWS was a lot of leaders saw something in me that I potentially didn't see, which was, yeah you might be great at running that big account but we need help over here doing go to market for a new product launch and boom, there you go. Now I'm in a different org helping solve that problem and getting something launched. And I think if you don't box yourself in to I'm only good at this, or, you know put a label on yourself as being the rockstar in that. It leaves room for opportunities to present themselves but also it leaves room within your own mind to see yourself as somebody capable of doing anything. Right, I don't know if I answered the question accurately. >> No, that's good, no, that's awesome. I love the sharing, Yeah, great, great share there. Question is, what do you see, what do you currently during now you're building a business of Persistent for the cloud, obviously AWS and Persistent's a leader global system integrator around the world, thousands and thousands of customers from what we know and been reporting on theCUBE, what's next for you? Where do you see yourself going? Obviously you're going to knock this out of the park. Where do you see yourself as you kind of look at the continuing journey of your mission, personal, professional what's on your mind? Where do you see yourself going next? >> Well, I think, you know, again, going back to not boxing yourself in. This role is an amazing one where I have an opportunity to take all the pieces of my career in tech and apply them to building a business within a business. And that involves all the goodness of coaching and mentoring and strategizing. And I'm loving it. I'm loving the opportunity to work with such great leaders. Persistent itself is very, very good at providing opportunities, very diverse opportunities. We just had a huge Semicolon; Hackathon. Some of the winners were females. The turnout was amazing in the CTO's office. We have very strong women leading the charge for innovation. I think to answer your question about the future and where I may see myself going next, I think now that my job, well they say the job is never done. But now that Chloe's kind of settled into Stanford and kind of doing her own thing, I have always had a passion to continue leading in a way that brings me to, into the fold a lot more. So maybe, you know, maybe in a VC firm partner mode or another, you know CEO role in a startup, or my own startup. I mean, I never, I don't know right now I'm super happy but you never know, you know where your drive might go. And I also want to be able to very deliberately be in a role where I can continue to mentor and support up and coming women in tech. >> Well, you got the smarts but you got really the building mentality, the curiosity and the confidence really sets you up nicely. Dominique great story, great inspiration. You're a role model for many women, young girls out there and women in tech and in celebration. It's a great day and thank you for sharing that story and all the good nuggets there. Appreciate you coming on theCUBE, and it's been my pleasure. Thanks for coming on. >> Thank you, John. Thank you so much for having me. >> Okay, theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE here in Palo Alto getting all the content, check out the other interviews some amazing stories, lessons learned, and some, you know some funny stories and some serious stories. So have some fun and enjoy the rest of the videos here for International Women's Days, thanks for watching. (gentle inspirational music)

(light airy music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, AI and machine learning. "Top Startups Building Generative AI on AWS." This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about AI machine learning. We have three great guests Bratin Saha, VP, Vice President of Machine Learning and AI Services at Amazon Web Services. Tom Mason, the CTO of Stability AI, and Aidan Gomez, CEO and co-founder of Cohere. Two practitioners doing startups and AWS. Gentlemen, thank you for opening up this session, this episode. Thanks for coming on. >> Thank you. >> Thank you. >> Thank you. >> So the topic is hype versus reality. So I think we're all on the reality is great, hype is great, but the reality's here. I want to get into it. Generative AI's got all the momentum, it's going mainstream, it's kind of come out of the behind the ropes, it's now mainstream. We saw the success of ChatGPT, opens up everyone's eyes, but there's so much more going on. Let's jump in and get your early perspectives on what should people be talking about right now? What are you guys working on? We'll start with AWS. What's the big focus right now for you guys as you come into this market that's highly active, highly hyped up, but people see value right out of the gate? >> You know, we have been working on generative AI for some time. In fact, last year we released Code Whisperer, which is about using generative AI for software development and a number of customers are using it and getting real value out of it. So generative AI is now something that's mainstream that can be used by enterprise users. And we have also been partnering with a number of other companies. So, you know, stability.ai, we've been partnering with them a lot. We want to be partnering with other companies as well. In seeing how we do three things, you know, first is providing the most efficient infrastructure for generative AI. And that is where, you know, things like Trainium, things like Inferentia, things like SageMaker come in. And then next is the set of models and then the third is the kind of applications like Code Whisperer and so on. So, you know, it's early days yet, but clearly there's a lot of amazing capabilities that will come out and something that, you know, our customers are starting to pay a lot of attention to. >> Tom, talk about your company and what your focus is and why the Amazon Web Services relationship's important for you? >> So yeah, we're primarily committed to making incredible open source foundation models and obviously stable effusions been our kind of first big model there, which we trained all on AWS. We've been working with them over the last year and a half to develop, obviously a big cluster, and bring all that compute to training these models at scale, which has been a really successful partnership. And we're excited to take it further this year as we develop commercial strategy of the business and build out, you know, the ability for enterprise customers to come and get all the value from these models that we think they can get. So we're really excited about the future. We got hugely exciting pipeline for this year with new modalities and video models and wonderful things and trying to solve images for once and for all and get the kind of general value and value proposition correct for customers. So it's a really exciting time and very honored to be part of it. >> It's great to see some of your customers doing so well out there. Congratulations to your team. Appreciate that. Aidan, let's get into what you guys do. What does Cohere do? What are you excited about right now? >> Yeah, so Cohere builds large language models, which are the backbone of applications like ChatGPT and GPT-3. We're extremely focused on solving the issues with adoption for enterprise. So it's great that you can make a super flashy demo for consumers, but it takes a lot to actually get it into billion user products and large global enterprises. So about six months ago, we released our command models, which are some of the best that exist for large language models. And in December, we released our multilingual text understanding models and that's on over a hundred different languages and it's trained on, you know, authentic data directly from native speakers. And so we're super excited to continue pushing this into enterprise and solving those barriers for adoption, making this transformation a reality. >> Just real quick, while I got you there on the new products coming out. Where are we in the progress? People see some of the new stuff out there right now. There's so much more headroom. Can you just scope out in your mind what that looks like? Like from a headroom standpoint? Okay, we see ChatGPT. "Oh yeah, it writes my papers for me, does some homework for me." I mean okay, yawn, maybe people say that, (Aidan chuckles) people excited or people are blown away. I mean, it's helped theCUBE out, it helps me, you know, feed up a little bit from my write-ups but it's not always perfect. >> Yeah, at the moment it's like a writing assistant, right? And it's still super early in the technologies trajectory. I think it's fascinating and it's interesting but its impact is still really limited. I think in the next year, like within the next eight months, we're going to see some major changes. You've already seen the very first hints of that with stuff like Bing Chat, where you augment these dialogue models with an external knowledge base. So now the models can be kept up to date to the millisecond, right? Because they can search the web and they can see events that happened a millisecond ago. But that's still limited in the sense that when you ask the question, what can these models actually do? Well they can just write text back at you. That's the extent of what they can do. And so the real project, the real effort, that I think we're all working towards is actually taking action. So what happens when you give these models the ability to use tools, to use APIs? What can they do when they can actually affect change out in the real world, beyond just streaming text back at the user? I think that's the really exciting piece. >> Okay, so I wanted to tee that up early in the segment 'cause I want to get into the customer applications. We're seeing early adopters come in, using the technology because they have a lot of data, they have a lot of large language model opportunities and then there's a big fast follower wave coming behind it. I call that the people who are going to jump in the pool early and get into it. They might not be advanced. Can you guys share what customer applications are being used with large language and vision models today and how they're using it to transform on the early adopter side, and how is that a tell sign of what's to come? >> You know, one of the things we have been seeing both with the text models that Aidan talked about as well as the vision models that stability.ai does, Tom, is customers are really using it to change the way you interact with information. You know, one example of a customer that we have, is someone who's kind of using that to query customer conversations and ask questions like, you know, "What was the customer issue? How did we solve it?" And trying to get those kinds of insights that was previously much harder to do. And then of course software is a big area. You know, generating software, making that, you know, just deploying it in production. Those have been really big areas that we have seen customers start to do. You know, looking at documentation, like instead of you know, searching for stuff and so on, you know, you just have an interactive way, in which you can just look at the documentation for a product. You know, all of this goes to where we need to take the technology. One of which is, you know, the models have to be there but they have to work reliably in a production setting at scale, with privacy, with security, and you know, making sure all of this is happening, is going to be really key. That is what, you know, we at AWS are looking to do, which is work with partners like stability and others and in the open source and really take all of these and make them available at scale to customers, where they work reliably. >> Tom, Aidan, what's your thoughts on this? Where are customers landing on this first use cases or set of low-hanging fruit use cases or applications? >> Yeah, so I think like the first group of adopters that really found product market fit were the copywriting companies. So one great example of that is HyperWrite. Another one is Jasper. And so for Cohere, that's the tip of the iceberg, like there's a very long tail of usage from a bunch of different applications. HyperWrite is one of our customers, they help beat writer's block by drafting blog posts, emails, and marketing copy. We also have a global audio streaming platform, which is using us the power of search engine that can comb through podcast transcripts, in a bunch of different languages. Then a global apparel brand, which is using us to transform how they interact with their customers through a virtual assistant, two dozen global news outlets who are using us for news summarization. So really like, these large language models, they can be deployed all over the place into every single industry sector, language is everywhere. It's hard to think of any company on Earth that doesn't use language. So it's, very, very- >> We're doing it right now. We got the language coming in. >> Exactly. >> We'll transcribe this puppy. All right. Tom, on your side, what do you see the- >> Yeah, we're seeing some amazing applications of it and you know, I guess that's partly been, because of the growth in the open source community and some of these applications have come from there that are then triggering this secondary wave of innovation, which is coming a lot from, you know, controllability and explainability of the model. But we've got companies like, you know, Jasper, which Aidan mentioned, who are using stable diffusion for image generation in block creation, content creation. We've got Lensa, you know, which exploded, and is built on top of stable diffusion for fine tuning so people can bring themselves and their pets and you know, everything into the models. So we've now got fine tuned stable diffusion at scale, which is democratized, you know, that process, which is really fun to see your Lensa, you know, exploded. You know, I think it was the largest growing app in the App Store at one point. And lots of other examples like NightCafe and Lexica and Playground. So seeing lots of cool applications. >> So much applications, we'll probably be a customer for all you guys. We'll definitely talk after. But the challenges are there for people adopting, they want to get into what you guys see as the challenges that turn into opportunities. How do you see the customers adopting generative AI applications? For example, we have massive amounts of transcripts, timed up to all the videos. I don't even know what to do. Do I just, do I code my API there. So, everyone has this problem, every vertical has these use cases. What are the challenges for people getting into this and adopting these applications? Is it figuring out what to do first? Or is it a technical setup? Do they stand up stuff, they just go to Amazon? What do you guys see as the challenges? >> I think, you know, the first thing is coming up with where you think you're going to reimagine your customer experience by using generative AI. You know, we talked about Ada, and Tom talked about a number of these ones and you know, you pick up one or two of these, to get that robust. And then once you have them, you know, we have models and we'll have more models on AWS, these large language models that Aidan was talking about. Then you go in and start using these models and testing them out and seeing whether they fit in use case or not. In many situations, like you said, John, our customers want to say, "You know, I know you've trained these models on a lot of publicly available data, but I want to be able to customize it for my use cases. Because, you know, there's some knowledge that I have created and I want to be able to use that." And then in many cases, and I think Aidan mentioned this. You know, you need these models to be up to date. Like you can't have it staying. And in those cases, you augmented with a knowledge base, you know you have to make sure that these models are not hallucinating. And so you need to be able to do the right kind of responsible AI checks. So, you know, you start with a particular use case, and there are a lot of them. Then, you know, you can come to AWS, and then look at one of the many models we have and you know, we are going to have more models for other modalities as well. And then, you know, play around with the models. We have a playground kind of thing where you can test these models on some data and then you can probably, you will probably want to bring your own data, customize it to your own needs, do some of the testing to make sure that the model is giving the right output and then just deploy it. And you know, we have a lot of tools. >> Yeah. >> To make this easy for our customers. >> How should people think about large language models? Because do they think about it as something that they tap into with their IP or their data? Or is it a large language model that they apply into their system? Is the interface that way? What's the interaction look like? >> In many situations, you can use these models out of the box. But in typical, in most of the other situations, you will want to customize it with your own data or with your own expectations. So the typical use case would be, you know, these are models are exposed through APIs. So the typical use case would be, you know you're using these APIs a little bit for testing and getting familiar and then there will be an API that will allow you to train this model further on your data. So you use that AI, you know, make sure you augmented the knowledge base. So then you use those APIs to customize the model and then just deploy it in an application. You know, like Tom was mentioning, a number of companies that are using these models. So once you have it, then you know, you again, use an endpoint API and use it in an application. >> All right, I love the example. I want to ask Tom and Aidan, because like most my experience with Amazon Web Service in 2007, I would stand up in EC2, put my code on there, play around, if it didn't work out, I'd shut it down. Is that a similar dynamic we're going to see with the machine learning where developers just kind of log in and stand up infrastructure and play around and then have a cloud-like experience? >> So I can go first. So I mean, we obviously, with AWS working really closely with the SageMaker team, do fantastic platform there for ML training and inference. And you know, going back to your point earlier, you know, where the data is, is hugely important for companies. Many companies bringing their models to their data in AWS on-premise for them is hugely important. Having the models to be, you know, open sources, makes them explainable and transparent to the adopters of those models. So, you know, we are really excited to work with the SageMaker team over the coming year to bring companies to that platform and make the most of our models. >> Aidan, what's your take on developers? Do they just need to have a team in place, if we want to interface with you guys? Let's say, can they start learning? What do they got to do to set up? >> Yeah, so I think for Cohere, our product makes it much, much easier to people, for people to get started and start building, it solves a lot of the productionization problems. But of course with SageMaker, like Tom was saying, I think that lowers a barrier even further because it solves problems like data privacy. So I want to underline what Bratin was saying earlier around when you're fine tuning or when you're using these models, you don't want your data being incorporated into someone else's model. You don't want it being used for training elsewhere. And so the ability to solve for enterprises, that data privacy and that security guarantee has been hugely important for Cohere, and that's very easy to do through SageMaker. >> Yeah. >> But the barriers for using this technology are coming down super quickly. And so for developers, it's just becoming completely intuitive. I love this, there's this quote from Andrej Karpathy. He was saying like, "It really wasn't on my 2022 list of things to happen that English would become, you know, the most popular programming language." And so the barrier is coming down- >> Yeah. >> Super quickly and it's exciting to see. >> It's going to be awesome for all the companies here, and then we'll do more, we're probably going to see explosion of startups, already seeing that, the maps, ecosystem maps, the landscape maps are happening. So this is happening and I'm convinced it's not yesterday's chat bot, it's not yesterday's AI Ops. It's a whole another ballgame. So I have to ask you guys for the final question before we kick off the company's showcasing here. How do you guys gauge success of generative AI applications? Is there a lens to look through and say, okay, how do I see success? It could be just getting a win or is it a bigger picture? Bratin we'll start with you. How do you gauge success for generative AI? >> You know, ultimately it's about bringing business value to our customers. And making sure that those customers are able to reimagine their experiences by using generative AI. Now the way to get their ease, of course to deploy those models in a safe, effective manner, and ensuring that all of the robustness and the security guarantees and the privacy guarantees are all there. And we want to make sure that this transitions from something that's great demos to actual at scale products, which means making them work reliably all of the time not just some of the time. >> Tom, what's your gauge for success? >> Look, I think this, we're seeing a completely new form of ways to interact with data, to make data intelligent, and directly to bring in new revenue streams into business. So if businesses can use our models to leverage that and generate completely new revenue streams and ultimately bring incredible new value to their customers, then that's fantastic. And we hope we can power that revolution. >> Aidan, what's your take? >> Yeah, reiterating Bratin and Tom's point, I think that value in the enterprise and value in market is like a huge, you know, it's the goal that we're striving towards. I also think that, you know, the value to consumers and actual users and the transformation of the surface area of technology to create experiences like ChatGPT that are magical and it's the first time in human history we've been able to talk to something compelling that's not a human. I think that in itself is just extraordinary and so exciting to see. >> It really brings up a whole another category of markets. B2B, B2C, it's B2D, business to developer. Because I think this is kind of the big trend the consumers have to win. The developers coding the apps, it's a whole another sea change. Reminds me everyone use the "Moneyball" movie as example during the big data wave. Then you know, the value of data. There's a scene in "Moneyball" at the end, where Billy Beane's getting the offer from the Red Sox, then the owner says to the Red Sox, "If every team's not rebuilding their teams based upon your model, there'll be dinosaurs." I think that's the same with AI here. Every company will have to need to think about their business model and how they operate with AI. So it'll be a great run. >> Completely Agree >> It'll be a great run. >> Yeah. >> Aidan, Tom, thank you so much for sharing about your experiences at your companies and congratulations on your success and it's just the beginning. And Bratin, thanks for coming on representing AWS. And thank you, appreciate for what you do. Thank you. >> Thank you, John. Thank you, Aidan. >> Thank you John. >> Thanks so much. >> Okay, let's kick off season three, episode one. I'm John Furrier, your host. Thanks for watching. (light airy music)

(upbeat music) >> Hey everyone. Welcome back to "The Cube"'s live coverage of "Women in Data Science 2023". As every year we are here live at Stanford University, profiling some amazing women and men in the fields of data science. I have my co-host for this segment is Hannah Freitag. Hannah is from Stanford's Data Journalism program, really interesting, check it out. We're very pleased to welcome our first guest of the day fresh from the keynote stage, Gayatree Ganu, the VP of Data Science at Meta. Gayatree, It's great to have you on the program. >> Likewise, Thank you for having me. >> So you have a PhD in Computer Science. You shared some really cool stuff. Everyone knows Facebook, everyone uses it. I think my mom might be one of the biggest users (Gayatree laughs) and she's probably watching right now. People don't realize there's so much data behind that and data that drives decisions that we engage with. But talk to me a little bit about you first, PhD in Computer Science, were you always, were you like a STEM kid? Little Gayatree, little STEM, >> Yeah, I was a STEM kid. I grew up in Mumbai, India. My parents are actually pharmacists, so they were not like math or stats or anything like that, but I was always a STEM kid. I don't know, I think it, I think I was in sixth grade when we got our first personal computer and I obviously used it as a Pacman playing machine. >> Oh, that's okay. (all laugh) >> But I was so good at, and I, I honestly believe I think being good at games kind of got me more familiar and comfortable with computers. Yeah. I think I always liked computers, I, yeah. >> And so now you lead, I'm looking at my notes here, the Engagement Ecosystem and Monetization Data Science teams at Facebook, Meta. Talk about those, what are the missions of those teams and how does it impact the everyday user? >> Yeah, so the engagement is basically users coming back to our platform more, there's, no better way for users to tell us that they are finding value on the things that we are doing on Facebook, Instagram, WhatsApp, all the other products than coming back to our platform more. So the Engagement Ecosystem team is looking at trends, looking at where there are needs, looking at how users are changing their behaviors, and you know, helping build strategy for the long term, using that data knowledge. Monetization is very different. You know, obviously the top, top apex goal is have a sustainable business so that we can continue building products for our users. And so, but you know, I said this in my keynote today, it's not about making money, our mission statement is not, you know, maximize as much money as you can make. It's about building a meaningful connection between businesses, customers, users, and, you know especially in these last two or three funky, post-pandemic years, it's been such a big, an important thing to do for small businesses all over all, all around the world for users to find like goods and services and products that they care about and that they can connect to. So, you know, there is truly an connection between my engagement world and the monetization world. And you know, it's not very clear always till you go in to, like, you peel the layers. Everything we do in the ads world is also always first with users as our, you know, guiding principle. >> Yeah, you mentioned how you supported especially small businesses also during the pandemic. You touched a bit upon it in the keynote speech. Can you tell our audience what were like special or certain specific programs you implemented to support especially small businesses during these times? >> Yeah, so there are 200 million businesses on our platform. A lot of them small businesses, 10 million of them run ads. So there is a large number of like businesses on our platform who, you know use the power of social media to connect to the customers that matter to them, to like you, you know use the free products that we built. In the post-pandemic years, we built a lot of stuff very quickly when Covid first hit for business to get the word out, right? Like, they had to announce when special shopping hours existed for at-risk populations, or when certain goods and services were available versus not. We had grants, there's $100 million grant that we gave out to small businesses. Users could show sort of, you know show their support with a bunch of campaigns that we ran, and of course we continue running ads. Our ads are very effective, I guess, and, you know getting a very reliable connection with from the customer to the business. And so, you know, we've run all these studies. We support, I talked about two examples today. One of them is the largest black-owned, woman black-owned wine company, and how they needed to move to an online program and, you know, we gave them a grant, and supported them through their ads campaign and, you know, they saw 60% lift in purchases, or something like that. So, a lot of good stories, small stories, you know, on a scale of 200 million, that really sort of made me feel proud about the work we do. And you know, now more than ever before, I think people can connect so directly with businesses. You can WhatsApp them, I come from India, every business is on WhatsApp. And you can, you know, WhatsApp them, you can send them Facebook messages, and you can build this like direct connection with things that matter to you. >> We have this expectation that we can be connected anywhere. I was just at Mobile World Congress for MWC last week, where, obviously talking about connectivity. We want to be able to do any transaction, whether it's post on Facebook or call an Uber, or watch on Netflix if you're on the road, we expect that we're going to be connected. >> Yeah. >> And what we, I think a lot of us don't realize I mean, those of us in tech do, but how much data science is a facilitator of all of those interactions. >> Yeah! >> As we, Gayatree, as we talk about, like, any business, whether it is the black women-owned wine business, >> Yeah. >> great business, or a a grocer or a car dealer, everybody has to become data-driven. >> Yes. >> Because the consumer has the expectation. >> Yes. >> Talk about data science as a facilitator of just pretty much everything we are doing and conducting in our daily lives. >> Yeah, I think that's a great question. I think data science as a field wasn't really defined like maybe 15 years ago, right? So this is all in our lifetimes that we are seeing this. Even in data science today, People come from so many different backgrounds and bring their own expertise here. And I think we, you know, this conference, all of us get to define what that means and how we can bring data to do good in the world. Everything you do, as you said, there is a lot of data. Facebook has a lot of data, Meta has a lot of data, and how do we responsibly use this data? How do we use this data to make sure that we're, you know representing all diversity? You know, minorities? Like machine learning algorithms don't do well with small data, they do well with big data, but the small data matters. And how do you like, you know, bring that into algorithms? Yeah, so everything we do at Meta is very, very data-driven. I feel proud about that, to be honest, because while data gets a bad rap sometimes, having no data and making decisions in the blind is just the absolute worst thing you can do. And so, you know, we, the job as a data scientist at Facebook is to make sure that we use this data, use this responsibly, make sure that we are representing every aspect of the, you know, 3 billion users who come to our platform. Yeah, data serves all the products that we build here. >> The responsibility factor is, is huge. You know, we can't talk about AI without talking about ethics. One of the things that I was talking with Hannah and our other co-host, Tracy, about during our opening is something I just learned over the weekend. And that is that the CTO of ChatGPT is a woman. (Gayatree laughs) I didn't know that. And I thought, why isn't she getting more awareness? There's a lot of conversations with their CEO. >> Yeah. >> Everyone's using it, playing around with it. I actually asked it yesterday, "What's hot in Data Science?" (all laugh) I was like, should I have asked that to let itself in, what's hot? (Gayatree laughs) But it, I thought that was phenomenal, and we need to be talking about this more. >> Yeah. >> This is something that they're likening to the launch of the iPhone, which has transformed our lives. >> I know, it is. >> ChatGPT, and its chief technologist is a female, how great is that? >> And I don't know whether you, I don't know the stats around this, but I think CTO is even less, it's even more rare to have a woman there, like you have women CEOs because I mean, we are building upon years and years of women not choosing technical fields and not choosing STEM, and it's going to take some time, but yeah, yeah, she's a woman. Isn't it amazing? It's wonderful. >> Yes, there was a great, there's a great "Fast Company" article on her that I was looking at yesterday and I just thought, we need to do what we can to help spread, Mira Murati is her name, because what she's doing is, one of the biggest technological breakthroughs we may ever see in our lifetime. It gives me goosebumps just thinking about it. (Gayatree laughs) I also wanted to share some stats, oh, sorry, go ahead, Hannah. >> Yeah, I was going to follow up on the thing that you mentioned that we had many years with like not enough women choosing a career path in STEM and that we have to overcome this trend. What are some, like what is some advice you have like as the Vice-President Data Science? Like what can we do to make this feel more, you know, approachable and >> Yeah. >> accessible for women? >> Yeah, I, there's so much that we have done already and you know, want to continue, keep doing. Of course conferences like these were, you know and I think there are high school students here there are students from my Alma Mater's undergrad year. It's amazing to like get all these women together to get them to see what success could look like. >> Yeah. >> What being a woman leader in this space could look like. So that's, you know, that's one, at Meta I lead recruiting at Meta and we've done a bunch to sort of open up the thinking around data science and technical jobs for women. Simple things like what you write in your job description. I don't know whether you know this, or this is a story you've heard before, when you see, when you have a job description and there are like 10 things that you need to, you know be good at to apply to this job, a woman sees those 10 and says, okay, I don't meet the qualifications of one of them and she doesn't apply. And a man sees one that he meets the qualifications to and he applies. And so, you know, there's small things you can do, and just how you write your job description, what goals you set for diversity and inclusion for your own organization. We have goals, Facebook's always been pretty up there in like, you know, speaking out for diversity and Sheryl Sandberg has been our Chief Business Officer for a very long time and she's been, like, amazing at like pushing from more women. So yeah, every step of the way, I think, we made a lot of progress, to be honest. I do think women choose STEM fields a lot more than they did. When I did my Computer Science I was often one of one or two women in the Computer Science class. It takes some time to, for it to percolate all the way to like having more CTOs and CEOs, >> Yeah. >> but it's going to happen in our lifetime, and you know, three of us know this, women are going to rule the world, and it (laughs) >> Drop the mic, girl! >> And it's going to happen in our lifetime, so I'm excited about it. >> And we have responsibility in helping make that happen. You know, I'm curious, you were in STEM, you talked about Computer Science, being one of the only females. One of the things that the nadb.org data from 2022 showed, some good numbers, the number of women in technical roles is now 27.6%, I believe, so up from 25, it's up in '22, which is good, more hiring of women. >> Yeah. >> One of the biggest challenges is attrition. What keeps you motivated? >> Yeah. >> To stay what, where you are doing what you're doing, managing a family and helping to drive these experiences at Facebook that we all expect are just going to happen? >> Yeah, two things come to mind. It does take a village. You do need people around you. You know, I'm grateful for my husband. You talked about managing a family, I did the very Indian thing and my parents live with us, and they help take care of the kids. >> Right! (laughs) >> (laughs) My kids are young, six and four, and I definitely needed help over the last few years. It takes mentors, it takes other people that you look up to, who've gone through all of those same challenges and can, you know, advise you to sort of continue working in the field. I remember when my kid was born when he was six months old, I was considering quitting. And my husband's like, to be a good role model for your children, you need to continue working. Like, just being a mother is not enough. And so, you know, so that's one. You know, the village that you build around you your supporters, your mentors who keep encouraging you. Sheryl Sandberg said this to me in my second month at Facebook. She said that women drop out of technical fields, they become managers, they become sort of administrative more, in their nature of their work, and her advice was, "Don't do that, Don't stop the technical". And I think that's the other thing I'd say to a lot of women. Technical stuff is hard, but you know, keeping up with that and keeping sort of on top of it actually does help you in the long run. And it's definitely helped me in my career at Facebook. >> I think one of the things, and Hannah and I and Tracy talked about this in the open, and I think you'll agree with us, is the whole saying of you can't be what you can't see, and I like to way, "Well, you can be what you can see". That visibility, the great thing that WiDS did, of having you on the stage as a speaker this morning so people can understand, everyone, like I said, everyone knows Meta, >> Yeah. >> everyone uses Facebook. And so it's important to bring that connection, >> Yeah. >> of how data is driving the experiences, the fact that it's User First, but we need to be able to see women in positions, >> Yes. >> like you, especially with Sheryl stepping down moving on to something else, or people that are like YouTube influencers, that have no idea that the head of YouTube for a very long time, Susan Wojcicki is a woman. >> (laughs) Yes. Who pioneered streaming, and I mean how often do you are you on YouTube every day? >> Yep, every day. >> But we have to be able to see and and raise the profile of these women and learn from them and be inspired, >> Absolutely. >> to keep going and going. I like what I do, I'm making a difference here. >> Yeah, yeah, absolutely. >> And I can be the, the sponsor or the mentor for somebody down the road. >> Absolutely. >> Yeah, and then referring back to what we talked in the beginning, show that data science is so diverse and it doesn't mean if you're like in IT, you're like sitting in your dark room, >> Right. (laughs) >> coding all day, but you know, >> (laughs) Right! >> to show the different facets of this job and >> Right! >> make this appealing to women, >> Yeah. for sure. >> And I said this in my keynote too, you know, one of the things that helped me most is complimenting the data and the techniques and the algorithms with how you work with people, and you know, empathy and alignment building and leadership, strategic thinking. And I think honestly, I think women do a lot of this stuff really well. We know how to work with people and so, you know, I've seen this at Meta for sure, like, you know, all of these skills soft skills, as we call them, go a long way, and like, you know, doing the right things and having a lasting impact. And like I said, women are going to rule the world, you know, in our lifetimes. (laughs) >> Oh, I can't, I can't wait to see that happen. There's some interesting female candidates that are already throwing their hats in the ring for the next presidential election. >> Yes. >> So we'll have to see where that goes. But some of the things that are so interesting to me, here we are in California and Palo Alto, technically Stanford is its own zip code, I believe. And we're in California, we're freaking out because we've gotten so much rain, it's absolutely unprecedented. We need it, we had a massive drought, an extreme drought, technically, for many years. I've got friends that live up in Tahoe, I've been getting pictures this morning of windows that are >> (laughs) that are covered? >> Yes, actually, yes. (Gayatree laughs) That, where windows like second-story windows are covered in snow. >> Yeah. >> Climate change. >> Climate change. >> There's so much that data science is doing to power and power our understanding of climate change whether it's that, or police violence. >> Yeah. (all talk together) >> We had talk today on that it was amazing. >> Yes. So I want more people to know what data science is really facilitating, that impacts all of us, whether you're in a technical role or not. >> And data wins arguments. >> Yes, I love that! >> I said this is my slide today, like, you know, there's always going to be doubters and naysayers and I mean, but there's hard evidence, there's hard data like, yeah. In all of these fields, I mean the data that climate change, the data science that we have done in the environmental and climate change areas and medical, and you know, medicine professions just so much, so much more opportunity, and like, how much we can learn more about the world. >> Yeah. >> Yeah, it's a pretty exciting time to be a data scientist. >> I feel like, we're just scratching the surface. >> Yeah. >> With the potential and the global impact that we can make with data science. Gayatree, it's been so great having you on theCUBE, thank you. >> Right, >> Thank you so much, Gayatree. >> So much, I love, >> Thank you. >> I'm going to take Data WiD's arguments into my personal life. (Gayatree laughs) I was actually just, just a quick anecdote, funny story. I was listening to the radio this morning and there was a commercial from an insurance company and I guess the joke is, it's an argument between two spouses, and the the voiceover comes in and says, "Let's watch a replay". I'm like, if only they, then they got the data that helped the woman win the argument. (laughs) >> (laughs) I will warn you it doesn't always help with arguments I have with my husband. (laughs) >> Okay, I'm going to keep it in the middle of my mind. >> Yes! >> Gayatree, thank you so much. >> Thank you so much, >> for sharing, >> Thank you both for the opportunity. >> And being a great female that we can look up to, we really appreciate your insights >> Oh, likewise. >> and your time. >> Thank you. >> All right, for our guest, for Hannah Freitag, I'm Lisa Martin, live at Stanford University covering "Women in Data Science '23". Stick around, our next guest joins us in just a minute. (upbeat music) I have been in the software and technology industry for over 12 years now, so I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCUBE. (upbeat music) Being a host on theCUBE has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, "Hey, I'm really interested in this. I love talking with customers, gimme a shot, let me come into the studio and do an interview and see if we can work together". I think where I really impact theCUBE is being a female in technology. We interview a lot of females in tech, we do a lot of women in technology events and one of the things I.

(low-key music) >> The cube's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (uplifting electronic music) (crowd chattering in background) >> Welcome back to Spain, everybody. My name's Dave Vellante. I'm here with Dave Nicholson. We are live at the Fira in Barcelona, covering MWC23 day four. We've been talking about, you know, 5G all week. We're going to talk about it some more. Jillian Kaplan is here. She's the head of Global Telecom Thought Leadership at Dell Technologies, and we're pleased to have Meg Knauth, who's the Vice President for Digital Platform Engineering at T-Mobile. Ladies, welcome to theCUBE. Thanks for coming on. >> Thanks for having us. >> Yeah, thank you. >> All right, Meg, can you explain 5G and edge to folks that may not be familiar with it? Give us the 101 on 5G and edge. >> Sure, I'd be happy to. So, at T-Mobile, we want businesses to be able to focus on their business outcomes and not have to stress about network technology. So we're here to handle the networking behind the scenes for you to achieve your business goals. The main way to think about 5G is speed, reduced latency, and heightened security. And you can apply that to so many different business goals and objectives. You know, some of the use cases that get touted out the most are in the retail manufacturing sectors with sensors and with control of inventory and things of that nature. But it can be applied to pretty much any industry because who doesn't need more (chuckles) more speed and lower latency. >> Yeah. And reliability, right? >> Exactly. >> I mean, that's what you're going to have there. So it's not like it's necessarily going to- you know, you think about 5G and these private networks, right? I mean, it's not going to, oh, maybe it is going to eat into, there's a Venn there, I know, but it's not going to going to replace wireless, right? I mean, it's new use cases. >> Yeah. >> Maybe you could talk about that a little bit. >> Yeah, they definitely coexist, right? And Meg touched a little bit on like all the use cases that are coming to be, but as we look at 5G, it's really the- we call it like the Enterprise G, right? It's where the enterprise is going to be able to see changes in their business and the way that they do things. And for them, it's going to be about reducing costs and heightening ROI, and safety too, right? Like being able to automate manufacturing facilities where you don't have workers, like, you know, getting hit by various pieces of equipment and you can take them out of harm's way and put robots in their place. And having them really work in an autonomous situation is going to be super, super key. And 5G is just the, it's the backbone of all future technologies if you look at it. We have to have a network like that in order to build things like AI and ML, and we talk about VR and the Metaverse. You have to have a super reliable network that can handle the amount of devices that we're putting out today, right? So, extremely important. >> From T-Mobile's perspective, I mean we hear a lot about, oh, we spent a lot on CapEx, we know that. You know, trillion and a half over the next seven years, going into 5G infrastructure. We heard in the early keynotes at MWC, we heard the call to you know, tax the over the top vendors. We heard the OTT, Netflix shot back, they said, "Why don't you help us pay for the content that we're creating?" But, okay, so I get that, but telcos have a great business. Where's T-Mobile stand on future revenue opportunities? Are you looking to get more data and monetize that data? Are you looking to do things like partner with Dell to do, you know, 5G networks? Where are the opportunities for T-Mobile? >> I think it's more, as Jillian said, it's the opportunities for each business and it's unique to those businesses. So we're not in it just for ourselves. We're in it to help others achieve their business goals and to do more with all of the new capabilities that this network provides. >> Yeah, man, I like that answer because again, listening to some of the CEOs of the large telcos, it's like, hmm, what's in it for me as the customer or the business? I didn't hear enough of that. And at least in the early keynotes, I'm hearing it more, you know, as the show goes on. But I don't know, Dave, what do you think about what you've heard at the event? >> Well, I'm curious from T-Mobile's perspective, you know when a consumer thinks about 5G, we think of voice, text, and data. And if we think about the 5G network that you already have in place, I'm curious, if you can share this kind of information, what percentage of that's being utilized now? How much is available for the, you know, for the Enterprise G that we're talking about, and maybe, you know, in five years in the future, do you have like a projected mix of consumer use versus all of these back office, call them processes that a consumer's not aware of, but you know the factory floor being connected via 5G, that frontiers that emerges, where are we now and what are you looking towards? Does that make sense? Kind of the mixed question? >> Hand over the business plan! (all laugh) >> Yeah! Yeah, yeah, yeah. >> Yeah, I- >> I want numbers Meg, numbers! >> Wow. (Dave and Dave laugh) I'm probably actually not the right person to speak to that. But as you know, T-Mobile has the largest 5G network in North America, and we just say, bring it, right? Let's talk- >> So you got room, you got room for Jillian's stuff? >> Yeah, let's solve >> Well, we can build so many >> business problems together. >> private 5G networks, right? Like I would say like the opportunities are... There's not a limit, right? Because as we build out these private networks, right? We're not on a public network when we're talking about like connecting these massive factories or connecting like a retail store to you and your house to be able to basically continue to try on the clothes remotely, something like that. It's limitless and what we can build- >> So they're related, but they're not necessarily mutually exclusive in the sense that what you are doing in the factory example is going to interfere with my ability to get my data through T-mobile. >> No, no, I- >> These are separated. >> Yeah. Yeah. >> Okay. >> As we build out these private networks and these private facilities, and there are so many applications in the consumer space that haven't even been realized yet. Like, when we think about 4G, when 4G launched, there were no applications that needed 4G to run on our cell phones, right? But then the engineers got to work, right? And we ended up with Uber and Instagram stories and all these applications that require 4G to launch. And that's what's going to happen with 5G too, it's like, as the network continues to get built, in the consumer space as well as the enterprise space, there's going to be new applications realized on this is all the stuff that we can do with this amazing network and look how many more devices and look how much faster it is, and the lower latency and the higher bandwidth, and you know, what we can really build. And I think what we're seeing at this show compared to last year is this stuff actually in practice. There was a lot of talk last year, like about, oh, this is what we can build, but now we're building it. And I think that's really key to show that companies like T-Mobile can help the enterprise in this space with cooperation, right? Like, we're not just talking about it now, we're actually putting it into practice. >> So how does it work? If I put in a private network, what are you doing? You slice out a piece of the network and charge me for it and then I get that as part of my private network. How does it actually work for the customer? >> You want to take that one? >> So I was going to say, yeah, you can do a network slice. You can actually physically build a private network, right? It depends, there's so many different ways to engineer it. So I think you can do it either way, basically. >> We just, we don't want it to be scary, right? >> Yep. >> So it starts with having a conversation about the business challenges that you're facing and then backing it into the technology and letting the technology power those solutions. But we don't want it to be scary for people because there's so much buzz around 5G, around edge, and it can be overwhelming and you can feel like you need a PhD in engineering to have a conversation. And we just want to kind of simplify things and talk in your language, not in our language. We'll figure out the tech behind the scenes. Just tell us what problems we can solve together. >> And so many non-technical companies are having to transform, right? Like retail, like manufacturing, that haven't had to be tech companies before. But together with T-Mobile and Dell, we can help enable that and make it not scary like Meg said. >> Right, so you come into my factory, I say, okay, look around. I got all these people there, and they're making hoses and they're physically putting 'em together. And we go and we have to take a physical measurement as to, you know, is it right? And because if we don't do that, then we have to rework it. Okay, now that's a problem. Okay, can you help me digitize that business? I need a network to do that. I'm going to put in some robots to do that. This is, I mean, I'm making this up but this has got to be a common use case, right? >> Yeah. >> So how do you simplify that for the business owner? >> So we start with what we can provide, and then in some cases you need additional solution providers. You might need a robotics company, you might need a sensor company. But we have those contacts to bring that together for you so that you don't have to be the expert in all those things. >> And what do I do with all the data that I'm collecting? Because, you know, I'm not really a data expert. Maybe, you know, I'm good at putting hoses together, but what's the data layer look like here? (all laughing) >> It's a hose business! >> I know! >> Great business. >> Back to the hoses again. >> There's a lot of different things you can do with it, right? You can collect it in a database, you can send it up to a cloud, you can, you know, use an edge device. It depends how we build the network. >> Dave V.: Can you guys help me do that? Can you guys- >> Sure, yeah. >> Help me figure that out. Should I put it into cloud? Should I use this database or that data? What kind of skills do I need? >> And it depends on the size of the network, right? And the size of the business. Like, you know, there's very simple. You don't have to be a massive manufacturer in order to install this stuff. >> No, I'm asking small business questions. >> Yeah. >> Right, I might not have this giant IT team. I might not have somebody who knows how to do ETL and PBA. >> Exactly. And we can talk to you too about what data matters, right? And we can, together, talk about what data might be the most valuable to you. We can talk to you about how we use data. But again, simplifying it down and making it personal to your business. >> Your point about scary is interesting, because no one has mentioned that until you did in four days. Three? Four days. Somebody says, let's do a private 5G network. That sounds like you're offering, you know, it's like, "Hey, you know what we should do Dave? We'll build you a cruise ship." It's like, I don't need a cruise ship, I just want to go bass fishing. >> Right, right, right. >> But in fact, these things are scalable in the sense that it can be scaled down from the trillions of dollars of infrastructure investment. >> Yeah. >> Yeah. It needs to be focused on your outcome, right? And not on the tech. >> When I was at the Dell booth I saw this little private network, it was about this big. I'm like, how much is that? I want one of those. (all laugh) >> I'm not the right person to talk about that! >> The little black one? >> Yes. >> I wanted one of those, too! >> I saw it, it had a little case to carry it around. I'm like, that could fit in my business. >> Just take it with you. >> theCUBE could use that! (all laugh) >> Anything that could go in a pelican case, I want. >> It's true. Like, it's so incredibly important, like you said, to focus on outcomes, right? Not just tech for the sake of tech. What's the problem? Let's solve the problem together. And then you're getting the outcome you want. You'll know what data you need. If you know what the problem is, you're like, okay this is the data I need to know if this problem is solved or not. >> So it sounds like 2022 was the year of talking about it. 2023, I'm inferring is the year of seeing it. >> Yep. >> And 2024 is going to be the year of doing it? >> I think we're doing it now. >> We're doing it now. >> Yeah. >> Okay. >> Yeah, yeah. We're definitely doing it now. >> All right. >> I see a lot of this stuff being put into place and a lot more innovation and a lot more working together. And Meg mentioned working with other partners. No one's going to do this alone. You've got to like, you know, Dell especially, we're focused on open and making sure that, you know, we have the right software partners. We're bringing in smaller players, right? Like ISVs too, as well as like the big software guys. Incredibly, incredibly important. The sensor companies, whatever we need you've got to be able to solve your customer's issue, which in this case, we're looking to help the enterprise together to transform their space. And Dell knows a little bit about the enterprise, so. >> So if we are there in 2023, then I assume 2024 will be the year that each of your companies sets up a dedicated vertical to address the hose manufacturing market. (Meg laughing) >> Oh, the hose manufacturing market. >> Further segmentation is usually a hallmark of the maturity of an industry. >> I got a lead for you. >> Yeah, there you go. >> And that's one thing we've done at Dell, too. We've built like this use case directory to help the service providers understand what, not just say like, oh, you can help manufacturers. Yeah, but how, what are the use cases to do that? And we worked with a research firm to figure out, like, you know these are the most mature, these are the best ROIs. Like to really help hone in on exactly what we can deploy for 5G and edge solutions that make the most sense, not only for service providers, right, but also for the enterprises. >> Where do you guys want to see this partnership go? Give us the vision. >> To infinity and beyond. To 5G! (Meg laughing) To 5G and beyond. >> I love it. >> It's continuation. I love that we're partnering together. It's incredibly important to the future of the business. >> Good deal. >> To bring the strengths of both together. And like Jillian said, other partners in the ecosystem, it has to be approached from a partnership perspective, but focused on outcomes. >> Jillian: Yep. >> To 5G and beyond. I love it. >> To 5G and beyond. >> Folks, thanks for coming on theCUBE. >> Thanks for having us. >> Appreciate your insights. >> Thank you. >> All right. Dave Vellante for Dave Nicholson, keep it right there. You're watching theCUBE. Go to silliconANGLE.com. John Furrier is banging out all the news. theCUBE.net has all the videos. We're live at the Fira in Barcelona, MWC23. We'll be right back. (uplifting electronic music)

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. >> All right, welcome back to the Fira in Barcelona. This is Dave Vellante with Dave Nicholson. Day 4 of coverage MWC 23. We've been talking all week about the disaggregation of the telco networks, how telcos need to increase revenue how they're not going to let the over the top providers do it again. They want to charge Netflix, right? And Netflix is punching back. There maybe are better ways to do revenue acceleration. We're going to talk to that topic with Dave Trigg who's the Global Vice President of Telecom systems business at Dell Technologies. And Ken Burns, who's a global telecom partner, sales lead. Guys, good to see you. >> Good to see you. Great to be here. >> Dave, you heard my, you're welcome. You heard my intro. It's got to be better ways to, for the telcos to make money. How can they accelerate revenue beyond taxing Netflix? >> Yeah, well, well first of all, sort of the promise of 5G, and a lot of people talk about 5G as the enterprise G. Right? So the promise of 5G is to really help drive revenue enterprise use cases. And so, it's sort of the promise of the next generation of technology, but it's not easy to figure out how we monetize that. And so we think Dell has a pretty significant role to play. It's a CEO conversation for every telco and how they accelerate. And so it's an area we're investing heavily into three different areas for telcos. One is the IT space. Dell's done that forever. 90% of the companies leaning in on that. The other places network, network's more about cost takeout. And the third area where we're investing in is working with what we call their line of businesses, but it's really their business units, right? How can we sit down with them and really understand what services do they take to market? Where do they go? So, we're making significant investments. So one way they can do it is working with Dell and and we're making big investments 'cause in most Geos we have a fairly significant sales force. We've brought in an industry leader to help us put it together. And we're getting very focused on this space and, you know, looking forward to talking more about it. >> So Ken, you know, the space inside and out, we just had at AT&T on... >> Dave Trigg: Yep. >> And they were saying we have to be hypersensitive because of our platinum brand to the use of personal information. >> Ken: Yeah. >> So we're not going to go there yet. We're not going to go directly monetize, but yet I'm thinking well, Netflix knows what I'm watching and they're making recommendations and they're, and and that's how they make money. And so the, the telcos are, are shy about doing that for right reasons, but they want to make better offers. They want to put, put forth better bundles. You know, they don't, they don't want to spend all their time trying to figure that out and not being able to change when they need to change. So, so what is the answer? If they're not going to go toward that direct monetization of data? >> Ken: Yeah. >> How do they get there? >> So I, I joined Dell in- at the end of June and brought on, as David said, to, to build and lead this what we call the line of business strategy, right? And ultimately what it is is tying together Dell technology solutions and the best of breed of what the telecoms bring to bear to solve the business outcomes of our joint customers. And there's a few jewels inside of Dell. One of it is that we have 35,000 sellers out there all touching enterprise business customers. And we have a really good understanding of what those customer needs are and you know what their outcomes needs to be. The other jewel is we have a really good understanding of how to solve those business outcomes. Dell is an open company. We work with thousands of integrators, and we have a really good insight in terms of how to solve those business outcomes, right? And so in my conversations with the telecom companies when you talk about, you know combining the best assets of Dell with their capabilities and we're all talking to the same customers, right? And if we're giving them the same story on these solutions solving business outcomes it's a beautiful thing. It's a time to market. >> What's an example of a, of a, of a situation where you'll partner with telcos that's going to drive revenue for, for both of you and value for the customer? >> Yeah, great question. So we've been laser focused on four key areas, cyber, well, let me start off with connected laptops, cyber, private mobility, and edge. Right? Now, the last two are a little bit squishy, but I'll I'll get to that in a bit, right? Because ultimately I feel like with this 5G market, we could actually make the market. And the way that we've been positioning this is almost, almost on a journey for IOT. When we talk about laptops, right? Dell is the, is the number one company in the world to sell business laptops. Well, if we start selling connected laptops the telcos are starting to say, well, you know what? If all of those laptops get connected to my network, that's a ton of 5G activations, right? We have the used cases on why having a connected workforce makes sense, right? So we're sharing that with the telcos to not simply sell a laptop, but to sell the company on why it makes sense to have that connected workforce. >> Dave Vellante: Why does it make sense? It could change the end customer. >> Ken: Yeah. So, you know, I'm probably not the best to answer that one right? But, but ultimately, you know Dell is selling millions and millions of laptops out there. And, and again, the Verizon's, the AT&T's, the T-mobile's, they're seeing the opportunity that, you know, connecting those laptops, give those the 5G activations right? But Dave, you know, the way that we've been positioning this is it's not simply a laptop could be really a Trojan horse into this IOT journey. Because ultimately, if you sell a thousand laptops to an enterprise company and you're connecting a thousand of their employees, you're connecting people, right? And we can give the analytics around that, what they're using it for, you know, making sure that the security, the bios, all of that is up to date. So now that you're connecting their people you could open up the conversation to why don't we we connect your place and, you know, allowing the telecom companies to come in and educate customers and the Dell sales force on why a private 5G mobility network makes sense to connecting places. That's a great opportunity. When you connect the place, the next part of that journey is connecting things in that place. Robotics, sensors, et cetera, right? And, and so really, so we're on the journey of people, places, things. >> So they got the cyber angle angle in there, Dave. That, that's clear benefit. If you, you know, if you got all these bespoke laptops and they're all at different levels you're going to get, you know, you're going to get hacked anyway. >> Ken: That's right. >> You're going to get hacked worse. >> Yeah. I'm curious, as you go to market, do you see significant differences? You don't have to name any names, but I imagine that there are behemoths that could be laggards because essentially they feel like they're the toll booth and all they have to do is collect, keep collecting the tolls. Whereas some of the smaller, more nimble, more agile entities that you might deal with might be more receptive to this message. That seems to be the sort of way the circle of life are. Are you seeing that? Are you seeing the big ones? Are you seeing the, you know, the aircraft carriers realizing that we got to turn into the wind guys and if we don't start turning into the wind now we're going to be in trouble. >> So this conference has been absolutely fantastic allowing us to speak with, you know, probably 30 plus telecom operators around this strategy, right? And all of the big guys, they've invested hundreds of billions of dollars in their 5G network and they haven't really seen the ROI. So when we're coming into them with a story about how Dell can help monetize their 5G network I got to tell you they're pretty excited >> Dave Nicholson: So they're receptive? >> Oh my God. They are very receptive >> So that's the big question, right? I mean is, who's, is anybody ever going to make any money off of 5G? And Ken, you were saying that private mobility and edge are a little fuzzy but I think from a strategy standpoint I mean that is a potential gold mine. >> Yeah, but it, for, for lot of the telcos and most telcos it's a pretty significant shift in mentality, right? Cause they are used to selling sim cards to some degree and how many sim cards are they selling and how many, what other used cases? And really to get to the point where they understand the use case, 'cause to get into the enterprise to really get into what can they do to help power a enterprise business more wholly. They've got to understand the use case. They got to understand the more complete solution. You know, Dell's been doing that for years. And that's where we can bring our Salesforce, our capabilities, our understanding of the customer. 'cause even your original question around AT&T and trying to understand the data, that's just really a how do you get better understanding of your customer, right? >> Right. Absolutely. >> And, and combined we're better together 'cause we bring a more complete picture of understanding our customers and then how can we help them understand what the edge is. Cause nobody's ever bought an Edge, right? They're buying an Edge to get a business outcome. You know, back in the day, nobody ever bought a data lake, right? Like, you know, they're buying an outcome. They want to use, use that data lake or they want to use the edge to deliver something. They want to use 5G. And 5G has very real capabilities. It's got intrinsic security, which, you know a lot of the wifi doesn't. It's got guaranteed on time, you know, for areas where you can't lose connectivity: autonomous vehicles, et cetera. So it's got very real capabilities that helps deliver that outcome. But you got to be able to translate that into the en- enterprise language to help them solve a problem. And that's where we think we need the help of the telcos. I think the telcos we can help them as well and, and really go drive that outcome. >> So Dell's bringing its go to market expertise and its technology. The telcos obviously have the the connectivity piece and what they do. There's no overlap in terms of the... >> Yeah. >> The, the equipment and the software that you're selling. I mean, they're going to, they're going to take your equipment and create new networks. Beautiful. And, and it's interesting you, like, you think about how Dell has transformed prior to EMC, Dell was, you know, PC maker with a subpar enterprise business, right? Kind of a wannabe enterprise business. Sorry Dell, it's the truth. And then EMC was largely, you know, a company sold storage boxes, but you owned VMware and then brought those two together. Now all of a sudden you had Dell powerhouse leader and Michael Dell, you had VMware incredibly strategic and important and it got EMC with amazing go to market. All of a sudden this Dell, Dell technologies became incredibly attractive to CIOs, C-level executives, board level. And you've come out of that transition VMware's now a separate company, right? And now, but now you have these relationships and you got the shops to be able to go into these edge locations at companies And actually go partner with the telcos. And you got a very compelling value proposition. >> Well, it's been interesting as in, in this show, again most telcos think of Dell as a server provider, you know? Important, but not overly strategic in their journey. But as we've started to invest in this business we've started to invest in things like automation. We've brought together things in our Infra Blocks and then we help them develop revenue. We're not only helping 'em take costs out of their network we're not helping 'em take risk out of deploying that network. We're helping them accelerate the deployment of that network. And then we're helping 'em drive revenue. We are having, you know, they're starting to see us in a new light. Not done yet, but, you know, you can start to see, one, how they're looking at Dell and two, and then how we can go to market. And you know, a big part of that is helping 'em drive and generate revenue. >> Yeah. Well, as, as a, as a former EMC person myself, >> Yeah? >> I will assert that that strategic DNA was injected into Dell by the acquisition of, of EMC. And I'm sticking... >> I won't say that. Okay I'll believe you on that. >> I'm sticking with the story. And it makes sense when you think about moving up market, that's the natural thing. What's, what's what's nearly impossible is to say, we sell semi-trucks but we want to get into the personal pickup truck market. That's that, that doesn't work. Going the other way works. >> Dave Trigg: Yeah. >> Now, now back to the conversation that you had with, with, with AT&T. I'm not buying this whole, no offense to AT&T, but I'm not buying this whole story that, you know, oh we're concerned about our branded customer data. That sounds like someone who's a little bit too comfortable with their existing revenue stream. If I'm out there, I want to be out partnering with folks who are truly aggressive about, about coming up with the next cool thing. You guys are talking about being connected in a laptop. Someone would say, well I got wifi. No, no, no. I'm thinking I want to sim in my laptop cause I don't want to screw around with wifi. Okay, fine. If I know I'm going to be somewhere with excellent wifi connectivity, great. But most of the time it's not excellent. >> That's right. >> So the idea that I could maybe hit F2 and have it switch over to my sim and know that anywhere that I've got coverage, I have high speed connections. Just the convenience of that. >> Ken: Absolutely. >> I'd pay extra for that as an end user consumer. >> Absolutely. >> And I pay for the service. >> Like I tell you, if it interests AT&T I think it's more not, they ask, they're comfortable. They don't know how to monetize that data. Now, of course, AT&T has a media >> Dave Nicholson: Business necessity is the mother of invention. If they don't see the necessity then they're not going to think about it. >> It's a mentality shift. Yes, but, but when you start talking about private mobility and edge, there's there's no concern about personal information there. You're going in with basically a business transformation. Hey, your, your business is, is not, not digital. It's not automated. Now we're going to automate that and digitize that. It's like the, the Dell booth with the beer guys. >> Right. >> You saw that, right? >> I mean that's, I mean that's a simple application. Yeah, a perfect example of how you network and use this technology. >> I mean, how many non-digital businesses are that that need to go digital? >> Dave Nicholson: Like, hundred percent of them. >> Everyone. >> Dave Nicholson: Pretty much. >> Yeah. And this, and this jewel that we have inside of Dell our global industries group, right, where we're investing really heavily in terms of what is the manufacturing industry looking for retail, finance, et cetera. So we have a CTO that came in, that it would be the CTO of manufacturing that gives us a really good opportunity to go to at AT&T or to Verizon or any telco out there, right? To, to say, these are the outcomes. There's Dell technology already in place. How do we connect it to your network? How do we leverage your assets, your manager professional services to provide a richer experience? So it's, there's, you said before Dave, there's really no overlap between Dell and, and our telecom partners. >> You guys making some serious investments here. I mean I, I've been, I was been critical over the years of, hey, you can't just take an X86 block, put a name on it that says edge something and throw it over the fence because that's what you were doing. >> Dave Trigg: And we would agree. >> Yeah. Right. But, of course, but that's all you had at the time. And so you put some... >> We may not have agreed then, but we would agree. >> You bought, brought some people in, you know, like Ken, who really know the business. You brought people into the technical side and you can really see it happening. It's not going to happen overnight. You know, I mean, you know if I were an investor in Dell, I'd be like, okay when are you going to start making money at this business? I'd be like, be patient. You know, it's going to take some time but look at the TAM. >> Yep. >> You know, you guys do a good, good TAM. Tennis is a pro at this stuff. >> We've been at, we've been at this two, three years and we're just now coming with some real material products. You've seen our server line really start to get more purpose-built, really start to get in there as we've started to put out some software that allows for quicker automation, quicker deployments. We have some telcos that are using it to deploy at 10,000 locations. They're literally turning up thousands of locations a week. And so yeah, we're starting to put out some real capability. Got a long way to go. A lot of exciting things on the roadmap. But to your point, it doesn't, you know the ship doesn't turn overnight, you know. >> It could be a really meaningful portion of Dell's business. I'm, I'm excited for the day that Tom Sweet starts reporting on it. Here's our telco business. Yeah. The telco business. But that's not going to happen overnight. But you know, Dell's pretty good at things like ROI. And so you guys do a lot of planning a lot of TAM analysis, a lot of technical analysis, bringing the ecosystem together. That's what this business needs. I, I just don't, it's, it feels unstoppable. You know, you're at this show everybody recognizes the need to open up. Some telcos are moving faster than others. The ones that move faster are going to disrupt. They're going to probably make some mistakes, you know but they're going to get there first. >> Well we've, we've seen the disruptors are making some mistakes and are kind of re- they're already at the phase where they're reevaluating, you know, their approach. Which is great. You know, you, you learn and adjust. You know, you run into a wall, you, you make a turn. And the interesting thing, one of the biggest learnings I've taken out of the show is talking to a bunch of the telcos that are a little bit more of the laggards. They're like, Nope, we, we don't believe in open. We don't think we can do it. We don't have the skillset. They're maybe in a geo that it's hard to find the skillset. As they've been talking to us, and we've been talking about, there's almost a glimmer of hope. They're not convinced yet, but they're like, well wait, maybe we can do this. Maybe open, you know, does give us choice. Maybe it can help us accelerate revenue. So it's been interesting to see a little bit of the, just a little bit, but a little bit of that shift. >> We all remember at 2010, 2011, you talked to banks and financial services companies about, the heck, the Cloud is happening, the Cloud's going to take over the world. We're never going to go into the Cloud. Now they're the biggest, you know Capital One's launching Cloud businesses, Western Union, I mean, they're all in the cloud, right? I mean, it's the same thing's going to happen here. Might, it might take a different pattern. Maybe it takes a little longer, but it's, it's it's a fate are completely >> I was in high school then, so I don't remember all that. >> Sorry, Dave. >> Wow, that was a low blow, like you know? >> But, but the, but the one thing that is for sure there's money to be made convincing people to get off of the backs of the dinosaurs they're riding. >> Dave Vellante: That's right. >> And also, the other thing that's a certainty is that it's not easy. And because it's not easy, there's opportunity there. So I know, I know it's, it, it, it, it, it all sounds great to talk about the the wonderful vision of the future, but I know how hard the the road is that you have to go down to get people, especially if you're comfortable with the revenue stream, if you're comfortable running the plumbing. If you're so comfortable that you can get up on stage and say, I want more money from you to pump your con- your content across my network. I love the Netflix retort, right Dave? >> Yeah, totally Dave. And, but the, the other thing is, telco's a great business. It's, they got monopolies that print money. So... >> Dave Nicholson: It's rational. It's rational. I understand. >> There's less of an incentive to move but what's going to be the incentive is guys like Dish Network coming in saying, we're going to, we're going to disrupt, we're going to build new apps. >> That's right. >> Yeah. >> Well and it's, you know, revenue acceleration, the board level, the CEO level know that they have to, you know, do things different. But to your point, it's just hard, and there's so much gravity there. There's hundreds of years literally of gravity of how they've operated their business. To your point, a lot of them, you know, lot- most of 'em were regulated and most Geos around the world at one point, right? They were government owned or government regulated entities. It's, it's a big ship to turn and it's really hard. We're not claiming we can help them turn the ship overnight but we think we can help evolve them. We think we can go along with the journey and we do think we are better together. >> IT the network and the line of business. Love the strategy. Guys, thanks so much for coming in theCUBE. >> Thank you so much. >> Thank you. >> All right, for Dave, Nicholson, Dave Vellante here, John Furrier is in our Palo Alto studio banging out all the news, keep it right there. TheCUBE's coverage of MWC 23. We'll be right back.

(gentle music) >> Narrator: "TheCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hello everybody, welcome back to day four of "theCUBE's" coverage of MWC '23. We're here live at the Fira in Barcelona. Wall-to-wall coverage, John Furrier is in our Palo Alto studio, banging out all the news. Really, the whole week we've been talking about the disaggregation of the telco network, the new opportunities in telco. We're really excited to have AT&T and Snowflake here. Dave Whittington is the AVP, at the Chief Data Office at AT&T. Roddy Tranum is the Assistant Vice President, for Channel Performance Data and Tools at AT&T. And Phil Kippen, the Global Head Of Industry-Telecom at Snowflake, Snowflake's new telecom business. Snowflake just announced earnings last night. Typical Scarpelli, they beat earnings, very conservative guidance, stocks down today, but we like Snowflake long term, they're on that path to 10 billion. Guys, welcome to "theCUBE." Thanks so much >> Phil: Thank you. >> for coming on. >> Dave and Roddy: Thanks Dave. >> Dave, let's start with you. The data culture inside of telco, We've had this, we've been talking all week about this monolithic system. Super reliable. You guys did a great job during the pandemic. Everything shifting to landlines. We didn't even notice, you guys didn't miss a beat. Saved us. But the data culture's changing inside telco. Explain that. >> Well, absolutely. So, first of all IoT and edge processing is bringing forth new and exciting opportunities all the time. So, we're bridging the world between a lot of the OSS stuff that we can do with edge processing. But bringing that back, and now we're talking about working, and I would say traditionally, we talk data warehouse. Data warehouse and big data are now becoming a single mesh, all right? And the use cases and the way you can use those, especially I'm taking that edge data and bringing it back over, now I'm running AI and ML models on it, and I'm pushing back to the edge, and I'm combining that with my relational data. So that mesh there is making all the difference. We're getting new use cases that we can do with that. And it's just, and the volume of data is immense. >> Now, I love ChatGPT, but I'm hoping your data models are more accurate than ChatGPT. I never know. Sometimes it's really good, sometimes it's really bad. But enterprise, you got to be clean with your AI, don't you? >> Not only you have to be clean, you have to monitor it for bias and be ethical about it. We're really good about that. First of all with AT&T, our brand is Platinum. We take care of that. So, we may not be as cutting-edge risk takers as others, but when we go to market with an AI or an ML or a product, it's solid. >> Well hey, as telcos go, you guys are leaning into the Cloud. So I mean, that's a good starting point. Roddy, explain your role. You got an interesting title, Channel Performance Data and Tools, what's that all about? >> So literally anything with our consumer, retail, concenters' channels, all of our channels, from a data perspective and metrics perspective, what it takes to run reps, agents, all the way to leadership levels, scorecards, how you rank in the business, how you're driving the business, from sales, service, customer experience, all that data infrastructure with our great partners on the CDO side, as well as Snowflake, that comes from my team. >> And that's traditionally been done in a, I don't mean the pejorative, but we're talking about legacy, monolithic, sort of data warehouse technologies. >> Absolutely. >> We have a love-hate relationship with them. It's what we had. It's what we used, right? And now that's evolving. And you guys are leaning into the Cloud. >> Dramatic evolution. And what Snowflake's enabled for us is impeccable. We've talked about having, people have dreamed of one data warehouse for the longest time and everything in one system. Really, this is the only way that becomes a reality. The more you get in Snowflake, we can have golden source data, and instead of duplicating that 50 times across AT&T, it's in one place, we just share it, everybody leverages it, and now it's not duplicated, and the process efficiency is just incredible. >> But it really hinges on that separation of storage and compute. And we talk about the monolithic warehouse, and one of the nightmares I've lived with, is having a monolithic warehouse. And let's just go with some of my primary, traditional customers, sales, marketing and finance. They are leveraging BSS OSS data all the time. For me to coordinate a deployment, I have to make sure that each one of these units can take an outage, if it's going to be a long deployment. With the separation of storage, compute, they own their own compute cluster. So I can move faster for these people. 'Cause if finance, I can implement his code without impacting finance or marketing. This brings in CI/CD to more reality. It brings us faster to market with more features. So if he wants to implement a new comp plan for the field reps, or we're reacting to the marketplace, where one of our competitors has done something, we can do that in days, versus waiting weeks or months. >> And we've reported on this a lot. This is the brilliance of Snowflake's founders, that whole separation >> Yep. >> from compute and data. I like Dave, that you're starting with sort of the business flexibility, 'cause there's a cost element of this too. You can dial down, you can turn off compute, and then of course the whole world said, "Hey, that's a good idea." And a VC started throwing money at Amazon, but Redshift said, "Oh, we can do that too, sort of, can't turn off the compute." But I want to ask you Phil, so, >> Sure. >> it looks from my vantage point, like you're taking your Data Cloud message which was originally separate compute from storage simplification, now data sharing, automated governance, security, ultimately the marketplace. >> Phil: Right. >> Taking that same model, break down the silos into telecom, right? It's that same, >> Mm-hmm. >> sorry to use the term playbook, Frank Slootman tells me he doesn't use playbooks, but he's not a pattern matcher, but he's a situational CEO, he says. But the situation in telco calls for that type of strategy. So explain what you guys are doing in telco. >> I think there's, so, what we're launching, we launched last week, and it really was three components, right? So we had our platform as you mentioned, >> Dave: Mm-hmm. >> and that platform is being utilized by a number of different companies today. We also are adding, for telecom very specifically, we're adding capabilities in marketplace, so that service providers can not only use some of the data and apps that are in marketplace, but as well service providers can go and sell applications or sell data that they had built. And then as well, we're adding our ecosystem, it's telecom-specific. So, we're bringing partners in, technology partners, and consulting and services partners, that are very much focused on telecoms and what they do internally, but also helping them monetize new services. >> Okay, so it's not just sort of generic Snowflake into telco? You have specific value there. >> We're purposing the platform specifically for- >> Are you a telco guy? >> I am. You are, okay. >> Total telco guy absolutely. >> So there you go. You see that Snowflake is actually an interesting organizational structure, 'cause you're going after verticals, which is kind of rare for a company of your sort of inventory, I'll say, >> Absolutely. >> I don't mean that as a negative. (Dave laughs) So Dave, take us through the data journey at AT&T. It's a long history. You don't have to go back to the 1800s, but- (Dave laughs) >> Thank you for pointing out, we're a 149-year-old company. So, Jesse James was one of the original customers, (Dave laughs) and we have no longer got his data. So, I'll go back. I've been 17 years singular AT&T, and I've watched it through the whole journey of, where the monolithics were growing, when the consolidation of small, wireless carriers, and we went through that boom. And then we've gone through mergers and acquisitions. But, Hadoop came out, and it was going to solve all world hunger. And we had all the aspects of, we're going to monetize and do AI and ML, and some of the things we learned with Hadoop was, we had this monolithic warehouse, we had this file-based-structured Hadoop, but we really didn't know how to bring this all together. And we were bringing items over to the relational, and we were taking the relational and bringing it over to the warehouse, and trying to, and it was a struggle. Let's just go there. And I don't think we were the only company to struggle with that, but we learned a lot. And so now as tech is finally emerging, with the cloud, companies like Snowflake, and others that can handle that, where we can create, we were discussing earlier, but it becomes more of a conducive mesh that's interoperable. So now we're able to simplify that environment. And the cloud is a big thing on that. 'Cause you could not do this on-prem with on-prem technologies. It would be just too cost prohibitive, and too heavy of lifting, going back and forth, and managing the data. The simplicity the cloud brings with a smaller set of tools, and I'll say in the data space specifically, really allows us, maybe not a single instance of data for all use cases, but a greatly reduced ecosystem. And when you simplify your ecosystem, you simplify speed to market and data management. >> So I'm going to ask you, I know it's kind of internal organizational plumbing, but it'll inform my next question. So, Dave, you're with the Chief Data Office, and Roddy, you're kind of, you all serve in the business, but you're really serving the, you're closer to those guys, they're banging on your door for- >> Absolutely. I try to keep the 130,000 users who may or may not have issues sometimes with our data and metrics, away from Dave. And he just gets a call from me. >> And he only calls when he has a problem. He's never wished me happy birthday. (Dave and Phil laugh) >> So the reason I asked that is because, you describe Dave, some of the Hadoop days, and again love-hate with that, but we had hyper-specialized roles. We still do. You've got data engineers, data scientists, data analysts, and you've got this sort of this pipeline, and it had to be this sequential pipeline. I know Snowflake and others have come to simplify that. My question to you is, how is that those roles, how are those roles changing? How is data getting closer to the business? Everybody talks about democratizing business. Are you doing that? What's a real use example? >> From our perspective, those roles, a lot of those roles on my team for years, because we're all about efficiency, >> Dave: Mm-hmm. >> we cut across those areas, and always have cut across those areas. So now we're into a space where things have been simplified, data processes and copying, we've gone from 40 data processes down to five steps now. We've gone from five steps to one step. We've gone from days, now take hours, hours to minutes, minutes to seconds. Literally we're seeing that time in and time out with Snowflake. So these resources that have spent all their time on data engineering and moving data around, are now freed up more on what they have skills for and always have, the data analytics area of the business, and driving the business forward, and new metrics and new analysis. That's some of the great operational value that we've seen here. As this simplification happens, it frees up brain power. >> So, you're pumping data from the OSS, the BSS, the OKRs everywhere >> Everywhere. >> into Snowflake? >> Scheduling systems, you name it. If you can think of what drives our retail and centers and online, all that data, scheduling system, chat data, call center data, call detail data, all of that enters into this common infrastructure to manage the business on a day in and day out basis. >> How are the roles and the skill sets changing? 'Cause you're doing a lot less ETL, you're doing a lot less moving of data around. There were guys that were probably really good at that. I used to joke in the, when I was in the storage world, like if your job is bandaging lungs, you need to look for a new job, right? So, and they did and people move on. So, are you able to sort of redeploy those assets, and those people, those human resources? >> These folks are highly skilled. And we were talking about earlier, SQL hasn't gone away. Relational databases are not going away. And that's one thing that's made this migration excellent, they're just transitioning their skills. Experts in legacy systems are now rapidly becoming experts on the Snowflake side. And it has not been that hard a transition. There are certainly nuances, things that don't operate as well in the cloud environment that we have to learn and optimize. But we're making that transition. >> Dave: So just, >> Please. >> within the Chief Data Office we have a couple of missions, and Roddy is a great partner and an example of how it works. We try to bring the data for democratization, so that we have one interface, now hopefully know we just have a logical connection back to these Snowflake instances that we connect. But we're providing that governance and cleansing, and if there's a business rule at the enterprise level, we provide it. But the goal at CDO is to make sure that business units like Roddy or marketing or finance, that they can come to a platform that's reliable, robust, and self-service. I don't want to be in his way. So I feel like I'm providing a sub-level of platform, that he can come to and anybody can come to, and utilize, that they're not having to go back and undo what's in Salesforce, or ServiceNow, or in our billers. So, I'm sort of that layer. And then making sure that that ecosystem is robust enough for him to use. >> And that self-service infrastructure is predominantly through the Azure Cloud, correct? >> Dave: Absolutely. >> And you work on other clouds, but it's predominantly through Azure? >> We're predominantly in Azure, yeah. >> Dave: That's the first-party citizen? >> Yeah. >> Okay, I like to think in terms sometimes of data products, and I know you've mentioned upfront, you're Gold standard or Platinum standard, you're very careful about personal information. >> Dave: Yeah. >> So you're not trying to sell, I'm an AT&T customer, you're not trying to sell my data, and make money off of my data. So the value prop and the business case for Snowflake is it's simpler. You do things faster, you're in the cloud, lower cost, et cetera. But I presume you're also in the business, AT&T, of making offers and creating packages for customers. I look at those as data products, 'cause it's not a, I mean, yeah, there's a physical phone, but there's data products behind it. So- >> It ultimately is, but not everybody always sees it that way. Data reporting often can be an afterthought. And we're making it more on the forefront now. >> Yeah, so I like to think in terms of data products, I mean even if the financial services business, it's a data business. So, if we can think about that sort of metaphor, do you see yourselves as data product builders? Do you have that, do you think about building products in that regard? >> Within the Chief Data Office, we have a data product team, >> Mm-hmm. >> and by the way, I wouldn't be disingenuous if I said, oh, we're very mature in this, but no, it's where we're going, and it's somewhat of a journey, but I've got a peer, and their whole job is to go from, especially as we migrate from cloud, if Roddy or some other group was using tables three, four and five and joining them together, it's like, "Well look, this is an offer for data product, so let's combine these and put it up in the cloud, and here's the offer data set product, or here's the opportunity data product," and it's a journey. We're on the way, but we have dedicated staff and time to do this. >> I think one of the hardest parts about that is the organizational aspects of it. Like who owns the data now, right? It used to be owned by the techies, and increasingly the business lines want to have access, you're providing self-service. So there's a discussion about, "Okay, what is a data product? Who's responsible for that data product? Is it in my P&L or your P&L? Somebody's got to sign up for that number." So, it sounds like those discussions are taking place. >> They are. And, we feel like we're more the, and CDO at least, we feel more, we're like the guardians, and the shepherds, but not the owners. I mean, we have a role in it all, but he owns his metrics. >> Yeah, and even from our perspective, we see ourselves as an enabler of making whatever AT&T wants to make happen in terms of the key products and officers' trade-in offers, trade-in programs, all that requires this data infrastructure, and managing reps and agents, and what they do from a channel performance perspective. We still ourselves see ourselves as key enablers of that. And we've got to be flexible, and respond quickly to the business. >> I always had empathy for the data engineer, and he or she had to service all these different lines of business with no business context. >> Yeah. >> Like the business knows good data from bad data, and then they just pound that poor individual, and they're like, "Okay, I'm doing my best. It's just ones and zeros to me." So, it sounds like that's, you're on that path. >> Yeah absolutely, and I think, we do have refined, getting more and more refined owners of, since Snowflake enables these golden source data, everybody sees me and my organization, channel performance data, go to Roddy's team, we have a great team, and we go to Dave in terms of making it all happen from a data infrastructure perspective. So we, do have a lot more refined, "This is where you go for the golden source, this is where it is, this is who owns it. If you want to launch this product and services, and you want to manage reps with it, that's the place you-" >> It's a strong story. So Chief Data Office doesn't own the data per se, but it's your responsibility to provide the self-service infrastructure, and make sure it's governed properly, and in as automated way as possible. >> Well, yeah, absolutely. And let me tell you more, everybody talks about single version of the truth, one instance of the data, but there's context to that, that we are taking, trying to take advantage of that as we do data products is, what's the use case here? So we may have an entity of Roddy as a prospective customer, and we may have a entity of Roddy as a customer, high-value customer over here, which may have a different set of mix of data and all, but as a data product, we can then create those for those specific use cases. Still point to the same data, but build it in different constructs. One for marketing, one for sales, one for finance. By the way, that's where your data engineers are struggling. >> Yeah, yeah, of course. So how do I serve all these folks, and really have the context-common story in telco, >> Absolutely. >> or are these guys ahead of the curve a little bit? Or where would you put them? >> I think they're definitely moving a lot faster than the industry is generally. I think the enabling technologies, like for instance, having that single copy of data that everybody sees, a single pane of glass, right, that's definitely something that everybody wants to get to. Not many people are there. I think, what AT&T's doing, is most definitely a little bit further ahead than the industry generally. And I think the successes that are coming out of that, and the learning experiences are starting to generate momentum within AT&T. So I think, it's not just about the product, and having a product now that gives you a single copy of data. It's about the experiences, right? And now, how the teams are getting trained, domains like network engineering for instance. They typically haven't been a part of data discussions, because they've got a lot of data, but they're focused on the infrastructure. >> Mm. >> So, by going ahead and deploying this platform, for platform's purpose, right, and the business value, that's one thing, but also to start bringing, getting that experience, and bringing new experience in to help other groups that traditionally hadn't been data-centric, that's also a huge step ahead, right? So you need to enable those groups. >> A big complaint of course we hear at MWC from carriers is, "The over-the-top guys are killing us. They're riding on our networks, et cetera, et cetera. They have all the data, they have all the client relationships." Do you see your client relationships changing as a result of sort of your data culture evolving? >> Yes, I'm not sure I can- >> It's a loaded question, I know. >> Yeah, and then I, so, we want to start embedding as much into our network on the proprietary value that we have, so we can start getting into that OTT play, us as any other carrier, we have distinct advantages of what we can do at the edge, and we just need to start exploiting those. But you know, 'cause whether it's location or whatnot, so we got to eat into that. Historically, the network is where we make our money in, and we stack the services on top of it. It used to be *69. >> Dave: Yeah. >> If anybody remembers that. >> Dave: Yeah, of course. (Dave laughs) >> But you know, it was stacked on top of our network. Then we stack another product on top of it. It'll be in the edge where we start providing distinct values to other partners as we- >> I mean, it's a great business that you're in. I mean, if they're really good at connectivity. >> Dave: Yeah. >> And so, it sounds like it's still to be determined >> Dave: Yeah. >> where you can go with this. You have to be super careful with private and for personal information. >> Dave: Yep. >> Yeah, but the opportunities are enormous. >> There's a lot. >> Yeah, particularly at the edge, looking at, private networks are just an amazing opportunity. Factories and name it, hospital, remote hospitals, remote locations. I mean- >> Dave: Connected cars. >> Connected cars are really interesting, right? I mean, if you start communicating car to car, and actually drive that, (Dave laughs) I mean that's, now we're getting to visit Xen Fault Tolerance people. This is it. >> Dave: That's not, let's hold the traffic. >> Doesn't scare me as much as we actually learn. (all laugh) >> So how's the show been for you guys? >> Dave: Awesome. >> What're your big takeaways from- >> Tremendous experience. I mean, someone who doesn't go outside the United States much, I'm a homebody. The whole experience, the whole trip, city, Mobile World Congress, the technologies that are out here, it's been a blast. >> Anything, top two things you learned, advice you'd give to others, your colleagues out in general? >> In general, we talked a lot about technologies today, and we talked a lot about data, but I'm going to tell you what, the accelerator that you cannot change, is the relationship that we have. So when the tech and the business can work together toward a common goal, and it's a partnership, you get things done. So, I don't know how many CDOs or CIOs or CEOs are out there, but this connection is what accelerates and makes it work. >> And that is our audience Dave. I mean, it's all about that alignment. So guys, I really appreciate you coming in and sharing your story in "theCUBE." Great stuff. >> Thank you. >> Thanks a lot. >> All right, thanks everybody. Thank you for watching. I'll be right back with Dave Nicholson. Day four SiliconANGLE's coverage of MWC '23. You're watching "theCUBE." (gentle music)