(upbeat music) >> Hello and welcome to the Cube Studios in Palo Alto California, for another Cube conversation. Where we go in-depth with thought leaders driving innovation across the tech industry. I'm your host Peter Burris. Every Enterprise is saddled with the challenge of how to get more value out of their data. While at the same time trying to find new ways of associating value with product or value with service and to work with the different technology suppliers to create an optimal relationship for how they can move their business forward within a data-driven world. It's a tall order but 2020 is going to feature an enormous amount of progress and how enterprises think about how to handle the people, process and technology of improving their overall stance towards getting value out of their data. So to have that conversation today, we're joined by a Guy Churchward, who's the CEO of Datera. Guy welcome back to the cube. >> Thank You Peter, I appreciate it. >> So before we go any further give us a quick update what's going on with Datera? >> We're doing pretty well. I mean this year's we're just going to close it off. So we're in Q4 right at the end of it. You mentioned data-driven, you know I mean that was obviously one of my key excitements, years ago we kind of moved from a hardware resiliency or Hardware-driven to software resiliency, Software defined and I do think that we've hit that data-defined, data-driven infrastructure right now. I've been in the CEO role now just about a year. I've been on the board since August of a year and change ago and part of it is we had a little bit of an impedance mismatch of message, technology and basically I go to market. So the team quite brilliantly produced this data services platform to do data driven architectures. >> Mmmh. >> But customers don't wake up every morning and go, I need to go buy a data-driven, how do I buy one? And so when I came in I realized that you know what they had was an exceptional solution but the market isn't ready yet for that thought process, and what they were really buying still was SDS, software defined storage. >> So it almost in a connect way. so I'm going to buy an SDS and connect it to something and get a little bit of flexibility over here but still worry about the lock in every where else. >> Yeah, exactly and in fact even on the SDS side. What they weren't looking for is bring your own server storage. What they were looking for was automation and they were looking to basically break out and have more data mobility and data freedom. And so that was good and then the second one was our technology really sells directly to enterprises, directly to large scale organizations and it's very difficult as a start-up, small company to basically be able to punch straight into a global account, you know. Because they'll sit back and say, well you know would you trust your family jewels to a company that's got 40 employees in Silicon Valley. >> Right. >> And so what you really have is this and get the message right and then make sure you have to flow through to the customer credibility right and we were fortunate to land a very strategic relationship with HP. And so that was our focus point. Right. So we basically got on board with HP, got into their complete program, started selling very closely to them of which their sales team has been marvelous and then we're just finishing that that year. The good news is and you know I'll give you a spoiler I care about Billings, you know I mean we actually move from an appliance business to a software business exclusively, and so we basically sell term agreement. So if you think about it from a bookings perspective, that's important but basically how much you bill out is more important. From a Billings perspective I think we're going to run roughly 350% up year-over-year. >> Ooh. >> Yeah which is kind of good. Right I mean in other words it was a bit of a pat on the back that seems very happy with that and then even from new account acquisitions if I count the amount of accounts that we bought in this year and to date, entirely since 2013 we've only had one customer churn, so all the customers are coming with us but if I count this year, if I look at 16 17 and 18 we've actually bought more customers on board in 19 than all three pulled together. So we're actually finishing a very very strong year. >> Congratulations. Now if we think about going into 2020 you're closing this quarter, but every startup has to have a notion of what's going to happen next and what role you're going to play. And what happens next. So if I look back I I see the enterprise starting to assert themselves in the cloud businesses. That's having an effect on on everybody. But it really becomes concrete you know, the rubber really meets the road at the level of data. So as you start to grow you're talking more customers, as you talk to more customers and they expressed what they need out of this new cloud oriented world, what kinds of problems are they bringing to the table as far as you're concerned? >> Yeah, I mean they initially come to us so what I would say is every account that we've run we've replaced traditional arrays storage arrays and every account we've run, we've actually competed against SDS vendors and whether that's something like Dells, VxFlex or even vSAN, VMware's vSAN and which are probably the two most well-known ones. A lot of cases I mean we actually have 100% win rate against that in these competitive situations, but interestingly most customers now are putting dual source in place. So in fact the reason that we've ridden pretty quickly and we've run lots of deals, isn't because we're going in and saying VxFlex is failing or vSAN is failing, but they want something extra, they want automation, they want desegregation, they want scale >> They want second source. In many respects of sales is, it's succeeding but you have to push a little bit harder and that is ease most easily done by bringing in another platform with crucial functionality... >> Yeah >> ...and a second source. >> And I think you're on the money there Peter because if I look at second source in the traditional array business, no CIO worth their soul is a single source vendor so they they will have Dell and they'll have HP or they'll have HP and they'll have Pure, doesn't matter and and even on HCI you'll see the HCI vendors, Nutanix is doing very well, so is Dell. So therefore they'll have that from second source if its critical. So if an environment is critical they always have a second source and so even now when you look into software-defined, this market in 2019 was very much like the, let's get the second source in place. And that shows you where we are on the maturity curve because people is basically moving on this en mass. Now that's 2019 you're asking about 20, 21, 22 moving forward. The reason that the traditional arrays weren't working for them is whether it's flexibility or it's basically management costs or maintenance, but it's data freedom. It's what they're really looking for. You know, what is a data center? Is it on-premise, is it cloud? It's definitely cloud but the question is is it on-premise cloud? Is it hybrid cloud, is it public cloud? And then you mention edge. You know we actually find customers who are looking and are saying look, the most important thing for us is being data-driven and what data-driven basically articulates is we get data in, we analyze it, we make decisions on it and we win and lose against our competition as fast as we can be accurate on that data set. And a lot of the decisions are getting made at the edge. So a lot of people are looking at saying my data center is actually at the edge, it's not in the center in the cloud, right. >> Well in many respects, it's for the first time a data center actually is what it says it is, right. Because the data center used to be where the hardware was and now increasingly enterprises are realizing that the services and the capabilities have to be where the data is. >> Yeah. >> Where the data is being produced, where the data is being utilized and certainly where the data, where decisions are being made about what to keep what not to keep, how much of it etc, and that that does start to drive forward an increased recognition that at some point in time we are going to talk more about the services that these platforms, or these devices or these software-defined environments provide. Have I got that right? >> Yeah, yeah you have and even if you look at that, you know ... what the AI/ML, you know I mean if I if I kind of step back and I look at what a customer's trying to do which is to utilize as much data as possible, in a way that they have data freedom that allows them to make decisions and that's really where AI and machine learning comes in. Right you know everybody employs that. I recently bought a camera, shockingly inside the camera it's got ML functionality into it, it's got AI built into it, my new photo editing software on my iPad is actually an ML-based system. They don't do it because it's a buzz word, they do it because basically they can get a much higher level of accuracy and then use data for enrichment, right. And then in the ML track, the classic route was I'm going to create a data lake, right. So I got my data lake and I've got everything in it then I'm going to analyze off the back of it. But everybody was analyzing once it's in the data lake. And what they've realized is to compete, they actually have to analyze much quicker. >> Right. >> And that's at the edge, and that's in real-time and that string based. And so that's really where people are sort of saying I can't ... I'm not going to have any long pole in my technology tent. I'm not going to have anything slow me down, I have to beat my competition and as part of that they need complete fluidity on their data. So I don't care whether it's at the edge or it's in the center or in the cloud, I need instant access to it for enrichment purposes and to make fast and accurate decisions. So they don't want data silos. You know, so any product out there that basically says me me me me give me my data and therefore I'm going to encrypt in such a ways you can't read it and it's not available to anybody else. They are just trying to eradicate that. And and we've sort of moved. It's a weird way of putting it but we've moved from hardware-defined to software-defined and I think we've moved into this data-defined era. But at the same time, it's the most stupid thing for me to say, because we've never not been in a data-defined era. But it's the way in which people think with their architecture as they sign up a data center now or a cloud and they're not saying, hey so about the hardware, it's based on that or it's the software. It's always going to be about the data. The access to the data, however before you get excited. (laughs) The thing that I kind of look at I say so what has fundamentally changed? And it's the fact that we always used to have to make a decision. You know, I ran a security analytics business and when you do things like log management, it's about collecting as much as data so in other words accuracy beats speed. And then security event management is speed beats accuracy. Because you can't ask questions of the same data. But technology is caught up now. So we've actually moved from the do you want accuracy? Or do you want speed? It's like "or arena". So people were building architectures in this "or" world, you know. Do you want software-defined? If you want software-defined you can't have Enterprisilities. Why not? Well, if you want an enterprise application, I mean remember the age-old adage. You should never buy a version 1.0 of an app. >> Right. But what happens is they want they want this ... people are turning around saying I need an enterprise application, I want full data access to the back of it, I actually need it to be fluid, I need it Software-defined, I don't know where it's going to be based and I don't want to do forklift upgrades. I want and and and and and. Not or, so what we've actually moved to is a software-defined era you know, and a data-defined architecture in an "and arena". And where customers are truly winning and where they're going to beat their competition, is where they don't settle and say oh I remember back two years ago, this happened and therefore we should learn from that, and we shouldn't do that. They're actually just breaking through and saying I'm going to fire the application up I want it up and running within 30 days, I want it to be an enterprise application, I need it to be flexible, I needed to have a hype of scale and then I'm going to break it down and by the way I'm not going to pay contractually to an organization to build all that infrastructure. And that's really why soup to nuts, as we move forward not only they sort of building an infrastructure is data-defined infrastructure, they don't want lock-in. They want optionality and that means they want term licenses which is sure, they don't want these proprietary silos and they need data flexibility on the back of it. And those are the progressive customers, and by the way I've not had to convince a single customer to move to software-defined or data-defined. Every client knows they're going there, the question on the journey is, how fast they want to get. >> Right, when? >> Yeah. >> So if so look every single every single enterprise, every single business person takes a look at what are regarded as the most valuable assets and then they hire people to take care of those assets, to get value out of those assets, to maintain those assets, and when we move from a hardware world where the most valuable asset is hardware that leads to one organization, one set of processes, one set of activities. Move into a software world to get the same thing. But we agree with you, we think that we are moving to a world that is data first, where data is increasingly going to be the primary citizen and as a consequence we're seeing firms reinstitutionalize how work is done, redefine the type of people they have, alter their sourcing arrangements, I mean there's an enormous amount of change happening because data is now becoming the primary citizen. So how is Datera going to help accelerate that in 2020? >> Yeah I mean and again that's part of data access. And then also part of data scale. Back probably six seven eight years ago. EMC we were even I remember Steve Manley is a good buddy of mine, we went on stage and we talked about bringing sexy back to back up. We were trying to move away from backup admins just being backup admins to backup admins actually morphing their job into being AI/ML. You know, I remember a big client of mine, and it wasn't in the EMC days, it was before that were basically saying they have to educate their IT staff, they want to bring them up as they move forward. In other words, you can't ... what you don't want is you don't want your team, because it all comes down to people. You don't want them stuck in an area to say we can't innovate forward because we can't get you away from this product, right. So one of our customers at Datera is a SaaS vendor. And their challenge is they had traditional array business even though it was in a SaaS model, it was basically hardware in the background and they would buy instances and they found that their HR cost, their headcount cost was scaling, >> With the hardware. >> Exactly, and and they were looking at and going, what does that do to my business? It does one or two things, either one is it means that cost I mean do I bear that I don't make profitability and I can't drive my business or do I lay that on my customers and then the cost goes up and therefore I'm actually not a cloud scale. And I can't hire all the people I need to hire into it. So they really needed to move to a point of saying how do I get to hyper scale? How do I drive the automation that allows me to basically take staff and do what they need to do. And so our thing isn't removing staff, it's actually taking the work that you have and the people and put them in a way they really matter. So in other words if you think about the old days of I'm going to mess this up but, I talked to somebody recently about what IT stands for. And they said IT should stand for information technology, right. I mean that's really what it is. But, but you know for the last 20 years it stood for infrastructure technology? >> Yeah. >> And that's frustrating, because in essence we got way too many people managing a lot of crap. And what they really should be doing is focusing on what makes the business happen. >> Yeah. >> And for instance I like to run a business by money in and money out, everybody else does and then you look at it and you say well, how do I get more money coming in? By being smarter and quicker than somebody else. How do I do that? By data analytics. Where do I want to put my work? Well I want to put it into the ML/AI and I want more analysts to work on it. I want my IT staff to do that. Let's move them into that. I don't want them you know rooms and reams of people trying to make it you know manage arrays that don't function the way they should or... >> One more percent out of that array of productivity. >> Yeah, abnormally trying to scale HCI solutions to a hyper scale that actually is impossible for them to do it. >> Right. >> You know and and that was the thing that really what Mark, who was the founder of Datera and the team really did is they looked at it from a cloud perspective and said it's got to be easier than this. There must be a way of doing low lights-out automation on storage. And that's why I was saying when I took over, I kind of did the company an injustice by calling it an SDS Tier 1 vendor. But in reality that was what customers could assume. And we're basically a data services platform that allows them to scale and then if you hop forward you go how do you open up the platform? How do you become data movement? How do you handle multi-cloud? How do you make sure that they don't have this issue? And the policies that they put in place and the way in which they've innovated, it allows that open and flexible choice. So for me, one is you get the scale, two you don't have forklift upgrade three is you don't have human capital cost on every decision you make, and it actually fits in in a very fluid way. And so even though customers move to us and buy us as a second source for SDS, once they've got the power of this thing they realize actually now they've got a data service platform and they start then layering in other policies and other systems and what we've seen is then a good uptick of us being seen as a strategic part of their data movement infrastructure. >> You expand. >> Exactly. >> Guy Churchward, CEO of Datera, thanks again for being on the Cube. >> My pleasure. Thank you Peter. >> And thank you for joining us for another CUBEConversation. I'm Peter Burris, see you next time. (upbeat music)

>> From our studios in the heart of Silicon Valley. Holloway Alto, California. It is a cube conversation. >> He will come back and ready Geoffrey here with the Cuban Interpol about those details for acute conversation. We've got a really great guess. He's been on many, many times. We're always excited. Have them on to a bunch of different companies a lot of years and really a great perspective. So we're excited. Guy. Church word. The CEO of Da Terra. Back >> in the politest. EEO guy. Great to see you. >> Thank you, Jeff. Appreciate it. >> Absolutely. So I think last time you were here, I was looking it up. Actually, Was November of twenty eighteen. You were >> kind of just getting started on your day. Terror of the adventure. Give us kind of the update. >> Yeah, I was gonna say last time we had Mark in whose CEO when found a cofounder of Data and I was edging in. So I was executive chairman at the time, you know? And obviously I found the technology. I was looking for an organization that had some forward thinking on storage. Andi, we started to get very close with a large strategic and actually We re announced it on the go to market, I think in February with HP, and I thought that myself and Mark kind of sat down, did a pinky swear and said, OK, maybe it's time for me to step in and take the CEO role just to make sure that we had that sort of marriage of innovation and then some of the operations stuff they could bring inside the business. >> So you've been at this for a >> while, but in the industry for a long time. What was it that you saw? Um, that really wanted you to get deeper in with date. Eriks. Obviously, I'm sure you have tons of opportunities coming your way. You know, to kind of move from the board seat into the CEO position. >> Yeah. Yeah, a bad bet. Maybe stupidity or being drunk. It, to be honest, it was. You know, the first thing is, I was looking for this technology that basically spanned forward, and I had this gut hunch that organizations were looking for data freedom. There's why did the Data Analytics job before that? I did security analytics, and, you know, we were looking at that when we were you know, back when we talk to things like I'm seeing Del and so from appear technology standpoint, I wanted to be in that space, but in the last few months, because you know, jobs are all about learning and then adjusting and learning and adjusting and learning. Adjusting on what I saw is a great bunch of guys, good technology. But we were sort of flapping around on DH had an idea that we were an Advanced data services platform. It's to do with multi, you know, multi cloud. And in essence, I've kind of come to this fundamental kind of understanding because I've been on both sides, which is date era is a bunch of cloud people trying to solve storage needs for what the cloud needs. But they have the experience. They walked that mile. You know, when people say you've gotta learn by walking in their shoes, right? Right on DH there, Done that versus where? Bean. In the past, where we were a ray specialists pushing towards the future that we didn't quite understand, you know, and and there is a fundamental philosopher philosophical difference between the two. Andi weirdly, my analogy or my R har moment came with the Tessler piece. And I know that, you know, you've pinned me a few times on Twitter over this, right? I'm not a tesler. Bigger to the extent of, you know, and probably am now, I should have a test a T shirt on, But I always thought it was an electric car and all they've done is electrified a car and there was on DH, You know, I've resisted it for years and bean know exactly an advocate, but I ended up buying one because I just I felt from a technology standpoint, their platform that they were the right thing. And once I started to really understand what they were about, I saw these severe differences. And, you know, we've chatted a little bit about this Onda again. It's part of the analogy of what's happening in the storage industry, but what's happening in the industry in in a global position. But if you compare contrast something like Tesler, too, maybe Volkswagon and it might be a bad example. But you know, Audi there first trance into electric vehicles was the Audi A three, and I could imagine that they were traditional car people pushing their car forward sort is a combustion engine will if I change that and put some salt powertrain in place, which is the equivalent of a you know, a system to basically drive the wheels and then a bunch of batteries Job done or good, right? Right. And I assume the test it was the same. But I had a weird experience, which is, once you get it into autopilot, you can actually set the navigation direction, and then it will indicate it'll it'Ll hint to you went to change lanes. And so, for instance, I'm driving to the office and I'm going along eight eighty and I want to go toe Wanna one? It says, You know you need to pull across. They hit the indicator will change lanes and they'LL do some of the stuff and that's all well and good. But I was up going to a board meeting on two eighty, going off for the Rosewood. You know, Sandra El Santo and I was listening to a book one of these, you know, audiobooks, and I wasn't really paying much attention. I'm in the outside lane, obviously hitting the speed limit gnome or but I wasn't paying attention. And all of a sudden the car basically indicates form A changes lanes, slows down, change lane again and then takes a junction, slows down, comes up to a junction, and you start to realize that actually Tesla's know about electrified vehicles. It's actually about the telemetry and the analytics and then feeding that back into the system. And I always thought Tesler might be collecting how faster cars going when they break. You know the usual thing. Everybody has this conversation. It's always over worked. But if you've sort of look at it and he said no, maybe they collect everything and then maybe what they're doing is they're collecting, hitting the indicator stalk. So when I'm coming out to a junction and I indicate, How long do I stay? Indicating before I break? And then I changed lanes and then I basically slow down and I go into the junction. And then what they do is they take that live information and crowdsource it, pull it back into the system, and then when they're absolutely bulletproof, that junction, then is exactly as a human would normally do this. They then let the car take over So the difference between the two junctions is one they totally understood, the other one there still learning from right when you look at it and you go done. So they're basically an edge telemetry at a micro level organization, you know, And that is a massive difference between what Tesla's doing and a lot of the other car manufacturers are doing. They're catching up, which is really why I believe that they're going to be a head for a long time. >> It's really interesting. I was >> Elektronik wholesale for ten years before come back to school. Can't got in the tech industry. And so really distribution was king from the manufacturer point of view. Always. They just like ship their products for ages, right? These distribution to break bulk thes distribution, educate the customer these distribution just to get this stuff out. But they never knew how people actually operate their products. Whether that be a car, a washing machine. Ah, cassette player, whatever. So what? What What fascinates me about thes connected devices is what, what a fundamentally different set of data. Now manufacturers have people have in how people actually use the product. But even more importantly, that as you said, they could take that data and make adjustments on the fly because since so much of its software now, we talked again before we turned on some of your software upgrades that you've gotten in the Tesla over the last six months, which we're all driven by customers. But they had a platform in place that enabled them to update functionality and to basically repurpose hardware elements for a new function, which is which is, you know, so in sync with Dev ops and kind of this dev up culture in this continuous this continuous upgrade, this continuous innovation with actual data from real people operating the products that they should come to the market. >> Andi, think once you step back. And that was really why was keen to sit down and talk. And it's not specifically around software defined storage, which is the data. A piece in our example is yes, I am the Tessler because we can do all of the analytics and all of the telemetry versus of standard array. If you scratch that away and you say let's have a look at our whole lives are macro lives. Another example was my wife and I. We've got friends of ours always banging on about these sleep by number beds and and so we went past the store wandered in, and the sales rep got us lying on a bed and he was doing there, you know, pumping the bed up to a size. It's just Well, you are sixty five, a US seventy or seventy five, and I kind of got bored of that. And I went here, Okay, I'm that and he goes, Okay, your wife's of fifty and you're a seventy five, Andi said. But let's kind of daft. And he goes, Well, here's and he shows them a map and it shows a thermal image of me lying on the bed. I'm a side sleeper back sleeper, and then what they do is they feed the information so that comes back off their edge, which is now Abed. And then what they do is they then analyzing continuously prove it to try and increase my bed sleeping patterns. So you look at it and you say what they're not doing is just manufacturing of mattress and throwing it out. What they've done is they said, we're going to treat each individual that lies on the mattress differently on, we're going to take feedback and we're going to make that experience even better. So that the same thing, which is this asset telemetry my crisis telemetry happens to be on the edge is identical to what they have, you know. And then I look at it and I go, Why don't I like the array systems? Will, because the majority of stuff is I'm a far system. My brain is inherently looking at the Dr types underneath and saying, As long as that works fine, everything that sits inside that OK, it'LL do its thing right, and that was built around the whole process and premise of an application has a single function. But now applications create data. That data has multiple functions, and as people start to use it in different ways, you need to feed that data on the way in which is processed differently. And so it all has the intelligence houses in home automation. I'm a junkie on anything that has a plug on it, and I've now got to a point where I have light switches or light fittings would have multiple bulbs on every bulb now is actually Khun B has telemetry around it, which I can adjust it dynamically based on the environment. Right? Right. And I wish it got wine. You know, I got the wine. Fridge is that's my biggest beef right now is you gotta wine, fridge. You can have Jules, you know, you have jewels climates, which means that you don't fan to one side of it and they overheat the bottom right. But it'LL break the grapes down. Would it be really cool if the cork actually had some way of figuring out what it needs to be fed? And then each of them could be individual, right? But our entire being, you know, if you think about it's not just technology or technologies driving it, but it's not the IT industry, but our entire lives. And now driven around exactly what you just described, which is manufacturers dropping something out into the wild to the edge and then having enough telemetry to be able to enhance that experience and then provide over the air, you know, enhancements, >> right? And the other thing, I think it's fascinating as it's looking up. We interviewed Derek Curtain >> from the architect council on. That's a group locally that just try work, too, along with municipalities and car manufacturers, tech companies. But >> he made a really interesting >> comment because there's the individual adjustment to you to know that you want to get off it at Page Milan or sandhill on DH. You've got a counter on your point of this is meeting the Rosewood. But >> then the other thing is, when you aggregate >> that now back up. You know, not that you're going to be sharing other people's data, but when he start to get usage patterns from a large population that you can again incorporate best practices into upgrades of the product and used a really good example of this was right after the one pedestrian got killed by the test of the lady with the bike that ran across the front of the street and it it it literally happened a week before. I think the conference so very hot topic at an autonomous vehicle conference and >> what he said, which is really important. You know, if if I get >> in an automobile accident and I'm going to learn something, the person I hits pride gonna learn something. The insurance adjusters going to take some notes and we're going to learn it's a bad intersection. I made a mistake, whatever, but when an autonomous vehicle gets in a Brack when it's connected, all that telemetry goes back up into the system to feed the system, to make improvements for the whole system. So every car learns every time one car has a problem every time one car gets into a sticky situation. So again, kind of this crowd sourced. Learning an optimization opportunity is fundamentally different than I'm just shipping stuff out, and I don't know what's going to happen to it, and maybe a couple pieces come back. So I think people that are not into this into the direct connection are so missing out on those you said this whole different level of data, this whole different level of engagement, a whole different level of product improvement and road map that's not a PR D. It's not an M R G. It's all about Get it out there, you know, get feedback from the usage and make those improvements on this >> guy finish improvements and micro analytics. I mean, even, you know, we talk back when you were adjusting how you deliver content for the Cube, you know, rather than a big blob, You really want to say, Well, I need more value for that. My clients need more value for that. So you've almost done that Mike segmentation by taking the information and then met attacking every single word in every single interview right to enrich the customer's experience, you know, And it kind of Then you Matt back and you say, We've got to the age now where the staff, the execs that we talked to over the other side, the table there, us they're living our lives. They've got the same kids as we've got the same ages we've got. They do the same person's we've got. They understand the same things and they get frustrated when things naturally don't work the way they should. Like I've got a home theater system and I've still got three remote controls. I can't get down. I've got a universal remote control, but it won't work because the components don't think so. What's happened is we've got to a world where everything's kind of interconnected and everything kind of learns and everything gets enriched when something doesn't it now stands out like a sore thumb and goes, That doesn't That is not the right way to do business on DH. Then you look that you say, translate that then into it and then into data centers. And there's these natural big red flag that says That's an old way of doing things. That's the old economy that doesn't enable me to go forward. I need to go forward. I need more agility. You know, I've got to get data freedom and then how do I solve that issue? And then what? Cos they're going to take me there because they're thinking the same ways as we are. This is why Tesler screamingly successful. This is why something like these beds are there. This is why things like Philips Hue systems are good and the list just goes on. And right now we're naturally inclined to work with products that enable us to enrich our lives and actually give feedback and then benefit us over the air. We don't like things that are too static now, and actually, there is this whole philosophy of cloud, which I think from an economic standpoint, is superb, you know? I mean, our product is Tier one enterprise storage in an SD s fashion for public private hybrid clouds. But we're seeing a lot of people doing bring backs. You know, out of the cloud is a whole thread of it right now, but I would actually say maybe it's not because the cloud philosophy is right, but it's the business model of the cloud guise of God. Because a lot of people have looked at cloud as they're setting. Forget, dump my stuff in the cloud. I get good economics. But what we're talking about now is data gets poked and prodded and moved and adjusted constantly. But the movement of the data is such that if you put in, the cloud is going to impinge you based on the business model. So that whole thing is going to mature as well, right? >> You're such a good position to because >> the, you know the growth of date is going. Bananas were just at at Arcee a couple weeks ago. In one of the conversation was about smart smart buildings, another zip zip devices on shades that tie back to the HBC, and if anybody's in the room or not, should be open should be closed. Where's the sun? But >> there was really interesting comment about >> you know, if you look at things from a software to find way you take what was an independent system that ran the elevator and independent system that ran the HBC and independent system that ran the locks? One that ran the fire alarm. But guess what? If the fire alarm goes off, baby, it would be convenient to unlock all the doors and baby. It would convenient automatically throw the elevator control system into fire mode, which is don't move. Maybe, you know so in reconnecting these things in new and imaginative ways, and then you tie it back to the I T side of the house. You know, it's it's it's it's getting a one plus one makes three effect. With all these previously silent systems that now can be, you know, connected. They can be software defined, you know, they can kind of take the operation till I would have never thought of that one hundred years. I thought that just again this fascinating twist of the Linz and how to get more value out of the existing systems by adding some intelligence and adding this back and forth telemetry. >> Yeah, and and and again, part of May is being the CEO of date era. I want advocates the right platform for people to use. But part of this is my visceral obsession of this market is moving through this software defined pattern. So it's going from being hardware resilient to software resilient to allow youto have flexibility across it. But things have to kind of interconnecting work, as you just described on SDF software to find storage as an example comes in different forms. HD is an example of it and clouds an example. I mean, everything is utterly software defined in Amazon. It so is the term gets misused, could be suffered to find you could say data centric data to find or you could say software resilient. But the whole point is what you've just described, which is open it up, allow data freedom, allow access to it and then make sure that your business is agile and whatever you do, Khun, take the feedback in a continuous loop on at lashing. Move forward as opposed to I've just got this sentence forget or lock mentality that allows me just to sort of look down the stack and say, I've got the silo. I'm owning that customer of owning the data and by the way, that's the job. It's going to doe, right? So this is just the whole concept of kind of people opening their eyes on DH. My encouragement on DI we can encourage anybody, whether customers or basically vendors, is to look around your life and figure out what enriches it from a technology standpoint. On odds on it will be something in the arena that we've just described, right? >> Do you think it's It's because I think software defined, maybe in its early days was >> just kind of an alternative thought to somebody doing it to flipping switches. But as you said in the early example, with the car, propulsion wasn't kind of a fundamentally different way to attack the problem. It was just applying a different way to execute action. What we're talking about now is a is a totally higher order of magnitude because now you've got analytics. You actually want to enable action based on the analytics based on the data for your card. Actually take action, not just a guy. Maybe you should you know, give given alert and notice that pops up on your phone. So, you know, >> maybe we need something different because it's not just redoing >> what we did a different way. It's actually elevating the whole interaction on a whole different kind of love. >> And this is this is kind of thank you for that. It was the profound kind of high got wasn't joining data and watching it. It was I got a demo off the cloud. You I the callback piece of what cloud? What data has. And I was watching a dashboard off a live data stream. You know of information that we were getting back from multiple customers and in each of the customers, it would make recommendations of, you know, how many gets on, how many times it would hear cash on DH. So it was actually coming back dynamically and recommending moving workloads across onto or flash systems. You, Khun, do things where once you've got this freedom on application, a data set isn't unknown. It's now basically in a template, and you say this is what priority has. And so you say it's got high priority. So whatever the best legacy you could give me. Give me right, You drop it onto a disk. And at the moment I've got hybrid. That's all I've got, but I decide to addle flash. So I put some all flash into the into the system. Now it becomes part of this fabric and its spots it and goes well on our second. That will disservice me better and then migrates the workload across onto it without you touching it, right? So, in other words, complete lights out so that the whole thing of this is what Mark and the team have done is looked at and said the only way forward is running this massively agile data center based on a swarm of servers that will basically be plugged together into something that would look like a fabric array. But but you can't. Then you've got to assume that you can now handle application life cycles across onto it. It'LL make recommendations like the bed thing. You know what I was saying? I was lying there and what I liked about it. So So I set my thing to fifty nine, and then it realizes I'm not sleeping very well. It's not suggested. Sixty sixty one sixty. Sleeping well, OK, that's it. And then that's good. We'LL do the same thing where an application will actually say, Here's my template. This is what it looks like. The top priority, by the way. I need the most expensive drives you've got, drops it onto it, and then it look at it and go. Actually, we could do just as good a job if there's on hybrid and then migrated across and optimize the workload, right? And so it's not again. Part of it is not. Data is the best STDs, and it is for Tier one for enterprise storage. It's the fact that the entire industry, no matter where you look at it, not just our industry but everybody is providing tech is doing is exactly the same thing, which is, and you kind of look it and you go. It's kind of edge asset micro telemetry, and then that feedback loop and then continuous adjustment allows you to be successful. That's what products are basically getting underpants. >> Just, you know, it's when he's traveling. Just No, we're almost out of time, but I just can't help it but >> say it, you know, because we used to make decisions >> based on samples of old data with samples. And it was old. And now, because of where we are on the technology lifecycle of drives and networks and CPS and GPS, we can now make decisions based on all the data now. And what a fundamentally different, different decision that's going to drive this too. And then to your point, it's like, What do you optimizing for? And you don't necessarily optimize for the same thing all the time that maybe low priority work, load optimized for cost and maybe a super high value workload optimized for speeding late in sea. And that might change >> over time when Anu workload comes in. So it's such a different way to look at the world >> and it is temporal, right? I mean, again, I know you're going kick me off now, but think about it right the old days and writing a car building a car is you thought, well, what's going to need to be in the car in three years time, put it in now, build manufacture, coming out and then with a Tesler i by the current December. Since December, I've now got pinned based authentication I've got century mode. I've got Dash Cam, They've got all free. I've got a pet mode into it now. My car's got more range. It's got high performance. This guy highest top speed, and I haven't even taken the current or it's all over the air And this is all about, continues optimization. They've done around the platform and you just go. That's the way this linked in. Recently, someone posted something said, You know, keep the eyes are dead. Well, the reason there saying that isn't because there's a stupid thing to do Q. B. Ours is because if you're not measuring your business and adjusting on a continuous basis, you're gonna be dead anyway. So our whole economy is moving this way. So you need an infrastructure architecture to support that. But where everybody's the same, we're all thinking the same. And it doesn't matter what industry or, you know, proclivity have this. This adjustment and this speed of adjustment is what you need. And like I said, I'm That's why I wanted to get to date era. That's what I'm excited about it and that is the are hard I had I kinda looked. It went Oh my God, I'm now working with cloud people who understand what they've walked in the shoes And I kind of got this way of sense of can Imagine what it had been like if you were ill on the first time You saw a hundred thousand cars worth of life data spilling in of what power you have right to adjust and to basically help your client base. And you can't do that if you are in fixed things, right? And so that's That's the world moving forward >> just in time for twenty twenty one will all have great insight in a few short months. We'LL all know >> everything Well, guy great Teo Great to >> sit down Love to keep keeping tabs on you on Twitter and social And thanks for stopping by. I >> appreciate it. All >> right. He's guy. I'm Jeff. You're watching the cube within a cube conversation Or Paulo? What? The studio's thanks for watching >> we'LL see you next time

(orchestral music playing) >> Hi. I'm Peter Burris. Welcome to another Cube Conversation. Brought to you by theCUBE from our beautiful studios in Palo Alto, California. Great conversation today. We're going to be speaking with Datera about some of the new trends and how we're going to utilize data within the business, with greater success, generating more value to superior customer objectives. To do that, we've got Marc Fleischmann, who's the CEO and Founder of Datera. Marc, welcome to theCUBE. >> Thank you. >> And Guy Churchward, who's the Executive Chairman of Datera. >> Yeah, thank you Peter. >> So guys, this is a great topic, great conversation, very very timely industry. One of the reasons is we've heard a lot about the Cloud-native stack. Now the Cloud-native stack is increasingly going to reach into the enterprise and not just demand that everything come back to the cloud, but bring the cloud more to the enterprise. Well one of the things that's still something of a challenge is and how do we bring data given it's native attributes into that model more successfully. Marc, what are the issues? So look, ultimately we believe it's all about data freedom, the capability to extract the value of data across the enterprise. However, as long as we continue to think about proprietary systems silos, where data is trapped, where it can't move freely across the enterprise, we're not going to be able to get there. So ultimately what it requires is changing our thinking of infrastructure from a hard for centric prospective to a service centric prospective. Ready applications drive the needs from the data, where it's an application centric perspective that automatically drives how data is actually consumed across the enterprise. >> But the, we've been thinking about that through software defined, ECI, and other, you know, hyperconversion infrastructure in other things. But at the end of the day, we really have to make sure that we're doing so in a way that marries to the realities of data. >> Absolutely. >> Talk to us a little bit about how Datera is providing that substrate that is native to data, but also native to the cloud. >> Absolutely. So I would describe Datera as Datera is to data what Kubernetes is to compute. What do I mean by that? First of all, it's all about data orchestration. We orchestrate the data just like Kubernetes would orchestrate compute. That's the foundation of our platform. Now if we don't deliver enterprise performance, so that we can actually, you know, replace existing storage, we wouldn't be able to actually broadly deploy. So we have enterprise performance as well. And lastly, to get away from a hard for centric model, we offer wide variety, wide choice, future ready choice of Harver. Those are the three key tenants that we actually see as getting to that vision. >> So Guy, you've been in this business a long time. You've looked at a lot of changes in technology, for rays where we were mainly focused on persisting data to now some of the new technologies, we were more focusing on delivering data to new classes of applications. From your perspective, how does this message Marc's bringing line up with customer needs? >> Yeah, I know, appreciate it. I mean that was one of the reasons that when I had the opportunity to work closely with Datera, I kind of jumped into it. You know, because part of this is, as Marc said, data freedom. Unlocking, in other words, unlocking from the boundaries of basically a physical location. I think, you know, we always aspire and believe that we want to move towards a cloud, a pure cloud model. But we're going to be in this transition for five, six, seven years where we have on premise a bit of hybrid and a bit of distributed and things like Intelligent Edge. So in other words, the whole concept is to say how do I utilize data no matter where it is into a fabric or a mesh. And I think that the industry that we all live in sort of, by accident, tries to own the data, you know. It doesn't matter whether you own it in a physical construct of a data center or we own it in a physical construct of a piece of hardware or a proprietary format. But in essence you have these data silos absolutely everywhere. And so for me to move to a cloud, you've got the simplicity you need. You've got the orchestration that you actually need. But you need this freedom outside of the bounds of a physical location or a piece of tent. >> I want to return back to the issues of performance >> Yeah. >> and the need for performance because the world that you just laid out guys, makes an enormous amount of sense to me and the Wikibon community. But it does mean that this data generated by that application in this location may have value to some other applications somewhere else that may have completely different performance action. >> Absolutely. >> So let's talk about that need for ensuring, that again, this notion of a native data approach to incorporating data into the cloud. How does the performance angle really work? >> I would argue where traditional self defined storage, SDS, fell short was exactly on the promise of performance. We saw that we contributed a significant part of the Linux data path itself. The way we architected the system, we delivered true, primary application performance. So that in combination with the ability to orchestrate data across the data center, across multiple data centers, and ultimately across the data center and the cloud gives you the best of both worlds. It gives you primary workloads, the ability to actually serve primary workloads across multiple protocols, but to serve them location dependent, wherever you like, because we orchestrate the data through those places. >> And- >> So- >> Oops! Go ahead. >> Sorry. It's the coffee. It's going to kick in. (Peter laughs) So I mean part of it is not just that, but it's also the life cycle. >> Ah, very true. Right, I mean and, you know, this is the thing that kind of attracts me is, and you mentioned, you know, what you learn with the amount of hair I don't have now and the gray beard I've got is, you know, there's one thing about this sort of data boundaries and things getting locked in. The other one is the speed of which people want to build an application. They need it to be have the enterprisilities, and then they'll take the application down. You know, if you kind of think when we started in the industry and it would last 20 years. And then 10 years. And then five years. And now you look at it saying somebody wants an enterprisility application up and running within two or three months, which is preposterous, but needs to be done. And then it might be down within a month. Because- >> Oh 15 years ago it took us two or three months to create the test data required for the application to follow up. >> Right, and how many people would ever used to tell you never use an application if it's a window zero. But we're talking about, in a window zero period, they're actually going to serve their communities, the most critical thing. Data is it for a company. If you're analytics don't run as fast as your company's competitive space, you're behind. So if you're going to analyze something that application that you bring up to analyze has to be critical to your business. And that's going to go up and it's going to go down. So in other words, it's going to go from test and dev, up into production, tier zero, then tier one, tier two, tier three, and then out into an archive in a period of time that normally a window zero would gestate. And so you need a platform that has that ultimate agility and again it can't be bound by anything. And this is something that, you know, Datera has as unique. This was why I like software defined and why I believe that this market's place is now for this space. Everything prior to SDS is basically what I call new legacy. You know, it doesn't matter whether it's a ray or it's hyperconversion, and they're great and they've got their place. But each one of them has this fixed boundary that allows you to flex but inside of its own control. Businesses aren't like that. They can't be done like that and applications can't be done like that now. So it's all multi-cloud, it's all going to be versed. >> Well let's build on that. So the Kubernetes describes, as you said, a cluster of compute. When you pull away the- It's really a network of compute. >> That's right. >> It's a network of compute resources that Kubernetes has visibility into so we can move resources >> That's right. >> Or move elements where they need to be to be optimally utilized. Let's build on that. So what where is Datera in this relationship between resources as it starts to build a an orchestrator, a manager, a network of data elements, and pull that into something that makes it easier for developers to do what they need to do, operators to do what they need to do, and the business to do what it needs to do? >> Yeah, so you can call Kubernetes the network of compute or a swarm of compute, right? So the power of Kubernetes is that it abstracts the infrastructure to a level where it gets delivered continuously to the application on demand. We do exactly the same thing for data, for the ability to store, manage, and ultimately life cycle data. So simply label based, like Kubernetes is, you specify the service level objectives for every individual application, and Kubernetes pretty much does all the rest of the job, completely independent of the hardware underneath. Again, we do that for data. You have certain access requirements, protocols, authentications, security. You have certain performance requirements. You have certain reliability requirements. You articulate them simply in similar SLO, service level objectives. Datera does all the actual implementation automatically across the data center. So now you get to a point where in the modern data center and the soft defined data center, I would argue we are the data foundation in those kinds of scenarios, we can co-orchestrate data along, since you said Kubernetes specifically with Kubernetes, with its compute. Obviously we work in other environments as well. We work equally well for Enver. We work for some other, a number of other cloud orchestration frameworks. But Kubernetes is a really good example here. >> So who's going to buy it? I mean cause going back to this issue of the orchestrator, the developers clearly need this because they want access to real data, but they typically don't think in terms of underlying data structures. If it's available that's all they care about. Data administrators, business people. Who do you find your customers today are really making that, not the initial contact, but actually driving the adoption of this new data fabric? >> So Marc, I mean I know you'll answer it more accurately than I will. But just from a higher level to step down, there seems to be two types of people inside of large companies. One is a project owner. So for instance, you know, I've been blessed with a job inside of BMW that I have to do, autonomic cars. And I'm tying together a very complicated pipeline that has to be extremely agile. So that's the type of person that would basically look to buy and move us forward. And the other one is an internal service provider to the enterprise. So in other words, instead of being a group that has a physical job, what I'm actually doing is I'm saying I'm now going to be a service provider, or a cloud provider, or a resource provider to an organization that now has complexity that's moving into and embracing the digital economy or digital transformation. So if those are the two types of person inside of an organization, I think if you get a tie kicker, you know, there are places that we struggle with, I think it would be fair to say, is there's always going to be a geek somewhere that wants to kick the latest, cool technology, so we get involved with that. And then by the time you go all the way through it, there's no project there. They just really enjoyed themselves and so have we. But in essence there's enough people now who recognize my business is going through this transformation, I need to get out of my technical debt, I'm throwing business into, you know, this economy. It's normally around machine learning applications, Kubernetes, things that are fast moving, you know. And they need that level of ility that they're used to getting through fixed bounded technology, you know. And so we're actually seeing that as a service provider, both external and internal. But internal, inside the enterprises, is something which we're very key on. >> And let me give you perhaps a few examples. We're looking at Fortune 2000 companies. A good example, for instance, would be one of the top airlines in the world that is replatforming from a more rigid siloed IT to really deliver all their applications to internal and external customers as a service. It would also be digital businesses where there currency really is speed, agility, and obviously data is their currency. So if you're looking here at one of the top travel fare aggregators, that's one of the customers, actually interestingly we are in their tier zero at Storch. That's quite an endorsement of the performance aspect. We are also in one of, I would say, the leading service providers outside of the typical crowd you think, those are one of the up and coming guys. So those are typical markets and customers we're looking at. Really Fortune 2000 companies that are replatforming to cloud, hybrid cloud, and digital service businesses. Digital businesses. >> But it is most people who are basically going from, they're transforming their data center into a metadata center. They're embracing the distribution and then cloud. But they're not going wholesale and just saying (claps hands) we're over. They have this practicality of first thing I need to do is to free up my data, make my data center agile, and then decide how I want to distribute it across. >> Marc Fleischmann. Guy Churchward. Datera. Thank you very much for being on theCUBE. >> Thank you very much Peter. >> A pleasure. Thank you. >> And once again, this is Peter Burris from our CUBE studios in Palo Alto, California. Thanks very much for participating in this CUBE conversation with Datera. (orchestral music plays)

(inspirational music) >> Hello, I'm Peter Burris of Wikibon. Welcome to another Cube Conversation. We're going to have a great time talking over the next few minutes about the role that performance in the data plain is going to play at making possible both the options provided by the Cloud, but at the same time in a way that actually allows us to actually run the applications at the speed and the scale the business requires. To do that, we've got Marc Fleishman, who's the CEO and founder of Datera, and Marc Churchward who's the Executive Chairman at Datera. Welcome back to theCube, guys. >> Thank you for having us, Peter. >> So, Marc, I want to start with what I started with. That at the end of the day we've got this enormous agility that we're provided with in the CloudStack, but you still have to run on real computers that have real constraints and everybody knows that there is no greater constraint than maintaining the state of data and moving data. So how does Datera address those issues? >> Part of data freedom obviously is not only about automation, the the promise of software-defined storage is seamless automation. But unfortunately in many cases with unimpressive performance. In our case, we've engineered the whole data path down to the physical devices, ourselves. The levels of performance we can deliver is millions of IOPS across the data center at less than two hundred microseconds latency. Most importantly, on standard servers, over standard protocols, so nothing fancy in terms of hardware required. That's the true promise of software-defined storage. >> Now you mentioned automation. That kind of performance has got to open up new classes of automation potential, so that the storage or the data resources are that much easier to envision, that much easier to apply, that much easier to exploit by the development community. Tell us a little bit about how automation plays into this. >> Absolutely, once you've made data delivery frictionless, and you've made data orchestration and data automation frictionless, you do unlock new classes of applications. What we're specifically seeing is folks who traditionally run an array of databases on very dedicated proprietary hardware, and then again they get the data trapped in those silos, and they have a real hard time to extract the value off that data, we see a lot of database farms coming on our unified platform across the data center, basically being able to really extract the value of the data across a range of applications. >> Now we've been in the last few years investing pretty heavily in storage area networks and arrays and those types of resources. Flash is changing that, but it sounds as though you guys are actually making it easier to bring servers into the mix of this. What's the real direction you see? Where's this resource going to be managed by and what's the opportunity? >> Ultimately the resource should be managed by the applications, it should be driven by the applications and managed by machine learning. So as we understand the requirements of the applications, every individual application, it should be managed by machine learning in terms of the physical resources on the servers. The server capabilities you put underneath it, and then obviously start rolling the server hardware, as technology improves as well over time. >> So it's really being driven by the server, that's where the market opportunity's coming from. >> That's right, yes. >> The last question I have here is, when we think about new technology, new classes of automation, new trends in the industry, people always immediately go, "Yeah, but new companies?" Where does Datera fit in its lifecycle as it works with customers and as it delivers value out? >> If you look at the market today, server-based storage is already larger than traditional array-based storage. It's growing at five X, year by year. Since we've been on theCUBE the last time, about two years ago, we are now looking at a 240% kegger every year, so the market has clearly come our way. This is the time for this kind of product. >> So the market's good, company's good, trends are good. As we think ultimately about where this ends up in a few years, what role will Datera play within the evolved computing industry? What do you see for it? >> Given that we have the broad data orchestration, enterprise performance and choice on hardware, we really do see ourselves as the data foundation for the software-defined data center. What I mean by that, again, just in an operational model, we are to data what Cooper Ladies is to compute, across a number of operating environments. So it's a really broad data foundation for everyone who wants to deliver ITS service. >> Guy, I have a very simple question for you, very complex answer. One of the places where this seems to be especially important, where the need is especially great, is in that world of analytics. Especially as we try to close the loop between the analytical systems and the operational systems. How does Datera and analytics come together, not just in the use of analytics to make Datera better, but Datera in making analytics applications run better. >> Yeah, and as you said, an easy question, complicated answer. In reality, what companies are trying to do is to run the analytics at the speed of which they're competing in their market space. Which means that it has to get a lot faster. Today's classic environment is an ETL with a data leak, so parking stale data and analyzing it, post-event and tomorrow, in an environment where people are using AI and ML is now in stream and it's in real time. So part of that is you actually have very very fast applications, both from a performance perspective, but also how long their lifecycle is. Because people are doing AB testing on the web, they're doing analytics on the fly, and it really is a kind of a different world. It's a different pace. When I started this business, or when I was in business early and I had hair, we used to look at organizations that had applications that were lasting 10 or 20 years. Now we're looking at enterprise applications that are up and down within a period of months, if not weeks. So managing that lifecycle and not having to invest in infrastructure to support something, that age-old adage of you don't buy an application if it's in 1.0 is gone. Because by the time you're into 1.1, that opportunity's disappeared as well. So, part of what I saw in the attraction with Datera is because it's absolutely software-defined, and all the resilience handles in the software not the hardware. There's not the infrastructure burden. It has much more agility to get up. It can provide tier zero, tier one. Again, you land and expand, so in test endeavor you have the same environment. By a matter of flipping a few switches, you can have tier-one-ilities and then you can drop down in that lifecycle. It doesn't matter whether it's on premise, whether it's a distributed environment or on Cloud. It's the same infrastructure, same architecture, so, back to what Marc said, you have data freedom. >> So we're trying to tie the physical realities of data, to the virtual realities of machine resources in IT, to the Cloud realities of the new wave of applications. >> That's exactly right. >> Marc Fleishman, CEO and founder of Datera, Guy Churchward, Executive Chairman of Datera, thanks very much for being on theCUBE. >> Thanks for having us Peter. >> Thank you Peter. >> And once again, this is Peter Burris, Wikibon, thanks for watching theCUBE. (inspirational music)

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. (upbeat music) >> Hi, everybody, welcome to this CUBE Conversation. My name is Dave Vellante and we're here in theCUBE Studios in Marlborough, Massachusetts. We're gonna talk about storage and some of the trends that are going on in storage, and things have changed quite dramatically. It's not just about what media you're using today, you've got a lot of other considerations. Cloud, on-prem, in comes the edge, and it really drives new considerations for customers. Sundip Arora is here. He's the director of North America Storage and Big Data Solutions at Hewlett Packard Enterprise. He's gonna talk to me about some of these trends, the customer point of view, and what HPE is doing to solve some of these problems. Sundip, thanks very much for coming on theCUBE. >> Dave, thanks for having me, I'm super excited. >> So you heard my little narrative upfront about some of the big picture trends, what do you see as some of the tectonic shifts in the storage marketplace? >> Yeah, Dave. So listen, we've traveled around the continent here and I spend a lot of time with customers in North America, and what I hear from customers is their center of universe revolves around being able to map with their cloud journey and what does that mean for their data. Now, I look at our cloud operating model and I map that to HPE's own point of view. Our point of view is bringing the intelligent data platform to our customers. And when we talk about mapping the cloud operating model to our customer, what does that really mean for us? When I talk to customers, they tell me three things. It means that you have extreme cost efficiency, you've got super ease of use, and you've got resource optimization, how to utilize them in the best manner. >> So, let me ask you on that. Big Data is in your title, and one of the things that we observed early on in the big data days was it was about bringing five megabytes of code to a petabyte of data. Well, that sounded great and it was great, but it also caused problems because you're pushing, now, storage is everywhere. I mentioned the edge. So, I'm sure you're seeing that with customers. There is no more perimeter. Storage is just everywhere, wherever you want it to be. So when you talk about the cloud operating model, are you talking about bringing that experience to your data wherever that data lives? >> That's a great question. It used to be that you had an accounting system and that had a database, and that was delivering you a ton of data that you could analyze and store and read and write. And now, you've got data that's being produced at the edge, you've got point of sales systems, you've got autonomous vehicles, you've got data that's being produced on the cloud itself, and you've got data that's being produced at the core. So, what we are talking about is not just the automation of bringing that data in, but also how that data is being utilized. And to us, the way we map that challenge is through intelligence. >> Let's break down those three things: cost efficiency, ease of use, and resource optimization. Let's start with cost-efficiency. So, obviously, there's TCO. There's also the way in which I consume. The people, I presume, are looking for a different pricing model. Are you hearing that? >> Yeah, absolutely. So, as part of the cost of running their business and being able to operate like a cloud, everybody's looking at a variety of different procurement and utilization models. One of the ways HPE provides utilization model that can map to their cloud journey, a public cloud journey, is through GreenLake. The ability to use and consume data on demand, consume compute on demand, across the entire portfolio of products HPE has, essentially is what a GreenLake journey looks like. >> Let's go into ease of use. So, what do you mean by that? I mean, people, they think cloud, they think swipe the credit card and start deploying machines. What do you mean by ease of use? >> For us, ease of use translates back to how do you map to a simpler operating and support model. For us, the support model is the key for customers to be able to realize the benefits of going to the cloud. To get to a simpler support model, we use AIOps. And for us, AIOps means using a product called InfoSight. InfoSight is a product that uses deep learning and machine learning algorithms to look at a wide net of call-home data from physical resources out there and then be able to take that data and make it actionable. And the action behind that is predictiveness, the prescriptiveness of creating automated support tickets, and closing automated support tickets without anybody ever having to pick up a phone and call IT support. That InfoSight model now is being expanded across the board to all HPE products. It started with Nimble. Now InfoSight is available on 3PAR it's available on Synergy, and a recent announcement said it's also on ProLiants. And we expect that InfoSight becomes the glue, the automation AI glue, that goes across the entire portfolio of HPE products. >> So this is a great example of applying AI to data, so it's like call home taking to a whole level, isn't it? >> Yeah, it absolutely is. And in fact, what it does is it uses the call-home data that we've had for a long time with products like 3PAR, which essentially was amazing data but not being actioned on in an automated fashion. It takes that data and now, it creates an automation task around it. And many times, that automation task leads to much simpler support experience. >> Okay, the third item you mentioned was resource optimization. Let's drill down into that. I infer from that there are performance implications, there's maybe governance compliance, physical placement, can you elaborate, add some color to that? >> I think it's all of the above that you just talked about. It's definitely about applying the right performance level to the right set of applications. We call this application-aware storage. The ability to be able to understand which application is creating the data allows us to understand how that data needs to be accessed which in turn means we know where it needs to reside. One of the things that HPE is doing in the storage domain is creating a common storage fabric with the cloud. We call that the fabric for the cloud. The idea there is that we have a single layer between the on-premises and off-premises resources that allows us to move data as needed depending on the application needs and depending on the user needs. >> Okay, so that brings me to multi-cloud. It's the hot buzzword now. Some people don't like it but it's reality. And so, you've got data on-prem, you want to look like the cloud operating model, you got data in the cloud, the edge confuses things even more. And so, what is your perspective on multi-cloud, and then I have a follow-up for you. >> For us, multicloud means the ability to be able to run your business whether it's on-premises or off-premises based on the needs or the requirements of the application and the business user. We don't want to force a model down our customer's throat. We want them to have optimization across both models. The way we do that is using a couple of different products. We've got a product known as Cloud Bank, which maps to StoreOnce. StoreOnce is our purpose-built backup appliance where our customer can store a copy, a backup copy of the data on-premises, and then a backup copy of that on a public cloud like Azure, AWS, or Google. Similarly, we've got products with Nimble and 3PAR that allows to have tight integration with both public and private cloud domains. And in the future, the idea is to bring all of that together where the automation and the orchestration allows customers not to worry about what product they're using but more about what are the requirements of the application. >> Okay, because sometimes you gonna wanna bring data back, whether it's, pick and, yeah, I wanna put it in the cloud for bursting, I wanna bring it back for more control, whatever it is, when it comes back, I wanna have that cloud operating model, that's where the AIOps fits in that you were just describing. >> Yeah, absolutely. >> Okay, and so, let's get into, more specifically, what HPE is doing. You've referenced some of the things that you and your partners are doing, but what specifically are you doing from the standpoint of products, you mentioned what I call data plan and control plan. What do you have there that we can actually buy and employ? >> What we have, as I talked about earlier from an AIOps point of view, is our product called InfoSight, and InfoSight is available to all customers that today use 3PAR, Nimble, or ProLiant servers. As long as you have a valid support contract, it comes available to them. >> So I remember when HPE acquired Nimble, you said one of the things you're gonna do is take that technology and push it across the portfolio, so that's something that you've really done in a pretty short timeframe. >> We have, and what it does, it gives us the opportunity now not just to look at call-home data from storage, but then also look at call-home data from the compute side. And then what we can do is correlate the data coming back to have better predictability and outcomes on your data center operations as opposed to doing it at the layer of infrastructure. >> And you also said about the vision of this orchestration layer, can you talk more about that? Are we talking about across all clouds, whether it's on-prem or at the edge or the public cloud? >> Yeah, we are. We're talking about making it as simple as possible where the customers are not necessarily picking and choosing. It allows them to have a strategy that allows them to go across the data center, whether it's a public cloud, building their own private infrastructure, or running on a traditional on-premises SAN structure. So this vision for us, Cloud Fabric vision for us, allows for customers to do that. >> And what about software-defined storage? Where does that fit into this whole equation? >> I'm glad you mentioned that because that was the third tenet of what HPE truly brings to our customers. Software-defined is something that allows us to maximize the utilization of the existing resources that our customers have. So, what we've done is we've partnered with a great deal of really strong software-defined vendors, such us Commvault, Cohesity, Qumulo, Datera. We work very closely with the likes of Veeam, Zerto. And the goal there is to provide our customers with a whole range of options to drive building a software-defined infrastructure built off the Apollo Series of products. Apollo servers, our storage products for us, are extremely dense storage products that allow for both cost and resource optimization. >> What's the nature of these technology partners, partnerships? Are you doing engineering integration or is it just kind of going to market together? >> We bundle our partners into three main categories. We've got a set of complete partners. These complete partners are relationship where we do joint reference architecture. We create joint pricing list and we bring them in to the family. We've got a set of partners that's part of the Pathfinder program. The Pathfinder program are partners that we've made strategic, HPE has made strategic investments in. And then the third set is partners that we resell through HPE. So, depending on which partner it is, they fall into a different bucket, and we have all sets of resources, including engineering collaboration to make sure that the customer's buying a solution as opposed to a product. >> That's great, Sundip, thank you. Thank you for watching. But before we go, how do people learn more? >> The way you learn more is make sure you contact your partner and make sure you come to Discover. So, we'll hopefully see you at the Discover. (upbeat music)