>>Welcome back, everyone live here at Supercomputing 22 in Dallas, Texas. I'm John for host of the Queue here at Paul Gillin, editor of Silicon Angle, getting all the stories, bringing it to you live. Supercomputer TV is the queue right now. And bringing all the action Bresniker, chief architect of Hewlett Packard Labs with HP Cube alumnis here to talk about Supercomputing Road to Quantum. Kirk, great to see you. Thanks for coming on. >>Thanks for having me guys. Great to be >>Here. So Paul and I were talking and we've been covering, you know, computing as we get into the large scale cloud now on premises compute has been one of those things that just never stops. No one ever, I never heard someone say, I wanna run my application or workload on slower, slower hardware or processor or horsepower. Computing continues to go, but this, we're at a step function. It feels like we're at a level where we're gonna unleash new, new creativity, new use cases. You've been kind of working on this for many, many years at hp, Hewlett Packard Labs, I remember the machine and all the predecessor r and d. Where are we right now from your standpoint, HPE standpoint? Where are you in the computing? It's as a service, everything's changing. What's your view? >>So I think, you know, you capture so well. You think of the capabilities that you create. You create these systems and you engineer these amazing products and then you think, whew, it doesn't get any better than that. And then you remind yourself as an engineer. But wait, actually it has to, right? It has to because we need to continuously provide that next generation of scientists and engineer and artists and leader with the, with the tools that can do more and do more frankly with less. Because while we want want to run the program slower, we sure do wanna run them for less energy. And figuring out how we accomplish all of those things, I think is, is really where it's gonna be fascinating. And, and it's also, we think about that, we think about that now, scale data center billion, billion operations per second, the new science, arts and engineering that we'll create. And yet it's also what's beyond what's beyond that data center. How do we hook it up to those fantastic scientific instruments that are capable to generate so much information? We need to understand how we couple all of those things together. So I agree, we are at, at an amazing opportunity to raise the aspirations of the next generation. At the same time we have to think about what's coming next in terms of the technology. Is the silicon the only answer for us to continue to advance? >>You know, one of the big conversations is like refactoring, replatforming, we have a booth behind us that's doing energy. You can build it in data centers for compute. There's all kinds of new things. Is there anything in the paradigm of computing and now on the road to quantum, which I know you're involved, I saw you have on LinkedIn, you have an open rec for that. What paradigm elements are changing that weren't in play a few years ago that you're looking at right now as you look at the 20 mile stair into quantum? >>So I think for us it's fascinating because we've had a tailwind at our backs my whole career, 33 years at hp. And what I could count on was transistors got at first they got cheaper, faster and they use less energy. And then, you know, that slowed down a little bit. Now they're still cheaper and faster. As we look in that and that Moore's law continues to flatten out of it, there has to be something better to do than, you know, yet another copy of the prior design opening up that diversity of approach. And whether that is the amazing wafer scale accelerators, we see these application specific silicon and then broadening out even farther next to the next to the silicon. Here's the analog computational accelerator here is now the, the emergence of a potential quantum accelerator. So seeing that diversity of approaches, but what we have to happen is we need to harness all of those efficiencies and yet we still have to realize that there are human beings that need to create the application. So how do we bridge, how do we accommodate the physical of, of new kinds of accelerator? How do we imagine the cyber physical connection to the, to the rest of the supercomputer? And then finally, how do we bridge that productivity gap? Especially not for people who like me who have been around for a long time, we wanna think about that next generation cuz they're the ones that need to solve the problems and write the code that will do it. >>You mentioned what exists beyond silicon. In fact, are you looking at different kinds of materials that computers in the future will be built upon? >>Oh absolutely. You think of when, when we, we look at the quantum, the quantum modalities then, you know, whether it is a trapped ion or a superconducting, a piece of silicon or it is a neutral ion. There's just no, there's about half a dozen of these novel systems because really what we're doing when we're using a a quantum mechanical computer, we're creating a tiny universe. We're putting a little bit of material in there and we're manipulating at, at the subatomic level, harnessing the power of of, of quantum physics. That's an incredible challenge. And it will take novel materials, novel capabilities that we aren't just used to seeing. Not many people have a helium supplier in their data center today, but some of them might tomorrow. And understanding again, how do we incorporate industrialize and then scale all of these technologies. >>I wanna talk Turkey about quantum because we've been talking for, for five years. We've heard a lot of hyperbole about quantum. We've seen some of your competitors announcing quantum computers in the cloud. I don't know who's using these, these computers, what kind of work they're being used, how much of the, how real is quantum today? How close are we to having workable true quantum computers and what can you point to any examples of how it's being, how that technology is being used in the >>Field? So it, it remains nascent. We'll put it that way. I think part of the challenge is we see this low level technology and of course it was, you know, professor Richard Fineman who first pointed us in this direction, you know, more than 30 years ago. And you know, I I I trust his judgment. Yes. You know that there's probably some there there especially for what he was doing, which is how do we understand and engineer systems at the quantum mechanical level. Well he said a quantum mechanical system's probably the way to go. So understanding that, but still part of the challenge we see is that people have been working on the low level technology and they're reaching up to wondering will I eventually have a problem that that I can solve? And the challenge is you can improve something every single day and if you don't know where the bar is, then you don't ever know if you'll be good enough. >>I think part of the approach that we like to understand, can we start with the problem, the thing that we actually want to solve and then figure out what is the bespoke combination of classical supercomputing, advanced AI accelerators, novel quantum quantum capabilities. Can we simulate and design that? And we think there's probably nothing better to do that than than an next to scale supercomputer. Yeah. Can we simulate and design that bespoke environment, create that digital twin of this environment and if we, we've simulated it, we've designed it, we can analyze it, see is it actually advantageous? Cuz if it's not, then we probably should go back to the drawing board. And then finally that then becomes the way in which we actually run the quantum mechanical system in this hybrid environment. >>So it's na and you guys are feeling your way through, you get some moonshot, you work backwards from use cases as a, as a more of a discovery navigational kind of mission piece. I get that. And Exoscale has been a great role for you guys. Congratulations. Has there been strides though in quantum this year? Can you point to what's been the, has the needle moved a little bit a lot or, I mean it's moving I guess to some, there's been some talk but we haven't really been able to put our finger on what's moving, like what need, where's the needle moved I >>Guess in quantum. And I think, I think that's part of the conversation that we need to have is how do we measure ourselves. I know at the World Economic Forum, quantum Development Network, we had one of our global future councils on the future of quantum computing. And I brought in a scene I EEE fellow Par Gini who, you know, created the international technology roadmap for semiconductors. And I said, Paulo, could you come in and and give us examples, how was the semiconductor community so effective not only at developing the technology but predicting the development of technology so that whether it's an individual deciding if they should change careers or it's a nation state deciding if they should spend a couple billion dollars, we have that tool to predict the rate of change and improvement. And so I think that's part of what we're hoping by participating will bring some of that road mapping skill and technology and understanding so we can make those better reasoned investments. >>Well it's also fun to see super computing this year. Look at the bigger picture, obviously software cloud natives running modern applications, infrastructure as code that's happening. You're starting to see the integration of, of environments almost like a global distributed operating system. That's the way I call it. Silicon and advancements have been a big part of what we see now. Merchant silicon, but also dpu are on the scene. So the role role of silicon is there. And also we have supply chain problems. So how, how do you look at that as a a, a chief architect of h Hewlett Packard Labs? Because not only you have to invent the future and dream it up, but you gotta deal with the realities and you get the realities are silicon's great, we need more of that quantums around the corner, but supply chain, how do you solve that? What's your thoughts and how do you, how, how is HPE looking at silicon innovation and, and supply chain? >>And so for us it, it is really understanding that partnership model and understanding and contributing. And so I will do things like I happen to be the, the systems and architectures chapter editor for the I eee International Roadmap for devices and systems, that community that wants to come together and provide that guidance. You know, so I'm all about telling the semiconductor and the post semiconductor community, okay, this is where we need to compute. I have a partner in the applications and benchmark that says, this is what we need to compute. And when you can predict in the future about where you need to compute, what you need to compute, you can have a much richer set of conversations because you described it so well. And I think our, our senior fellow Nick Dubey would, he's coined the term internet of workflows where, you know, you need to harness everything from the edge device all the way through the extra scale computer and beyond. And it's not just one sort of static thing. It is a very interesting fluid topology. I'll use this compute at the edge, I'll do this information in the cloud, I want to have this in my exoscale data center and I still need to provide the tool so that an individual who's making that decision can craft that work flow across all of those different resources. >>And those workflows, by the way, are complicated. Now you got services being turned on and off. Observability is a hot area. You got a lot more data in in cycle inflow. I mean a lot more action. >>And I think you just hit on another key point for us and part of our research at labs, I have, as part of my other assignments, I help draft our AI ethics global policies and principles and not only tell getting advice about, about how we should live our lives, it also became the basis for our AI research lab at Shewl Packard Labs because they saw, here's a challenge and here's something where I can't actually believe, maintain my ethical compliance. I need to have engineer new ways of, of achieving artificial intelligence. And so much of that comes back to governance over that data and how can we actually create those governance systems and and do that out in the open >>That's a can of worms. We're gonna do a whole segment on that one, >>On that >>Technology, on that one >>Piece I wanna ask you, I mean, where rubber meets the road is where you're putting your dollars. So you've talked a lot, a lot of, a lot of areas of, of progress right now, where are you putting your dollars right now at Hewlett Packard Labs? >>Yeah, so I think when I draw, when I draw my 2030 vision slide, you know, I, for me the first column is about heterogeneous, right? How do we bring all of these novel computational approaches to be able to demonstrate their effectiveness, their sustainability, and also the productivity that we can drive from, from, from them. So that's my first column. My section column is that edge to exoscale workflow that I need to be able to harness all of those computational and data resources. I need to be aware of the energy consequence of moving data, of doing computation and find all of that while still maintaining and solving for security and privacy. But the last thing, and, and that's one was a, one was a how one was aware. The last thing is a who, right? And is is how do we take that subject matter expert? I think of a, a young engineer starting their career at hpe. It'll be very different than my 33 years. And part of it, you know, they will be undaunted by any, any scale. They will be cloud natives, maybe they metaverse natives, they will demand to design an open cooperative environment. So for me it's thinking about that individual and how do I take those capabilities, heterogeneous edge to exito scale workflows and then make them productive. And for me, that's, that's where we were putting our emphasis on those three. When, where and >>Who. Yeah. And making it compatible for the next generation. We see the student cluster competition going on over there. This is the only show that we cover that we've been to that is from the dorm room to the boardroom and this cuz Supercomputing now is elevating up into that workflow, into integration, multiple environments, cloud, premise, edge, metaverse. This is like a whole nother world. >>And, and, but I think it's, it's the way that regardless of which human pursuit you're in, you know, everyone is going to be demand simulation and modeling ai, ML and massive data m l and massive data analytics that's gonna be at heart of, of everything. And that's what you see. That's what I love about coming here. This isn't just the way we're gonna do science. This is the way we're gonna do everything. >>We're gonna come by your booth, check it out. We've talked to some of the folks, hpe obviously HPE Discover this year, GreenLake with center stage, it's now consumption is a service for technology. Whole nother ballgame. Congratulations on, on all this. I would say the massive, I won't say pivot, but you know, a change >>It >>Is and how you guys >>Operate. And you know, it's funny sometimes you think about the, the pivot to as a services benefiting the customer, but as someone who has supported designs over decades, you know, that ability to to to operate and at peak efficiency, to always keep in perfect operating order and to continuously change while still meeting the customer expectations that actually allows us to deliver innovation to our customers faster than when we are delivering warranted individual packaged products. >>Kirk, thanks for coming on Paul. Great conversation here. You know, the road to Quantum's gonna be paved through computing supercomputing software integrated workflows from the dorm room to the boardroom to Cube, bringing all the action here at Supercomputing 22. I'm Jacque Forer with Paul Gillin. Thanks for watching. We'll be right back.

(atmospheric music) >> Welcome to theCUBE Coverage of Data Citizens 2022 Collibra's Customer event. My name is Dave Vellante. With us is Kirk Haslbeck, who's the Vice President of Data Quality of Collibra. Kirk, good to see you, welcome. >> Thanks for having me, Dave. Excited to be here. >> You bet. Okay, we're going to discuss data quality, observability. It's a hot trend right now. You founded a data quality company, OwlDQ, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >> Yeah, absolutely. It's definitely exciting times for data quality which you're right, has been around for a long time. So why now? And why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before, and the variety has changed and the volume has grown. And while I think that remains true there are a couple other hidden factors at play that everyone's so interested in as to why this is becoming so important now. And I guess you could kind of break this down simply and think about if Dave you and I were going to build a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, what the ramifications could be, what those incidents would look like. Or maybe better yet, we try to build a new trading algorithm with a crossover strategy where the 50 day crosses the 10 day average. And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, kind of starts there, where everybody's realizing that we're all data companies, and if we are using bad data we're likely making incorrect business decisions. But I think there's kind of two other things at play. I bought a car not too long ago and my dad called and said, "How many cylinders does it have?" And I realized in that moment, I might have failed him cause I didn't know. And I used to ask those types of questions about any lock breaks and cylinders, and if it's manual or automatic. And I realized, I now just buy a car that I hope works. And it's so complicated with all the computer chips. I really don't know that much about it. And that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the individuals loading and consuming all of this data for the company actually may not know that much about the data itself and that's not even their job anymore. So, we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >> You know, the other thing too about data quality, and for years we did the MIT, CDO, IQ event. We didn't do it last year at COVID, messed everything up. But the observation I would make there, your thoughts is, data quality used to be information quality, used to be this back office function, and then it became sort of front office with financial services, and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well they sort of flipped the bit from sort of a data as a risk to data as an asset. And now as we say, we're going to talk about observability. And so it's really become front and center, just the whole quality issue because data's so fundamental, hasn't it? >> Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my favorite stock ticker app, and I check out the Nasdaq market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And that's kind of what's going on. There's so many numbers and they're coming from all of these different sources, and data providers, and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before Collibra. And what's been so exciting is, we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting, and why I think the CDO is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale, and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's not ever going to be based on one or two domain experts anymore. >> So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they cousins? What's your perspective on that? >> Yeah, it's super interesting. It's an emerging market. So the language is changing, a lot of the topic and areas changing. The way that I like to say it or break it down because the lingo is constantly moving, as a target on the space is really breaking records versus breaking trends. And I could write a condition when this thing happens it's wrong, and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. Everybody's talking about fresh data and stale data, and why would that matter? Well, if your data never arrived, or only part of it arrived, or didn't arrive on time, it's likely stale, and there will not be a condition that you could write that would show you all the good and the bads. That was kind of your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data. But it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there, there's more than a couple of these happening every day. >> So what's the Collibra angle on all this stuff? Made the acquisition, you got data quality, observability coming together. You guys have a lot of expertise in this area, but you hear providence of data. You just talked about stale data, the whole trend toward realtime. How is Collibra approaching the problem and what's unique about your approach? >> Well I think where we're fortunate is with our background. Myself and team, we sort of lived this problem for a long time in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with, before it was called data observability or reliability, was basically the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution. It's more advanced than some of the observation techniques that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights. And they want to see break records and breaking trends together, so they can correlate the root cause. And we hear that all the time. "I have so many things going wrong just show me the big picture. Help me find the thing that if I were to fix it today would make the most impact." So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows you can actually achieve total data governance. At this point with the acquisition of what was a Lineage company years ago, and then my company OwlDQ, now Collibra Data Quality. Collibra may be the best positioned for total data governance and intelligence in the space. >> Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was. They would just say, "Oh, it's a glitch." So they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you got to announce new products, right? It is your yearly event. What's new? Give us a sense as to what products are coming out but specifically around data quality and observability. >> Absolutely. There's this, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and BigQuery, and Databricks, Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook into these databases, and while we've always worked with the same databases in the past they're supported today. We're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now? Is everyone's concerned with something called Egress. Did my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? And with these native integrations that we're building and about to unveil here as kind of a sneak peak for next week at Data Citizens, we're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >> So this is interesting because what you just described, you mentioned Snowflake, you mentioned Google, oh actually you mentioned yeah, Databricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool. But then Google's got the open data cloud. If you heard, Google next. And now Databricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way, up until now I'm hearing, to really understand the relationships between all those and have confidence across, it's like yamarket AMI, you should just be a note on the mesh. I don't care if it's a data warehouse or a data lake, or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And that's what you're bringing to the table. Is that right? Did I get that right? >> Yeah, that's right. And it's, for us, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now we can send them the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network cost, zero egress cost, zero latency of time. And so when you were to log into BigQuery tomorrow using our tool, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage in access, privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there just like all of the major brands that you mentioned but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And we think that this positions us to be the leader there. >> I love this example because, we've got talks about well the cloud guys you're going to own the world. And of course now we're seeing that the ecosystem is finding so much white space to add value connect across cloud. Sometimes we call it super cloud and so, or inter clouding. Alright, Kirk, give us your final thoughts on the trends that we've talked about and data Citizens 22. >> Absolutely. Well I think, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there they want to know where everything is, where their sensitive data is, if it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're going to see more one click solutions, more SaaS based solutions, and solutions that hopefully prove faster time to value on all of these modern cloud platforms. >> Excellent. All right, Kirk Haslbeck, thanks so much for coming on theCUBE and previewing Data Citizens 22. Appreciate it. >> Thanks for having me, Dave. >> You're welcome. All right. And thank you for watching. Keep it right there for more coverage from theCUBE. (atmospheric music)

(bright upbeat music) >> Welcome to theCUBE's Coverage of Data Citizens 2022 Collibra's Customer event. My name is Dave Vellante. With us is Kirk Hasselbeck, who's the Vice President of Data Quality of Collibra. Kirk, good to see you. Welcome. >> Thanks for having me, Dave. Excited to be here. >> You bet. Okay, we're going to discuss data quality, observability. It's a hot trend right now. You founded a data quality company, OwlDQ and it was acquired by Collibra last year. Congratulations! And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >> Yeah, absolutely. It's definitely exciting times for data quality which you're right, has been around for a long time. So why now, and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as to why this is becoming so important now. And I guess you could kind of break this down simply and think about if Dave, you and I were going to build, you know a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, what the ramifications could be? What those incidents would look like? Or maybe better yet, we try to build a new trading algorithm with a crossover strategy where the 50 day crosses the 10 day average. And imagine if the data underlying the inputs to that is incorrect. We'll probably have major financial ramifications in that sense. So, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. I bought a car not too long ago and my dad called and said, "How many cylinders does it have?" And I realized in that moment, I might have failed him because 'cause I didn't know. And I used to ask those types of questions about any lock brakes and cylinders and if it's manual or automatic and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips. I really don't know that much about it. And that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the individuals loading and consuming all of this data for the company actually may not know that much about the data itself and that's not even their job anymore. So, we'll talk more about that in a minute but that's really what's setting the foreground for this observability play and why everybody's so interested, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >> You know, the other thing too about data quality and for years we did the MIT CDOIQ event we didn't do it last year at COVID, messed everything up. But the observation I would make there love thoughts is it data quality used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a a risk to data as an asset. And now, as we say, we're going to talk about observability. And so it's really become front and center, just the whole quality issue because data's fundamental, hasn't it? >> Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And that's kind of what's going on. There's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor. But with the scale that we've achieved in early days, even before Collibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is listening right intently nowadays to this topic is so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's not ever going to be based on one or two domain experts anymore. >> So, how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they cousins? What's your perspective on that? >> Yeah, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the lingo is constantly moving as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens it's wrong and when it doesn't, it's correct. Or I could look for a trend and I'll give you a good example. Everybody's talking about fresh data and stale data and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good and the bads. That was kind of your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >> So what's the Collibra angle on all this stuff made the acquisition you got data quality observability coming together, you guys have a lot of expertise in this area but you hear providence of data you just talked about stale data, the whole trend toward real time. How is Collibra approaching the problem and what's unique about your approach? >> Well, I think where we're fortunate is with our background, myself and team we sort of lived this problem for a long time in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution. It's more advanced than some of the observation techniques that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong just show me the big picture. Help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact connecting it with lineage and catalog, metadata. And as that grows, you can actually achieve total data governance. At this point, with the acquisition of what was a lineage company years ago and then my company OwlDQ, now Collibra Data Quality, Collibra may be the best positioned for total data governance and intelligence in the space. >> Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, they just said, "Oh, it's a glitch." So they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens '22 that you're announcing you got to announce new products, right? Your yearly event, what's new? Give us a sense as to what products are coming out but specifically around data quality and observability. >> Absolutely. There's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks, Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a salike model. And we've started to hook in to these databases. And while we've always worked with the same databases in the past they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did my data that I've spent all this time and money with my security team securing ever leave my hands? Did it ever leave my secure VPC as they call it? And with these native integrations that we're building and about to unveil here as kind of a sneak peek for next week at Data Citizens, we're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration you could log into the Collibra Data Quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >> So this is interesting because what you just described you mentioned Snowflake, you mentioned Google, oh actually you mentioned yeah, the Data Bricks. Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool but then Google's got the open data cloud. If you heard Google Nest and now Data Bricks doesn't call it the data cloud but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm hearing to really understand the relationships between all those and have confidence across, it's like (indistinct) you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And that's what you're bringing to the table. Is that right? Did I get that right? >> Yeah, that's right. And for us, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now we can send them the operating ability to crunch all of the calculations, the governance, the quality and get the answers. And what that's doing, it's basically zero network cost, zero egress cost, zero latency of time. And so when you were to log into Big BigQuery tomorrow using our tool or let or say Snowflake, for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls things of that nature that just become less onerous. What we're seeing is there's so much technology out there just like all of the major brands that you mentioned but how do we make it easier? The future is about less clicks, faster time to value faster scale, and eventually lower cost. And we think that this positions us to be the leader there. >> I love this example because every talks about wow the cloud guys are going to own the world and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. Alright, Kirk, give us your final thoughts and on the trends that we've talked about and Data Citizens '22. >> Absolutely. Well I think, one big trend is discovery and classification. Seeing that across the board people used to know it was a zip code and nowadays with the amount of data that's out there, they want to know where everything is where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases, how fast they can get controls and insights out of their tools. So I think we're going to see more one click solutions, more SAS-based solutions and solutions that hopefully prove faster time to value on all of these modern cloud platforms. >> Excellent, all right. Kurt Hasselbeck, thanks so much for coming on theCUBE and previewing Data Citizens '22. Appreciate it. >> Thanks for having me, Dave. >> You're welcome. All right, and thank you for watching. Keep it right there for more coverage from theCUBE.

>> Narrator: From around the globe. It's theCUBE covering Data Citizens, 21 brought to you by Collibra. >> Hi everybody, John Walls here on theCUBE continuing our coverage of Data Citizens 2021. And I'm with now Kirk Haslbeck was the vice president of engineering at Collibra. Kirk joins us from his home, Kirk good to see you today. Thanks for joining us here on theCUBE. >> Well, thanks for having me, I'm excited to be here. >> Yeah, no, this is all about data quality, right? That's your world, you know, making sure that you're making the most of this great asset, right? That continues to evolve and mature. And yet I'm wondering from your perspective from your side of the fence, I assume data quality has always been a concern, right? Making the most of this asset, wherever it is. And whenever you can get it. >> Yeah, absolutely. I mean, the challenge hasn't slowed down, right? We're looking at more data coming in all the time laws of large numbers, but you kind of have to wonder a lot of the large organizations have been trying to solve this for quite some time, right? So what is going on? Why isn't it just easier to get our arms around it? And there's so many reasons, but if I were to list maybe the top one it's the diminishing value of static rules and a good example of that might just be something as simple as starting with a gender column. And back in the day, we might have assumed that it had to be an M or an F male or female. And over the last couple of years, we've actually seen that column evolve into six or seven different types. So just the very act of assuming that we could go in and write rules about our business and that they're never going to change and that the data's not evolving. And we start to think about zip codes and addresses that are changing, you know, Google street view. However you want to think of it. Every column and every record is just changing all the time. And so what, you know, many large organizations have done they've written maybe forty thousand, fifty thousand rules and they have to continue to manage them. So I think we all try to get our arms around rule creation. And it's not even just about that. It would also be about if you had all the rules in place could you even keep up with them on a day-to-day changing basis? And so one of the largest companies in the U.S sat down with myself and team early on and said, so what am I up against? I'm really either going to continue to hire a mountain of rule writers, you know, as they put it per department to get my arms around this and that'll never end, or I need to think of a better way which was the solution that we were ultimately providing at that time. And, you know, and what that solution really entails is using data mining to learn and observe all the data that's already there and to curate the rules based on the data itself, right? That's where all the information is. And then ultimately we have this concept of adaptive ruling which means all the variants in that column all the new values that come in every day, the roll counts, the sizes are all being managed. It's an automatic program, so that the rule is recalibrating itself and I think this is where most most chief data officers sit back and say if I have to protect the franchise, right? If I have to put a trusted data program in place what are my options and how does it scale? And they have to take a really hard look at something like this. >> You know, the process that you're talking about too it just kind of reminds me of, of like, of a diet in that nobody wants to go through that pain, right? We all want to eat, what we want to eat but you're really happy when you get there at the end of the day, you like the way you look like the way you feel, like the way you act, all those things, so it'd be almost like when you're talking about in terms of this data, you know, in terms of a rule setting, right? Governance and accessibility and all these things, it's, it can be a tough process. Can be, but it certainly seems well worth it because you make your data all the more valuable and essential to your business, Is that about right? >> Yeah, that's right, that's right. And you know, it's funny you compare it to a diet. Sometimes I think of a patient stress test, you know, almost like a health exam and we're spending so much time testing the analytics or testing the models and looking at accuracy and can anybody achieve 89 to 90% but we're probably not spending enough time testing our data assumptions, right? Running that diet or health check against the data itself. And I would say that every fortune 100 or even fortune 1000 probably considers themselves a data-driven business at this point in time, which means they're going to make decisions quickly based on data. And if we really pull that thread a little bit, what about what's the cost of making decisions on incorrect data? I mean it's terribly scary as we start to unfold that, so you're absolutely right. They're taking it very seriously. And it takes a lot of thought of how to get enough coverage and how to create trust in that type of environment. >> Yeah, it's almost too, it's like, you know the concept of input bias a little bit here where were if you're assuming that certain data sets are accurate and pertinent, relevant, all those things and then you're making decisions based on those data sets but you might be looking at kind of an input bias if I'm hearing you right, that you're maybe you're not keeping your mind open as to what really should be important or influential in your decision-making in terms of data. And then obviously acting on that appropriately. So you have to decide maybe on the front side, you know, what data matters and you help people do that. And then help me make decisions based on good data basically, right? >> Right, that's right and to be fully transparent and candid we weren't as strong in the what data matters piece of it. We were very strong early on in giving you broad coverage meaning we made no assumptions, right? We wanted to go out and attack the whole surface of the problem and then sort of have a consistent scoring methodology. And as we've partnered and now become acquired by Collibra which is an exciting path, they are very good at what's called critical data elements and lineage and doing graph analysis to sort of identify the assets that are most used. And that's where we see a huge benefit in combining those two powers. So you kind of got there quickly, but ultimately we are combining the forces of total coverage at scale with what is most important to you. >> Imagine we coming OwlDQ, you were the founder of that, that was purchased by Collibra. Tell us a little bit about, just about how that came to be in first off, we did a OwlDQ, what that was all about and then how this, this a marriage, if you will how this relationship with Collibra evolved and then you were eventually purchased. >> Yeah, absolutely, so, I mean, I had this passion that I couldn't hold back on in the data community. Once you see it this way, where you can use data mining and compute power to curate and manage rules and then take it much beyond there and to predicting and seeing around the corner for tomorrow, you have to go that direction. So that's exactly what myself and team did. And what we started to see with the early adopters of our software was that they were getting a seven figure return on investment per department. And they were able to replicate this across many departments, so we've had a great lifespan with those customers, staying and growing and expanding but we were getting a little bit of market pressure from the investment community, as well as that same customer community that they wanted us to integrate with their data catalog and the data catalog of choice. Every time the conversation was Collibra. And interestingly enough, you know, I ran into the likes of Jim Cushman and in the, you know, the whole thing unfolds from there. I think they were seeing a little bit of a similar story saying doesn't catalog and lineage belong together with quality. And when we sat together it was like three market forces suggesting the same answer. And as we laid out the roadmap and the integration we just can't see it any other way. There's no way I'll be bold and say that it goes back the other way, not just for this company but for the industry, data governance and data intelligence will absolutely combine quality, lineage, catalog and all of the above in the future. It is becoming that clear, I think. >> You know, this has kind of a big picture question, about all of that data quality right now, what's driving this avid interest that organizations showing and it's you know, small, medium enterprise it's everybody but in your mind, you know, you've been involved in this for a number of years now. You know, why now, what is it now? Is it just that we have so much more data available that so much of it's own use that, that, you know, we know what we have. And we're realizing that what we have is pretty valuable but you know, what's the driver, what's the big push here? >> Yeah, it is a tough question. And I have gotten this one before and it's interesting because it's been around since the nineties, right? So it's a very fair question. There's a couple things I think that are driving it. One as we start to see more data in Tableau dashboards and pick your favorite BI tool you start to realize the data's not correct. You know, you look at your house on Zillow or whatever you find out it's mislabeled. It doesn't have the right bedrooms. Maybe humans are entering into the listings and as data's become more available visually we're more critical of it. And now businesses are becoming more data-driven where they're humans aren't involved as much and the actions are automatically being taken. And it becomes an embarrassing moment if your data is incorrect and we can really measure that cost at this point. You do see some other factors like cloud migration. Well, that adds a risk to your business. Could you possibly port everything, not just the servers not just the software, but all of your data into another system and think that there would be no errors in that process. So as people are kind of creating their next generation platforms, and then probably even a touch of COVID accelerating that cloud migration adoption and even just technology adoption. So for a multitude of reasons, there's just more data and there's more data quality concerns than ever before. >> So if you're talking to a prospective client right now, which you probably are, you know, what do you want to share with them? Or what would you encourage them to consider in terms of kind of their data venture their data journey if you will, in terms of, you know, refining what they have in terms of mining appropriately in terms of governing it appropriately, all these things that maybe haven't been given a lot of consideration or deep consideration. >> Yeah, I think the two things although if you listen to my other talks I can talk forever about, about all of those items. It probably, you know, maybe just do the napkin math of all the tables, all the files all the Kafka messages, right? All the columns and fields and attributes and kind of just multiply that out and and try to figure out how you would get coverage. And if you could, how you could maintain it. And why shouldn't we be trading compute power for domain knowledge and things at that point I think that's the first place to start. And probably the second is actually the act of traditional data quality rules puts you in a binary situation. It basically says you will either have a break record or you will not. So it's a yes, no question, what it never will tell you is what the answer should have been. And if you take a deeper look at the solution that we're providing to the market we're actually predicting to you what the correct value is and it's a complete paradigm shift it obviously is much more scientific, but it's much more powerful to get you to the end answer more quickly instead of just going through break records. >> Right? Tremendous capability that you just described. And on that, I'm going to thank you for the time but just think about it, right? We're we're not only going to help you make more sense of your data. We're also going to help you make better decisions and show you what that path might be or what you probably should be considering. So it certainly opens up a lot of doors for a lot of companies in that respect. Kirk, thanks for the time, sorry we didn't have enough time to hear that guitar in the background, but next time I'm going to hold you to it, okay. >> Yeah, that sounds good, John, I really appreciate it. >> All right very good Kirk Haslbeck joining us from Collibra, we continue our coverage here at Data Citizens 21 on theCUBE and I'm John Walls. (bright music)

>>from the cube studios >>in Palo alto in >>boston connecting with thought leaders all around the world. This >>is a cute >>conversation. Hello welcome to the cubes coverage of HPD discovered 2021 virtual. I'm john for your host of the cube we're here with CUBA alumni. One of the original cube guests 2020 11 back in the day kurt president and chief architect of Hewlett Packard labs. He's also a Hewlett Packard enterprise fellow and vice president. Great to see you and you're in Vegas. I'm in Palo Alto. We've got a little virtual hybrid going on here. Thanks for spending time. >>Thanks john it's great to be back with you >>so much going on. I love to see you guys having this event kind of everyone in one spot. Good mojo. Great CHP, you know, back in the saddle again. I want to get your, take, your in the, in the, in the action right now on the lab side, which is great disruptive innovation is the theme. It's always been this year, more than ever coming out of the pandemic, people are looking for the future, looking to see the signs, they want to connect the dots. There's been some radical rethinking going on that you've been driving and in the labs, you hope you look back at last, take us through what's going on, what you're thinking, what's the, what's the big trends? >>Yeah, John So it's been interesting, you know, over the last 18 months, all of us had gone through about a decade's worth of advancement in decentralization, education, healthcare, our own work, what we're doing right now suddenly spread apart. Uh, and it got us thinking, you know, we think about that distributed mesh and as we, as we try and begin to return to normal and certainly think about all that we've lost, we want to move forward, we don't want to regress. And we started imagining, what does that world look like? And we think about the world of 20 2500 and 35 zeta bytes, 100 and 50 billion connected things out there. And it's the shape of the world has changed. That's where the data is going to be. And so we started thinking about what's it like to thrive in that kind of world. We had a global Defense research institute came to us, Nasa's that exact question. What's the edge? What do we need to prepare for for this age of insight? And it was kind of like when you had those exam questions and I was one of those kids who give you the final exam and if it's a really good question, suddenly everything clicked. I understood all the material because there was that really forcing question when they asked us that for me, it it solidified what I've been thinking about all the work we've done at labs over the last the last 10 years. And it's really about what does it take to survive and thrive. And for me it's three things. One is, success is going to go to whoever can reason over more information, who can gain the deepest insights from that information in time that matters and then can turn that insight into action at scale. So reason, insight and action. And it certainly was clear to me everything we've been trying to push for in labs, all those boundaries. We've been pushing all those conventions we've been defying are really trying to do that for, for our customers and our partners to bring in more information for them to understand, to be able to allow them to gain insight across departments across disciplines and then turn that insight into action at scale where scale is no longer one cloud or one company or one country, let alone one data center >>lot there. I love the dot I love that metadata and meta reasoning incites always been part of that. Um and you mentioned decentralization. Again, another big trend. I gotta ask you where is the big opportunity because a lot of people who are attending discover people watching are trying to ask what should they be thinking about. So what is that next big opportunity? How would you frame that and what should attendees look for coming out at HP discover. >>So one thing we're seeing is that this is actually a ubiquitous trend, whether we're talking about transportation or energy or communications, they all are trying to understand and how will they admit more of that data to make those real time decisions? Our expectation in the middle of this decade when we have the 125 petabytes, You know, 30% of that data will need real time action out of the edge where the speed of light is now material. And also we expect that at that point in time three out of four of those 185 petabytes, they'll never make it back to the data center. So understanding how we will allow that computation, that understanding to reach out to where the data is and then bringing in that's important. And then if we look at at those, all of those different areas, whether it's energy and transportation, communications, all that real time data, they all want to understand. And so I I think that as many people come to us virtually now, hopefully in person in the future when we have those conversations that labs, it's almost immediate takes a while for them and then they realize away that's me, this is my industry too, because they see that potential and suddenly where they see data, they see opportunity and they just want to know, okay, what does it take for me to turn that raw material into insight and then turn that insight into >>action, you know, storage compute never goes away, it gets more and more, you need more of it. This whole data and edge conversations really interesting. You know, we're living in that data centric, you know, everyone's gonna be a date a couple, okay. That we know that that's obvious. But I gotta ask you as you start to see machine learning, um cloud scale cloud operations, a new Edge and the new architecture is emerging and clients start to look at things like AI and they want to have more explain ability behind I hear that all the time. Can you explain it to me? Is there any kind of, what is it doing? Good as our biases, a good bad or you know, is really valuable expect experimental experiential. These are words are I'm hearing more and more of >>not so much a speeds >>and feeds game, but these are these are these are these are outcomes. So you got the core data, you've got a new architecture and you're hearing things like explainable ai experiential customer support, a new things happening, explain what this all means, >>You know, and it's it's interesting. We have just completed uh creating an Ai ethical framework for all of Hewlett Packard enterprise and whether we're talking about something that's internal improving a process, uh something that we sell our product or we're talking about a partnership where someone wants to build on top of our services and infrastructure, Build an AI system. We really wanted to encompass all of those. And so it was it was challenging actually took us about 18 months from that very first meeting for us to craft what are some principles for us to use to guide our our team members to give them that understanding. And what was interesting is we examined our principles of robustness of uh making sure they're human centric that they're reliable, that they are privacy preserving, that they are robust. We looked at that and then you look at where people want to apply these Ai today's AI and you start to realize there's a gap, there's actually areas where we have a great challenge, a human challenge and as interesting as possibly efficacious as today's A. I. S. R. We actually can't employ them with the confidence in the ethical position that we need to really pull that technology in. And what was interesting is that then became something that we were driving at labs. It began gave us a viewpoint into where there are gaps where, as you say, explica bility, you know, as fantastic as it is to talk into your mobile phone and have it translated into another one of hundreds of languages. I mean that is right out of Star trek and it's something we can all do. And frankly, it's, you know, we're expecting it now as efficacious as that is as we echo some other problems, it's not enough. We actually need to be explainable. We need to be able to audit these decisions. And so that's really what's informed now are trustworthy ai research and development program at Hewlett Packard Labs. Let's look at where we want to play. I I we look at what keeps us from doing it and then let's close the technology gap and it means some new things. It means new approaches. Sometimes we're going back back back to some of the very early ai um that things that we sort of left behind when suddenly the computational capability allowed us to enter into a machine learning and deep neural nets. Great applications, but it's not universally applicable. So that's where we are now. We're beginning to construct that second generation of AI systems where that explica bility where that trustworthiness and were more important that you said, understanding that data flow and the responsibility we have to those who created that data, especially when it's representing human information, that long term responsibility. What are the structures we need to support that ethically? >>That's great insight, Kirk, that's awesome stuff. And it reminds me of the old is new again, right? The cycles of innovation, you mentioned a I in the eighties, reminds me of dusting off and I was smiling because the notion of reasoning and natural language that's been around for a while, these other for a lot of Ai frame which have been around for a while But applied differently becomes interesting. The notion of Meta reasoning, I remember talking about that in 1998 around ontology and syntax and data analysis. I mean, again, well formed, you know, older ways to look at data. And so I gotta ask you, you know, you mentioned reasoning over information, getting the insights and having actions at scale. That doesn't sound like an R and D or labs issue. Right? I mean that that should be like in the market today. So I know you, there's stuff out there, what's different around the Hewlett Packard labs challenge because you guys, you guys are working on stuff that's kind of next gen, so why, what's next gen about reasoning moreover, information and getting insights? Because you know, there's a zillion startups out there that claim to be insights as a service, um, taking action outcomes >>and I think there were going to say a couple things. One is the technologies and the capabilities that God is this far. Uh, they're actually in an interesting position if we think of that twilight of moore's law is getting a little darker every day. Um, there's been such a tail wind behind us tremendous and we would have been foolish not to take advantage of it while it lasted, but as it now flattens out, we have to be realistic and say, you know what that ability to expect anticipate and then planned for a doubling and performance in the next 18 to 24 months because there's twice as many transistors in that square of silicon. We can't count on that anymore. We have to look now broader and it's not just one of these technology inflection points. There's so many we already mentioned ai it's voraciously vowing all this data at the same time. Now that data is all at the edge is no longer in the data center. I mean we may find ourselves laughing chuckling at the term itself data center. Remember when we sent it all the data? Because that's where the computers were. Well, that's 2020 thinking right, that's not even 2025. Thinking also security, that cyber threat of Nation State and criminal enterprises, all these things coming together and it's that confluence of discontinuities, that's what makes a loud problem. And the second piece is we don't just need to do it the way that we've been doing it because that's not necessarily sustainable. And if something is not sustainable is inherently inequitable because we can't afford to let everyone enjoy those benefits. So I think that's all those things, the technology confluence of technology, uh, disruptions and this desire to move to really sustainable, really inherently inequitable systems. That's what makes it a labs problem. >>I really think that's right on the money. And one of things I want to get your thoughts on, cause I know you have a unique historic view of the trajectory arc. Cloud computing that everyone's attention lift and shift cloud scale. Great cloud native. Now with hybrid and multi cloud clearly happening, all the cloud players were saying, oh, it's never gonna happen. All the data set is going to go away. Not really. The, the data center is just an edge big age. So you brought up the data center concept and you mentioned decentralization there, it's a distributed computing architecture, There is no line anymore between what's cloud and what's not the cloud is just the cloud and the data center is now a big fat edge and edges are smaller and bigger. Their nodes distribute computing now is the context. So this is not a new thing for Hewlett Packard enterprise. I mean you guys been doing distributed computing paradigms, supplying software and hardware and solutions Since I can remember since it was founded, what's new now, what do you say that folks are saying, what is HP doing for this new architecture? Because now an operating system is the word, the word that they want. They want to have an operating model, deV ops to have sex shops, all this is happening. What's the what's the state of the art from H. P. E. And how does the lab play into that vision? >>And it's so wonderful that you mentioned in our heritage because if you think about it was the first thing that Bill and they did, they made instruments of unparalleled value and quality for engineers and scientists. And the second thing they did was computerized that instrument control. And then they network them together and then they connect to the network measurement sensing systems to business computing. Right. And so that's really, that's exactly what we're talking about here. You know, and yesterday it was H. B. I. B. Cables. But today it is everything from an Aruba wireless gateway to a green Lake cloud that comes to you to now are cray exa scale supercomputing. And we wanted to look at that entire gamut and understand exactly what you said. How is today's modern developer who has been distinct in agile development in seven uh and devops and def sec ops. How can we make them as comfortable and confident deploying to any one of those systems or all of them in conjunction as confident as they've been deploying to a cloud. And I think that's really part of what we need to understand. And as you move out towards the edge things become interesting. A tiny amount of resources, the number of threats, physical and uh um cyber increased dramatically. It is no longer the healthy happy environment of that raised floor data center, It is actually out in the world but we have to because that's where the data is and so that's another piece of it that we're trying to bring with the labs are distributed systems lab trying to understand how do we make cloud native access every single bite everywhere from the tiniest little Edge embedded system, all the way up through that exa scale supercomputer, how do we admit all of that data to this entire generation and then the following subsequent generation, who will no longer understand what we were so worried about with things being in one place or another, they want to digest all the world's data regardless of where it is. >>You know, I was just having a conversation, you brought this up. Uh that's interesting around the history and the heritage, embedded systems is changing the whole hardware equations, changes the software driven model. Now, supply chain used to be constrained to software. Now you have a software supply chain, hardware, now you have software supply chain. So everything is happening in these kind of new use cases. And Edge is a great example where you want to have compute at the edge not having pulled back to some central location. So, again, advantage hp right, you've got more, you've got some solutions there. So all these like memory driven computing, something that you've worked on and been driving the machine product that we talked about when you guys launched a few years ago, um, looks like now a good R and D project, because all the discussions, I'm I'm hearing whether it's stuff in space or inside hybrid edges is I gotta have software running on an embedded system, I need security, I gotta have, you know, memory driven architecture is I gotta have data driven value in real time. This is new as a kind of a new shift, but you still need to run it. What's the update on the machine and the memory driven computing? And how does that connect the dots for this intelligent Edge? That's now super important in the hybrid equation. >>Yeah, it's fantastic you brought that up. You know, it's uh it's gratifying when you've been drawing pictures on your white board for 10 or 15 years and suddenly you see them printed uh and on the web and he's like, OK Yeah, you guys were there were there because we always knew it had to be bigger than us. And for a while you wonder, well is this the right direction? And then you get that gratification that you see it repeated. And I think one of the other elements that you said that was so important was talking about that supply chain uh and especially as we get towards these edge devices uh and the increasing cyber threat, you know, so much more about understanding the provenance of that supply chain and how we get beyond trust uh to prove. And in our case that proof is rooted in the silicon. Start with the silicon establish a silicon root of trust, something that can't be forged that that physically uncomfortable function in the silicon. And then build up that chain not of trust but a proof of measurable confidence. And then let's link that through the hardware through the data. And I think that's another element, understanding how that data is flowing in and we establish that that that provenance that's provable provenance and that also enables us to come back to that equitable question. How do we deal with all this data? Well, we want to make sure that everyone wants to buy in and that's why you need to be able to reward them. So being able to trace data into an AI model, trace it back out to its effect on society. All these are things that we're trying to understand the labs so that we can really establish this data economy and admit the day that we need to the problems that we have that really just are crying out for that solution bringing in that data, you just know where is the data, Where is the answer? Now I get to work with, I've worked for several years with the German center for your Degenerative Disease Research and I was teasing their director dr nakata. I said, you know, in a couple of years when you're getting that Nobel prize for medicine because you cracked Alzheimer's I want you to tell me how long was the answer hiding in plain sight because it was segregated across disciplines across geography and it was there. But we just didn't have that ability to view across the breath of the information and in a time that matters. And I think so much about what we're trying to do with the lab is that that's that reasoning moreover, more information, gaining insights in the time that matters and then it's all about action and that is driving that insight into the world regardless of whether it has to land in an exa scale supercomputer or tiny little edge device, we want today's application development teams to feel that degree of freedom to range over all of those that infrastructure and all of that data. >>You know, you bring up a great call out there. I want to just highlight that cause I thought that was awesome. The future breakthroughs are hiding in plain sight. It's the access to the people and the talent to solve the problems and the data that's stuck in the silos. You bring those together, you make that seamless and frictionless, then magic happens. That's that's really what we're talking about in this new world, isn't it? >>Absolutely, yeah. And it's one of those things that sometimes my kids as you know, why do you come in every day? And for me it is exactly that I think so many of the challenges we have are actually solvable if the right people knew the right information at the right time and that we all have that not again, not trust, but that proof that confidence, that measurable conference back to the instruments that that HP was always famous for. It was that precision and they all had that calibration tag. So you could measure your confidence in an HP instrument and the same. We want people to measure their confidence when data is flowing through Hewlett Packard Enterprise infrastructure. >>It's interesting to bring up the legacy because instrumentation network together, connecting to business systems. Hey, that sounds like the cloud observe ability, modern applications, instant action and actionable insights. I mean that's really the the same almost exact formula. >>Yeah, For me that's that, that the constant through line from the garage to right now is that ability to handle and connect people to the information that they need. >>Great, great to chat. You're always an inspiration and we could go for another hour talking about extra scale, green leg, all the other cool things going on at H P E. I got to ask you the final question, what are you most excited about for h B and his future and how and how can folks learn more to discover and what should they focus on? >>Uh so I think for me um what I love is that I imagine that world where the data you know today is out there at the edge and you know we have our Aruba team, we have our green Lake team, we have are consistent, you know, our core enterprise infrastructure business and now we also have all the way up through X scale compute when I think of that thriving business, that ability to bring in massive data analytics, machine learning and Ai and then stimulation and modeling. That's really what whether you're a scientist and engineer or an artist, you want to have that intersectionality. And I think we actually have this incredible, diverse set of resources to bring to bear to those problems that will span from edge to cloud, back to core and then to exit scale. So that's what really, that's why I find so exciting is all of the great uh innovators that we get to work with and the markets we get to participate in. And then for me it's also the fact it's all happening at Hewlett Packard Enterprise, which means we have a purpose. You know, if you ask, you know, when they did ask Dave Packer, Dave, why hp? And he said in 1960, we come together as a company because we can do something we could not do by ourselves and we make a contribution to society and I dare anyone to spend more than a couple of minutes with Antonio Neary and he won't remind you. And this is whether it is here to discover or in the halls at labs remind me our purpose, that Hewlett Packard Enterprise is to advance the way that people live and work. And for me that's that direct connection. So it's, it's the technology and then the purpose and that's really what I find so exciting about HPV. >>That's a great call out, Antonio deserves props. I love talking with him, he's the true Bill and Dave Bill. Hewlett Dave package spirit And I'll say that I've talked with him and one of the things that resident to me and resonates well is the citizenship and be interesting to see if Bill and Dave were alive today, that now it's a global citizenship. This is a huge part of the culture and I know it's still alive there at H P E. So, great call out there and props to Antonio and yourself and the team. Congratulations. Thanks for spending the time, appreciate it. >>Thank you john it's great to be with you again. >>Okay. Global labs. Global opportunities, radical. Rethinking this is what's happening within HP. Hewlett Packard Labs, Great, great contribution there from Kirk, have them on the cube and always fun to talk so much, so much to digest there. It's awesome. I'm john Kerry with the cube. Thanks for watching. >>Mm >>mhm Yeah.

>>Kirk focuses on the approach to modern data quality and how it can enable the continuous delivery of trusted data. Take it away. Kirk >>Trusted data has been a focus of mine for the last several years. Most particularly in the area of machine learning. Uh, I spent much of my career on wall street, writing models and trying to create a healthy data program, sort of the run the bank and protect the franchise and how to do that at scale for larger organizations. Uh, I'm excited to have the opportunity today sitting with me as Victor to have a fireside chat. He is an award-winning and best-selling author of delete big data and most currently framers. He's also a professor of governance at Oxford. So Victor, my question for you today is in an era of data that is always on and always flowing. How does CDOs get comfortable? You know, the, I can sleep at night factor when data is coming in from more angles, it's being stored in different formats and varieties and probably just in larger quantities than ever before. In my opinion, just laws of large numbers with that much data. Is there really just that much more risk of having bad data or inaccuracy in your business? >>Well, thank you Kirk, for having me on. Yes, you're absolutely right. That the real problem, if I were to simplify it down to one statement is that incorrect data and it can lead to wrong decisions that can be incredibly costly and incredibly costly for trust for the brand, for the franchise incredibly costly, because they can lead to decisions that are fundamentally flawed, uh, and therefore lead the business in the wrong direction. And so the, the, the real question is, you know, how can you avoid, uh, incorrect data to produce incorrect insights? And that depends on how you view trust and how you view, uh, data and correctness in the first place. >>Yeah, that's interesting, you know, in my background, we were constantly writing models, you know, we're trying to make the models smarter all the time, and we always wanted to get that accuracy level from 89% to 90%, you know, whatever we could be, but there's this popular theme where over time the models can diminish an accuracy. And the only button we really had at our disposal was to retrain the model, uh, oftentime I'm focused on, should we be stress testing the data, it almost like a patient health exam. Uh, and how do we do that? Where we could get more comfortable thinking about the quality of the data before we're running our models and our analytics. >>Yeah, absolutely. When we look at the machine learning landscape, even the big data landscape, what we see is that a lot of focus is now put on getting the models, right, getting it worked out, getting the kinks worked out, but getting sort of the ethics, right. The value, right. That is in the model. Um, uh, and what is really not looked at what is not focused enough that, um, is the data. Now, if you're looking at it from a compliance viewpoint, maybe it's okay if you just look at the model, maybe not. But if you understand that actually using the right data with the right model gives you a competitive advantage that your competitors don't have, then it is far more than compliance. And if it is far more compliance, then actually the aperture for strategy opens up and you should not just look at models. You should actually look at the data and the quality and correctness of the data as a huge way by which you can push forward your competitive advantage. >>Well, I haven't even trickier one for you. I think, you know, there's so much coming in and there's so much that we know we can measure and there's so much we could replay and do what if analysis on and kind of back tests, but, you know, do you see organizations doing things to look around the corner? And maybe an interesting analogy would be something like with Tesla is doing whether it's sensors or LIDAR, and they're trying to bounce off every object they know, and they can make a lot of measurements, but the advancements in computer vision are saying, I might be able to predict what's around the corner. I might be able to be out ahead of the data error. I'm about to see tomorrow. Um, you know, do you see any organizations trying to take that futuristic step to sort of know the unknown and be more predictive versus reactive? >>Absolutely. Tesla is doing a bit Lincoln, uh, but so are others in that space and not autonomous driving space, um, uh, Waymo, the, uh, the, the, uh, Google company that is, uh, doing autonomous driving for a long period of time where they have been doing is collecting training data, uh, through their cars and then running a machine learning on the training data. Now they hit a wall a couple of years ago because the training data wasn't diverse enough. It didn't have that sort of Moore's law of insight anymore, even though it was more and more training data. Um, and so the, the Delta, the additional learning was just limited. So what they then decided to do was to build a virtual reality called car crafting, which were actually cars would drive around and create, uh, uh, predictive training data. Now, what is really interesting about that is that that is isn't a model. It is a model that creates predictive data. And this predictive is the actual value that is added to the equation here. And with this extra predictive data, they were able to improve their autonomous driving quite significantly. Uh, five years ago, their disengagement was, uh, raped was every, uh, 2000 miles on average. And, uh, last year, uh, five years later, it was every 30,000 miles on average, that's a 15 K improvement. And that wasn't driven by a mysterious model. It was driven by predictive data. >>Right, right. You know, that's interesting. I, I'm also a fan of trying to use data points that don't exist in the data sets. So it sounds like they were using more data data that was derived from other sources. And maybe the most simple format that I usually get started with was, you know, what, if I was looking at data from Glassdoor and I wanted to know if it was valid, if it was accurate, but of course there's going to be numbers in the age, field and salary and years of experience in different things. But what if the years of experience and age and academic level of someone no longer correlates to the salary yet that correlation component is not a piece of data that even lives in the column, the row, the cell. So I do think that there's a huge area for improvement and just advancement in the role data that we see in collect, but also the data science metrics, something like lift and correlation between the data points that really helped me certify and feel comfortable that this data makes sense. Otherwise it could just be numbers in the field >>Indeed. And, and this challenge of, of finding the data and focusing on the right subset of the data and manipulating it, uh, in the right, in a qualitatively right way is really something that has been with us for quite a number of years. There's a fabulous, uh, case, um, a few years back, uh, when, um, in Japan, when there was the suspicion that in Sumo wrestling, there was match fixing going on massive max fiction. Um, and, and so investigators came in and they took the data from the championship bouts and analyzed them and, uh, didn't find anything. And, uh, what was, what was really interesting is then later researchers came in and read the rules and regulations of Sumo wrestling and understood that it's not just the championship bouts that matter, but it's also sometimes the relegation matches that matter. And so then they started looking at those secondary matches that nobody looked at before and that subset of data, and they discovered there's massive match fixing going on. It's just, nobody looked at it because nobody just, as you said, that connection, uh, between th those various data sources or the sort of causal connectivity there. And so it's, it's, it's really crucial to understand, uh, that, uh, driving insight out of data, isn't a black box thing where you feed the data in and get it out. It really requires deep thinking about how to wire it up from the very beginning. >>No, that's an interesting story. I kind of wonder if the model in that case is almost the, the wrestlers themselves or the output, but definitely the, the data that goes into it. Um, yeah. So, I mean, do you see a path where organizations will achieve a hundred percent confidence? Because we all know there's a, I can't sleep at night factor, but there's also a case of what do I do today. It's, I'm probably not living in a perfect world. I might be sailing a boat across an ocean that already has a hole in it. So, you know, we can't turn everything off. We have to sort of patch the boat and sail it at the same time. Um, what do you think the, a good approaches for a large organization to improve their posture? >>You know, if you focus on perfection, you never, you never achieved that perfection a hundred percent perfection or so is never achievable. And if you want some radical change, then that that's admirable. But a lot of times it's very risky. It's a very risky proposition. So rather than doing that, there is a lot of low hanging fruit than that incremental, pragmatic step-by-step approach. If I can use an analogy from history, uh, we, we, we talk a lot about, um, the data revolution and before that, the industrial revolution, and when we think about the industrial revolution, we think about the steam engine, but the reality is that the steam engine, wasn't just one radical invention. In fact, there were a myriad of small incremental invade innovations over the course of a century that today we call the industrial revolution. And I think it's the various same thing when the data revolution where we don't have this one silver bullet that radically puts us into data Nirvana, but it is this incremental, pragmatic step-by-step change. It will get us closer. Um, pragmatic, can you speak in closer to where we want to be, even though there was always more work for us left? >>Yeah, that's interesting. Um, you know, that one hits home for me because we ultimately at Collibra take an incremental approach. We don't think there's a stop the world event. There's, you know, a way to learn from the past trends of our data to become incrementally smarter each day. And this kind of stops us from being in a binary project mode, right. Where we have to wait right. Something for six months and then reassess it and hope, you know, we kind of wonder if you're at 70% accuracy today is being at 71% better tomorrow, right? At least there's a measurable amount of improvement there. Uh, and it's a sort of a philosophical difference. And it reminds me of my banking days. When you say, uh, you know, past performance is no guarantee of future results. And, um, it's a nice disclaimer, you can put in everything, but I actually find it to be more true in data. >>We have all of these large data assets, whether it's terabytes or petabytes, or even if it's just gigabytes sitting there on all the datasets to learn from. And what I find in data is that the past historical values actually do tell us a lot about the future and we can learn from that to become incrementally smarter tomorrow. And there's really a lot of value sitting there in the historical data. And it tells me at least a lot about how to forecast the future. You know, one that's been sitting on the top of my mind recently, especially with COVID and the housing market a long time back, I competed with automation, valuation modeling, which basically means how well can you predict the price of a house? And, you know, that's always a fun one to do. And there's some big name brands out there that do that pretty well. >>Back then when I built those models, I would look at things like the size of the yard, the undulation of the land, uh, you know, whether a pool would award you more or less money for your house. And a lot of those factors were different than they are now. So those models ultimately have already changed. And now that we've seen post COVID people look for different things in housing and the prices have gone up. So we've seen a decline and then a dramatic increase. And then we've also seen things like land and pools become more valuable than they were in the housing model before, you know, what are you seeing here with models and data and how that's going to come together? And it's just, is it always going to change where you're going to have to constantly recalibrate both, you know, our understanding of the data and the models themselves? >>Well, indeed the, the problem of course is almost eternal. Um, oftentimes we have developed beautiful models that work really well. And then we're so wedded to this model or this particular kind of model. And we can fathom to give them up. I mean, if I think of my students, sometimes, you know, they, they, they, they have a model, they collect the data, then they run the analysis and, uh, it basically, uh, tells them that their model was wrong. They go out and they collect more data and more data and more data just to make sure that it isn't there, that, that, that their model is right. But the data tells them what the truth is that the model isn't right anymore that has context and goals and circumstances change the model needs to adapt. And we have seen it over and over again, not just in the housing market, but post COVID and in the COVID crisis, you know, a lot of the epidemiologists looked at life expectancy of people, but when you, when you look at people, uh, in the intensive care unit, uh, with long COVID, uh, suffering, uh, and in ICU and so on, you also need to realize, and many have that rather than life expectancy. >>You also need to look at life quality as a mother, uh, kind of dimension. And that means your model needs to change because you can't just have a model that optimizes on life expectancy anymore. And so what we need to do is to understand that the data and the changes in the data that they NAMIC of the data really is a thorn in our thigh of revisiting the model and thinking very critically about what we can do in order to adjust the model to the present situation. >>But with that, Victor, uh, I've really enjoyed our chat today. And, uh, do you have any final thoughts, comments, questions for me? >>Uh, you know, Kirk, I enjoyed it tremendously as well. Uh, I do think that, uh, that what is important, uh, to understand with data is that as there is no, uh, uh, no silver bullet, uh, and there is only incremental steps forward, this is not actually something to despair, but to give and be the source of great hope, because it means that not just tomorrow, but even the day after tomorrow and the day after the day after tomorrow, we still can make headway can make improvement and get better. >>Absolutely. I like the hopeful message I live every day to, uh, to make data a better place. And it is exciting as we see the advancements in what's possible on what's kind of on the forefront. Um, well with that, I really appreciate the chat and I would encourage anyone. Who's interested in this topic to attend a session later today on modern data quality, where I go through maybe five key flaws of the past and some of the pitfalls, and explain a little bit more about how we're using unsupervised learning to solve for future problems. Thanks Victor. Thank you, Kurt. >>Thanks, Kirk. And Victor, how incredible was that?

>>okay. Getting data right is one of the top priorities for organizations to affect digital strategy. So right now we're going to dig into the challenges customers face when trying to deploy enterprise wide data strategies. And with me to unpack this topic is Kirk born principal data Scientists and executive advisor Booz Allen Hamilton. Kirk, great to see you. Thank you, sir, for coming on the program. >>Great to be here, Dave. >>So hey, enterprise scale data science and engineering initiatives there. Nontrivial. What do you see? Some of the challenges and scaling data science and data engineering ops. >>Well, one of the first challenge is just getting it out of the sandbox because so many organizations, they say, let's do cool things with data. But how do you take it out of that sort of play phase into an operational phase? And so being able to do that is one of the biggest challenges. And then being able to enable that for many different use cases then creates an enormous challenge. Because do you replicate the technology and the team for each individual use case, or can you unify teams and technologies to satisfy all possible use cases? And so those are really big challenges for companies, organizations everywhere to think about >>what about the idea of industrializing those those data operations? I mean, what does that? What does that mean to you? Is that a security connotation? A compliance? How do you think about it? >>It's actually all of those industrialized to me is sort of like How do you not make it a one off? But you make it sort of a reproducible, solid, risk compliant and so forth system that can be reproduced many different times and again using the same infrastructure and the same analytic tools and techniques, but for many different use cases, so we don't have to rebuild the will reinvent the wheel, reinvent the car, so to speak. Every time you need a different type of vehicle, you build a car or a truck or a race car. There's some fundamental principles that are common to all of those, and that's where that industrialization is, and it includes security, compliance with regulations and all those things. But it also means just being able to scale it out to to new opportunities beyond the ones that you dreamed of when you first invented the thing >>you know, data by its very nature. As you well know, it's distributed, but for you've been at this a while. For years, we've been trying to sort of shove everything into a monolithic architecture and and in hardening infrastructures around that and many organizations, it's It's become a block to actually getting stuff done. But so how? How are you seeing things like the edge emerged? How do you How do you think about the edge? How do you see that evolving? And how do you think customers should be dealing with with edge and edge data? >>Well, it's really kind of interesting. I had many years at NASA working on data systems, and back in those days, the the idea was you would just put all the data in a big data center, and then individual scientists would retrieve that data and do analytics on it, do their analysis on their local computer. And you might say that sort of like edge analytics, so to speak, because they're doing analytics at at their home computer. But that's not what edge means. It means actually doing the analytics, the insights, discovery at the point of data collection, and so that's that's really real time Business decision making. You don't bring the data back and then try to figure out sometime in the future what to do. And I think an autonomous vehicle is a good example of why you don't want to do that. Because if you collect data from all the cameras and radars and light ours that are on a self driving car and you move that data back to a data cloud while the car is driving down the street and let's say a child walks in front of the car, you send all the data back. It computes and does some object recognition and pattern detection, and 10 minutes later sent a message to the car. Hey, you need to put your brakes on. Well, it's a little kind of late at that point, and so you need to make those discoveries, insight, discoveries, those pattern discoveries and hence the proper decisions from the patterns in the data at the point of data collection. And so that's Data Analytics at the edge. And so, yes, you can bring the data back to a central cloud or distributed cloud. It almost doesn't even matter if if your data is distributed, so any use case in any data, scientists or any analytic team in the business can access it. Then what you really have is a data mesh or a data fabric that makes it accessible at the point that you need it, whether it's at the edge or in some static post, uh, event processing. For example, typical business quarter reporting takes a long look at your last three months of business. Well, that's fine in that use case, but you can't do that for a lot of other real time analytic decision making. Well, >>that's interesting. I mean, it sounds like you think the the edge not as a place, but as you know, where it makes sense to actually, you know, the first opportunity, if you will, to process the data at low latency, where it needs to be low latency. Is that a good way to think about it? >>Absolutely. It's a little late and see that really matters. Uh, sometimes we think we're gonna solve that with things like five G networks. We're gonna be able to send data really fast across the wire. But again, that self driving cars yet another example because what if you all of a sudden the network drops out, you still need to make the right decision with the network not even being there, >>that darn speed of light problem. Um, and so you use this term data mash or or data fabric? Double click on that. What do you mean by that? >>Well, for me, it's it's, uh, it's a sort of a unified way of thinking about all your data. And when I think of mesh, I think of like weaving on a loom, or you're you're creating a blanket or a cloth and you do weaving, and you do that. All that cross layering of the different threads and so different use cases in different applications and different techniques can make use of this one fabric, no matter where it is in the in the business. Or again if it's at the edge or or back at the office. One unified fabric, which has a global name space so anyone can access the data they need, sort of uniformly, no matter where they're using it. And so it's a way of this unifying all the data and use cases and sort of a virtual environment that that no longer you need to worry about. So what's what's the actual file name or what's the actual server of this thing is on? Uh, you can just do that for whatever use case you have. But I think it helps Enterprises now to reach a stage which I like to call the self driving enterprise. Okay, so it's modeled after the self driving car. So the self driving enterprise needs the business leaders in the business itself. You would say it needs to make decisions oftentimes in real time, all right. And so you need to do sort of predictive modeling and cognitive awareness of the context of what's going on. So all these different data sources enable you to do all those things with data. And so, for example, any kind of a decision in a business, any kind of decision in life, I would say, is a prediction, right? You say to yourself, If I do this such and such will happen If I do that, this other thing will happen. So a decision is always based upon a prediction about outcomes, and you want to optimize that outcome so both predictive and prescriptive analytics need to happen in this in this same stream of data and not statically afterwards, so that self driving enterprises enabled by having access to data wherever and whenever you need it. And that's what that fabric that data fabric and data mesh provides for you, at least in my opinion. >>Well, so like carrying that analogy like the self driving vehicle, your abstracting, that complexity away in this metadata layer that understands whether it's on prem or in the public cloud or across clouds or at the edge where the best places to process that data, what makes sense? Does it make sense to move it or not? Ideally, I don't have to. Is that how you're thinking about it? Is that why we need this notion of a data fabric >>right? It really abstracts away all the sort of complexity that the I T aspects of the job would require. But not every person in the business is going to have that familiarity with the servers and the access protocols and all kinds of it related things, and so abstracting that away. And that's in some sense what containers do. Basically, the containers abstract away that all the information about servers and connectivity protocols and all this kind of thing You just want to deliver some data to an analytic module that delivers me. And inside our prediction, I don't need to think about all those other things so that abstraction really makes it empowering for the entire organization. You like to talk a lot about data, democratization and analytics democratization. This really gives power to every person in the organization to do things without becoming an I t. Expert. >>So the last last question, we have time for years. So it sounds like Kirk the next 10 years of data not going to be like the last 10 years will be quite different. >>I think so. I think we're moving to this. Well, first of all, we're going to be focused way more on the why question. Why are we doing this stuff? The more data we collect, we need to know why we're doing it. And one of the phrases I've seen a lot in the past year, which I think is going to grow in importance in the next 10 years, is observe ability, so observe ability to me is not the same as monitoring. Some people say monitoring is what we do. But what I like to say is, yeah, that's what you do. But why you do it is observe ability. You have to have a strategy. Why what? Why am I collecting this data? Why am I collecting it here? Why am I collecting it at this time? Resolution? And so getting focused on those why questions create be able to create targeted analytic solutions for all kinds of different different business problems. And so it really focuses it on small data. So I think the latest Gartner data and Analytics trending reports said we're gonna see a lot more focused on small data in the near future. >>Kirk born your dot connector. Thanks so much >>for coming on. The Cuban >>being part of the program. >>My pleasure. Mm mm.

(soft music) >> Getting data right, is one of the top priorities for organizations to affect digital strategy. So, right now we're going to dig into the challenges customers face when trying to deploy enterprise wide data strategies and with me to unpack this topic is Kirk Borne, Principal-Data Scientist, and Executive Advisor Booz Allen Hamilton. Kirk, great to see you, thank you sir for coming on the program. >> Great to be here, Dave. >> So hey, enterprise scale, data science and engineering initiatives, they're non-trivial. What do you see as some of the challenges in scaling data science and data engineering ops? >> First challenge is just getting it out of the sandbox, because so many organizations, they say let's do cool things with data but how do you take it out of that sort of play phase into an operational phase? And so being able to do that is one of the biggest challenges and then being able to enable that for many different use cases then creates an enormous challenge, because do you replicate the technology and the team for each individual use case or can you unify teams and technologies to satisfy all possible use cases? And so those are really big challenges for companies, organizations everywhere to think about. >> Well, what about the idea of you know, industrializing those data operations? I mean, what does that mean to you, is that a security connotation, a compliance? How do you think about it? >> It's actually all of those. And industrialized to me is sort of like, how do you not make it a one-off but you make it a sort of a reproducible, solid risk compliant and so forth system that can be reproduced many different times. And again, using the same infrastructure and the same analytic tools and techniques but for many different use cases. So we don't have to rebuild the wheel, reinvent the wheel, reinvent the car so to speak every time you need a different type of vehicle. You can either build a car, or a truck, or a race car there's some fundamental principles that are common to all of those. And that's where that industrialization is. And it includes security, compliance with regulations and all those things but it also means just being able to scale it out to to new opportunities beyond the ones that you dreamed of when you first invented the thing. >> Yeah, data by its very nature as you well know, is it's distributive but for you you've been at this awhile, for years we've been trying to sort of shove everything into a monolithic architecture, and in hardening infrastructures around that. And in many organizations it's become, you know, a block to actually getting stuff done. But, so how are you seeing things like the Edge emerge you know, how do you think about the edge, how do you see that evolving and how do you think customers should be dealing with edge and edge data? >> Well, that's really kind of interesting. I had many years at NASA working on data systems, and back in those days the idea was you would just put all the data in a big data center and then individual scientists would retrieve that data and do analytics on it, do their analysis on their local computer. And you might say that's sort of like edge analytics so to speak because they're doing analytics at their home computer, but that's not what edge means. It means actually doing the analytics, the insights discovery at the point of data collection. And so that's really real time business decision-making. You don't bring the data back and then try to figure out sometime in the future what to do. And I think autonomous vehicles is a good example of why you don't want to do that because if you collect data from all the cameras and radars and lidars that are on a self-driving car, and you move that data back to a data cloud while the car is driving down the street and let's say a child walks in front of the car, you send all the data back it computes and does some object recognition and pattern detection. And 10 minutes later, it sends a message to the car, "Hey, you need to put your brakes on." Well, it's a little kind of late at that point (laughs) and so you need to make those discoveries those insight discoveries, those pattern discoveries and hence the proper decisions from the patterns in the data at the point of data collection. And so that's data analytics at the edge. And so yes, you can bring the data back to a central cloud or distributed cloud. It almost doesn't even matter. If your data is distributed at sort of any use case in any data scientist or any analytic team and the business can access it then what you really have is a data mesh or a data fabric that makes it accessible at the point that you need it, whether it's at the edge or in some static post event processing, for example, typical business quarter reporting takes a long look at your last three months of business. Well, that's fine in that use case, but you can't do that for a lot of other real time analytic decision-making >> Well that's interesting. I mean, it sounds like you think of the edge not as a place, but as you know where it makes sense to actually, you know the first opportunity, if you will, to process the data at low latency where it needs to be low latency, is that a good way to think about it? >> Yeah, absolutely. It's the low latency that really matters. Sometimes we think we're going to solve that with things like 5G networks. We're going to be able to send data really fast across the wire, but again, that self-driving car is yet another example because what if all of a sudden the network drops out you still need to make the right decision with the network not even being there. >> Yeah that darn speed of light problem. And so you use this term data mesh or data fabric, double click on that, what do you mean by that? >> Well, for me, it's sort of a unified way of thinking about all your data. And when I think of mesh, I think of like weaving on a loom, you're creating a a blanket or a cloth and you do weaving and you do that all that cross layering of the different threads. And so different use cases in different applications in different techniques can make use of this one fabric no matter where it is in the business or again, if it's at the edge or back at the office. One unified fabric, which has a global namespace so anyone can access the data they need, sort of uniformly no matter where they're using it. And so it's a way of unifying all of the data and use cases and sort of a virtual environment that you no longer need to worry about. So what's the actual file name or what's the actual server this thing is on, you can just do that for whatever use case you have. I think it helps the enterprises now to reach a stage which I like to call the self-driving enterprise, okay? So it's modeled after the self-driving car. So the self-driving enterprise, the business leaders and the business itself you would say needs to make decisions, oftentimes in real time, All right? And so you need to do sort of predictive modeling and cognitive awareness of the context of what's going on. So all of these different data sources enable you to do all those things with data. And so, for example, any kind of a decision in a business, any kind of decision in life, I would say is a prediction, right? You say to yourself, if I do this such and such will happen. If I do that, this other thing will happen. So a decision is always based upon a prediction about outcomes and you want to optimize that outcome. So both predictive and prescriptive analytics need to happen in this same stream of data and not statically afterwards. And so that self-driving enterprise is enabled by having access to data wherever and whenever you need it and that's what that fabric, that data fabric and data mesh provides for you, at least in my opinion. >> Also like carrying that analogy like the self-driving vehicle, you're abstracting that complexity away and there's a metadata layer that understands whether it's on prem or in the public cloud or across clouds, or at the edge, where are the best places to process that data, what makes sense, does it make sense to move it or not, ideally, I don't have to, Is that how you're thinking about it? Is that why we need this notion of a data fabric? >> Right, it really abstracts away all the, sort of the complexity that the IT aspects of the job would require, but not every person in the business is going to have that familiarity with the servers and the access protocols and all kinds of IT related things. And so abstracting that away, and that's in some sense what containers do. Basically the containers abstract away all the information about servers and connectivity, you know, and protocols and all this kind of thing. You just want to deliver some data to an analytic module that delivers me an insight or a prediction, I don't need to think about all those other things. And so that abstraction really makes it empowering for the entire organization. We like to talk a lot about data democratization and analytics democratization. This really gives power to every person in the organization to do things without becoming an IT expert. >> So the last question we have time for here is, so it sounds like Kirk, the next 10 years of data are not going to be like the last 10 years, it will be quite different. >> I think so. I think we're moving to this, well, first of all, we're going to be focused way more on the why question, like, why are we doing this stuff? The more data we collect we need to know why we're doing it. And what are the phrases I've seen a lot in the past year which I think is going to grow in importance in next 10 years is observability. So observability to me is not the same as monitoring. Some people say monitoring is what we do but what I like to say is, "Yeah, that's what you do, but why you do it is observability." You have to have a strategy. Why am I collecting this data? Why am I collecting it here? Why am I collecting it at this time resolution? And so getting focused on those why questions, be able to create targeted analytics solutions for all kinds of different business problems. And so it really focuses it on small data. So, I think the latest Gartner data and analytics trending report, so we're going to see a lot more focus on small data in the near future. >> Kirk Borne, you're a dot connector. Thanks so much for coming on The Cube and being of the part of the program. >> My pleasure. (soft music)

>> Live, from New York City, it's theCUBE. Covering Lenovo Transform 2.0. Brought to you by, Lenovo. (electronic music) >> Welcome back to theCUBE's live coverage of Lenovo Transform, here in New York City. I'm your host, Rebecca Knight, along with my cohost Stu Miniman. We have two guests here on this segment, We have Kirk Skaugen, he is the president of Lenovo Data Center Group, and Brad Anderson, the Corporate Vice President of Enterprise Mobility for NetApp. Thanks for coming on the program. >> Thank you for having us. >> So the big news of the day, the NetApp Lenovo partnership, explain to our viewers exactly what this means. These are two global powerhouses joining forces. >> Yeah sure, so I think Lenovo has had an amazing year. Last year in our Transform 1.0 we announced the largest server portfolio in our history. And this year we announced the largest data center, data management storage portfolio in our history. With a partnership with NetApp, so we're creating a multi-billion dollar global alliance, a multi-year alliance and it has a place in a joint venture in China as well as we'll be distributing NetApp products in over 160 countries in the world. >> So tell us about the background to this partnership. How did it come about? >> Well, you know, for NetApp we were looking for expanding our reach, and there was two markets that were kind of underserved in. One being kind of the commercial SMB SME channel, and Lenovo has a high-velocity channel there, a strong position. So Lenovo made complete sense in that space as well as in China, where we have a strong brand but we're underserved there as well, so who is better in China than Lenovo? So for us this is all about global market and then the fact that they're a server vendor is just icing on the cake, because the other two server vendors in the marketplace are also our competitors. And then, Lenovo is so much more compatible and complementary to our entire business. >> Kirk, maybe you could spend a little more, because when you look at storage today, storage is really built on servers. You know, NetApp is, you know, at it's heart a software company, even back in the day NetApp was never, some of the other storage companies spent a lot of time and money on the hardware pieces. And of course had reliable, good, trustable hardware, but maybe explain how much, kind of, I.P. goes into this partnership. >> Yeah, sure. So I think today we have about 15 percent coverage of the overall storage market within Lenovo. We've grown our flash array business over 100 percent over the last four quarters. IDC had us at 30% quarter to quarter growth. So we've done well, but we've only cover 15% of the market. After this announcement, and shipping now today, we'll cover over 90% of the market in more than 160 countries. So we're using our global supply chain which is ranked number five in the world by Garner. Manufacturing in Europe, in China, in Mexico et cetera. Really expand this out through our channel partnership program. And in China we're taking a very unique approach to this joint venture. This isn't about taking global products and just trying to force fit them into China. China has unique software solutions, unique hyper scale requirements. So we're pooling our R and D there. Lenovo will be a 51% owner, NetApp a 49% owner. Brad's going to be on the board and there we're going to be delivering products in China for China. >> Yeah, is it, you've got a lot of experience with that. You talk about coming in the future there's an NFV software and hardware solution in China, so Lenovo has some experience doing this kind of engagement, you know. >> Yeah, I think we have a more than 50% growth now, year on year in China. We retooled a lot of the operations that we had there. We have a really nice, broad portfolio now since we launched Think System and Think Agile so it's a nice place to grow on. But today we talked about the joint venture with NetApp and also the fact that over the next year we'll be building out a telecom NFV company after having China Mobile and China Telecom with us as at Mobile World Congress. As well as new edge gateway and edge server solutions. >> Brad, I know cloud is in your title for what you are doing, when I hear NetApp talking, I see NetApp at all the cloud shows we go to. It's a very different world than when I think about NetApp ten years or twenty years ago as like, you know, the Nas Filer company. So bring us up to speed of kind of the NetApp today the momentum and what this brings. >> Yeah, I mean we are going through our own transformation where we were principally a storage company and now we want to be a data company, and increasingly to be a data company you got to be a cloud company. And so, we continue to develop what we think are the, you know, the best storage products in the world, but they are all cloud connected. 'Cause we want data to be able to flow from prim to cloud and customers be able to, you know. That's what really kind of fuels these digital enterprises is that data is the new oil. And so in doing that we have kind of expanded NetApp's charter significantly to being the data authority in hybrid cloud. Hybrid being both the private and the public. And so part of my business is really focused on providing products and solutions so customers can have the same experience in building their own private clouds that they enjoy in the public. And then on the public side we have partnerships with all the hyper scalers to put NetApp's in there so they can deliver native cloud data services. And so, this is a very different company where we're getting more and more cloudy every day. (Rebecca laughs). And that's part of our transformation intentionally. >> So, the transformation, it's the theme of this conference and you were up on the main stage talking about Lenovo's turning this corner and really accelerating its growth, and also talking about the transformation from within the company. Changing the look of the leadership team in particular. Can you tell our viewers a little bit more about that strategy. >> Sure, so we acquired the IBM system X business in late 2014 and we did some things really well and we did some things that we've learned from. So we spent, you know, basically the last 18 months transforming the whole company. New channel programs, new system integrator partnerships, new training certifying over 11,000 people in the world now. Tripling the number of our solution recipes. And we have transformed The management team as well. We have replaced about 19 executives because we wanted the right balance of external and internal perspectives from our competitors as well as from ex-Lenovo and ex-IBM employees. So we feel like we have a very customer-centric organization now and, again, Gardner now is saying we are growing 49% year on year in units, IDC said we are growing 87% year on year in revenues. So I think customers are responding to the new product line. Over the last year the Think System brand truly meant the highest customer performance, the highest reliability, the highest customer satisfaction. And as a result it does take a while to transform. And I think that over the last 12 months you've seen that and we're exponentially growing now as a company. >> And you see it in your results. I mean, they are outstanding. >> So Brad, bring us inside the products a little bit. So we've got, it's the Think System DE and DM. Of course the storage industry very much, they need to trust it, they need to understand it. Gives little to understand, I believe DE maybe has something to do with the >> The E series >> The E series there and tell us the DM series, what's underneath there and how do people understand what's different and what's the same. >> Yeah, I mean the. We're taking platforms across our E series, our FAS and our all flash arrays. So the DE corresponds to the E series. The DM will have our FAS products as well as our all flash array products in there. So that's kind of the mapping. We're putting initially I think, ten products in there. We have the capacity to expand and I'm sure we're going to learn a lot because these are serving markets that NetApp doesn't typically serve. So I think not only is this going to give Lenovo the tools to compete, it's going to give us a lot of information to even build better products, better solutions for both NetApp and our Lenovo customers. So we're super excited about that. The second thing is, it's OnTab, it's the same core software, and all the value and performance testing and validation you get with NetApp. That all goes into the Lenovo branded products as well. And we have made it one of our hallmarks is our data fabric. All of the data services that are on top of this that you can move data and manage data between platforms, that is really important for the NetApp customer. All those values extend to the Lenovo customer. So if they also have NetApp in their environment, or vice-versa, they can share or move data between both those platforms. So that's, nowhere else in the industry is that possible across vendors let alone within. >> So how does it work when you are in the product development process. Two companies, both relentlessly focused on customers. This is part of your culture, part of your DNA. So how do you work together in terms of innovating and collaborating. >> Well, I think the first thing is you just look at the core business: our server business and NetApp branded storage, or Lenovo branded storage based on NetApp's portfolio. We're going to have a better together solution. So the first thing we're looking at is a set of solution recipes so that when you use NetApp and Lenovo together, you're going to get a better experience as a customer base. So that's why I am excited today. We've launched three times as many engineered solutions as we did a year ago. And trained these 11,000 people because we have a very solution oriented sales force and a very complementary channel. From a development perspective, we're going to be building X Clarity management into our portfolio. So the same systems management software that is mission critical for Lenovo server products will now manage the big system DE and DM products. So it's a very familiar management interface for customers, there's an engineering effort gone with that. And then on service and support, we're going to use over 10,000 people around the world that Lenovo has to go service and support these products. So we can deliver a premium customer experience. Whether you're buying the server or the storage. And back to the customer base: we're going to, especially in China, have deep engineering collaborations. Where we're walking into those customer bases and asking what's unique about the China market. >> And, and. It really helps that the two companies are very complementary. So NetApp has deep storage expertise, Lenovo has tremendous compute expertise. So they are very complementary and as customers want more and more complete solutions, we are learning, our engineers are learning from each other and it doesn't hurt the fact that we have a large engineering. We NetApp, have a large engineering population in the research triangle where Kirk's people are at. >> That's right. We're probably one kilometer away from each other in research triangle park. >> Geography matters, location location location. >> No, and our two support organizations are next door as well. So I think that proximity will only contribute to the collaboration. >> Yeah, exactly. >> Alright, so the storage industry actually has a relatively good track record of some deep, long partnerships. NetApp has had a number of them over the years. Tell us, what does success look like if we look back three years from now, what's this partnership. >> Well, what we said publicly is we plan to have a multi-billion dollar, multi-year alliance. So that's going to be fantastic as we grow in over 160 countries. We're going to use Lenovo's extensive supply chain network. So you know as one of the largest kind of procurers of componentry and things around the world, we get to leverage this global factory network to build even more value into that situation. And in China specifically, we've set a goal of being a top three storage player. So we both have probably single digit share in China but together with this collaboration we are setting sights quite high to be in the top three over the next several years. >> I think that's exactly right and I think those are all achievable goals. But right now, we want to get out the gate fast. I mean this is a partnership with two companies with a lot of momentum and I see this as a huge opportunity for both our companies to kind of amplify that momentum near term. And so while there's a lot of excitement on the future, I think success is going to look like, you know, some very exciting results that Kirk can share at Transform 3.0 next year. >> That's right. And for our customer base, we have already gone into production. Taking orders, as of today and tons of engineering, tons of manufacturing development. So we'll have a whole host of seed units and early access units. Our customers can get their hands on this stuff right away and start testing it in their environment. >> As you said, it is an audacious vision. You announced an audacious vision last year, you did another one again this year. So when you think about what you want to be talking about next year. You said what success looks like. What are some other things that you're working on? You said, this is a process, Lenovo has turned the corner and it's got a lot of momentum. But what else are you, what else do you have on tap that you're... >> Well, if we tell all of you that, (Rebecca laughs) we won't have this here next year. >> Yeah >> But I think today is about entry and midrange. About expanding Lenovo's breadth from 15 to 90% of the market and being very aggressive against our top competitors that have a combined server storage portfolio. And I think as I've gone around the world, I've been in Latin America, in India, our channel partners are incredibly excited about this. So I think while other customers might be taking business more direct, we've traditionally been very channel-centric. So, I've seen a lot of pull for choice in the market and I think that's what we're going to deliver to our channel partners. But we will have a lot more in store, that I can promise you. This is phase one of a multi-phase, multi-year plan. >> I think there's a lot of things, there's a lot of possibilities on the product development side and how we can do better products, but I think a lot of success is going to look, it's going to come in our global market. Already, Kirk, since I've been here, I've had a channel partner come up and said "Hey, this makes me rethink my channel partners all over again", because now that channel partner who's a Lenovo has the full breadth of the storage portfolio. So I think this is going to be really good for both of us. Particularly when, you know, Lenovo and NetApp are both very channel friendly partners and companies and I think this I going to be a catalyst to have more people on our side than ever before. >> Kirk, just last thing, just give you the opportunity to talk about some of the other breadth and choice and other things that Lenovo has going on. We're going to talk to some of your team about, you know, hyper converge and hyper scale and other hyper things, but yeah. (Rebecca laughs) >> Well I think the good news about our growth now is that we're doing it across multiple segments in the industry. There isn't just one part of the market that growing. So last year we set an audacious goal of being the largest supercomputer company in the world by 2020. We've now crossed that actually this year. So we are the largest supercomputer company in the world. About one in every four supercomputers now are there. And we're expanding that into a lot of AI offerings as well with our four artificial intelligence centers, from China, Germany, Taipei, Beijing. All having customers bring their AI workloads into a controlled environment with our partners where there's intel and video or the FGBA vendors. So super-computing is alive and well and we continue to innovate with our warm-water cooling technology that's going to be here on display. We think we're building one of the largest supercomputers in Europe right now using that technology. So not just helping solve global warming but being more energy-efficient while we are computing on that as well. In hyper scale we've grown to about delivering six of the top ten hyper scalers products. And we're doing that through, basically starting with a white sheet of paper with our customers and building more than thirty customized products. In the motherboard, in the system, in putting it through our entire supply chain. Versus just, in the past maybe two years ago, maybe just leveraging ODM products, so. Significant growth in hyper scale where we're bringing on new billion dollar customers on a regular basis now. And then in flash arrays, our traditional business, we were over 100% growth year on year. So we're building off of momentum. We had great products but only covering 15% of the market, now much larger. Last but not least, we did announce since Transform, new divisions in embedded and IoT as well as in telecommunications NFVF software. We think each of those can be billion dollar groups within Lenovo, so that's probably a lot of what we would be talking about next year is announcements and innovations we've had. Would be Transform 3.0 probably. (Rebecca laughs) >> Well, we're already looking forward to the next Transform. >> 3.0 will be CUBEd so we look forward to that. >> Stu, very nice. Very nice. Excellent. Well thank you so much Brad and Kirk for being on the show, I really appreciate it. >> Thank you very much. >> I'm Rebecca Knight for Stu Miniman. We will have more from Lenovo Transform and theCUBE's live coverage, just after this. (intense electronic music)

>> Voiceover: Live, from Washington, DC. It's the Cube covering .NEXT Conference. (upbeat music) Brought to you by Nutanix. >> We're back at Nutanix .NEXT, everybody. This is the Cube, the leader in live tech coverage. This is day two of our wall-to-wall coverage of .NEXT Conf. Kirk Skaugen is here, he's the president of the Lenovo Data Center Infrastructure Group. Sudheesh Nair is the president of Nutanix. Gentlemen, welcome to the Cube. I'm Dave Vellante, this is Stu Miniman. We're part of the nerd herd here at the conference. So Kirk, let's start with you. We've been talking to Nutanix all week. You guys got the great booth, we've been looking at your booth all week. Transform, last week you guys had a big conference. Lenovo, obviously undergoing major transformations, as are your customers and your partners. Give us the update, how's it going? >> Well, it was a big event for us. We've been working for about two and a half years since the acquisition, the IBM X-Series team. So we launched basically our biggest data center portfolio in history, about 14 new servers, seven new storage boxes, five new network machines, and, probably more importantly to our relationship, we announced two big new brands. So Think System is kind of for the traditional infrastructure and then Think Agile, and our appliances with Nutanix for hyper-converge infrastructure. >> You guys have been talking to analysts and your community about what I call choice. You know, you've got a lot of different choices of partners, of even now processor types, hyper-visors, etc. So talk about how that's important to your partnership strategy, generally and specifically unpack some of the Lenovo specifics. >> I think it is important to have a point of view, when you're talking to customers nowadays. The problem is: is the point of view about your own company's thought process, Wall Street expectations or the point of view's doing by what is right for the customer. Take it for example, an SSD, a commodity SSD from Samsung or Toshiba. If you take that SSD and put it inside a Solar and try to sell it, you probably will get X dollars for it. That same SSD, if you put it inside a high-end SAN, you can probably take like 10X more that, right? Where do you you are-- >> Those were the days. (laughing) >> The thing is where do you think you will be going first? What will you be trying to sell first? The thing I like about Lenovo is that they're made to be efficient. That it is going to be a software defined world. But hardware does matter, the library matters, support matters and along with Lenovo, we are able to go to customers and completely re-transform, you know sort of change their architecture without being caged by any sort of Wall Street expectation that goes counter to what is right for customers. >> Kirk, I know there are many milestones you talked about at Lenovo Transform. I think if I remember it, one of them was the 20 millionth x86 server is going to be shipping sometime in the next couple weeks. >> That's right. >> To think Agile line to kind of look at software defined, how does Nutanix fit into that? You've been OEM-ing them since before you went into this branding so tell us how that came together to the new line. >> So I think we're celebrating this year 25 years an x86 servers and so you're right, we are looking at a software defined world and what I constantly hear is that Lenovo is getting pulled in because we don't have a legacy infrastructure of a big SAN business or a big router business, so we're kind of unencumbered by that but we're shipping our 20 millionth x86 server in July, next month. But with Nutanix, what we're basically doing is we're tightly integrating our management software with their prism software, we're looking at integrating some of the network topology work now with innovation because rather than kind of a legacy network that people are used to now, well we moved to a hyper-converge infrastructure, some of those pain points move onto networking but we've been innovating together now for almost two years and I think we're crossing almost 300 customer deployments now, almost 200% growth since we've started. At least Lenovo's goal is we're going to be Nutanix's largest growing OEM partner this year. >> So talk more about that innovation strategy because, you know, the general consensus is well, it's x86, they're all the same. How do you guys differentiate from an innovation standpoint? >> Well, what we talked about at Transform is our legacy now is we're number one in customer satisfaction in Lenovo on x86 systems in actually 21-22 categories. And that's a third party survey that's done across like 700 customers in 20 countries. Number one in reliability. So we're building off of this infrastructure, off of a really strong customer base. What we're trying to do on Think Agile is completely redefine the customer experience. From the way you configure the system, we can now do configure to order in three weeks. Which we think is about half of what anyone else in the industry can do relative to our competitors. And then we're innovating down the the manageability layer, the networking stack, all of those pieces to really build the best solutions together. >> Sudheesh, there's an interesting two differing things if I look at Lenovo and your partnership. Number one is Kirk says they don't have any legacy, but one of the reasons you're in OEM with them is because they do have history, they've got brand, they've got channel, how do those come together in the partnership? >> So remember, I think before XEI, servers used to be a stateless machine, being they would move the VM's back and forth because the data lives somewhere else in the storage system. So what you expect out of the server, when it comes to reliability and serviceability are very different. What we did with XEI when we came on for the first time, we took the liable storage piece, sharded into small segments and move them inside the servers. All of a sudden, the library of the server has become exponentially more important. Affordability, serviceability, how you do things like form guard management, all those things become important now because your entire core banking application is running inside a bunch of servers, there is no SAN sitting behind protecting all of this. One of the reasons why Lenovo's ex-clarity project is one of the first apps on our app store is because we want to make sure that customers have a fully integrated souped enough experience of not just managing the product but also experiencing the day one and day two. Upgrades, replacements, failure replacement, all of those things. So between our relationship with Lenovo's hardware and engineering, plus the support, we are able to deliver a one plus one equals three experience for our customers. >> So Sudheesh, I heard almost 300 customers you're at. Could you give us a little bit of kind of either verticals or geography that you're being successful? >> What we've seen with Lenovo that is a little different from the rest of the business that we do is that majority of the business is coming from large customers and second, I would say financial sectors were the biggest initial moment it seem to be. And the repeat business is following the same pattern that the customers who buy are coming back and buying again. In fact, one of the largest financial institutions in the country, New York, bought last quarter a decent size, a seven figure plus deal, and they'll probably come back and buy again this quarter. So that pick-up is happening really fast and customers are happy with the overall experience. And it's also about the courting process, the shipping process that he talked about, these are all simple things but these are extremely important in the customer buying experience. >> I think from our perspective, we operate in over 160 countries, a lot of people don't realize we have over 10,000 support specialists that call with more than a 90% customer sat rating. So when we're bringing in Think Agile, what we're bundling now with Think Agile and the Nutanix appliances is premiere customer support so you don't even go to an automated system, you go directly to a local language speaking person on the phone immediately and you get one vendor to support you across your server, your storage, your networking in the whole configuration. That has gotten customers like for us, Jiffy Lube, Holloway, Beam Suntory who's the third largest premium spirits vendor in the world, one of the largest Japanese auto-manufacturers, I mean, I think it's been across all verticals that we've seen success together. >> I was in Asia last week, two weeks ago, and the business there is tremendously picking up speed. It goes through the story, you know, they have local language support, local marketing, local channel enablement, those things matter significantly. Lenovo's very strong in all those areas. >> We live in a world that's data driven. Data is the new oil. You've got to montage your data. You guys have big volumes, you have a lot of data. In relation to partnerships, in this day and age, what role does the data play? Is there an integration of data, is there a way to get more value, how are you getting more value out of the data that you share with your customers? >> I started maybe working China as well in one of the areas, this is an extremely important question, don't think of this as a hardware and infrastructure software play, this is about what customers want. In one area, for example, SAP. One of the largest SAP's partner is Lenovo and by partnering with Lenovo, we are now able to deliver, in fact, there is a specific product CD's that we've built for Lenovo HANA customers called Bridge to HANA where we deliver certified HANA platform on Lenovo along with the Nutanix software as a prediction and testing and wiring IB's next to that. By lapping the Lenovo SAP expertise, the hardware expertise, and the Nutanix's infrastructure expertise, the customers can have a single one-stop shop for analytics, ERT, and everything. Those kind of experiences are what customers are looking for. >> I think one of the reasons people are coming to Lenovo is we're not trying to compete with them necessarily far up the stack like we would think some of our competitors are doing. But if you look at SAP, we're excited because we've had a relationship in software defined with SAP since probably eight years ago. We were actually blazing the trail, I think, with them on software defined and we got rid of the legacy SAN out of that solution probably in 2010, started eliminating some of the costs associated with that. And now we're proud that SAP runs Lenovo, and Lenovo runs SAP. We're starting to pull some big customers together like V-Grass which is one of the largest, fastest growing clothing manufacturers in China, but we're not trying to like hoard the data and use the data, or compete with our customers on data. >> Alright, guys, we're out of time. But just to sort of last questions relates to the future. Where do you guys want to take this? A couple years down the road, where are we going to see this partnership, what's your shared vision? >> You saw today, we moved from that hyper-converge to a multi-cloud world. A multi-cloud world where we are redefining what hybrid cloud really means. There's a lot of work to be done to bring applications, infrastructure, and uses togethers. And partners like Lenovo is how we are going to get there. >> Yeah, absolutely, I think this is just the beginning. We're looking to a transposable world, hyper-convergence is one path along the way. We've been participating in public cloud and now the world is moving into hybrid cloud. We've got great partnerships I think we'll see strong growth with both companies for the next few years. >> Can't do it alone. Kirk and Sudheesh, thanks very much for coming to the Cube, I really appreciate it. >> Thanks so much. >> You're welcome. Keep right there, buddy, Stu and I will be back with our next guest right after this short break. We're live from Nutanix .NEXT, we'll be right back. (upbeat music)

>> Narrator: Live from New York City, it's theCUBE. Covering Lenovo Transform 2017. Brought to you by: Lenovo. >> Welcome back to theCUBE's coverage of the Lenovo Transform Event. I'm your host, Rebecca Knight, along with my co-host, Stu Miniman, who is a Senior Analyst at Wikibon. We are joined by Kirk Skaugan. He is the Executive Vice President and President of Lenovo Data Group. Welcome back to theCUBE, Kirk. You're a veteran. >> Yeah, we're doing this on a monthly basis. It's great. >> So you're fresh off the keynote. The theme of this conference is transform. Lenovo has undergone a massive transformation in recent years. What is your focus, and where do you see the biggest points of change in the company? >> Well, I think we're sort of celebrating today, this transformation to the next phase of our growth. If you think about us as a company, we've kind of acquired the x86 business, server business from IBM a few years ago, and we are also building off more than a decade of our China heritage, for the ThinkServer business, so that's combining the two together. Kind of driving to our next phase of growth. The whole purpose of today is really transforming the customer experience, and starting with the customer first. We're incredibly proud that we just got ranked number one in customer satisfaction, again but we're not kind of stopping there. We're going to use this announcement today to catapult us ahead. >> Customer service has always been a strength of Lenovo, and as you said, you're going to continue to drive toward that. You said in the keynote that you're incentivizing employees around customer service. Can you talk a little bit about how you plan on maintaining the edge? >> So this year, every Lenovo employee is getting incentivized on customer experience. We're making them take a personal goal of how they can better improve the customer. Regardless of whether you're an engineer, or you're in phone support, or these kind of things, so it really starts at the grass-roots level. It gets everybody thinking customer first, which is great. Again, we're excited, because we're in 21 of the 22 categories, number one is x86 servers, but we're constantly learning and wanting to improve. That's where we're starting. >> Kirk, Y.Y. in his keynote, talked about, just the pace of change. That, forget about 18 years ago, 18 months ago we probably couldn't predict how fast things are going. How does that drive your strategy? How you work with customers, and drive the product line? >> So I think customers are asking for simplicity. It's getting so complex, and the rate of change is so much, so when we did this design of both our server storage and networking, we're kind of future-proofing it. We are actually dramatically reducing the number of products, but building to be more flexible, so you can qualify less solutions, but have them live longer in your data-center. That's been a key attribute as we look at future-proofing. Also, as we move to software-defined, that's going to be a key element as well, because people aren't looking to change out the hardware as much as they are the software part. Everything from our configuration managers to our system hardware management, and with Xclarity, the whole design experience, we're changing to simply the experience for the customer. 'Cause the change is almost getting to the point that it's too much for people to handle, from a technology transformation perspective. >> You're celebrating 25 years of the x86 server that you're offering, so explain to us the new branding. You've got two new brands that you've announced today. The kind of, thinking behind that, and walk us through what they are. >> Sure, so today, we're announcing ThinkSystem and ThinkAgile. So on the server side, we had both the ThinkServer brand from Lenovo and the SystemX brand from IBM. We're building those two together. The engineers were given the charter years ago, to say how do you stay number one in reliability, Number one in customer satisfaction, and then we have a legacy now of over 150 world-record benchmarks. So it's a brand that's highly flexible, premium, and it's going to span now, not only our server products, but server, storage, and networking. One of the surprises I had joining Lenovo is just, we have hundreds of engineers in networking that the old IBM had acquired from companies like Blade Network Technologies, and now things like hyper-converged storage. Once you've solved the storage-compute integration, networking becomes the next bottleneck. The products we're announcing today on ThinkAgile, which is our software-defined products, are helping solve not only the hyper-converge storage problems, but also some of the challenges that brings to networking, and moving traffic from a traditional north-south architecture to east-west. Simply put, ThinkSystem for network, storage, and server; and ThinkAgile for software-defined. >> On the ThinkAgile, the two partners that I saw highlighted up on screen were Nutanix, which you've had in OEM for awhile, and the Microsoft Azure, with Azure staff, we knew is coming this year. Both of those companies have a lot of partners. Why is Lenovo positioned to be a strong contender with both of those companies. >> I think that when we talk to CIOs, what we're hearing pretty constantly is that Lenovo's lack of legacy... We don't have a huge legacy router business, or a huge legacy sand business, and all the associated costs and services. We see our competitors sometimes up, pushing one more generation of the legacy technology, and so we feel like we're getting pulled in to leap ahead, not being encumbered by the past. Then I always say, little things don't mean a lot; little things mean everything. It's the thousands of Lenovo engineers that are tuning this for both of those solutions, especially for Nutonix, we've got integrated networking now, in the stack, so we're not just solving the storage problem, but we're addressing that network solution as well. There's a reason why we have 150 world-record benchmarks. It's that fine-tuning with our partners to get the last few bits of performance out of the systems. >> I wonder if you could talk a little bit more about this lack of legacy, as well as the cost-efficiencies that you referenced in the keynote, in terms of having everything in China, and you described going left to make the servers, and going right to make the PCs. Can you talk a little bit about how that helps Lenovo improve it's offerings? >> So I think that we have the benefits of being an autonomous data center group, and making our own decisions, but we're taking care of the manufacturing, taking advantage of the manufacturing capability of Lenovo. If you look at the devices inside, Lenovo's building about four devices a second. On the server side, we build a little bit over a hundred servers an hour. But if you go into, for example, we have factories in Sárvár, Hungary; Monterrey, Mexico; North Carolina; and even Shenzen. If you go into our Shenzen factory, the parts warehouse is common on the first floor. It comes up through the second floor, and actually goes left for notebooks, right for servers. So all that vendor-managed inventory, we're taking advantage of that scale of four devices a second, and we get that advantage, unlike some of our competitors. What that really means to our customers is we can compete with the best commodity costs, and the best manufacturing costs in the industry. Some of our third-party analysts are saying we have manufacturing rates that could be almost half of our competition, because some of the scale that we have. >> Kirk, one of the things that caught my attention in the keynote was talking about using the intelligence, and inside your supply-chain through the whole life-cycle of the product. Can you give us a little bit of insight as to how you're using it internally, and what customers see from that. >> So we just hired our Chief Technology Officer, was Dr. Rui, Yong, who's ex-Microsoft. He's one of the world leaders in Artificial Intelligence. Our CIO and us in the data-center group, we've all been collaborating to bring Artificial Intelligence deeper into everything we do, but even from our supply chain to our order delivery, which is why I think our customer satisfaction rates are so high, because we can predict the supply chain, the right amount of inventory, and shipping it all the way through, and predicting the dock-date to our customers incredibly well. One of the key learners we had over the last couple of years of acquiring the IBM x86 server business, it took us almost two years to get off the IT systems, right? We had over forty different databases that we had to integrate in, and now that, as of January 1, they've all become part of Lenovo, pulling those big data analytics together and using Artificial Intelligence, we can now track the aged population of all of the installed base of over about two and a half million servers that we have out there, who's coming up for warranty replacements, who's coming up for hardware replacements, and it's almost that predictive analytics that customers are really valuing. >> In terms of Lenovo and it's aspirations for the future, in terms of becoming the world's biggest super-computing company, you are the fastest growing, but let's talk about impact. This is something that Y.Y. talked about in his keynote, and really making sure that Lenovo is working, not just helping companies sell more widgets, but also with scientific breakthroughs, curing diseases, predicting the effect of climate change. How big a part is that of your job? >> I think it's something that's incredibly motivating to Lenovo employees, beyond financial return to shareholders, is every day I get internal texts or WeChats from Lenovo employees that are feeling really proud to be part of a company that's off trying to do something good for humanity, as well. I mean on the PC side, we're selling ChromeBooks and bridging the digital divide between kids in Africa and kids in the major metropolitan areas of the world, but on the data center side, things like we did with the Barcelona supercomputer, where we now have the fastest, next-generation Intel computer on the planet. It is one of the breakthroughs of predicting weather and climate change, predicting and tracking the next tsunami to evacuate coastlines faster, trying to find cures to some of the most terrible diseases on Earth. It's a huge part of the culture, of trying to do good for the world, not just make a financial return. >> Kirk, I want to go back to ThinkAgile for a second, because you dropped a hint that we couldn't let pass. Said that it's likely that we should expect M and A, from Lenovo here, now, I don't expect you to tell us who you're looking at, but what do you look for, what type of company to look for, or what would fit well into the Lenovo portfolio? >> Well, it's funny, because we're Lenovo, so we're not Huawei, or Cisco, or EMC, right? Big names, without saying traditional networking and storage. All of these startups out there that are essentially competing with those large legacy companies are coming to us saying, we want either access to China, given our strong China presence, but also global-scale. Because once they get to a couple hundred million dollars in revenue, they have a real tough time scaling, and as I said, we're participating now in over 160 countries, 50 call-centers. That's a pretty big investment, even for some of the fastest growing software-defined companies in the world, to set up. I think we want to build our own internal intellectual property, but we're also going to look at joint ventures and M and A's in the areas of software-defined networking, software-defined storage, because our customers, again, see us with that lack of legacy and are really pushing us to go even faster, which is great. >> So those are the business that you're interested in, but what are the kind of cultures that you're looking for, particularly because culture is such an important part of Lenovo? >> One of the reasons I moved from Intel to Lenovo is that they're just fierceless innovators, right? And we became number one in PC through innovation, not just cost-cutting and I see that on the data-center side as well. All of those little things that matter. So I think we want to have people who have the highest of aspirations. When we go into something, we want to be number one in it. People who are fearless, they're not afraid of companies that might be three or four times our size, but that want to make a global impact. A lot of these customers, they've already made their financial returns in a previous startup, and now they're looking at how they can go change the world, and the scale that Lenovo brings, I think is something that's pretty exciting to them. >> Kirk, on the Intel point, I think this is the fourth show that we've done theCUBE at this year, where Intel's been up on stage, arm and arm with a partner, in the cloud-space, in the server-space, talking about that next generation chipset. What's going to set Lenovo apart with this next wave, and what are your customers excited about for this next spin of the Intel chipset? >> We're seeing 59% performance improvement on things like SAP HANA, where we're number one in the world in installations. We're seeing better total cost of ownership productions. So particularly in hyper-scale and HPC, we see a step-function transition over, almost immediately on the new Intel chips. We're looking at all architectures, of course, as well, but I think with Intel, we've put in the largest omni-pass solutions on the planet. With Barcelona supercomputer, we're working not just on processors, but on the SSDs, on their accelerator, on high technology, on the fabrics. So we have a really tight innovation relationship with them, so we're selling probably more content per box, therefore we're obviously able to fine-tune the entire portfolio together with them. I think customers are excited about us continuing this world-record performance that we've had. The TCO reductions, of getting to lower power. Most of these supercomputers are still constrained by power. We have more than 25 patents now in water-cooling technology to try to be greener for the Earth. I think that those are some of the things that we're seeing from Intel. >> Those are the selling points. >> Yeah, higher performance. We have a very tight, close relationship, so there's not a lot of finger-pointing. We get into an issue, as all companies do, we can solve it very, very quickly. I think again, being number one in customer satisfaction from a third-party is our testament to that. >> Kirk, this last question I had on that, the hyperscale market. Can you just give us the update, as kind of Lenovo's position there. Heard a lot about the HPC market, we know, kind of a traditional enterprise market, but hyperscale, I think, is one of the areas you differentiate yourself. >> We obviously sell Dubai, to Alibaba, Tencent is part of China, we're one of their largest suppliers and partners, and we're now expanding, through this new segment-focus into the west coast of the United States. We don't necessarily go out and say the names of those customers, but there are multiple hyperscale customers in the top ten, many of which are based in the US that we are already now shipping into significantly more units this year than last year. It's a function of really getting cost-optimized. Again, we're taking advantage of PC economics and bringing them to hyperscale computing, so we're not afraid of low-margin, high-volume business, because that's what we're doing in the PC space every day. So, we're going to continue to expand, not just in the top-tier ones, but also moving into the tier two, tier three, kind of customer bases as well, so we're expanding that sales force. Looking at it only end-to-end, only burdening it with what it needs to be burdened with, right? Relative to the cost structure so that we can compete with the best, most cost-effective companies in the world, and still make a little bit of money for Lenovo shareholders. >> Kirk, thanks so much for joining us. It's been a pleasure having you on the show. >> Yeah, the excitement around here's been great. We appreciate you guys coming, and appreciate your time. >> Great. I'm Rebecca Knight for Stu Miniman, we'll be back with more of Lenovo Transform after this.

>> Announcer: Live from Las Vegas, it's The Cube, covering HPE Discover, 2017 brought to you by Hewlett Packard Enterprise. >> Okay, welcome back everyone. We're here live in Las Vegas for our exclusive three day coverage from The Cube Silicon Angle media's flagship program. We go out to events, talk to the smartest people we can find CEOs, entrepreneurs, R&D lab managers and of course we're here at HPE Discover 2017 our next two guests, Andrew Wheeler, the Fellow, VP, Deputy Director, Hewlett Packard Labs and Kirk Bresniker, Fellow and VP, Chief Architect of HP Labs, was on yesterday. Welcome back, welcome to The Cube. Hewlett Packard Labs well known you guys doing great research, Meg Whitman really staying with a focused message and one of the comments she mentioned at our press analyst meeting yesterday was focusing on the lab. So I want ask you where is that range in the labs? In terms of what you guys, when does something go outside the lines if you will? >> Andrew: Yeah good question. So, if you think about Hewlett Packard Labs and really our charter role within the company we're really kind of tasked for looking at things that will disrupt our current business or looking for kind of those new opportunities. So for us we have something we call an innovation horizon and you know it's like any other portfolio that you have where you've got maybe things that are more kind of near term, maybe you know one to three years out, things that are easily kind of transferred or the timing is right. And then we have kind of another bucket that says well maybe it's more of a three to five year kind of in that advanced development category where it needs a little more incubation but you know it needs a little more time. And then you know we reserve probably you know a smaller pocket that's for more kind of pure research. Things that are further out, higher risk. It's a bigger bet but you know we do want to have kind of a complete portfolio of those, and you know over time throughout our history you know we've got really success stories in all of those. So it's always finding kind of that right blend. But you know there's clearly a focus around the advanced development piece now that we've had a lot of things come from that research point and really one of the... >> John: You're looking for breakthroughs. I mean that's what you're... Some-- >> Andrew: Clearly. >> Internal improvement, simplify IT all that good stuff, you guys still have your eyes on some breakthroughs. >> That's right. Breakthroughs, how do we differentiate what we're doing so but yeah clearly, clearly looking for those breakthrough opportunities. >> John: And one of the things that's come up really big in this show is the security and chip thing was pretty hot, very hot, and actually wiki bonds public, true public cloud report that they put out sizing up on prem the cloud mark. >> Dave: True private cloud. >> True private cloud I'm sorry. And that's not including hybrids of $265 billion tam but the notable thing that I want to get your thoughts on is the point they pushed was over 10 years $150 billion is going to shift out of IT on premise into other differentiated services. >> Andrew: Out of labor. >> Out of labor. So this, and I asked them what that means, as he said that means it's going to shift to vendor R&D meaning the suppliers have to do more work. So that the customers don't have to do the R&D. Which we see a lot in cloud where there's a lot of R&D going on. That's your job. So you guys are HP Labs, what's happening in that R&D area that's going to off load that labor so they can move to some other high yield tasks. >> Sure. Take first. >> John: Go ahead take a stab at it. >> When we've been looking at some of the concepts we had in the memory driven computing research and advanced development programs the machine program, you know one of the things that was the kick off for me back in 2003 we looked at what we had in the unix market, we had advanced virtualization technologies, we had great management of resources technologies, we had memory fabric technologies. But they're all kind of proprietary. But Silicon is thinking and back then we were saying how does risk unix compete with industry standards service? This new methodology, new wave, exciting changing cost structures. And for us it was that it was a chance to explore those ideas and understand how they would affect our maintaining the kind of rich set of customer experiences, mission criticality, security, all of these elements. And it's kind of funny that we're sort of just coming back to the future again and we're saying okay we have this move we want to see these things happen on the cloud and we're seeing those same technologies, the composable infrastructure we have in synergy and looking forward to see the research we've done on the machine advanced development program and how will that intersect hardware composability, converged infrastructure so that you can actually have that shift, those technologies coming in taking on more of that burden to allow you freedom of choice, so you can make sure that you end up with that right mix. The right part on a full public cloud, the right mix on a full private cloud, the right mixing on that intelligent edge. But still having the ability to have all of those great software development methodologies that agile methodology, the only thing the kids know how to do out of school is open source and agile now. So you want to make sure that you can embrace that and make sure regardless of where the right spot is for a particular application in your entire enterprise portfolio that you have this common set of experiences and tools. And some of the research and development we're doing will enable us to drive that into that existing, conventional, enterprise market as well as this intelligent edge. Making a continuum, a continuum from the core to the intelligent edge. And something that modern computer science graduates will find completely comfortable. >> One attracting them is going to be the key, I think the edge is kind of intoxicating if you think about all the possibilities that are out there in terms of what you know just from a business model disruption and also technology. I mean wearables are edge, brain implants in the future will be edge, you know the singularities here as Ray Kersewile would say... >> Yeah. >> I mean but, this is the truth. This is what's happened. This is real right now. >> Oh absolutely. You know we think of all that data and right now we're just scratching the surface. I remember it was 1994 the first time I fired up a web server inside of my development team. So I could begin thinning out design information on prototype products inside of HP, and it was a novelty. People would say "What is that thing "you just sent me an email, W W whatever?" And suddenly we went from, like almost overnight, from a novelty to a business necessity, to then it transformed the way that we created the applications for the... >> John: A lot of people don't know this but since you brought it up this historical trivia, HP Labs, Hewlett Packard Labs had scientists who actually invented the web with Tim Berners-Lee, I think HTML founder was an HP Labs scientist. Pretty notable trivia. A lot of people don't know that so congratulations. >> And so I look at just what you're saying there and we see this new edge thing is it's going to be similarly transformative. Now today it's a little gimmicky perhaps it's sort of scratching the surface. It's taking security and it can be problematic at times but that will transform, because there is so much possibility for economic transformation. Right now almost all that data on the edge is thrown away. If you, the first person who understands okay I'm going to get 1% more of that data and turn it into real time intelligence, real time action... That will unmake industries and it will remake new industries. >> John: Andrew this the applied research vision, you got to apply R&D to the problem... >> Andrew: Correct. >> That's what he's getting at but you got to also think differently. You got to bring in talent. The young guns. How are you guys bringing in the young guns? What's the, what's the honeypot? >> Well I think you know for us it's, the sell for us, obviously is just the tradition of Hewlett Packard to begin with right? You know we have recognition on that level even it's not just Hewlett Packard Labs as well it's you know just R&D in general right? Kind of it you know the DNA being an engineering company so... But it's you know I think it is creating kind of these opportunities and whether it's internship programs you know just the various things that we're doing whether it's enterprise related, high performance computing... I think this edge opportunity is a really interesting one as a bridge because if you think about all the things that we hear about in enterprise in terms of "Oh you know I need this deep analytics "capability," or you know even a lot of the in memories things that we're talking about, real time response, driving information, right? All of that needs to happen at the edge as well for various opportunities so it's got a lot of the young graduates excited. We host you know hundreds of interns every year and it's real exciting to see kind of the ideas they come in with and you know they're all excited to work in this space. >> Dave: So Kirk you have your machine button, three, of course you got the logo. And then the machine... >> I got the labs logo, I got the machine logo. >> So when I first entered you talked about in the early 1980s. When I first got in the business I remembered Gene Emdall. "The best IO is no IO." (laughter) >> Yeah that's right. >> We're here again with this sort of memory semantics, centric computing. So in terms of the three that Andrew laid out the three types of sort of projects you guys pursue... Where does the machine fit? IS it sort of in all three? Or maybe you could talk about that a little bit. >> Kirk: I think it is, so we see those technologies that over the last three years we have brought so much new and it was, the critical thing about this is I think it's also sort of the prototyping of the overall approach our leaning in approach here... >> Andrew: That's right. >> It wasn't just researchers. Right? Those 500 people who made that 160 terabyte monster machine possible weren't just from labs. It was engineering teams from across Hewlett Packard Enterprise. It was our supply chain team. It was our services team telling us how these things fit together for real. Now we've had incredible technology experiences, incredible technologist experiences, and what we're seeing is that we have intercepts on conventional platforms where there's the photonics, the persistent memories. Those will make our existing DCIG and SDCG products better almost immediately. But then we also have now these whole cloth applications and as we take all of our learnings, drive them into open source software, drive them into the genesys consortium and we'll see you know probably 18, 24 months from now some of those first optimized silicon designs pop out of that ecosystem then we'll be right there to assemble those again, into conventional systems as well as more expansive, exo-scale computing, intelligent edge with large persistent memories and application specific processing as that next generation of gateways, I think we can see these intercept points at every category Andrew talked about. >> Andrew: And another good point there that kind of magnifies the model we were talking about, if we were sitting here five years ago, we would talking about things like photonics and non-volatile memory as being those big R projects. Those higher risk, longer term things, that right? As those mature, we make more progress innovation happens, right? It gets pulled into that shorter time frame that becomes advanced development. >> Dave: And Meg has talked about that... >> Yeah. >> Wanting to get more productivity out of the labs. And she's also pointed out you guys have spent more on R&D in the last several years. But even as we talked about the other day you want to see a little more D and keep the R going. So my question is, when you get to that point, of being able to support DCIG... Where do you, is it a hand off? Are you guys intimately involved? When you're making decisions about okay so member stir for example, okay this is great, that's still in the R phase then you bring it in. But now you got to commercialize this and you got 3D nan coming out and okay let's use that, that fits into our framework. So how much do you guys get involved in that handoff? You know the commercialization of this stuff? >> We get very involved. So it's at the point where when we think have something that hey we think you know maybe this could get into a product or let's see if there's good intercept here. We work jointly at that point. It's lab engineers, it's the product managers out of the group, engineers out of the business group, they essentially work collectively then on getting it to that next step. So it's kind of just one big R&D effort at that point. >> Dave: And so specifically as it relates to the machine, where do you see in the next in the near term, let's call near term next three years, or five years even, what do you see that looking like? Is it this combination of memory width capacitors or flash extensions? What does that look like in terms of commercial terms that we can expect? >> Kirk: So I really think the palette is pretty broad here. That I can see these going into existing rack and tower products to allow them to have memory that's composable down to the individual module level. To be able to take that facility to have just the right resources applied at just the right time with that API that we have in one view. Extend down to composing the hardware itself. I think we look at those edge line systems and want to have just the right kind of analytic capability, large persistent memories at that edge so we can handle those zeta bytes and zeta bytes of data in full fidelity analyzed at the edge sending back that intelligence to the core but also taking action at the edge in a timeframe that matters. I also see it coming out and being the basis of our exoscale high performance computing. You know when you want to have a exoscale system that has all of the combined capacity of the top 500 systems today but 1/20th of their power that is going to take rather novel technologies and everything we've been working on is exactly what's feeding that research and soon to be advanced development and then soon to be production in supply chain. >> Dave: Great. >> John: So the question I have is obviously we saw some really awesome Gen 10 stuff here at this show you guys are seeing that obviously you're on stage talking about a lot of the cool R&D, but really the reality is that's multiple years in the works some of this root of trust silicon technology that's pretty, getting the show buzzed up everyone's psyched about it. Dreamworks Animation's talking about how inorganic opportunities is helping their business and they got the security with the root of trust NIST certified and compliant. Pretty impressive. What's next? What else are you working on because this is where the R&D is on your shoulders for that next level of innovation. Where, what do you guys see that? Because security is a huge deal. That's that great example of how you guys innovated. Cause that'll stop the vector of a tax in the service area of IOT if you can get the servers to lock down and you have firmware that's secure, makes a lot of sense. That's probably the tip of the iceberg. What else is happening with security? >> Kirk: So when we think about security and our efforts on advanced development research around the machine what you're seeing here with the proliance is making the machines more secure. The inherent platform more secure. But the other thing I would point to you is the application we're running on the prototype. Large scale graph inference. And this is security because you have a platform like the machine. Able to digest hundreds and hundreds of tera bytes worth of log data to look for that fingerprint, that subtle clue that you have a system that has been compromised. And these are not blatant let's just blast everything out to some dot dot x x x sub domain, this is an advanced persistent thread by a very capable adversary who is very subtle in their reach out from a system that has been compromised to that command and control server. The signs are there if you can look at the data holistically. If you can look at that DNS log, graph of billions of entries everyday, constantly changing, if you can look at that as a graph in totality in a timeframe that matters then that's an empowering thing for a cyber defense team and I think that's one of the interesting things that we're adding to this discussion. Not only protect, detect and recover, but giving offensive weapons to our cyber defense team so they can hunt, they can hunt for those events for system threats. >> John: One of the things, Andrew I'll get your thoughts and reaction to this because Ill make an observation and you guys can comment and tell me I'm all wet, fell off the deep end or what not. Last year HP had great marketing around the machine. I love that Star Trek ad. It was beautiful and it was just... A machine is very, a great marketing technique. I mean use the machine... So a lot of people set expectations on the machine You saw articles being written maybe these people didn't understand it. Little bit pulled back, almost dampered down a little bit in terms of the marketing of the machine, other than the bin. Is that because you don't yet know what it's going to look like? Or there's so many broader possibilities where you're trying to set expectations? Cause the machine certainly has a lot of range and it's almost as if I could read your minds you don't want to post the position too early on what it could do. And that's my observation. Why the pullback? I mean certainly as a marketer I'd be all over that. >> Andrew: Yeah, I think part of it has been intentional just on how the ecosystem, we need the ecosystem developed kind of around this at the same time. Meaning, there are a lot of kind of moving parts to it whether it's around the open source community and kind of getting their head wrapped around what is this new architecture look like. We've got things like you know the Jin Zee Consortium where we're pouring a lot of our understanding and knowledge into that. And so we need a lot of partners, we know we're in a day and an age where look there's no single one company that's going to do every piece and part themselves. So part of it is kind of enough to get out there, to get the buzz, get the excitement to get other people then on board and now we have been heads down especially this last six months of... >> John: Jamming hard on it. >> Getting it all together. You know you think about what we showed first essentially first booted the thing in November and now you know we've got it running at this scale, that's really been the focus. But we needed a lot of that early engagement, interaction to get a lot of the other, members of the ecosystem kind of on board and starting to contribute. And really that's where we're at today. >> John: It's almost you want it let it take its own course organically because you mentioned just on the cyber surveillance opportunity around the crunching, you kind of don't know yet what the killer app is right? >> And that's the great thing of where we're at today now that we have kind of the prototype running at scale like this, it is allowing us to move beyond, look we've had the simulators to work with, we've had kind of emulation vehicles now you've got the real thing to run actual workloads on. You know we had the announcement around DZ and E as kind of an early early example, but it really now will allow us to do some refinement that allows us to get to those product concepts. >> Dave: I want to just ask the closing question. So I've had this screen here, it's like the theater, and I've been seeing these great things coming up and one was "Moore's Law is dead." >> Oh that was my session this morning. >> Another one was block chain. And unfortunately I couldn't hear it but I could see the tease. So when you guys come to work in the morning what's kind of the driving set of assumptions for you? Is it just the technology is limitless and we're going to go figure it out or are there things that sort of frame your raison d'etre? That drive your activities and thinking? And what are the fundamental assumptions that you guys use to drive your actions? >> Kirk: So what's been driving me for the last couple years is this exponential growth of information that we create as a species. That seems to have no upper bounding function that tamps it down. At the same time, the timeframe we want to get from information, from raw information to insight that we can take action on seems to be shrinking from days, weeks, minutes... Now it's down to micro seconds. If I want to have an intelligent power grid, intelligent 3G communication, I have to have micro seconds. So if you look at those two things and at the same time we just have to be the lucky few who are sitting in these seats right when Moore's Law is slowing down and will eventually flatten out. And so all the skills that we've had over the last 28 years of my career you look at those technologies and you say "Those aren't the ones that are going "to take us forward." This is an opportunity for us to really look and examine every piece of this, because if was something we could of just can't we just dot dot dot do one thing? We would do it, right? We can't just do one thing. We have to be more holistic if we're going to create the next 20, 30, 40 years of innovation. And that's really what I'm looking at. How do we get back exponential scaling on supply to meet this unending exponential demand? >> Dave: So technically I would imagine, that's a very hard thing to balance because the former says that we're going to have more data than we've ever seen. The latter says we've got to act on it fast which is a great trend for memory but the economics are going to be such a challenge to meet, to balance that. >> Kirk: We have to be able to afford the energy, and we have to be able to afford the material cost, and we have to be able to afford the business processes that do all these things. So yeah, you need breakthroughs. And that's really what we've been doing. And I think that's why we're so fortunate at Hewlett Packard Enterprise to have the labs team but also that world class engineering and that world class supply chain and a services team that can get us introduced to every interesting customer around the world who has those challenging problems and can give us that partnership and that insight to get those kind of breakthroughs. >> Dave: And I wonder if there will be a tipping point, if the tipping point will be, and I'm sure you've thought about this, a change in the application development model that drives so much value and so much productivity that it offsets some of the potential cost issues of changing the development paradigm. >> And I think you're seeing hints of that. Now we saw this when we went from systems of record, OLTP systems, to systems of engagement, mobile systems, and suddenly new ways to develop it. I think now the interesting thing is we move over to systems of action and we're moving from programmatic to training. And this is this interesting thing if you have those data bytes of data you can't have a pair of human eyeballs in front of that, you have to have a machine learning algorithm. That's the only thing that's voracious enough to consume this data in a timely enough fashion to get us answers, but you can't program it. We saw those old approaches in old school A.I., old school autonomous vehicle programs, they go about 10 feet, boom, and they'd flip over, right? Now you know they're on our streets and they are functioning. They're a little bit raw right now but that improvement cycle is fantastic because they're training, they're not programming. >> Great opportunity to your point about Moore's Law but also all this new functionality that has yet been defined, is right on the doorstep. Andrew, Kirk thank you so much for sharing. >> Andrew: Thank you >> Great insight, love Hewlett Packard Labs love the R&D conversation. Gets us a chance to go play in the wild and dream about the future you guys are out creating it congratulations and thanks for spending the time on The Cube, appreciate it. >> Thanks. >> The Cube coverage will continue here live at Las Vegas for HPE Discover 2017, Hewlett Packard Enterprises annual event. We'll be right back with more, stay with us. (bright music)

>> Announcer: Live from Las Vegas, it's the CUBE! Covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Hey, welcome back, everyone. We are live here in Las Vegas for SiliconANGLE Media's CUBE exclusive coverage of HPE Discover 2017. I'm John Furrier, my co-host, Dave Vellante. Our next guest is Kirk Bresniker, fellow and VP chief architect of Hewlett Packard Labs, and Natalia Vassilieva, senior research manager, Hewlett Packard Labs. Did I get that right? >> Yes! >> John: Okay, welcome to theCUBE, good to see you. >> Thank you. >> Thanks for coming on, really appreciate you guys coming on. One of the things I'm most excited about here at HPE Discover is, always like to geek out on the Hewlett Packard Labs booth, which is right behind us. If you go to the wide shot, you can see the awesome display. But there's some two things in there that I love. The Machine is in there, which I love the new branding, by the way, love that pyramid coming out of the, the phoenix rising out of the ashes. And also Memristor, really game-changing. This is underlying technology, but what's powering the business trends out there that you guys are kind of doing the R&D on is AI, and machine learning, and software's changing. What's your thoughts as you look at the labs, you look out on the landscape, and you do the R&D, what's the vision? >> One of the things what is so fascinating about the transitional period we're in. We look at the kind of technologies that we've had 'til date, and certainly spent a whole part of my career on, and yet all these technologies that we've had so far, they're all kind of getting about as good as they're going to get. You know, the Moore's Law semiconductor process steps, general-purpose operating systems, general-purpose microprocessors, they've had fantastic productivity growth, but they all have a natural life cycle, and they're all maturing. And part of The Machine research program has been, what do we think is coming next? And really, what's informing us as what we have to set as the goals are the kinds of applications that we expect. And those are data-intensive applications, not just petabytes, exabytes, but zettabytes. Tens of zettabytes, hundreds of zettabytes of data out there in all those sensors out there in the world. And when you want to analyze that data, you can't just push it back to the individual human, you need to employ machine learning algorithms to go through that data to call out and find those needles in those increasingly enormous haystacks, so that you can get that key correlation. And when you don't have to reduce and redact and summarize data, when you can operate on the data at that intelligent edge, you're going to find those correlations, and that machine learning algorithm is going to be that unbiased and unblinking eye that's going to find that key relationship that'll really have a transformational effect. >> I think that's interesting. I'd like to ask you just one follow-up question on that, because I think, you know, it reminds me back when I was in my youth, around packets, and you'd get the buffer, and the speeds, and feeds. At some point there was a wire speed capability. Hey, packets are moving, and you can do all this analysis at wire speed. What you're getting at is, data processing at the speed of, as fast as the data's coming in and out. Is that, if I get that right, is that kind of where you're going with this? Because if you have more data coming, potentially an infinite amount of data coming in, the data speed is going to be so high-velocity, how do you know what a needle looks like? >> I think that's a key, and that's why the research Natalia's been doing is so fundamental, is that we need to be able to process that incredible amount of information and be able to afford to do it. And the way that you will not be able to have it scale is if you have to take that data, compress it, reduce it, select it down because of some pre-determined decision you've made, transmit it to a centralized location, do the analysis there, then send back the action commands. Now, we need that cycle of intelligence measurement, analysis and action to be microseconds. And that means it needs to happen at the intelligent edge. I think that's where the understanding of how machine learning algorithms, that you don't program, you train, so that they can work off of this enormous amount of data, they voraciously consume the data, and produce insights. That's where machine learning will be the key. >> Natalia, tell us about your research on this area. Curious. Your thoughts. >> We started to look at existing machine learning algorithms, and whether their limiting factors today in the infrastructure which don't allow to progress the machine learning algorithms fast enough. So, one of the recent advances in AI is appearance, or revival, of those artificial neural networks. Deep learning. That's a very large hype around those types of algorithms. Every speech assistant which you get, Siri in your phone, Cortana, or whatever, Alexa by Amazon, all of them use deep learning to train speech recognition systems. If you go to Facebook and suddenly it starts you to propose to mark the faces of your friends, that the face detection, face recognition, also that was deep learning. So that's a revival of the old artificial neural networks. Today we are capable to train byte-light enough models for those types of tasks, but we want to move forward. We want to be able to process larger volumes of data, to find more complicated patterns, and to do that, we need more compute power. Again, today, the only way how you can add more compute power to that, you scale out. So there is no compute device on Earth today which is capable to do all the computation. You need to have many of them interconnect together, and they all crunch numbers for the same problem. But at some point, the communication between those nodes becomes a bottleneck. So you need to let know laboring node what you achieved, and you can't scale out anymore. Adding yet another node to the cluster won't lead up to the reduction of the training time. With The Machine, when we have added the memory during computing architecture, when all data seeds in the same shared pool of memory, and when all computing nodes have an ability to talk to that memory. We don't have that limitation anymore. So for us, we are looking forward to deploy those algorithms on that type of architecture. We envision significant speedups in the training. And it will allow us to retrain the model on the new data, which is coming. To do not do training offline anymore. >> So how does this all work? When HP split into two companies, Hewlett Packard Labs went to HPE and HP Labs went to HP Ink. So what went where, and then, first question. Then second question is, how do you decide what to work on? >> I think in terms of how we organize ourselves, obviously, things that were around printing and personal systems went to HP Ink. Things that were around analytics, enterprise, hardware and research, went to Hewlett Packard Labs. The one thing that we both found equally interesting was security, 'cause obviously, personal systems, enterprise systems, we all need systems that are increasingly secure because of the advanced, persistent threats that are constantly assaulting everything from our personal systems up through enterprise and public infrastructure. So that's how we've organized ourselves. Now in terms of what we get to work on, you know, we're in an interesting position. I came to Labs three years ago. I used to be the chief technologist for the server global business unit. I was in the world of big D, tiny R. Natalia and the research team at Labs, they were out there looking out five, 10, 15, or 20 years. Huge R, and then we would meet together occasionally. I think one of the things that's happened with our machine advanced development and research program is, I came to Labs not to become a researcher, but to facilitate that communication to bring in the engineering, the supply chain team, that technical and production prowess, our experience from our services teams, who know how things actually get deployed in the real world. And I get to set them at the bench with Natalia, with the researchers, and I get to make everyone unhappy. Hopefully in equal amounts. That the development teams realize we're going to make some progress. We will end up with fantastic progress and products, both conventional systems as well as new systems, but it will be a while. We need to get through, that's why we had to build our prototype. To say, "No, we need a construction proof of these ideas." The same time, with Natalia and the research teams, they were always looking for that next horizon, that next question. Maybe we pulled them a little bit closer, got a little answers out of them rather than the next question. So I think that's part of what we've been doing at the Labs is understanding, how do we organize ourselves? How do we work with the Hewlett Packard Enterprise Pathfinder program, to find those little startups who need that extra piece of something that we can offer as that partnering community? It's really a novel approach for us to understand how do we fill that gap, how do we still have great conventional products, how do we enable breakthrough new category products, and have it in a timeframe that matters? >> So, much tighter connection between the R and the D. And then, okay, so when Natalia wants to initiate a project, or somebody wants Natalia to initiate a project around AI, how does that work? Do you say, "Okay, submit an idea," and then it goes through some kind of peer review? And then, how does it get funded? Take us through that. >> I think I'll give my perspective, I would love to hear what you have from your side. For me, it's always been organic. The ideas that we had on The Machine, for me, my little thread, one of thousands that's been brought in to get us to this point, started about 2003, where we were getting ready for, we're midway through Blade Systems C-class. A category-defining product. A absolute home run in defining what a Blade system was going to be. And we're partway through that, and you realize you got a success on your hands. You think, "Wow, nothing gets better than this!" Then it starts to worry, what if nothing gets better than this? And you start thinking about that next set of things. Now, I had some insights of my own, but when you're a technologist and you have an insight, that's a great feeling for a little while, and then it's a little bit of a lonely feeling. No one else understands this but me, and is it always going to be that way? And then you have to find that business opportunity. So that's where talking with our field teams, talking with our customers, coming to events like Discover, where you see business opportunities, and you realize, my ingenuity and this business opportunity are a match. Now, the third piece of that is someone who can say, a business leader, who can say, "You know what?" "Your ingenuity and that opportunity can meet "in a finite time with finite resources." "Let's do it." And really, that's what Meg and leadership team did with us on The Machine. >> Kirk, I want to shift gears and talk about the Memristor, because I think that's a showcase that everyone's talking about. Actually, The Machine has been talked about for many years now, but Memristor changes the game. It kind of goes back to old-school analog, right? We're talking about, you know, login, end-login kind of performance, that we've never seen before. So it's a completely different take on memory, and this kind of brings up your vision and the team's vision of memory-driven computing. Which, some are saying can scale machine learning. 'Cause now you have data response times in microseconds, as you said, and provisioning containers in microseconds is actually really amazing. So, the question is, what is memory-driven computing? What does that mean? And what are the challenges in deep learning today? >> I'll do the machine learning-- >> I will do deep learning. >> You'll do the machine learning. So, when I think of memory-driven computing, it's the realization that we need a new set of technologies, and it's not just one thing. Can't we just do, dot-dot-dot, we would've done that one thing. This is more taking a holistic approach, looking at all the technologies that we need to pull together. Now, memories are fascinating, and our Memristor is one example of a new class of memory. But they also-- >> John: It's doing it differently, too, it's not like-- >> It's changing the physics. You want to change the economics of information technology? You change the physics you're using. So here, we're changing physics. And whether it's our work on the Memristor with Western Digital and the resistive RAM program, whether it's the phase-change memories, whether it's the spin-torque memories, they're all applying new physics. What they all share, though, is the characteristic that they can continue to scale. They can scale in the layers inside of a die. The die is inside of a package. The package is inside of a module, and then when we add photonics, a transformational information communications technology, now we're scaling from the package, to the enclosure, to the rack, cross the aisle, and then across the data center. All that memory accessible as memory. So that's the first piece. Large, persistent memories. The second piece is the fabric, the way we interconnect them so that we can have great computational, great memory, great communication devices available on industry open standards, that's the Gen-Z Consortium. The last piece is software. New software as well as adapting existing productive programming techniques, and enabling people to be very productive immediately. >> Before Natalia gets into her piece, I just want to ask a question, because this is interesting to me because, sorry to get geeky here, but, this is really cool because you're going analog with signaling. So, going back to the old concepts of signaling theory. You mentioned neural networks. It's almost a hand-in-glove situation with neural networks. Here, you have the next question, which is, connect the dots to machine learning and neural networks. This seems to be an interesting technology game-changer. Is that right? I mean, am I getting this right? What's this mean? >> I'll just add one piece, and then hear Natalia, who's the expert on the machine learning. For me, it's bringing that right ensemble of components together. Memory technologies, communication technologies, and, as you say, novel computational technologies. 'Cause transistors are not going to get smaller for very much longer. We have to think of something more clever to do than just stamp out another copy of a standard architecture. >> Yes, you asked about changes of deep learning. We look at the landscape of deep learning today, and the set of tasks which are solved today by those problems. We see that although there is a variety of tasks solved, most of them are from the same area. So we can analyze images very efficiently, we can analyze video, though it's all visual data, we can also do speech processing. There are few examples in other domains, with other data types, but they're much fewer. It's much less knowledge how to, which models to train for those applications. The thing that one of the challenges for deep learning is to expand the variety of applications which it can be used. And it's known that artificial neural networks are very well applicable to the data where there are many hidden patterns underneath. And there are multi-dimensional data, like data from sensors. But we still need to learn what's the right topology of neural networks to do that. What's the right algorithm to train that. So we need to broaden the scope of applications which can take advantage of deep learning. Another aspect is, which I mentioned before, the computational power of today's devices. If you think about the well-known analogy of artificial neural network in our brain, the size of the model which we train today, the artificial neural networks, they are much, much, much smaller than the analogous thing in our brain. Many orders of magnitude. It was shown that if you increase the size of the model, you can get better accuracy for some tasks. You can process a larger variety of data. But in order to train those large models, you need more data and you need more compute power. Today, we don't have enough compute power. Actually did some computation, though in order to train a model which is comparable in size with our human brain, you will need to train it in a reasonable time. You will need a compute device which is capable to perform 10 to the power of 26 floating-point operations per second. We are far, far-- >> John: Can you repeat that again? >> 10 to the power of 26. We are far, far below that point now. >> All right, so here's the question for you guys. There's all this deep learning source code out there. It's open bar for open source right now. All this goodness is pouring in. Google's donating code, you guys are donating code. It used to be like, you had to build your code from scratch. Borrow here and there, and share in open source. Now it's a tsunami of greatness, so I'm just going to build my own deep learning. How do customers do that? It's too hard. >> You are right on the point to the next challenge of deep learning, which I believe is out there. Because we have so many efforts to speed up the infrastructure, we have so many open source libraries. So now the question is, okay, I have my application at hand. What should I choose? What is the right compute node to the deep learning? Everybody use GPUs, but is it true for all models? How many GPUs do I need? What is the optimal number of nodes in the cluster? And we have a research effort towards to answer those questions as well. >> And a breathalyzer for all the drunk coders out there, open bar. I mean, a lot of young kids are coming in. This is a great opportunity for everyone. And in all seriousness, we need algorithms for the algorithms. >> And I think that's where it's so fascinating. We think of some classes of things, like recognizing written handwriting, recognizing voice, but when we want to apply machine learning and algorithms to the volume of sensor data, so that every manufactured item, and not only every item we manufacture, but every factory that can be fully instrumented with machine learning understanding how it can be optimized. And then, what of the business processes that are feeding that factory? And then, what are the overall economic factors that are feeding that business? And instrumenting and having this learning, this unblinking, unbiased eye examining to find those hidden correlations, those hidden connections, that could yield a very much more efficient system at every level of human enterprise. >> And the data's more diverse now than ever. I'm sorry to interrupt, but in Voice you mentioned you saw Siri, you see Alexa, you see Voice as one dataset. Data diversity's massive, so more needles, more types of needles than ever before. >> In that example that you gave, you need a domain expert. And there's plenty of those, but you also need a big brain to build the model, and train the model, and iterate. And there aren't that many of those. Is the state of machine learning and AI going to get to the point where that problem will solve itself, or do we just need to train more big brains? >> Actually, one of the advantages of deep learning that you don't need that much effort from the domain experts anymore, from the step which was called future engineering, like, what do you do with your data before you throw machine learning algorithm into that? So they're, pretty thing, cool thing about deep learning, artificial neural network, that you can throw almost raw data into that. And there are some examples out there, that the people without any knowledge in medicine won the competition of the drug recognition by applying deep neural networks to that, without knowing all the details about their connection between proteins, like that. Not domain experts, but they still were able to win that competition. Just because algorithm that good. >> Kirk, I want to ask you a final question before we break in the segment because, having spent nine years of my career at HP in the '80s and '90s, it's been well-known that there's been great research at HP. The R&D has been spectacular. Not too much R, I mean, too much D, not enough applied, you mention you're bringing that to market faster, so, the question is, what should customers know about Hewlett Packard Labs today? Your mission, obviously the memory-centric is the key thing. You got The Machine, you got the Memristor, you got a novel way of looking at things. What's the story that you'd like to share? Take a minute, close out the segment and share Hewlett Packard Labs' mission, and what expect to see from you guys in terms of your research, your development, your applications. What are you guys bringing out of the kitchen? What's cooking in the oven? >> I think for us, it is, we've been given an opportunity, an opportunity to take all of those ideas that we have been ruminating on for five, 10, maybe even 15 years. All those things that you thought, this is really something. And we've been given the opportunity to build a practical working example. We just turned on the prototype with more memory, more computation addressable simultaneously than anyone's ever assembled before. And so I think that's a real vote of confidence from our leadership team, that they said, "Now, the ideas you guys have, "this is going to change the way that the world works, "and we want to see you given every opportunity "to make that real, and to make it effective." And I think everything that Hewlett Packard Enterprise has done to focus the company on being that fantastic infrastructure, provider and partner is just enabling us to get this innovation, and making it meaningful. I've been designing printed circuit boards for 28 years, now, and I must admit, it's not as, you know, it is intellectually stimulating on one level, but then when you actually meet someone who's changing the face of Alzheimer's research, or changing the way that we produce energy as a society, and has an opportunity to really create a more sustainable world, then you say, "That's really worth it." That's why I get up, come to Labs every day, work with fantastic researchers like Natalia, work with great customers, great partners, and our whole supply chain, the whole team coming together. It's just spectacular. >> Well, congratulations, thanks for sharing the insight on theCUBE. Natalia, thank you very much for coming on. Great stuff going on, looking forward to keeping the progress and checking in with you guys. Always good to see what's going on in the Lab. That's the headroom, that's the future. That's the bridge to the future. Thanks for coming in theCUBE. Of course, more CUBE coverage here at HP Discover, with the keynotes coming up. Meg Whitman on stage with Antonio Neri. Back with more live coverage after this short break. Stay with us. (energetic techno music)

>> Announcer: Live from San Francisco, it's theCUBE covering Informatica World 2017. Brought to you by Informatica. (light techno music) >> Welcome back to Informatica World 2017. I'm Peter Burris, and once again theCUBE is broadcasting morning to night two days in a row to bring you The Signal from the Noise, this very very important conference. There's a lot going on here as we talk about the increasing role that data's playing in the world. Now, to get a user perspective, and not just any user perspective, a leading user perspective, on some of these issues, we've asked Mel Kirk to come on board. Mel, welcome to theCUBE . >> Thank you sir. Glad to be here. >> Mel is the senior vice president chief information officer for Ryder Systems. For those of you who don't know Ryder, it's a trucking company, a trucking and leasing company. >> Mel: Absolutely. >> And my background is I used to actually do a lot of research around transportation-related things, and I always found the ability to use queuing theory, >> Mel: Ah. in both technology and in transportation, >> Yes. to be very fascinating. So again, Mel, welcome here, but tell us a little bit about what you're here at Informatica World for, and what's your interest in all this? >> You know it's interesting, this was one of the conferences that I set out this year that I wanted to come to because I wanted to learn more about where Informatica is going in terms of leveraging data. Transportation company, we generate a lot of data. We have three business units, we have a fleet management company, a 3PL traditional transportation, supply chain company, and a dedicated transportation company. All three of those businesses generate a lot of data, and we're on a journey to try to figure out how, what's the best way of using that data to improve business outcomes. So that's what I'm here for this week, is to learn more about the tools that are here, the applications that are here, that we can use to do just that. >> So one of the things that I'm fascinated, often the new branding of Informatica, which we think is good: enterprise, Cloud, data management, leader. We know what enterprise is, we know what Cloud is, we know what leader is. One of the dynamics is, what is the new data management? We've talked to a couple of people about it. From your perspective, all this data coming in, what is the new data management function at Ryder, or the new requirements and capabilities? >> I think the biggest thing for us, from a data management standpoint, is mastering our data. Like I said, we generate a lot of data. We've got two really important domains in which that data revolves around. It's a customer and it's a vehicle. And so our objective this year is to master both the customer and the vehicle, the information around those, so that our marketing team can create better solutions by understanding all of the ways that a particular customer may interact with our business. It's also our operating team is leveraging that same data to win at the local level on a day-to-day basis. When a driver comes to one of our facilities, and he wants work done on his truck, our account people and our service people at that location will be able to pull up specific information about that customer and perform the work that they need based on the contract they have with us. That's a win for the customer and a win for our local team. >> So key, handle the customers, handle the crucial assets. That seems to be a general trend in the industry, is you look across both the conversations that you're having here at Informatica World, but also beyond. Where do you think the industry is going, from a trend standpoint, with some of these questions around data? >> I think we're all on a journey to try to figure out the best ways to leverage the data, treat data as an enterprise asset, right? A real enterprise asset that may have more value to it than some of the physical assets that sit in our business. And as I've talked to people during the week here, it's really about that journey of trying to figure out how do you get better value out of the investment that you make, and understanding, cleansing, liberating your data. And for us, again that's creating products, new products, from the data that we have, and it's improving productivity and efficiency in our operations with that data. >> So you must be excited about some of the new capabilities Informatica's announcing about being able to discover, you know, inventory, and then use metadata in new and different ways. What do you think about some of the metadata issues that Informatica's talking about here? >> Yeah, I think, you know, both metadata and Cloud for me is very important. The metadata is important because, again, we've got multiple business units, right, that are operating with elements of data that are not associated across the enterprise. And so, you know, getting more deliberate about understanding the data at the metadata level will help us as we try to bridge everything together across our enterprise. The Cloud's important because more and more of our customers are moving from a batch world to a near real time world. And what's happening there is we need the ability to spin up operations in a very quick way, receive data in large swaths. So having burst capacity is what the Cloud is going to give us. The immediacy of capacity is important to us, so the Cloud-based applications that I've seen here, even the enterprise information catalog is important because as we go through and we cleanse and harness our data, having it in a structured, governed pattern is important to us as well. >> So you had been in the business. You're ex-GE before you came to Ryder, a couple iterations before, you know, Master Black Belt, Six Sigma, that kind of stuff. You're an operations guy. >> I'm an operations guy. >> So as you think about going from an operations guy, and great operations guys are very focused on data, into the CIO, how was that transition? >> It was more than what I thought. You know it's interesting, I've said that as an operator, I'm not sure that I would've been effective in this role five, ten years ago, because it was a different type of role. >> Peter: Right. >> Today I don't know how you'd not do this role, how you could do this type of role, the CIO role, without having an operational background because the technology is integral to everything we do now. So, you know, where before, companies differentiated themselves on, you know, operational rigor and process, which is what I live in, >> Yep. >> Now it's about data. Now it's about data and the technology tools that can free up capacity, create productivity, and again, generate products. And so, this has been a great exercise for me, a great learning experience for me getting involved in technology at a time when it's moving so fast, right? Every day is a different day from a technology standpoint, and bridging that with my operating background, I think it's been a great experiment for both me and Ryder. >> Well a lot of CIOs that have great job satisfaction at heart are operations people who have figured out how to be operations people as opposed to people who often, CIOs who often don't have that satisfaction are spending their days putting out fires, and they never get into that groove. But think about as the role of the CIO changes at Ryder, but just in general, how do you see yourself organizing your groups around data assets, because it used to be that the key assets were, you know, the hardware. >> Right. >> Or the network. How is that catalyzing a new way of thinking about getting your talent mobilized to do what Ryder needs your function to do? >> You know, the big shift is away from keeping the lights on and keeping the phones working to delivering outcomes for the business. So that's that operational view, right? It's really whether there's an application development team or a talent on our, employee on our infrastructure team, it's about delivering outcomes for the operating team, for the business team. And so an example of that is in our fleet management business, right, we run 850 shops around the US and Canada, repair centers, and our core application in that business, our technicians in those shops say, "Mel, if you can do one thing for us, "make the application faster." That's both an application problem and an infrastructure problem. >> Peter: Sure. >> Right? In terms of trying to find the right solve. What I've been able to do and what I've been focusing on is translating that ask, of give me more speed, to the infrastructure team and the application team in a way that they understand that that incremental speed means better customer service, better outcomes for the business as well as our customer. That driver that comes to that repair center, he or she is on the clock. >> Peter: Right. And they want to get out as fast as, they are more, of more value to the customer when they're on the road doing their job. >> And a truck is typically not a cheap thing. >> Mel: It's not a cheap thing. >> So a truck's on the clock too. >> Mel: Absolutely. >> So as you think about the new, these new disciplines, and then acculturating the application team to, at least in this case, speed, the infrastructure team to speed, are there any new skills or any new disciplines that you are finding need to be filled within your shop? >> You know, the thing that's been interesting, and I'm going to go back to my Six Sigma background, the thing that's really been interesting, and when I take into consideration the pace of change of technology, it's been change management, right? I mean, the application team can come up with the best, the absolute best solution. I'm going to add two, it's change management and the UI, the user interface is important to that journey, right? >> Peter: Absolutely. >> And so they can come up with the greatest application, it could be the best solution ever, but you've got to get people, like in our organization it's nothing to see employees that have been with the company for 20 years. And getting them to fundamentally change how they do work, that's a challenge. And so we, what we've been focusing on is educating both the IT organization as well as the business team on how to drive change, especially in an organization with such a long, rich heritage. >> So as these changes start to manifest themselves, your relationship with the executive staff, how's that evolving? >> Yes, so when I went over to, when I came over into this role, you know, I'd left the operating role as a peer, and I came over to the IT role, and I think they felt sorry for me because of all of the challenges. But what's evolved is that as I've learned more about the technology and how to deploy, I've been able to actually balance between communicating with the technology team on the needs of the operating side of the business, and then translating the technical challenges to the operating team so that they've got a better sense of if we're going to launch a new product, or if we're going to onboard a new account, right, there's some lead time, there's some pre-thinking that needs to happen to get the technology right for you to be successful when you deploy for that customer. So I think bridging the gap between the two sides of the company has been very important for us, especially now given that, again, the pace of change with technology. >> Peter: So does Ryder have a COO? >> Ryder actually doesn't have a COO at the corporate level. We have a COO in our fleet management business, but I'm playing kind of a hybrid role I'd say. >> Peter: Yes. >> You know, kind of a CIO/COO because I can blend the two. >> Excellent! And how's that, how's that going? >> It's actually good. When I first moved into the CIO role, I was very deliberate about not encroaching on the role of the operating teams, right, even though my heritage and all of the things I had done in the company was around operations, I didn't want to make operating decisions from the CIO role. What I'm realizing now is the best value, the best benefit for Ryder and the customers is for me to bring all of the skills that I have, right, plus the talents of the team, to bear on a problem for the company. So I've thought less about boundaries and more about delivering outcomes. And if that means I have to put a, you know, a little bit of an operating perspective on a technical challenge, so be it. >> Which is really quite frankly what any real great Chief anything does. >> Yes. >> How do I take shareholder capital and translate it into an outcome through my purview. >> Mel: Right. >> So, Mel, let's pretend we got five CIOs sitting here, >> Mel: Okay. >> All about ready to start the journey that you're quite a ways along. What is the one thing you want to say to them? Say, here's how you're going to get started, and here's the pothole that you have to look out for. >> You know, I think one of the most important things that I would advise is to divide, especially if you're like me coming from a different purview and even folks that have been in technology for a while, establish a board of directors, right, your own personal board of directors. For me that was, I had to identify, you know, a couple of folks that had been in this role before that I could call and reach out to and get unfiltered advice, right? It was also identifying, the second one was identifying a short list of vendor partners that I could go to for technical questions in their domain, plus beyond their domain where I felt comfortable with the autonomy of the answer. >> Good ideas. >> Right, just good ideas. No sale, just good ideas. Then I had to reach inside of my team and figure out who are the one or two people in the organization that I'd go bounce ideas across for the sake of the change management that I talked about, right? Some for technology but also from a change management standpoint. And then build a couple of key partners at the leadership level within the organization, again to help with some of the concepts and the ideas. A lot of what a CIO is going to bring to bear now is going to be disruptive to the way a business, a company does business today, and so they're going to need constituents or partners from the executive leadership team. >> Yeah, none of it happens if the CIO doesn't recognize the change management that they have to drive. >> Absolutely. >> About their role within the business. >> Absolutely. So I used my board of directors, this board of directors, as a way of getting smarter about the job, you know, secondly, to help facilitate the change that we need, and three, just to bounce ideas. For sanity. >> Awesome. Fantastic. Mel Kirk is senior vice president, chief information officer of Ryder Systems Inc. Mel, great conversation. Thank you very much for being here in theCUBE . >> Okay, thank you for your time. >> Once again, Peter Burris, Informatica World 2017, we'll be back with more in a moment. (light techno music)

>> Narrator: Live from Boston, Massachusetts, it's The Cube, covering Red Hat Summit 2017, brought to you by Red Hat. >> Welcome back to The Cube's coverage of the Red Hat Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We are joined by Kirk Skaugen, he is the Executive Vice President and President of Lenovo Data Center group, Lenovo. So thanks so much for joining us, Kirk. >> Thanks for having me. >> I want to start out by talking about Lenovo's commitment to open source, right. We're hearing a lot about this in this summit, It's the real deal! >> Yeah, well I was at for 24 years and had a long partnership with Red Hat there so as I moved over to Lenovo on that, open source is a key aspect of our strategy. Kind of foundational for us and where we sit with the days in our company, because we don't have this legacy. We're not someone who's trying to protect an old router business or an old storage business. So as we look at open source as part of our, kind of, open partnerships commitment, it's pretty foundational to what we're doing. >> Kirk, could you help us unpack that a little bit? We heard in Keynote this morning they talked about open source hardware. I know you guys have been involved in OCP. How much is software, how much is hardware? Where do you guys put commitment in? How much of it is partners? >> Yeah, so I think we're in about over 30 different standards bodies now committed to open source. It really happened after our acquisition of the IBM xSeries server business, so now we're the third largest x86 server provider in the world and we're expanding ahead in the data center, so we're participating about 30 standards bodies. We have about 12 open source projects going on with Red Hat, and we're really at the base level, announcing today something called Open Platform at Lenovo. It's something we said we would do a year ago at this conference, and now here at the Red Hat summit we're showing it in our booth actually there. It's a base open platform with an optimized stack which you can put NFE and other solutions on top of, so that's one example of things we said we were going to do a year ago today and then are doing today. It's really about, from our perspective, optimizing the base hardware for all these platforms. >> Interesting, we look at things. I hear people look at open source and there's more transparency. It's not like '08; there's a secret project we're working on and here it is. You worked at Intel. Everybody kind of understood the tick-tock that went on there, how does open source influence the planing that you guys go into and do you feel the road maps at a company like Lenovo are more transparent since you're part of open source? I mean, again, what you should expect from us is we're a leader in x86 system technology but we've also acquired assets like blade network technologies in the past as well. We're expanding as a company out of our server routes into networking and storage. We think containerization is going to be the future. Today we're sitting with, something like 32 world record benchmarks and our theme is kind of "different is better" which means it's the little things that we're doing with all these partners to tune out the best performance of these systems working with our partners. We're not trying to go far up the stack and compete with our partners. I think that makes us a little bit unique. We're in trying to be the best x86 system provider in the world. Expand that into storage and networking as we get the software defined. >> Great, and absolutely. It would be useful to kind of explain your role in the data center group itself. As you said, you've got in some pieces. >> Some came from the IBM, there's various acquisitions. >> Kirk: Mmmhmm. >> Lay out a little bit more of what you guys do and what your partner does. >> Sure, so I think a lot of people know Lenovo as being number one in PCs. This is the 25th year of ThinkPad and we look at our Think Server brand today and our X series brand that we acquired from IBM. >> So we're, again, the third largest server provider but expanding that into storage and networking and then we acquired the Motorola phone business, so we just crossed to be number four in the world outside of China, with a presence in India. So we basically have three businesses within Lenovo but Data Center group, we believe, is a big growth driver for the future. A lot of people I think, 25 years ago, would have never thought Lenovo would be number one in PCs worldwide. I think we're kind of sitting there as a server provider with number one in customer satisfaction, number one in server reliability, number one in quality by all these third party measures. Our biggest issue is people don't realize we acquired this amazing asset from IBM so we're here at the summit basically showing and promoting our brand, but also promoting the proof points underneath that. >> This event is very global, multicultural. Lenovo's also a global company. Maybe speak a little bit to that; where your teams live, where development happens and what your customer base looks like. >> I live in Raleigh. We have a dual headquarters in Raleigh and Beijing, but we operate in over 160 countries. We have over 10,000 IT professionals now within the data center group. We have manufacturing in the United States, in Mexico, in Hungary, in China, so we can basically globally ship everywhere. When I looked at moving from Intel to another company, number one this enabled me to get one step closer to the customer, but I thought Lenovo's one of the best companies I saw that we're partnering. I think in the data center group, you look at our list of partners and it's unprecedented partly because we don't have a legacy business, so almost every startup and everybody who wants to do something new ends up wanting access to our presence in China, being number one in China, but also because we're not protecting a legacy so they see us as someone interesting and unique to partnership with. So open source is one of those areas where I think, now that we separated from IBM we're clearly an x86 provider committed to open source and the way we're getting into telecom, where we hadn't been, and competing with our big customers is because we're open and ideally we're more agile and partner better. >> I'm wondering if you could comment on the culture of these culture of these various places. As you said, you've been in Portland for a long time. You're now new to Raleigh. Your company is Beijing and Raleigh and you do business all over the world. How do you experience how these engineers, are they different in different parts of the world? Or is open source really transcending that and there is a much more of an openness and a transparency? >> Yeah, I thought I'd fit really well into the Lenovo culture. I think six months into the job, I feel like it's exceeded my expectations. If you look at the executive staff at Lenovo there's something like seven different nationalities on there from Italy, and Switzerland, and Australia, and the U.S., and China, Hong Kong, Singapore, India. >> Rebecca: And that's by design. >> Yeah, by design. So I think it provides a really unique perspective as you're looking at market trends, and then customers and things like that. When you look at the engineering aspect of it I'm looking at this efficiencies of the PC, the cost economics of the PC, having some of these factors. We're actually one of the last companies who's designing our own systems and putting them in our own factory, so from that perspective we get the efficiencies of being part of a larger PC company, but listen, data center's very different, right? We have a completely autonomous data center group now but we get the efficiencies of that, so we can kind of get the best of all the cultures that we participate in with development in Romania, in India, in China, Raleigh and again, we can manufacture in any place the customer wants us to manufacture pretty much. >> You mentioned that you're one of the last companies that's designing your own systems and putting them into your machines. Is that going to go by the wayside? You're one of the last, so all these other companies have decided it's just not sustainable. Can you comment on that? >> Well I think consolidation is absolutely key. If you look at the PC industry, and I managed the PC business at Intel the last three years. There's absolutely been consolidation in that market. You should look at some of the Japanese suppliers going away, but that's what enabled Lenovo to continue to grow in a multi-hundred million unit market. Today we ship about 100 servers a minute. A hundred servers an hour, rather, about one a minute. If you look at the consolidation trends I think still going to be a lot of consolidation in the market around that, so we believe we can grow in that market. PCs through consolidation, and if the PC market flattens out, even in the data center space where I think there'll be fewer and fewer players that will be able to compete. It really gets down to just uber-efficiency. When you're running in a factory that's building as the number one PC company, you get manufacturing efficiencies that other people can't do at our subscale. So as an example, when we look at things like supercomputing we're now the fastest growing supercomputing company on the planet. 99 of the top 500 supercomputers. That's because we can build very, very efficient products in a market that typically runs on razor-thin margins, right. >> Kirk, we talk about that huge volume of servers. Can you speak to where Lenovo's playing in the service provider and cloud marketplace? >> Sure, I think we just reorganized into kind of, four customer-centric markets. So first is in hyperscale, we participate with Baidu, Alibaba, Tencent and we're expanding across some of the largest hyperscale providers in the West Coast. We believe designing our own board, putting in our own factories gives us the cost economics to compete with the largest data centers in the world, just 'cause we can make money in PC desktop towers which is a pretty commoditized business. We think we can make money there. Software-defined, I think what we're seeing is because of our lack of legacy hardware whether it's a legacy SAN or a legacy routing business, we can leap ahead there both through our own stack but also our partner's stack. Third is supercomputing, so this is something where we brought a lot of that application knowledge over from IBM to the acquisition, and our goal is to continue to be the fastest growing supercomputing company on the planet and right now we're number two in the world, so we're building our Barcelona supercomputer right now to be 12 times more powerful that what it is today. With the University of Adelaide, 30 times more powerful than their last computer. Supercomputing's the third, and then the fourth is just traditional data center. So there you look at things like SAP HANA, where we were solutions-lead. We're trying to not just ship the hardware, but deliver optimized solutons so we feel like the little things don't mean a lot, the little things mean everything. So why does Lenovo have 32 worldwide per benchmarks? 'Cause we're tuning things with SAP, and now, for example, SAP just went public that they're running their own internal HANA on Lenovo. So I think it's a testament, it's the fine tuning of the application. It's hyperscale, software-defined, supercomputing, and then legacy data center infrastructure lead by solutions. Those are our four segments. >> Kirk, you talked about, it was 25 years for ThinkPad. As I look out towards the future, the data center group, what's kind of the touchstone? What are people going to really understand and know that group for in the future? >> Well, I think we want to be most trusted from a data center provider, right. We're not trying to contain anyone in a legacy thinking. We want to leap ahead into software-defined. We think we have the base hardware, customer satisfaction, reliability to do that. So I think, number one, we want to be most trusted. Number two, we're trying to be incredibly agile. Much faster than companies that are larger than us. That's been an innovation culture that's lead us to be number one in PCs, not through cost, but through innovation. We want to be known for innovation and being faster to deploy innovation both with us, but as well was with our partners. So if you go into our both, you showcasing with Intel. We're showcasing with Juniper. We're showcasing with Red Hat. So that's a very decent foundation. I think we can leap ahead, not be encumbered by the past, and be trusted, innovative, cost-effective, and make a lead to software-defined. What's interesting to me is, I think when I joined Intel in 1992, there was something like 100 gigabytes a day. When I joined Lenovo 24 years later, it was like 250 million gigabytes a day of data, if I have my numbers correctly. It's going to leapfrog up just in a massive way over the next 10 years with 5G and the whole internet buildup so you hear that from almost every keynote speaker, but what it means to me is that, we're just at the beginning of cloud transformation. A company like Lenovo, we didn't invent the PC, we just became number one in it over 25 years. We didn't invent servers, but we acquired amazing people. They can then leap us ahead over the next, now, 25 years. (laughing) >> Well Kirk, thank you so much for joining us. Thank you for your time. >> Yeah. Thank you It's a pleasure, it's a great event. So thank you. >> I'm Rebecca Knight for Stu Miniman. We'll be more with the Red Hat summit after this. (electronic music)

>> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies creating technologies that drive human progress. (upbeat music) >> Hey everyone, welcome back to theCube's first day of coverage of MWC 23 from Barcelona, Spain. Lisa Martin here with Dave Vellante and Dave Nicholson. I'm literally in between two Daves. We've had a great first day of coverage of the event. There's been lots of conversations, Dave, on disaggregation, on the change of mobility. I want to be able to get your perspectives from both of you on what you saw on the show floor, what you saw and heard from our guests today. So we'll start with you, Dave V. What were some of the things that were our takeaways from day one for you? >> Well, the big takeaway is the event itself. On day one, you get a feel for what this show is like. Now that we're back, face-to-face kind of pretty much full face-to-face. A lot of excitement here. 2000 plus exhibitors, I mean, planes, trains, automobiles, VR, AI, servers, software, I mean everything. I mean, everybody is here. So it's a really comprehensive show. It's not just about mobile. That's why they changed the name from Mobile World Congress. I think the other thing is from the keynotes this morning, I mean, you heard, there's a lot of, you know, action around the telcos and the transformation, but in a lot of ways they're sort of protecting their existing past from the future. And so they have to be careful about how fast they move. But at the same time if they don't move fast, they're going to get disrupted. We heard some complaints, essentially, you know, veiled complaints that the over the top guys aren't paying their fair share and Telco should be able to charge them more. We heard the chairman of Ericsson talk about how we can't let the OTTs do that again. We're going to charge directly for access through APIs to our network, to our data. We heard from Chris Lewis. Yeah. They've only got, or maybe it was San Ji Choha, how they've only got eight APIs. So, you know the developers are the ones who are going to actually build out the innovation at the edge. The telcos are going to provide the connectivity and the infrastructure companies like Dell as well. But it's really to me all about the developers. And that's where the action's going to be. And it's going to be interesting to see how the developers respond to, you know, the gun to the head. If you want access, you're going to have to pay for it. Now maybe there's so much money to be made that they'll go for it, but I feel like there's maybe a different model. And I think some of the emerging telcos are going to say, you know what, here developers, here's a platform, have at it. We're not going to charge you for all the data until you succeed. Then we're going to figure out a monetization model. >> Right. A lot of opportunity for the developer. That skillset is certainly one that's in demand here. And certainly the transformation of the telecom industry is, there's a lot of conundrums that I was hearing going on today, kind of chicken and egg scenarios. But Dave, you had a chance to walk around the show floor. We were here interviewing all day. What were some of the things that you saw that really stuck out to you? >> I think I was struck by how much attention was being paid to private 5G networks. You sort of read between the lines and it appears as though people kind of accept that the big incumbent telecom players are going to be slower to move. And this idea of things like open RAN where you're leveraging open protocols in a stack to deliver more agility and more value. So it sort of goes back to the generalized IT discussion of moving to cloud for agility. It appears as though a lot of players realize that the wild wild west, the real opportunity, is in the private sphere. So it's really interesting to see how that works, how 5G implemented into an environment with wifi how that actually works. It's really interesting. >> So it's, obviously when you talk to companies like Dell, I haven't hit HPE yet. I'm going to go over there and check out their booth. They got an analyst thing going on but it's really early days for them. I mean, they started in this business by taking an X86 box, putting a name on it, you know, that sounded like it was edged, throwing it over, you know, the wall. That's sort of how they all started in this business. And now they're, you know, but they knew they had to form partnerships. They had to build purpose-built systems. Now with 16 G out, you're seeing that. And so it's still really early days, talking about O RAN, open RAN, the open RAN alliance. You know, it's just, I mean, not even, the game hasn't even barely started yet but we heard from Dish today. They're trying to roll out a massive 5G network. Rakuten is really focused on sort of open RAN that's more reliable, you know, or as reliable as the existing networks but not as nearly as huge a scale as Dish. So it's going to take a decade for this to evolve. >> Which is surprising to the average consumer to hear that. Because as far as we know 5G has been around for a long time. We've been talking about 5G, implementing 5G, you sort of assume it's ubiquitous but the reality is it is just the beginning. >> Yeah. And you know, it's got a fake 5G too, right? I mean you see it on your phone and you're like, what's the difference here? And it's, you know, just, >> Dave N.: What does it really mean? >> Right. And so I think your point about private is interesting, the conversation Dave that we had earlier, I had throughout, hey I don't think it's a replacement for wifi. And you said, "well, why not?" I guess it comes down to economics. I mean if you can get the private network priced close enough then you're right. Why wouldn't it replace wifi? Now you got wifi six coming in. So that's a, you know, and WiFi's flexible, it's cheap, it's good for homes, good for offices, but these private networks are going to be like kickass, right? They're going to be designed to run whatever, warehouses and robots, and energy drilling facilities. And so, you know the economics I don't think are there today but maybe they can be at volume. >> Maybe at some point you sort of think of today's science experiment becoming the enterprise-grade solution in the future. I had a chance to have some conversations with folks around the show. And I think, and what I was surprised by was I was reminded, frankly, I wasn't surprised. I was reminded that when we start talking about 5G, we're talking about spectrum that is managed by government entities. Of course all broadcast, all spectrum, is managed in one way or another. But in particular, you can't simply put a SIM in every device now because there are a lot of regulatory hurdles that have to take place. So typically what these things look like today is 5G backhaul to the network, communication from that box to wifi. That's a huge improvement already. So yeah, my question about whether, you know, why not put a SIM in everything? Maybe eventually, but I think, but there are other things that I was not aware of that are standing in the way. >> Your point about spectrum's an interesting one though because private networks, you're going to be able to leverage that spectrum in different ways, and tune it essentially, use different parts of the spectrum, make it programmable so that you can apply it to that specific use case, right? So it's going to be a lot more flexible, you know, because I presume the needs spectrum needs of a hospital are going to be different than, you know, an agribusiness are going to be different than a drilling, you know, unit, offshore drilling unit. And so the ability to have the flexibility to use the spectrum in different ways and apply it to that use case, I think is going to be powerful. But I suspect it's going to be expensive initially. I think the other thing we talked about is public policy and regulation, and it's San Ji Choha brought up the point, is telcos have been highly regulated. They don't just do something and ask for permission, you know, they have to work within the confines of that regulated environment. And there's a lot of these greenfield companies and private networks that don't necessarily have to follow those rules. So that's a potential disruptive force. So at the same time, the telcos are spending what'd we hear, a billion, a trillion and a half over the next seven years? Building out 5G networks. So they got to figure out, you know how to get a payback on that. They'll get it I think on connectivity, 'cause they have a monopoly but they want more. They're greedy. They see the over, they see the Netflixes of the world and the Googles and the Amazons mopping up services and they want a piece of that action but they've never really been good at it. >> Well, I've got a question for both of you. I mean, what do you think the odds are that by the time the Shangri La of fully deployed 5G happens that we have so much data going through it that effectively it feels exactly the same as 3G? What are the odds? >> That's a good point. Well, the thing that gets me about 5G is there's so much of it on, if I go to the consumer side when we're all consumers in our daily lives so much of it's marketing hype. And, you know all the messaging about that, when it's really early innings yet they're talking about 6G. What does actual fully deployed 5G look like? What is that going to enable a hospital to achieve or an oil refinery out in the middle of the ocean? That's something that interests me is what's next for that? Are we going to hear that at this event? >> I mean, walking around, you see a fair amount of discussion of, you know, the internet of things. Edge devices, the increase in connectivity. And again, what I was surprised by was that there's very little talk about a sim card in every one of those devices at this point. It's like, no, no, no, we got wifi to handle all that but aggregating it back into a central network that's leveraging 5G. That's really interesting. That's really interesting. >> I think you, the odds of your, to go back to your question, I think the odds are even money, that by the time it's all built out there's going to be so much data and so much new capability it's going to work similarly at similar speeds as we see in the networks today. You're just going to be able to do so many more things. You know, and your video's going to look better, the graphics are going to look better. But I think over the course of history, this is what's happening. I mean, even when you go back to dial up, if you were in an AOL chat room in 1996, it was, you know, yeah it took a while. You're like, (screeches) (Lisa laughs) the modem and everything else, but once you were in there- >> Once you're there, 2400 baud. >> It was basically real time. And so you could talk to your friends and, you know, little chat room but that's all you could do. You know, if you wanted to watch a video, forget it, right? And then, you know, early days of streaming video, stop, start, stop, start, you know, look at Amazon Prime when it first started, Prime Video was not that great. It's sort of catching up to Netflix. But, so I think your point, that question is really prescient because more data, more capability, more apps means same speed. >> Well, you know, you've used the phrase over the top. And so just just so we're clear so we're talking about the same thing. Typically we're talking about, you've got, you have network providers. Outside of that, you know, Netflix, internet connection, I don't need Comcast, right? Perfect example. Well, what about the over the top that's coming from direct satellite communications with devices. There are times when I don't have a signal on my, happens to be an Apple iPhone, when I get a little SOS satellite logo because I can communicate under very limited circumstances now directly to the satellite for very limited text messaging purposes. Here at the show, I think it might be a Motorola device. It's a dongle that allows any mobile device to leverage direct satellite communication. Again, for texting back to the 2,400 baud modem, you know, days, 1200 even, 300 even, go back far enough. What's that going to look like? Is that too far in the future to think that eventually it's all going to be over the top? It's all going to be handset to satellite and we don't need these RANs anymore. It's all going to be satellite networks. >> Dave V.: I think you're going to see- >> Little too science fiction-y? (laughs) >> No, I, no, I think it's a good question and I think you're going to see fragments. I think you're going to see fragmentation of private networks. I think you're going to see fragmentation of satellites. I think you're going to see legacy incumbents kind of hanging on, you know, the cable companies. I think that's coming. I think by 2030 it'll, the picture will be much more clear. The question is, and I think it's come down to the innovation on top, which platform is going to be the most developer friendly? Right, and you know, I've not heard anything from the big carriers that they're going to be developer friendly. I've heard "we have proprietary data that we're going to charge access for and developers are going to have to pay for that." But I haven't heard them saying "Developers, developers, developers!" You know, Steve Bomber running around, like bend over backwards for developers, they're asking the developers to bend over. And so if a network can, let's say the satellite network is more developer friendly, you know, you're going to see more innovation there potentially. You know, or if a dish network says, "You know what? We're going after developers, we're going after innovation. We're not going to gouge them for all this network data. Rather we're going to make the platform open or maybe we're going to do an app store-like model where we take a piece of the action after they succeed." You know, take it out of the backend, like a Silicon Valley VC as opposed to an East Coast VC. They're not going to get you in the front end. (Lisa laughs) >> Well, you can see the sort of disruptive forces at play between open RAN and the legacy, call it proprietary stack, right? But what is the, you know, if that's sort of a horizontal disruptive model, what's the vertically disruptive model? Is it private networks coming in? Is it a private 5G network that comes in that says, "We're starting from the ground up, everything is containerized. We're going to go find people at KubeCon who are, who understand how to orchestrate with Kubernetes and use containers in microservices, and we're going to have this little 5G network that's going to deliver capabilities that you can't get from the big boys." Is there a way to monetize that? Is there a way for them to be disrupted, be disruptive, or are these private 5G networks that everybody's talking about just relegated to industrial use cases where you're just squeezing better economics out of wireless communication amongst all your devices in your factory? >> That's an interesting question. I mean, there are a lot of those smart factory industrial use cases. I mean, it's basically industry 4.0 use cases. But yeah, I don't count the cloud guys out. You know, everybody says, "oh, the narrative is, well, the latency of the cloud." Well, not if the cloud is at the edge. If you take a local zone and put storage, compute, and data right next to each other and the cloud model with the cloud APIs, and then you got an asynchronous, you know, connection back. I think that's a reasonable model. I think the cloud guys figured out developers, right? Pretty well. Certainly Microsoft and, and Amazon and Google, they know developers. I don't see any reason why they can't bring their model to the edge. So, and that's really disruptive to the legacy telco guys, you know? So they have to be careful. >> One step closer to my dream of eliminating the word "cloud" from IT lexicon. (Lisa laughs) I contend that it has always been IT, and it will always be IT. And this whole idea of cloud, what is cloud? If AWS, for example, is delivering hardware to the edge where it needs to be, is that cloud? Do we go back to the idea that cloud is an operational model and not a question of physical location? I hope we get to that point. >> Well, what's Apex and GreenLake? Apex is, you know, Dell's as a service. GreenLake is- >> HPE. >> HPE's as a service. That's outposts. >> Dave N.: Right. >> Yeah. >> That's their outpost. >> Yeah. >> Well AWS's position used to be, you know, to use them as a proxy for hyperscale cloud. We'll just, we'll grow in a very straight trajectory forever on the back of net new stuff. Forget about the old stuff. As James T. Kirk said of the Klingons, "let them die." (Lisa laughs) As far as the cloud providers were concerned just, yeah, let, let that old stuff go away. Well then they found out, there came a point in time where they realized there's a lot of friction and stickiness associated with that. So they had to deal with the reality of hybridity, if that's the word, the hybrid nature of things. So what are they doing? They're pushing stuff out to the edge, so... >> With the same operating model. >> With the same operating model. >> Similar. I mean, it's limited, right? >> So you see- >> You can't run a lot of database on outpost, you can run RES- >> You see this clash of Titans where some may have written off traditional IT infrastructure vendors, might have been written off as part of the past. Whereas hyperscale cloud providers represent the future. It seems here at this show they're coming head to head and competing evenly. >> And this is where I think a company like Dell or HPE or Cisco has some advantages in that they're not going to compete with the telcos, but the hyperscalers will. >> Lisa: Right. >> Right. You know, and they're already, Google's, how much undersea cable does Google own? A lot. Probably more than anybody. >> Well, we heard from Google and Microsoft this morning in the keynote. It'd be interesting to see if we hear from AWS and then over the next couple of days. But guys, clearly there is, this is a great wrap of day one. And the crazy thing is this is only day one. We've got three more days of coverage, more news, more information to break down and unpack on theCUBE. Look forward to doing that with you guys over the next three days. Thank you for sharing what you saw on the show floor, what you heard from our guests today as we had about 10 interviews. Appreciate your insights and your perspectives and can't wait for tomorrow. >> Right on. >> All right. For Dave Vellante and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE's day one wrap from MWC 23. We'll see you tomorrow. (relaxing music)

(upbeat techno music) >> Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions, and they were largely confined to regulated industries that had to comply with public policy mandates. But as the cloud went mainstream the tech giants showed us how valuable data could become, and the value proposition for data quality and trust, it evolved from primarily a compliance driven issue, to becoming a linchpin of competitive advantage. But, data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper-specialized skills, to develop data architectures and processes, to serve the myriad data needs of organizations. And it resulted in a lot of frustration, with data initiatives for most organizations, that didn't have the resources of the cloud guys and the social media giants, to really attack their data problems and turn data into gold. This is why today, for example, there's quite a bit of momentum to re-thinking monolithic data architectures. You see, you hear about initiatives like Data Mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business users. You hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver, like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that but also, how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. In other words, while it's enticing to experiment, and run fast and loose with data initiatives, kind of like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated and intelligent. Governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is going to use data that is entrusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. Hello and welcome to theCUBE's coverage of Data Citizens made possible by Collibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Vellante and I'm one of the hosts of our program which is running in parallel to Data Citizens. Now at theCUBE we like to say we extract the signal from the noise, and over the next couple of days we're going to feature some of the themes from the keynote speakers at Data Citizens, and we'll hear from several of the executives. Felix Van de Maele, who is the co-founder and CEO of Collibra, will join us. Along with one of the other founders of Collibra, Stan Christiaens, who's going to join my colleague Lisa Martin. I'm going to also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Haslbeck. He's the Vice President of Data Quality at Collibra. He's an amazingly smart dude who founded Owl DQ, a company that he sold to Collibra last year. Now, many companies they didn't make it through the Hadoop era, you know they missed the industry waves and they became driftwood. Collibra, on the other hand, has evolved its business, they've leveraged the cloud, expanded its product portfolio and leaned in heavily to some major partnerships with cloud providers as well as receiving a strategic investment from Snowflake, earlier this year. So, it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. (upbeat rock music) Last year theCUBE covered Data Citizens, Collibra's customer event, and the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know starting with the Hadoop movement, we had Data lakes, we had Spark, the ascendancy of programming languages like Python, the introduction of frameworks like Tensorflow, the rise of AI, Low Code, No Code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives, and we said at the time, you know maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation. Meaning, making it easier for domain experts to both gain insights from data, trust the data, and begin to use that data in new ways, fueling data products, monetization, and insights. Data Citizens 2022 is back and we're pleased to have Felix Van de Maele who is the founder and CEO of Collibra. He's on theCUBE. We're excited to have you Felix. Good to see you again. >> Likewise Dave. Thanks for having me again. >> You bet. All right, we're going to get the update from Felix on the current data landscape, how he sees it why data intelligence is more important now than ever, and get current on what Collibra has been up to over the past year, and what's changed since Data citizens 2021, and we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends and we're not just snapping back to the 2010s, that's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s, from the previous decade, and what challenges does that bring for your customers? >> Yeah, absolutely, and and I think you said it well, Dave and the intro that, that rising complexity and fragmentation, in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use, has only gotten more more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under, respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well. Which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity, and fragmentation. So, it's become much more acute. And to your earlier point, we do live in a different world and and the past couple of years we could probably just kind of brute force it, right? We could focus on, on the top line, there was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, how do we truly get the value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale with data, not just from a a technology and infrastructure perspective, but how do we actually scale data from an organizational perspective, right? You said at the, the people and process, how do we do that at scale? And that's only, only, only becoming much more important, and we do believe that the, the economic environment that we find ourselves in today is going to be catalyst for organizations to really take that more seriously if, if, if you will, than they maybe have in the have in the past. >> You know, I don't know when you guys founded Collibra, if you had a sense as to how complicated it was going to get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >> Yeah, absolutely. We, we started Collibra in 2008. So, in some sense and the, the last kind of financial crisis and that was really the, the start of Collibra, where we found product market fit, working with large financial institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis. And kind of here we are again, in a very different environment of course 15 years, almost 15 years later, but data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So, what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it Data Citizens, we truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we still relatively early in that, in that journey. >> Well that's interesting, because you know, in my observation it takes 7 to 10 years to actually build a company, and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your current momentum? >> Yeah, absolutely. Again, there's a lot of tailwind organizations that are only maturing their data practices and we've seen that kind of transform or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world with its Adobe, Heineken, Bank of America and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in the, in the market with some of the cloud partners like Google, Amazon, Snowflake, Data Breaks, and and others, right? As those kind of new modern data infrastructures, modern data architectures, are definitely all moving to the cloud. A great opportunity for us, our partners, and of course our customers, to help them kind of transition to the cloud even faster. And so we see a lot of excitement and momentum there. We did an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course data quality isn't new but I think there's a lot of reasons why we're so excited about quality and observability now. One, is around leveraging AI machine learning again to drive more automation. And a second is that those data pipelines, that are now being created in the cloud, in these modern data architecture, architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously, has become absolutely critical so that they're really excited about, about that as well. And on the organizational side, I'm sure you've heard the term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believed in. Federated, focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations, and so that aligns really well with our vision and from a product perspective, we've seen a lot of momentum with our customers there as well. >> Yeah, you know, a couple things there. I mean, the acquisition of OwlDQ, you know Kirk Haslbeck and, and their team. It's interesting, you know the whole data quality used to be this back office function and and really confined to highly regulated industries. It's come to the front office, it's top of mind for Chief Data Officers. Data mesh, you mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So, let's chat a little bit about the, the products. We're going to go deeper into products later on, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the the under the covers in security, sort of making data more accessible for people, just dealing with workflows and processes, as you talked about earlier. Tell us a little bit about what you're introducing. >> Yeah, absolutely. We we're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission. Either customers are still start, are just starting on that, on that journey. We want to make it as easy as possible for the, for organization to actually get started, because we know that's important that they do. And for our organization and customers, that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again to make it easier for, really to, to accomplish that mission and vision around that Data Citizen, that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving, a lot of kind of ease of adoption, ease of use, but also then, how do we make sure that, as clear becomes this kind of mission critical enterprise platform, from a security performance, architecture scale supportability, that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme. From an innovation perspective, from a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One, is around data marketplace. Again, a lot of our customers have plans in that direction, How to make it easy? How do we make How do we make available to true kind of shopping experience? So that anybody in the organization can, in a very easy search first way, find the right data product, find the right dataset, that they can then consume. Usage analytics, how do you, how do we help organizations drive adoption? Tell them where they're working really well and where they have opportunities. Homepages again to, to make things easy for, for people, for anyone in your organization, to kind of get started with Collibra. You mentioned Workflow Designer, again, we have a very powerful enterprise platform, one of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a, a new Low-Code, No-Code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around Collibra protect, which in partnership with Snowflake, which has been a strategic investor in Collibra, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PIA data, is managed as a much more effective, effective rate. Really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily, and quickly, and widely as we can? Moving that to the cloud has been a big part of our strategy. So, we launch our data quality cloud product, as well as making use of those, those native compute capabilities and platforms, like Snowflake, Databricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down, so we're actually pushing down the computer and data quality, to monitoring into the underlying platform, which again from a scale performance and ease of use perspective, is going to make a massive difference. And then more broadly, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical, and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So that's a lot coming out, the team has been work, at work really hard, and we are really really excited about what we are coming, what we're bringing to market. >> Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you you talked about, you know, the marketplace, you know you think about Data Mesh, you think of data as product, one of the key principles, you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been, been so hard. So, how do you see sort of the future and, you know give us the, your closing thoughts please? >> Yeah, absolutely. And, and I think we we're really at a pivotal moment and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not going to fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to, deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can, as kind of our, as our mission. And so I'm really, really excited to see what we, what we are going to, how the marks are going to evolve over the next, next few quarters and years. I think the trend is clearly there. We talked about Data Mesh, this kind of federated approach focus on data products, is just another signal that we believe, that a lot of our organization are now at the time, they're understanding need to go beyond just the technology. I really, really think about how to actually scale data as a business function, just like we've done with IT, with HR, with sales and marketing, with finance. That's how we need to think about data. I think now is the time, given the economic environment that we are in, much more focus on control, much more focus on productivity, efficiency, and now is the time we need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >> Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much. Good luck in, in San Diego. I know you're going to crush it out there. >> Thank you Dave. >> Yeah, it's a great spot for an in-person event and and of course the content post-event is going to be available at collibra.com and you can of course catch theCUBE coverage at theCUBE.net and all the news at siliconangle.com. This is Dave Vellante for theCUBE, your leader in enterprise and emerging tech coverage. (upbeat techno music)