(upbeat music) >> Welcome back to Vegas. Lisa Martin and Dave Vellante here with theCUBE live on the Venetian Expo Hall Floor, talking all things AWS re:Invent 2022. This is the first full day of coverage. It is jam-packed here. People are back. They are ready to hear all the new innovations from AWS. Dave, how does it feel to be back yet again in Vegas? >> Yeah, Vegas. I think it's my 10th time in Vegas this year. So, whatever. >> This year alone. You must have a favorite steak restaurant then. >> There are several. The restaurants in Vegas are actually really good. >> You know? >> They are good. >> They used to be terrible. But I'll tell you. My favorite? The place that closed. >> Oh! >> Yeah, closed. In between where we are in the Wynn and the Venetian. Anyway. >> Was it CUT? >> No, I forget what the name was. >> Something else, okay. >> It was like a Greek sort of steak place. Anyway. >> Now, I'm hungry. >> We were at Pure Accelerate a couple years ago. >> Yes, we were. >> When they announced Cloud Block Store. >> That's right. >> Pure was the first- >> In Austin. >> To do that. >> Yup. >> And then they made the acquisition of Portworx which was pretty prescient given that containers have been going through the roof. >> Yeah. >> So I'm sort of excited to have these guys on and talk about that. >> We're going to unpack all of this. We've got one of our alumni back with us, Venkat Ramakrishna, VP of Product, Portworx by Pure Storage. And Dan Kogan joins us for the first time, VP of Product Management and Product Marketing, FlashArray at Pure Storage. Guys, welcome to the program. >> Thank you. >> Hey, guys. >> Dan: Thanks for having us. >> Do you have a favorite steak restaurant in Vegas? Dave said there's a lot of good choices. >> There's a lot of good steak restaurants here. >> I like SDK. >> Yeah, that's a good one. >> That's the good one. >> That's a good one. >> Which one? >> SDK. >> SDK. >> Where's that? >> It's, I think, in Cosmopolitan. >> Ooh. >> Yeah. >> Oh, yeah, yeah, yeah. >> It's pretty good, yeah. >> There's one of the Western too that's pretty. >> I'm an Herbs and Rye guy. Have you ever been there? >> No. >> No. >> Herbs and Rye is off strip, but it's fantastic. It's kind of like a locals joint. >> I have to dig through all of this great stuff today and then check that out. Talk to me. This is our first day, obviously. First main day. I want to get both of your perspectives. Dan, we'll start with you since you're closest to me. How are you finding this year's event so far? Obviously, tons of people. >> Busy. >> Busy, yeah. >> Yeah, it is. It is old times. Bigger, right? Last re:Invent I was at was 2019 right before everything shut down and it's probably half the size of this which is a different trend than I feel like most other tech conferences have gone where they've come back, but a little bit smaller. re:Invent seems to be the IT show. >> It really does. Venkat, are you finding the same? In terms of what you're experiencing so far on day one of the events? >> Yeah, I mean... There's tremendous excitement. Overall, I think it's good to be back. Very good crowd, great turnout, lot of excitement around some of the new offerings we've announced. The booth traffic has been pretty good. And just the quality of the conversations, the customer meetings, have been really good. There's very interesting use cases shaping up and customers really looking to solve real large scale problems. Yeah, it's been a phenomenal first day. >> Venkat, talk a little bit about, and then we'll get to you Dan as well, the relationship that Portworx by Pure Storage has with AWS. Maybe some joint customers. >> Yeah, so we... Definitely, we have been a partner of AWS for quite some time, right? Earlier this year, we signed what is called a strategic investment letter with AWS where we kind of put some joint effort together like to better integrate our products. Plus, kind of get in front of our customers more together and educate them on how going to how they can deploy and build vision critical apps on EKS and EKS anywhere and Outpost. So that partnership has grown a lot over the last year. We have a lot of significant mutual customer wins together both on the public cloud on EKS as well as on EKS anywhere, right? And there are some exciting use cases around Edge and Edge deployments and different levels of Edge as well with EKS anywhere. And there are pretty good wins on the Outpost as well. So that partnership I think is kind of like growing across not just... We started off with the one product line. Now our Portworx backup as a service is also available on EKS and along with the Portworx Data Services. So, it is also expanded across the product lanes as well. >> And then Dan, you want to elaborate a bit on AWS Plus Pure? >> Yeah, it's for kind of what we'll call the core Pure business or the traditional Pure business. As Dave mentioned, Cloud Block Store is kind of where things started and we're seeing that move and evolve from predominantly being a DR site and kind of story into now more and more production applications being lifted and shifted and running now natively in AWS honor storage software. And then we have a new product called Pure Fusion which is our storage as code automation product essentially. It takes you from moving and managing of individual arrays, now obfuscates a fleet level allows you to build a very cloud-like backend and consume storage as code. Very, very similar to how you do with AWS, with an EBS. That product is built in AWS. So it's a SaaS product built in AWS, really allowing you to turn your traditional Pure storage into an AWS-like experience. >> Lisa: Got it. >> What changed with Cloud Block Store? 'Cause if I recall, am I right that you basically did it on S3 originally? >> S3 is a big... It's a number of components. >> And you had a high performance EC2 instances. >> Dan: Yup, that's right. >> On top of lower cost object store. Is that still the case? >> That's still the architecture. Yeah, at least for AWS. It's a different architecture in Azure where we leverage their disc storage more. But in AWS were just based on essentially that backend. >> And then what's the experience when you go from, say, on-prem to AWS to sort of a cross cloud? >> Yeah, very, very simple. It's our replication technology built in. So our sync rep, our async rep, our active cluster technology is essentially allowing you to move the data really, really seamlessly there and then again back to Fusion, now being that kind of master control plan. You can have availability zones, running Cloud Block Store instances in AWS. You can be running your own availability zones in your data centers wherever those may happen to be, and that's kind of a unification layer across it all. >> It looks the same to the customer. >> To the customer, at the end of the day, it's... What the customer sees is the purity operating system. We have FlashArray proprietary hardware on premises. We have AWS's hardware that we run it on here. But to the customer, it's just the FlashArray. >> That's a data super cloud actually. Yeah, it's a data super cloud. >> I'd agree. >> It spans multiple clouds- >> Multiple clouds on premises. >> It extracts all the complexity of the underlying muck and the primitives and presents a common experience. >> Yeah, and it's the same APIs, same management console. >> Dave: Yeah, awesome. >> Everything's the same. >> See? It's real. It's a thing, On containers, I have a question. So we're in this environment, everybody wants to be more efficient, what's happening with containers? Is there... The intersection of containers and serverless, right? You think about all the things you have to do to run containers in VMs, configure everything, configure the memory, et cetera, and then serverless simplifies all that. I guess Knative in between or I guess Fargate. What are you seeing with customers between stateless apps, stateful apps, and how it all relates to containers? >> That's a great question, right? I think that one of the things that what we are seeing is that as people run more and more workloads in the cloud, right? There's this huge movement towards being the ability to bring these applications to run anywhere, right? Not just in one public cloud, but in the data centers and sometimes the Edge clouds. So there's a lot of portability requirements for the applications, right? I mean, yesterday morning I was having breakfast with a customer who is a big AWS customer but has to go into an on-prem air gap deployment for one of their large customers and is kind of re-platforming some other apps into containers in Kubernetes because it makes it so much easier for them to deploy. So there is no longer the debate of, is it stateless versus it stateful, it's pretty much all applications are moving to containers, right? And in that, you see people are building on Kubernetes and containers is because they wanted multicloud portability for their applications. Now the other big aspect is cost, right? You can significantly run... You know, like lower cost by running with Kubernetes and Portworx and by on the public cloud or on a private cloud, right? Because it lets you get more out of your infrastructure. You're not all provisioning your infrastructure. You are like just deploying the just-enough infrastructure for your application to run with Kubernetes and scale it dynamically as your application load scales. So, customers are better able to manage costs. >> Does serverless play in here though? Right? Because if I'm running serverless, I'm not paying for the compute the whole time. >> Yeah. >> Right? But then stateless and stateful come into play. >> Serverless has a place, but it is more for like quick event-driven decision. >> Dave: The stateless apps. >> You know, stuff that needs to happen. The serverless has a place, but majority of the applications have need compute and more compute to run because there's like a ton of processing you have to do, you're serving a whole bunch of users, you're serving up media, right? Those are not typically good serverless apps, right? The several less apps do definitely have a place. There's a whole bunch of minor code snippets or events you need to process every now and then to make some decisions. In that, yeah, you see serverless. But majority of the apps are still requiring a lot of compute and scaling the compute and scaling storage requirements at a time. >> So what Venkat was talking about is cost. That is probably our biggest tailwind from a cloud adoption standpoint. I think initially for on-premises vendors like Pure Storage or historically on-premises vendors, the move to the cloud was a concern, right? In that we're getting out the data center business, we're going all in on the cloud, what are you going to do? That's kind of why we got ahead of that with Cloud Block Store. But as customers have matured in their adoption of cloud and actually moved more applications, they're becoming much more aware of the costs. And so anywhere you can help them save money seems to drive adoption. So they see that on the Kubernetes side, on our side, just by adding in things that we do really well: Data reduction, thin provisioning, low cost snaps. Those kind of things, massive cost savings. And so it's actually brought a lot of customers who thought they weren't going to be using our storage moving forward back into the fold. >> Dave: Got it. >> So cost saving is great, huge business outcomes potentially for customers. But what are some of the barriers that you're helping customers to overcome on the storage side and also in terms of moving applications to Kubernetes? What are some of those barriers that you could help us? >> Yeah, I mean, I can answer it simply from a core FlashArray side, it's enabling migration of applications without having to refactor them entirely, right? That's Kubernetes side is when they think about changing their applications and building them, we'll call quote unquote more cloud native, but there are a lot of customers that can't or won't or just aren't doing that, but they want to run those applications in the cloud. So the movement is easier back to your data super cloud kind of comment, and then also eliminating this high cost associated with it. >> I'm kind of not a huge fan of the whole repatriation narrative. You know, you look at the numbers and it's like, "Yeah, there's something going on." But the one use case that looks like it's actually valid is, "I'm going to test in the cloud and I'm going to deploy on-prem." Now, I dunno if that's even called repatriation, but I'm looking to help the repatriation narrative because- >> Venkat: I think it's- >> But that's a real thing, right? >> Yeah, it's more than repatriation, right? It's more about the ability to run your app, right? It's not just even test, right? I mean, you're going to have different kinds of governance and compliance and regulatory requirements have to run your apps in different kinds of cloud environments, right? There are certain... Certain regions may not have all of the compliance and regulatory requirements implemented in that cloud provider, right? So when you run with Kubernetes and containers, I mean, you kind of do the transformation. So now you can take that app and run an infrastructure that allows you to deliver under those requirements as well, right? So that portability is the major driver than repatriation. >> And you would do that for latency reasons? >> For latency, yeah. >> Or data sovereign? >> Data sovereignty. >> Data sovereignty. >> Control. >> I mean, yeah. Availability of your application and data just in that region, right? >> Okay, so if the capability is not there in the cloud region, you come in and say, "Hey, we can do that on-prem or in a colo and get you what you need to comply to your EDX." >> Yeah, or potentially moves to a different cloud provider. It's just a lot more control that you're providing on customer at the end of the day. >> What's that move like? I mean, now you're moving data and everybody's going to complain about egress fees. >> Well, you shouldn't be... I think it's more of a one-time move. You're probably not going to be moving data between cloud providers regularly. But if for whatever reasons you decide that I'm going to stop running in X Cloud and I'm going to move to this cloud, what's the most seamless way to do? >> So a customer might say, "Okay, that's certification's not going to be available in this region or gov cloud or whatever for a year, I need this now." >> Yeah, or various commercial. Whatever it might be. >> "And I'm going to make the call now, one-way door, and I'm going to keep it on-prem." And then worry about it down the road. Okay, makes sense. >> Dan, I got to talk to you about the sustainability element there because it's increasingly becoming a priority for organizations in every industry where they need to work with companies that really have established sustainability programs. What are some of the factors that you talk with customers about as they have choice in all FlashArray between Pure and competitors where sustainability- >> Yeah, I mean we've leaned very heavily into that from a marketing standpoint recently because it has become so top of mind for so many customers. But at the end of the day, sustainability was built into the core of the Purity operating system in FlashArray back before it was FlashArray, right? In our early generation of products. The things that drive that sustainability of high density, high data reduction, small footprint, we needed to build that for Pure to exist as a company. And we are maybe kind of the last all-flash vendor standing that came ground up all-flash, not just the disc vendor that's refactored, right? And so that's sort of engineering from the ground up that's deeply, deeply into our software as a huge sustainability payout now. And we see that and that message is really, really resonating with customers. >> I haven't thought about that in a while. You actually are. I don't think there's any other... Nobody else made it through the knothole. And you guys hit escape velocity and then some. >> So we hit escape velocity and it hasn't slowed down, right? Earnings will be tomorrow, but the last many quarters have been pretty good. >> Yeah, we follow you pretty closely. I mean, there was one little thing in the pandemic and then boom! It's just kept cranking since, so. >> So at the end of the day though, right? We needed that level to be economically viable as a flash bender going against disc. And now that's really paying off in a sustainability equation as well because we consume so much less footprint, power cooling, all those factors. >> And there's been some headwinds with none pricing up until recently too that you've kind of blown right through. You know, you dealt with the supply issues and- >> Yeah, 'cause the overall... One, we've been, again, one of the few vendors that's been able to navigate supply really well. We've had no major delays in disruptions, but the TCO argument's real. Like at the end of the day, when you look at the cost of running on Pure, it's very, very compelling. >> Adam Selipsky made the statement, "If you're looking to tighten your belt, the cloud is the place to do it." Yeah, okay. It might be that, but... Maybe. >> Maybe, but you can... So again, we are seeing cloud customers that are traditional Pure data center customers that a few years ago said, "We're moving these applications into the cloud. You know, it's been great working with you. We love Pure. We'll have some on-prem footprint, but most of everything we're going to do is in the cloud." Those customers are coming back to us to keep running in the cloud. Because again, when you start to factor in things like thin provisioning, data reduction, those don't exist in the cloud. >> So, it's not repatriation. >> It's not repatriation. >> It's we want Pure in the cloud. >> Correct. We want your software. So that's why we built CBS, and we're seeing that come all the way through. >> There's another cost savings is on the... You know, with what we are doing with Kubernetes and containers and Portworx Data Services, right? So when we run Portworx Data Services, typically customers spend a lot of money in running the cloud managed services, right? Where there is obviously a sprawl of those, right? And then they end up spending a lot of item costs. So when we move that, like when they run their data, like when they move their databases to Portworx Data Services on Kubernetes, because of all of the other cost savings we deliver plus the licensing costs are a lot lower, we deliver 5X to 10X savings to our customers. >> Lisa: Significant. >> You know, significant savings on cloud as well. >> The operational things he's talking about, too. My Fusion engineering team is one of his largest customers from Portworx Data Services. Because we don't have DBAs on that team, it's just developers. But they need databases. They need to run those databases. We turn to PDS. >> This is why he pays my bills. >> And that's why you guys have to come back 'cause we're out of time, but I do have one final question for each of you. Same question. We'll start with you Dan, the Venkat we'll go to you. Billboard. Billboard or a bumper sticker. We'll say they're going to put a billboard on Castor Street in Mountain View near the headquarters about Pure, what does it say? >> The best container for containers. (Dave and Lisa laugh) >> Venkat, Portworx, what's your bumper sticker? >> Well, I would just have one big billboard that goes and says, "Got PX?" With the question mark, right? And let people start thinking about, "What is PX?" >> I love that. >> Dave: Got Portworx, beautiful. >> You've got a side career in marketing, I can tell. >> I think they moved him out of the engineering. >> Ah, I see. We really appreciate you joining us on the program this afternoon talking about Pure, Portworx, AWS. Really compelling stories about how you're helping customers just really make big decisions and save considerable costs. We appreciate your insights. >> Awesome. Great. Thanks for having us. >> Thanks, guys. >> Thank you. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from the cube in ETR. This is Breaking Analysis with Dave Vellante. >> On Friday, December 10th, Oracle announced a strong earnings beat and raise, on the strength of its licensed business, and slightly better than expected cloud performance. The stock was up sharply on the day and closed up nearly 16% surpassing 280 billion in market value. Oracle's success is due largely to its execution, of a highly differentiated strategy, that has really evolved over the past decade or more, deeply integrating its hardware and software, heavily investing in next generation cloud, creating a homogeneous experience across its application portfolio, and becoming the number one platform. Number one for the world's most mission critical applications. Now, while investors piled into the stock, skeptics will point to the beat being weighed toward licensed revenue and likely keep one finger on the sell button until they're convinced Oracle's cloud momentum, is more consistent and predictable. Hello and welcome to this week's Wikibond CUBE insights powered by ETR. In this breaking analysis, we'll review Oracle's most recent quarter, and pull in some ETR survey data, to frame the company's cloud business, the momentum of fusion ERP, where the company is winning and some gaps and opportunities that we see. The numbers this quarter was strong, particularly top line growth. Here are a few highlights. Oracle's revenues that grew 6% year on year that's in constant currency, surpassed $10 billion for the quarter. Oracle's non-gap operating margins, were an impressive 47%. Safra Catz has always said cloud is more profitable business and it's really starting to show in the income statement. Operating cash and free cash flow were 10.3 billion and 7.1 billion respectively, for the past four quarters, and would have been higher, if not for charges largely related to litigation expenses tied to the hiring of Mark Hurd, which the company said would not repeat in the future quarters. And you can see in this chart how Oracle breaks down its business, which is kind of a mishmash of items they lump into so-called the cloud. The largest piece of the revenue pie is cloud services, and licensed support, which in reading 10Ks, you'll find statements like the following; licensed support revenues are our largest revenue stream and include product upgrades, and maintenance releases and patches, as well as technical support assistance and statements like the following; cloud and licensed revenue, include the sale of cloud services, cloud licenses and on-premises licenses, which typically represent perpetual software licenses purchased by customers, for use in both cloud, and on-premises, IT environments. And cloud license and on-prem license revenues primarily represent amounts earned from granting customers perpetual licenses to use our database middleware application in industry specific products, which our customers use for cloud-based, on-premise and other IT environments. So you tell me, "is that cloud? I don't know." In the early days of Oracle cloud, the company used to break out, IaaS, PaaS and SaaS revenue separately, but it changed its mind, which really makes it difficult to determine what's happening in true cloud. Look I have no problem including same same hardware software control plane, et cetera. The hybrid if it's on-prem in a true hybrid environment like exadata cloud@customer or AWS outposts. But you have to question what's really cloud in these numbers. And Larry in the earnings call mentioned that Salesforce licenses the Oracle database, to run its cloud and Oracle doesn't count that in its cloud number, rather it counts it in license revenue, but as you can see it varies that into a line item that starts with the word cloud. So I guess I would say that Oracle's reporting is maybe somewhat better than IBM's cloud reporting, which is the worst, but I can't really say what is and isn't cloud, in these numbers. Nonetheless, Oracle is getting it done for investors. Here's a chart comparing the five-year performance of Oracle to some of its legacy peers. We excluded Microsoft because it skews the numbers. Microsoft would really crush all these names including Oracle. But look at Oracle. It's wedged in between the performance of the NASDAQ and the S&P 500, it's up over 160% in that five-year timeframe, well ahead of SAP which is up 59% in that time, and way ahead of the dismal -22% performance of IBM. Well, it's a shame. The tech tide is rising, it's lifting all boats but, IBM has unfortunately not been able to capitalize. That's a story for another day. As a market watcher, you can't help but love Larry Ellison. I only met him once at an IDC conference in Paris where I got to interview Scott McNealy, CEO at the time. Ellison is great for analysts because, he's not afraid to talk about the competition. He'll brag, he'll insult, he'll explain, and he'll pitch his stories. Now on the earnings call last night, he went off. Educating the analyst community, on the upside in the fusion ERP business, making the case that because only a thousand of the 7,500 legacy on-prem ERP customers from Oracle, JD Edwards and PeopleSoft have moved Oracle's fusion cloud ERP, and he predicted that Oracle's cloud ERP business will surpass 20 billion in five years. In fact, he said it's going to bigger than that. He slammed the hybrid cloud washing. You can see one of the quotes here in this chart, that's going on when companies have customers running in the cloud and they claim whatever they have on premise hybrid, he called that ridiculous. I would agree. And then he took an opportunity to slam the hyperscale cloud vendors, citing a telco customer that said Oracle's cloud never goes down, and of course, he chose the same week, that AWS had a major outage. And so to these points, I would say that Oracle really was the first tech company, to announce a true hybrid cloud strategy, where you have an entirely identical experience on prem and in the cloud. This was announced with cloud@customer, two years, before AWS announced outposts. Now it probably took Oracle two years to get it working as advertised, but they were first. And to the second point, this is where Oracle differentiates itself. Oracle is number one for mission critical applications. No other vendor really can come close to Oracle in this regard. And I would say that Oracle is recent quarterly performance to a large extent, is due to this differentiated approach. Over the past 10 years, we've talked to hundreds literally. Hundreds and hundreds of Oracle customers. And while they may not always like the tactics and licensing policies of Oracle in their contracting, they will tell you, that business case for investing and staying with Oracle are very strong. And yes, a big part of that is lock-in but R&D investments innovation and a keen sense of market direction, are just as important to these customers. When you're chairman and founder is a technologist and also the CTO, and has the cash on hand to invest, the results are a highly competitive story. Now that's not to say Oracle is not without its challenges. That's not to say Oracle is without its challenges. Those who follow this program know that when it comes to ETR survey data, the story is not always pretty for Oracle. So let's take a look. This chart shows the breakdown of ETR is net score methodology, Net score measures spending momentum and works ETR. Each quarter asks customers, are you adding in the platform, That's the lime green. Increasing spend by 6% or more, that's the fourth green. Is you're spending E+ or minus 5%, that's the gray. You're spending climbing by 6%, that's the pinkish. Or are you leaving the platform, that's the bright red retiring. You subtract the reds from the greens, and that yields a net score, which an Oracle's overall case, is an uninspiring -4%. This is one of the anomalies in the ETR dataset. The net score doesn't track absolute actual levels, of spending the dollars. Remember, as the leader in mission critical workloads, Oracle commands a premium price. And so what happens here is the gray, is still spending a large amount of money, enough to offset the declines, and the greens are spending more than they would on other platforms because Oracle could command higher prices. And so that's how Oracle is able to grow its overall revenue by 6% for example, whereas the ETR methodology, doesn't capture that trend. So you have to dig into the data a bit deeper. We're not going to go too deep today, but let's take a look at how some of Oracle's businesses are performing relative to its competitors. This is a popular view that we like to share. It shows net score or spending momentum on the vertical axis, and market share. Market share is a measure of pervasiveness in the survey. Think of it as mentioned share. That's on the x-axis. And we've broken down and circled Oracle overall, Oracle on prem, which is declining on the vertical axis, Oracle fusion and NetSuite, which are much higher than Oracle overall. And in the case of fusion, much closer to that 40% magic red horizontal line, remember anything above that line, we consider to be elevated. Now we've added SAP overall which has, momentum comparable to fusion in the survey, using this methodology and IBM, which is in between fusion and Oracle, overall on the y-axis. Oracle as you can see on the horizontal axis, has a larger presence than any of these firms that are below the 40% line. Now, above that 40% line, you see companies with a smaller presence in the survey like Workday, salesforce.com, pretty big presence still, Google cloud also, and Snowflake. Smaller presence but much much higher net score than anybody else on this chart. And AWS and Microsoft overall with both a strong presence, and impressive momentum, especially for their respective sizes. Now that view that we just showed you excluded on purpose Oracle specific cloud offering. So let's now take a look at that relative to other cloud providers. This chart shows the same XY view, but it cuts the data by cloud only. And you can see Oracle while still well below the 40% line, has a net score of +15 compared to a -4 overall that we showed you earlier. So here we see two key points. One, despite the convoluted reporting that we talked about earlier, the ETR data supports that Oracle's cloud business has significantly more momentum than Oracle's overall average momentum. And two, while Oracle is smaller and doesn't have the growth of the hyperscale giants, it's cloud is performing noticeably better than IBM's within the ETR survey data. Now a key point Ellison emphasized on the earnings call, was the importance of ERP, and the work that Oracle has done in this space. It lives by this notion of a cloud first mentality. It builds stuff for the cloud and then, would bring it on-prem. And it's been attracting new customers according to the company. He said Oracle has 8,500 fusion ERP customers, and 28,000 NetSuite customers in the cloud. And unlike Microsoft, it hasn't migrated its on-prem install base, to the cloud yet. Meaning these are largely new customers. Now this chart isolates fusion and NetSuite, within a sector ETR calls GPP. The very giant, public and private companies. And this is a bellwether of spending in the ETR dataset. They've gone back and it correlates to performance. So think large public companies, the biggest ones, and also privates big privates like Mars or Cargo or Fidelity. The chart shows the net score breakdown over time for fusion and NetSuite going back to 2019. And you can see, a big uptick as shown in the blue line from the October, 2020 survey. So Oracle has done a good job building and now marketing its cloud ERP to these important customers. Now, the last thing we want to show you is Oracle's performance within industry sectors. On the earnings call, Oracle said that it had a very strong momentum for fusion in financial services and healthcare. And this chart shows the net score for fusion, across each industry sector that ETR tracks, for three survey points. October, 2020, that's the gray bars, July 21, that's the blue bars and October, 2021, the yellow bars. So look it confirms Oracles assertions across the board that they're seeing fusion perform very well including the two verticals that are called out healthcare and banking slash financial services. Now the big question is where does Oracle go from here? Oracle has had a history of looking like it's going to break out, only to hit some bumps in the road. And so investors are likely going to remain a bit cautious and take profits off the table along the way. But since the Barron's article came out, we reported on that earlier this year in February, declaring Oracle a cloud giant, the stock is up more than 50% of course. 16 of those points were from Friday's move upward, but still, Oracle's highly differentiated strategy of integrating hardware and software together, investing in a modern cloud platform and selectively offering services that cater to the hardcore mission critical buyer, these have served the company, its customers and investors as well. From a cloud standpoint, we'd like to see Oracle be more inclusive, and aggressively expand its marketplace and its ecosystem. This would provide both greater optionality for customers, and further establish Oracle as a major cloud player. Indeed, one of the hallmarks of both AWS and Azure is the momentum being created, by their respective ecosystems. As well, we'd like to see more clear confirmation that Oracle's performance is being driven by its investments in technology IE cloud, same same hybrid, and industry features these modern investments, versus a legacy licensed cycles. We are generally encouraged and are reminded, of years ago when Sam Palmisano, he was retiring and leaving as the CEO of IBM. At the time, HP under the direction ironically of Mark Hurd, was the now company, Palmisano was asked, "do you worry about HP?" And he said in fact, "I don't worry about HP. I worry about Oracle because Oracle invests in R&D." And that statement has proven present. What do you think? Has Oracle hit the next inflection point? Let me know. Don't forget these episodes they're all available as podcasts wherever you listen, all you do is search it. Breaking Analysis podcast, check out ETR website at etr.plus. We also publish a full report every week on wikibon.com and siliconANGLE.com. You can get in touch with me on email David.vellante@siliconangle.com, you can DM me @dvellante on Twitter or, comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights. Powered by ETR. Have a great week everybody. Stay safe, be well, and we'll see you next time. (upbeat music)

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

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante as we watch an historic election unfold before our eyes we look back at the early days of the millennium with the memorable presidential race of 2000 that decade of course was defined by 911 which permanently reshaped our thinking and we exited that decade at the tail end of a massive financial crisis only to enter the 2010s with the hope and the momentum of fiscal stimulus a flat globe job growth and very importantly the ascendancy of the cloud cloud computing unquestionably powered the innovation engine over the last 10 years and the pandemic marks a new era where adoption of cloud data and ai have been accelerated by at least two to three years and that's what's going to shape the future of the technology industry and frankly all businesses and organizations hello everyone and welcome to this week's episode of thecube insights powered by etr in this breaking analysis we're going to update you on our latest cloud market share and dig in to some fresh october survey data from our partners over at etr let me start just with a brief summary of the latest action that's going on in cloud now quite interestingly each of the big three cloud players they showed nearly identical year-on-year growth rates in q3 as they did in q2 now we're going to dig into that in a moment but our data suggests that these three companies combined will account for more than 75 billion dollars in infrastructure as a service and platform as a service revenue in 2020 and they're potentially on track to hit 100 billion in 2021. customer survey data indicates that cio's top two infrastructure priorities remain security and cloud migration now that said as we previously reported the cloud it's not immune to the pandemic the remote worker pivot well it's a positive for cloud hasn't completely eradicated certain headwinds now what i mean here is that because the cloud vendors are now so large they're somewhat exposed to the softness in the overall i.t spending climate and also industries that have been hit hardest by the pandemic now would the cloud growth have been better if the pandemic didn't hit we'll never know for sure but our data suggests no covet has definitely been a benefactor to cloud in our view cloud will remain at the center of technological innovation for the foreseeable future the economics of cloud are becoming so compelling that we think the power of the big cloud companies will only increase this decade now importantly we're talking about the costs of running hyper-distributed systems we're not commenting here on what they charge customers that's a different story we believe the cost structure for the hyperscalers is superior to alternative approaches and we believe this advantage will only accelerate over the next several years we also believe that competition is going to continue to drive competitive pricing and innovation all right let's look at our latest market share numbers for the big three this chart shows our estimates of aws azure and the google cloud platform now viewers of this program know that these are is and pass figures and you also know that aws is the only company that provides clean numbers on that sector whereas azure and gcp are estimates that we make based on tidbits of guidance that the companies give us and survey data that we capture and other modeling that we do now as we've said we'll end this year it's about 75 billion in revenue or maybe even a little bit more note that for these three note that we've we've slightly restated some of our earlier estimates for azure to reconcile some differences that we had between constant currency and actual growth we try to keep things in constant currency where possible sorry for that but sometimes that happens azure according to our estimates as we reported last week is now 18 of microsoft's overall revenue number we had it at 19 that last week but when i dug in we made some adjustments so we toned it down a bit aws represents a much smaller percentage of course of amazon's revenues at about 12 percent but it represents 56 percent of amazon's profits gcp on the other hand accounts for less than five percent of google's overall revenue which as we've stated a few weeks ago needs more attention from google but look at the growth rates for these three platforms and the respective size of their is and pass businesses hear all this talk about repatriation i.e that what i mean by that is people go to the cloud but they're unhappy or the bill is too high it's too expensive so then they come back on prem well you just don't see that in the numbers so you gotta be careful when vendor a vendor tries to sell you on that trend i don't buy it except for selective situations now let's bring in some of the etr data and compare the spending momentum for each of the big three you've seen these wheel graphs before they show the breakdown of net score for aws microsoft and google now one note these figures represent these three companies overall within the etr technology taxonomy so for example they don't include amazon's retail business of course but they do include for example microsoft's entire tech portfolio not just the cloud the green portion of the wheel represents increases in spending via new adoptions and increased spending whereas the red sections show decreases via lower spending and defections net score which i've highlighted in the orange is calculated by subtracting the two reds from the two true greens in other words adoptions and increase minus decrease and replacements the takeaway here is these are all pretty strong with aws leading the pack microsoft is exceptionally strong as we pointed out last last week because they're so huge and they still have net scores comparable to aws which is a pure play gcp is a laggard and is showing softness in the data despite a sanguine outlook that we had back in 2019 based on survey data i don't know perhaps google's smaller presence muted their customers ability to take advantage of the platform the thinking there is the customers maybe needed to pivot to the cloud so quickly and aws and azure were the incumbents and that was maybe the most expedient path hence the higher increases in the spend more category but you do see gcp um they had 13 new adoptions which is pretty good so we'll keep looking at that regardless again these are not pure play cloud comparisons but they give a good indication of spending momentum i'd also note that all three show very low defections well each is showing solid increases in new adoptions especially google as i mentioned so that's kind of interesting to see but again google much much smaller you would expect that now i want to turn our attention to one of the hottest areas in cloud which is serverless and this is a pure play comparison so serverless let me start there it's a strange term because it's not really accurate but it's stuck serverless computing is a model where the cloud platform dynamically delivers services as the application requires so so you don't have to configure the compute and the containers for example rather when an application needs resources it goes and gets them and you only pay for when the services are actually invoked and in use so it's really good for workloads that spin up and spin down very frequently it kind of reminds me in concept anyway of the component tree that we saw in the days of soa if you remember that services oriented architecture but now this is cloud it's cloud native it's a whole new world and it's increasingly a popular model and as we'll show in a moment there's a lot of spending momentum in this area but before we do that i want to share some comments made by andy jassy a while back about serverless take a listen it's a good question and you know i really the comment i made was really about um directionally what amazon would do you know in this in the very earliest days of aws jeff used to say a lot if i were starting amazon today i'd have built it on top of aws we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point i think the same thing is true here with lambda which is i think if amazon were starting today it's a given they would build it on the cloud and i think with a lot of the applications that comprise amazon's consumer business we would build those on on our serverless capabilities now now lambda of course jesse referring to lambda that's amazon's serverless offering and if you think about amazon's retail business and take for example the frequent spin up and spin down of resources for something like black monday serverless would be a much more cost effective approach same for a managed data warehouse service for example where you know you don't want to pay for the compute if it's idle the app just calls for the compute when it's needed so it's a very popular model and it's got increased momentum today and you see that in this slide it shows the net score breakdown for serverless for azure aws is lambda which is again is their serverless offering and google cloud functions again you're shipping functions to the application that's why it's called functions look at the net scores azure functions nearly 70 aws at 65 google again lagging and that's a bit of a concern because this is a really really hot space all right let's move on and look at the competitive landscape as we like to do often and update you on that this xy graph is one of our favorites and it shows net score or spending momentum on the vertical axis and market share on the horizontal market share is a measure of pervasiveness in the data set in the upper right you also see a table that ranks each vendor my net score and it includes the shared n in other words the number of mentions in this sector for each vendor now you can you can see up top in the middle i've selected on the cloud computing category so this represents only the cloud businesses for each of these players there's a little bit of nuance here and that we've selected on microsoft azure there's a category in the etr taxonomy for that and we're comparing that with aws overall so there's there are things in the aws overall number that fit into the other parts of the taxonomy like maybe ai collaboration etc whereas azures and gcp are just the cloud segments so i i know it's a bit strange because aws is all cloud but don't get caught up in the taxonomical nuance the point is it's good to be azure in aws it's shown there when you look at the upper right of the chart here they stand out and they stand alone in cloud leadership google cloud is they have nice elevated levels but they're much much smaller they don't have the presence in the market now look at that hybrid cloud zone emerging we've talked about this sometimes in the past and and i want to call it vmware cloud on aws red hat open shift and vmware cloud itself like vmware cloud foundation and their other cloud services all of these appear to be gaining traction and you can see in the number of occurrences in the upper right that shared end that i talked about we're starting to see real numbers that are meaningful in this space vmware cloud on aws for example has a net score of 53 percent with 116 accounts within that total respondent sample that you see there in the middle left of 1438 that's how many cios and technology buyers responded to the etr survey in october you look at open shift at 45 net score and that's with 82 accounts now openshift is in beta with what looked to be some really strong offerings on aws and you can see for context i've added dell emc's cloud offerings hpe's cloud offerings and the oracle cloud and ibm cloud and also rackspace dell actually pretty strong with a net score of 20 and 185 shared accounts much much higher than dell overall which is kind of in the red zone oracle ibm you see those rackspace you know organizing not killing it rackspace is kind of in the big negative so that's a concern but anyway we'd like for these guys we'd like to see the data match the marketing rhetoric for the the guys that are in the red and look alibaba is starting to to show up in the server there's only 26 shared ends but we thought we'd we'd put it in there those three key points again aws and microsoft keep on trucking google needs to do better hybrid is becoming real and that bodes well for multi-cloud and the legacy on-prem guys they got a lot of work to do they're under a lot of pressure the pivot to cloud has not been easy for them uh and it's still a case where they're i've talked about this a lot they're they're declines in their on-premises offerings they're not being offset by the new stuff the cloud momentum all right i want to close out by sharing some of the conversations and thoughts that we've had in the community around sas and its impact on cloud we really have been focusing on ias and pass of the sas layer obviously up the stack so let me first share that there's a lot of talk around and has been for years about aws they're slowing growth rates and whether or not they'll have to enter the sas market to expand their total available market and i've said consistently while i never say never about aws i don't think so at least not yet this chart plots the big three cloud players note aws is a bigger piece of this pie now that i've turned off the cloud computing filter and i know more nuances but the data wonks will will find you know see this and they'll ask me about it this is all of aws portfolio and again it's only the microsoft azure portfolio so you see it aws now overtakes azure on the x-axis i.e market share now we've plotted some of the major sas vendors and you can see servicenow and salesforce both very large and they have really strong spending momentum and servicenow's you know pushing 100 billion dollars in market value they've surpassed workday quite some time ago workday's got less presence but they've got really really solid net score and i got to say i'm impressed with sap despite some of the earnings challenges that they've been having they're right up there with splunk and tableau splunk has softened in recent surveys and i've i've also plotted in there netsuite and oracle fusion which are just okay and that is i think for now anyway aws is going to position as the best place and the most friendly and highest quality cloud in which to run your sas for example workday runs on aws aws is salesforce's preferred infrastructure platform so my premise here is just like retail companies might want not want to run on aws a number of sas companies that compete with microsoft they might think twice about running on azure so aws would be better off for now trying to attract those sas players and drive their services and sticking to infrastructure and the pass layer snowflake is actually kind of interesting and i've added them for context because their netscore is always kind of a bellwether it's really off the charts and they're an isv running on the cloud they're different from some of the other sas players and the snowflake is a database okay and most of snowflake's business runs on aws and aws competes with snowflake with redshift but aws has the best cloud and drives a lot of business for snowflake and vice versa so it's kind of interesting snow snowflake to redshift and a much smaller example is kind of like netflix to amazon prime video to compete they both thrive so i think aws is going to continue to grow by attracting sas players as the preferred platform and they'll also attract developers and try to disrupt sas players like servicenow which runs on its own cloud i remember years ago david floyer and i said that servicenow was it was awesome but at some point its infrastructure cost structure its infrastructure cost structure is going to be less competitive than those companies that are running on hyperscale clouds certainly the hyperscale clouds themselves and servicenow they have this multi-instance architecture which just can't easily port over to the cloud but it can charge a lot which it does now at some point some sharp developers are going to look at all this and say whoa see that service now i can build this for less and they'll attack servicenow and their seat base license model maybe with the consumption pricing model and a platform that's perhaps or a set of services that are perhaps less expensive you're seeing this to a you know a certain degree with like elastic inside the application performance management space so there's some some things to watch there but there are those who firmly believe that aws will and must enter the sas space directly we talked last week about how beneficial microsoft's application business is for azure and what a flywheel that is but for me i think we're not there yet let's give it some time i think maybe four to five years before aws may even start to think about filling some of the space up the stack now maybe they'll find some unique opportunities to do that for instance at the edge but i think that's way off okay so bottom line it's good to be in tech these days it's even better to be in the cloud and it's best if you're aws and microsoft and i don't see that changing for a while now remember these episodes are all available as podcasts wherever you listen i publish each week on wikibon.com and siliconangle.com you can get in touch with me through email it's david at siliconangle.com feel free to dm me on twitter at d vallante i post on linkedin love your comments there thank you and don't forget to check out etr plus for all the survey action thanks for watching this episode of thecube insights powered by etr this is dave vellante stay safe stay sane and we'll see you next time you

>> From the Silicon Angle Media office, in Boston Massachusetts, it's the Cube. Now, here's your host, Dave Vellante. >> Hi everybody, welcome to this Cube Conversation here in our studios, outside of Boston. My name is Dave Vellante. I'm here with Matt Carroll, who's the CEO of Immuta. Matt, good to see ya. >> Good, nice to have me on. >> So we're going to talk about governance, how to automate governance, data privacy, but let me start with Immuta. What is Immuta, why did you guys start this company? >> Yeah, Immuta is an automated data governance platform. We started this company back in 2014 because we saw a gap in the market to be able to control data. What's happened in the market as changes is that every enterprise wants to leverage their data. Data's the new app. But, governments want to regulate it and consumers want to protect it. These were at odds with one another, so we saw a need of creating a platform that could meet the needs of everyone. To democratize access to data and in the enterprise, but at the same time, provide the necessary controls on the data to enforce any regulation, and ensure that there was transparency as to who is using it and why. >> So let's unpack that a little bit. Just try to dig into the problem here. So we all know about the data explosion, of course, and I often say data used to be a liability, now it's turned into an asset. People used to say get rid of the data, now everybody wants to mine it, and they want to take advantage of it, but that causes privacy concerns for individuals. We've seen this with Facebook and many others. Regulations now come into play, GDPR, different states applying different regulations, so you have all these competing forces. The business guys just want to go and get out to the market, but then the lawyers and the compliance officers and others. So are you attacking that problem? Maybe you could describe that problem a little further and talk about how you guys... >> Yeah, absolutely. As you described, there's over 150 privacy regulations being proposed over 25 states, just in 2019 alone. GDPR has created or opened the flood gates if you will, for people to start thinking about how do we want to insert our values into data? How should people use it? And so, the challenge now is, you're right, your most sensitive data in an enterprise is most likely going to give you the most insight into driving your business forward, creating new revenue channels, and be able to optimize your operational expenses. But the challenge is that consumers have awoken to, we're not exactly sure we're okay with that, right? We signed a YULU with you to just use our data for marketing, but now you're using it for other revenue channels? Why? And so, where Immuta is trying to play in there is how do we give the line of business the ability to access that instantaneously? But also give the CISO, the Chief Information Security Officer, and the governance seems the ability to take control back. So it's a delicate balance between speed and safety. And I think what's really happening in the market is we used to think about security from building firewalls, we invested in physical security controls around managing external adversaries from stealing our data. But now it's not necessarily someone trying to steal it, it's just potentially misusing it by accident in the enterprise. And the CISO is having to step in and provide that level of control. And it's also the collision of the cloud and these privacy regulations. Cause now, we have data everywhere, it's not just in our firewalls. And that's the big challenge. That's the opportunity at hand, democratization of data in the enterprise. The problem is data's not all in the enterprise. Data's in the cloud, data's in SaaS, data's in the infrastructure. >> It's distributed by it's very nature. All right, so there's a lot of things I want to follow up on. So first, there's GDPR. When GDPR came out of course, it was May of 2018 I think. It went into effect. It actually came out in 2017, but the penalties didn't take effect till '18. And I thought, okay, maybe this can be a framework for governments around the world and states. It sounds like yeah sort of, but not really. Maybe there's elements of GDPR that people are adopting, but then it sounds like they're putting in their own twists, which is going to be a nightmare for companies. So, are you not seeing a sort of, GDPR becoming this global standard? It sounds like, no. >> I don't think it's going to be necessarily global standard, but I do think the spirit of the GDPR, and at the core of it is, why are you using my data? What was the purpose? So traditionally, when we think about using data, we think about all right, who's the user, and what authorizations do they have, right? But now, there's a third question. Sure, you're authorized to see this data, depending on your role or organization right? But why are you using it? Are you using it for certain business use? Are you using it for personal use? Why are you using this? That's the spirit of GDPR that everyone is adopting across the board. And then of course, each state, or each federal organization is thinking about their unique lens on it, right? And so you're right. This is going to be incredibly complex. And the amount of policies being enforced at query time. I'm in my favorite, let's just say I'm in Tableau or Looker right? I'm just some simple analyst, I'm a young kid, I'm 22, my first job right? And I'm running these queries, I don't know where the data is, right? I don't know what I'm combining. And what we found is on average in these large enterprises, any query at any moment in time, might have over 500 thousand policies that need to be enforced in real time. >> Wow. >> And it's only getting worse. We have to automate it. No human can handle all those edge cases. We have to automate. >> So, I want to get into how you guys actually do that. Before I do, there seems to be... There's a lot of confusion in the marketplace. Take the word data management, data protection. All the backup guys are using that term, the database guys use that term, GOC folks use that term, so there's a lot of confusion there. You have all these adjacent markets coming together. You've got the whole governance risk and compliance space, you've got cyber security, there's privacy concerns, which is kind of two sides of the same coin. How do you see these adjacencies coming together? It seems like you sit in the middle of all that. >> Yeah, welcome to why my marketing budget is getting bigger and bigger. The challenge we're facing now is I think, who owns the problem right? The Chief Data Officer is taking on a much larger role in these organizations, the CISO is taking a much more larger role in reporting up to the board. You have the line of business who now is almost self-sustaining, they don't have to depend on IT as much any longer because of the cloud and because of the new compute layers to make it easier. So who owns it? At the end of the day, where we see it is we think there's a next generation of cyber tools that are coming out. We think that the CISO has to own this. And the reason is that the CISO's job is to protect the enterprise from cyber risk. And at the core of cyber risk is data. And they must own the data problem. The CDO must find the data, and explain what that data is, and make sure it's quality, but it is the CISO that must protect the enterprise from these threats. And so, I see us as part of this next wave of cyber tools that are coming out. There's other companies that are equally in our stratosphere, like BigID, we're seeing AWS with Macy doing sensitive data discovery, Google has their data loss prevention service. So the cloud players are starting to see, hey, we've got to identify sensitive data. There's other startups that are saying hey, we got to identify and catalog sensitive data. And for us, we're saying hey, we need to be able to consume all that cataloging, understand what's sensitive, and automatically apply policies to ensure that any regulation in that environment is met. >> I want to ask you about the cloud too. So much to talk to you about here, Matt. So, I also wanted to get your perspective on variances within industries. So you mentioned Chief Data Officers. The ascendancy of the Chief Data Officers started in financial services, healthcare, and government where we had highly regulation industries. And now it's sort of seeped into more commercial. But it terms of those regulated industries, take healthcare for example. There are specific nuances. Can you talk about what you're seeing in terms of industry variance. >> Yeah, it's a great point. Starting with like, healthcare. What does it mean to be HIPPA compliant anymore? There are different types of devices now where I can point it at your heartbeat from a distance away and I can have 99 percent accuracy of identifying you, right? It takes three data points in any data set to identify 87 percent of US citizens. If I have your age, sex, location, I can identify you. So, what does it mean anymore to be HIPPA compliant? So the challenge is how do we build guarantees of trust that we've de-identified these DESA's, cause we have to use it, right? No one's going to go into a hospital and say, "You know what, I don't want you to say my life. "Cause I want my data protected," right? No one's ever going to say that. So the challenges we face now across these regulated industries is the most sensitive data sets are critical for those businesses to operate. So there has to be a compromise. So, what we're trying to do in these organizations is help them leverage their data and build levels of proportionality, to access that right? So, the key isn't to stop people from using data. The key is to build the controls necessary to leverage a small bit of the data. Let's just say, we've made it indistinguishable. You can only ask Agriculture and Statistics the question. Well, you know what, we actually found some really interesting things there, we need to be a little bit more useful, it's this trade-off between privacy and utility. It's a pendulum that swings back and forth. As someone proves I need more of this, you can swing it, or just mask it. I need more of it? All right, we'll just redact some of the certain things. Nope, this is really important, it's going to save someone's life. Okay, completely unmasked, you have the raw data. But it's that control that's necessary in these environments, that's what's missing. You know, we came out of the US Intelligence community. We understood this better than anyone. Because highly regulated, very sensitive data, but we knew we needed the ability to rapidly control. Well is this just a hunch, or is this a 9-11 event? And you need the ability to switch like that. That's the difference and so, healthcare is going through a change of, we have all these new algorithms. Like Facebook the other day said, hey, we have machine learning algorithms that can look at MRI scans, and we're going to be better than anyone in the world at identifying these. Do you feel good about giving your data to Facebook? I don't know, but we can maybe provide guaranteed anonymization to them, to prove to the world they're going to do right. That's where we have to get to. >> Well, this is huge, especially for the consumer, cause you just gave several examples. Facebook's going to know a lot about me, a mobile device, a Fit Bit, and yet, if I want to get access to my own medical records, it's like Fort Knox to try to get, please, give this to my insurance company. You know, you got to go through all these forms. So, you've got those diverging objectives and so, as a consumer, I want to be able to trust that when I say yes you can use it, go, and I can get access to it, and other can get access to it. I want to understand exactly what it is that you guys do, what you sell. Is it software, is it SAS, and then let's get into how it works. So what is it? >> Yeah, so we're a software platform. We deploy into any infrastructure, but it is not multi-tenant so, we can deploy on any cloud, or on premises for any customer, and we do that with customers across the world. But if you think about at the core of what is Immuta, think of Immuta as a system of record for the CISO or the line of business where I can connect to any data, on any infrastructure, on any compute layer, and we connect into over 61 different storage platforms. We then have built a UI where lawyers... We actually have three lawyers as employees that act as product managers to help any lawyer of any stature take what's on paper, these regulations, these rules and policies, and they digitize it essentially, in active code. So they can build any policy they want on any data in the ecosystem, in the enterprise, and enforce it globally without having to write any code. And then because we're this plane where you can connect any tool to this data, and enforce any regulation because we're the man in the middle, we can audit who is using what data and why. In every action, in any change in policy. So, if you think about it, it's connect any tool to any data, control it, any regulation, and prove compliance in a court of law. >> So you can set the policy at the data set level? >> Correct. >> And so, how does one do that? Can you automate that on the creation of that data set? I mean you've got you know, dependencies. How does that all work? >> Yeah, what's a really interesting part of our secret sauce is that one, we could do that at the column level, we can do it at the row level, we can do it at the cell level. >> So very granular. >> Very, very granular. This is something again, we learned from the US Intelligence community, that we have to have very fine grained access to every little bit of the data. The reason is that, especially in the age of data, is people are going to combine many data sets together. The challenge isn't enforcing the policy on a static data set, the challenge is enforcing the policy across three data sets where you merge three pieces of data together, who have conflicting policies. What do you do then? That's the beauty of our system. We deal with that policy inheritance, we manage that lineage of the policy, and can tell you here's what the policy will be. >> In other words, you can manage to the highest common denominator as an example. >> Or we can automate it to the lowest common denominator, where you can work in projects together recognizing hey, we're going to bring someone into the project that's not going to have the level of access. Everyone else will automatically change it to the lowest common denominator. But then you share that work with another team and it'll automatically be brought to the highest common denominator. And we've built all these work flows in. That was what was missing and that's why I call it a system of record. It's really a symbiotic relationship between IT, the data owner, governance, the CISO, who are trying to protect the data, and the consumer, and all they want to do is access the data as fast as possible to make better, more informed decisions. >> So the other mega-trend you have is obviously, the super power of machine intelligence, or artificial intelligence, and then you've got edge devices and machine to machine communication, where it's just an explosion of IP addresses and data, and so, it sounds like you guys can attack that problem as well. >> Any of this data coming in on any system, the idea is that eventually it's going to land somewhere, right? And you got to protect it. We call that like rogue data, right? This is why I said earlier, when we talk about data, we have to start thinking about it as it's not in some building anymore. Data's everywhere. It's going to be on a cloud infrastructure, it's going to be on premises, and it's likely, in the future, going to be on many distributed data centers around the world cause business is global. And so, what's interesting to us is no matter where the data's sitting, we can protect it, we can connect to it, and we allow people to access it. And that's the key thing is not worrying about how to lock down your physical infrastructure, it's about logically separating it. And that's why what differentiates us from other people is one, we don't copy the data, right? That's the always the barrier for these types of platforms. We leave the data where it is. The second is we take all those regulations and we can actually, at query time, push it down to where that data is. So rather than bring it to us, we push the policy to the data. And what that does is that's what allows us, what differentiates us from everyone else is, it allows us to guarantee that protection, no matter where the data's living. >> So you're essentially virtualizing the data? >> Yeah, yeah. It's virtual views of data, but it's not all the data. What people have to realize is in the day of apps, we cared about storage. We put all the data into a database, we built some services on top of it and a UI, and it was controlled that way, right? You had all the nice business logic to control it. In the age of data, right? Data is the new app, right? We have all these automation tools, Data Robot, and H20, and Domino, and Tableau's building all these automation work flows. >> The robotic process automation. >> Yeah, RPA, UI Path, the Work Fusion, right? They're making it easier and easier for any user to connect to any data and then automate the process around it. They don't need an app to build a unique work flows, these new tools do that for them. The key is getting to the data. And the challenge with the supply chain of data is time to data is the most critical aspect of that. Cause, the time to insight is perishable. And so, what I always tell people, a little story, I came from the government, I worked in Baghdad, we had 42 minutes to know whether or not a bad guy in the environment, we could go after him. After that, that data was perishable, right? We didn't know where he was. It's the same thing in the real world. It's like imagine if Google told you, well, in 42 minutes it might be a good time to go 495. (laughter) It's not very useful, I need to know the information now. That's the key. What we see is policy enforcement and regulations are the key barrier of entry. So our ability to rapidly, with no latency, be able to connect anyone to that data and enforce those policies where the data lives, that's the critical nature. >> Okay, so you can apply the policies and you do it quickly, and so now you can help solve the problem. You mentioned a cloud before, or on prem. What is the strategy there with regard to various clouds and how do you approach multi-clouds? >> I think cloud is what used to be an infrastructure as a service game, is now becoming a compute game. I think large, regulated enterprises, government, healthcare, financial services, insurance, are all moving to cloud now in a different way. >> What do you mean by that? Cause people think infrastructure as service, they'll say oh that's compute storage and some networking. What do you mean by that? >> I think there's a whole new age of software that's being laid on top of the availability of compute and the availability of storage. That's companies like Databricks, companies like Snowflake, and what they're doing is dramatically changing how people interact with data. The availability zones, the different types of features, the ability to rip and replace legacy warehouses and main frames. It's changing the ability to not just access, but also the types of users that could even come on to leverage this data. And so these enterprises are now thinking through, "How do I move my entire infrastructure of data to them? "And what are these new capabilities "that I could get out of that?" Which, that is just happening now. A lot of people have been thinking, "Oh, this has been happening over the past five years," no, the compute game is now the new war. I used to think of like, Big Data, right? Big Data created, everyone started to understand, "Ah, if we've got our data assets together, "we can get value." Now they're thinking, "All right, let's move beyond that." The new cloud at our currents works is Snowflake and Databricks. What they're thinking about is, "How do I take all your meta-data "and allow anyone to connect any BI tool, "any data science tool, and provide highly performance, "and highly dependable compute services "to process petabytes of data?" It's pretty fantastic. >> And very cost efficient and being able to scale, compute independent of storage, from an architectural perspective. A lot of people claim they can do that, but it doesn't scale the same way. >> Yeah, when you're talking about... Cause that's the thing is you got to remember, these financial systems especially, they depend on these transactions. They cannot go down and they're processing petabytes of data. That's what the new war is over, is that data in the compute layer. >> And the opportunity for you is that data that can come from anywhere, it's not sitting in a God box, where you can enforce policies on that corpus. You don't know where it's coming from. >> We want to be invisible to that right? You're using Snowflake, it's just automatically enforced. You're using Databricks, it's automatically enforced. All these policies are enforced in flight. No one should even truly care about us. We just want to allow you to use the data the way you're used to using it. >> And you do this, this secret sauce you talked about is math, it's artificial intelligence? >> It's math. I wish I could say it was like super fancy, unsupervised neural nets or what not, it's 15 years of working in the most regulated, sticky environments. We learned about very simple novel ways of pushing it down. Great engineering's always simple. But what we've done is... At query time, what's really neat is we figured a way to take user attributes from identity management system and combine that with a purpose, and then what we do is we've built all these libraries to connect into all these dispert storage and compute systems, to push it in there. The nice thing about that is prior to this what people were doing, was making copies. They'd go to the data engineering team and they'd say hey, "I need to ETL this "and get a copy and it'll be anatomized." Think about that for a second. One, the load on your production systems, of all these copies, all the time, right? The second is CISO, the surface area. Now you've got all this data that in a snapshot in time, is legal and ethical, might change tomorrow. And so, now you've got an increase surface area of risk. Like that no-copy aspect. So the pushing it down and then the no-copy aspect really changed the game for enterprises. >> And you've got providence issues, like you say. You've got governance and compliance. >> And imagine trying, if someone said to you, imagine Congress said hey, "Any data source that you've processed "over the past five years, I want to know if "there was these three people in any of these data sources "and if there were, who touched that data "and why did they touch it?" >> Yeah and storage is cheap, but there's unintended consequences. People are, management isn't. >> We just don't have a unified way to look at all of the logs cross listed. >> So we started to talk about cloud and then I took you down a different path. But you offer your software on any cloud, is that right? >> Yeah, so right now, we are in production on Immuta's Marketplace. And that is a managed service, so you can go deploy in there, it'll go into your VPC, and we can manage the updates for you, we have no insight into your infrastructure, but we can push those updates, it'll automatically update, so you're getting our quarterly releases, we release every season. But yeah, we started with AWBS, and then we will grow out. We see cloud is just too ubiquitous. Currently, we still support though, Bigquery, Data Praq, we support Azure, Data Light Storage version two, as well as Azure Databricks. But you can get us through Immuta's Marketplace. We're also investing in ReInvent, we'll be out there in Vegas in a couple weeks. It's a big event for us just because obviously, the government has a very big stake in AWBS, but also commercial customers. It's been a massive endeavor to move. We've seen lots of infrastructure. Most of our deals now are on cloud infrastructure. >> Great, so tell us about the company. You've raised, I think in a Series B, about 28 million to date. Maybe you could give us the head count, and whatever you can share about momentum, maybe customer examples. >> Yeah, so we've raised 32 million to date. >> 32 million. >> From some great investors. The company's about 70 people now. So not too big, but not small anymore. Just this year, at this point, I haven't closed my fiscal year, so I don't want to give too much, but we've doubled our ARR and we've tripled our LOGO count this year alone and we've still got one more quarter here. We just started our fourth quarter. And some customer cases, the way I think about our business is I love healthcare, I love government, I love finance. To give you some examples is like, COGNO is a really great example. COGNO and what they're trying to solve is can they predict where a child is on the autism spectrum? And they're trying to use machine learning to be able to narrow these children down so that they can see patterns as to how a provider, a therapist is helping these families give these kids the skills to operate in the real world. And so it's like this symbiotic relationship utilizing software, surveys and video and what not, to help connect these kids that are in similar areas of the spectrum, to help say hey, this is a successful treatment, right? The problem with that is we need lots of training data. And this is children, one, two, this is healthcare, and so, how do you guarantee HIPPA compliance? How do you get through FDA trials, through third party, blind testing? And still continue to validate and retrain your models, while protecting the identity of these children? So we provide a platform where we can anonymize all the data for them, we can guarantee that there's blind studies, where the company doesn't have access to certain subsets of the data. We can also then connect providers to gain access to the HIPPA data as needed. We can automate the whole thing for them. And they're a startup too, there are 100 people. But imagine if you were a startup in this health-tech industry and you had to invest in the backend infrastructure to handle all of that. It's too expensive. What we're unlocking for them, I mean yes, it's great that they're HIPPA compliant and all that, that's what we want right? But the more important thing is like, we're providing a value add to innovate in areas utilizing machine learning, that regulations would've stymied, right? We're allowing startups in that ecosystem to really push us forward and help those families. >> Cause HIPPA compliance is table stay compulsory. But now you're talking about enabling new business models. >> Yeah, yeah exactly. >> How did you get into all this? You're CEO, you're business savvy, but it sounds like you're pretty technical as well. What's your background? >> Yeah I mean, so I worked in the intelligence community before this. And most of my focus was on how do we take data and be able to leverage it, either for counter-terrorism missions, to different non-kinetic operations. And so, where I kind of grew up in is in this age of, think about billions of dollars in Baghdad. Where I learned is that through the computing infrastructure there, everything changed. 2006 Baghdad created this boom of technology. We had drones, right? We had all these devices on our trucks that were collecting information in real time and telling us things. And then we started building computing infrastructure and it burst Hadoop. So, I kind of grew up in this era of Big Data. We were collecting it all, we had no idea what to do with it. We had nowhere to process it. And so, I kind of saw like, there's a problem here. If we can find the unique little, you know, nuggets of information out of that, we can make some really smart decisions and save lives. So once I left that community, I kind of dedicated myself to that. The birth of this company again, was spun out of the US Intelligence community and it was really a simple problem. It was, they had a bunch of data scientists that couldn't access data fast enough. So they couldn't solve problems at the speed they needed to. It took four to six months to get to data, the mission said they needed it in less than 72 hours. So it was orthogonal to one another, and so it was very clear we had to solve that problem fast. So that weird world of very secure, really sensitive, but also the success that we saw of using data. It was so obvious that we need to democratize access to data, but we need to do it securely and we need to be able to prove it. We work with more lawyers in the intelligence community than you could ever imagine, so the goal was always, how do we make a lawyer happy? If you figure that problem out, you have some success and I think we've done it. >> Well that's awesome in applying that example to the commercial business world. Scott McNeely's famous for saying there is no privacy in the internet, get over it. Well guess what, people aren't going to get over it. It's the individuals that are much more concerned with it after the whole Facebook and fake news debacle. And as well, organizations putting data in the cloud. They need to govern their data, they need that privacy. So Matt, thanks very much for sharing with us your perspectives on the market, and the best of luck with Immuta. >> Thanks so much, I appreciate it. Thanks for having me out. >> All right, you're welcome. All right and thank you everybody for watching this Cube Conversation. This is Dave Vellante, we'll see ya next time. (digital music)

>> From the SiliconANGLE Media Office in Boston, Massachusetts, it's theCUBE! Now here's your host, Dave Vellante. >> Hi, everybody, welcome to this Cube Insights, powered by ETR. In this breaking analysis I want to talk to you about what I learned this week at Dell Technology's financial analyst meeting in New York. They gathered all the financial analysts, Rob Williams hosted it, he's the head of IR, Michael Dell of course was there. They had Dennis Hoffman who is the head of strategic planning, Jeff Clarke who basically runs the business and Tom Sweet, of course, who was the star of the show, the CFO, all the analysts want to see him. Dell laid out its longterm goals, it provided much clearer understanding of its strategic direction, basically focused on three areas. Dell believes that IT is getting more complex, we know that, they want to capitalize on that by simplifying IT. We'll talk about that. And then they want to position for the wave of digital transformations that are coming and they also believe, Dell believes, that it can capitalize on the consolidation trend, consolidating vendors, so I'll talk about each of those. And so let me bring up the first slide, Alex, if you would. The takeaways from the Dell financial analyst meeting. Let me share with you the overall framework that Tom Sweet laid out. And I have to say, the messaging was very consistent, these guys were very well-prepared. I think Dell is, from a management perspective, very well-run company. They're targeting three to 5% growth on what they're saying is a 4% GDP forecast. Or sorry, 4%, I have GDP here, it's really 4% industry growth. GDP's a little lower than that obviously. So this is IDC data, Gartner data, 4% industry growth. So that's an error on my part, I apologize. The strategies to grow relative to their competition. So grow share on a relative basis. So whatever the market does, again, not GDP, but whatever the market does, Dell wants to grow faster than the market. So it wants to gain share, that's its primary metric. From there they want to grow operating income and they want to grow that faster than revenue, that's going to throw off cash. And then they're going to also continue to delever the balance sheet. I think they paid down 17 billion in debt since the EMC acquisition. They want to get to a two X debt to EBITA ratio within 18 months. And what they're saying is, you know, they talked about, Tom Sweet talked about this consistent march toward investment-grade rating. They've been talkin' about that for awhile. He made the comment, we don't need to have a triple A rating but we want to get to the point where we can reduce our interest expense, and that will, 'cause they'll drop right into the bottom line. So they talked about these various levers that they can turn, some of them under the P and L, gaining share, some are their operating structure and their organizational structure, and one big one is obviously their debt structure. The other key issue here is will this cut the liquidity discount that Dell faces? What do I mean by that? Well, VMware has about a $60 billion valuation. Dell owns about 80% of VMware, which would equate to 48 billion. But if you look at Dell's market cap, it's only 37 billion. So it essentially says that Dell's core business is worth minus 11 billion. We used to talk about this when EMC owned VMware. Its core business only comprised about 40% of the overall value of the company, in this case because of the high debt, Dell has a negative value. And it's not just the high debt. Michael Dell has control over the voting shares, it's essentially a conglomerate structure, there's very high debt, and it's a relatively low margin business, notwithstanding VMware. And so as a result, Dell trades at a discount relative to what you would think it should trade at, given its prominence in the market, $92 billion company, the leader in every category under the sun. So that's the big question is can Dell turn these levers, drop EBITA or cash to the bottom line, affect operating income, and then ultimately pay down its debt and affect that discount that it trades at? Okay, bring up, if you would, Alex, the next slide. Now I want to share with you the takeaways from the Dell line of business focus. This really was Jeff Clarke's presentations that I'm going to draw from. Servers, we know, they're softer demand, but the key there is they're really faced tough compares. Last year, Dell's server business grew like crazy. So this year the comparisons are lessened. But there's less spending on servers. I'll share with you some of the ETR data. Storage, they call it holding serve, you saw last quarter I did an analysis, I took the ETR data and the income statement, it showed Pure was gaining share at like 22% growth from the income statement standpoint. Dell was 0% growth but is actually growing faster than its competitors. With the exception of Pure. It's growing faster than the market. So Dell actually gained share with 0% growth. Dell's really focused on consolidating the portfolio. They've cut the portfolio down from 80, I think actually the right number is 88 products, down to 20 by May of 2020. They've got some new mid-range coming, they've just refreshed their data protection portfolio, so again, by May of next year, by Dell Technologies World they'll have a much, much more simplified portfolio. And they're gaining back share. They've refocused on the storage business. You might recall after the acquisition, EMC was kind of a mess. It was losing share before the acquisition, it was so distracted with all the Elliott Management stuff goin' on. And kind of took its eye off the ball, and then after the acquisition it took awhile for them to get their act together. They gained back about 375 basis points in the last 18 months. Remember a basis point is 1/100th of 1%. So gaining share and their consistent focus on trying to do that. Their PC business, which is actually doin' quite well, is focused on the commercial segment and focused on higher margins. They made the statement that the PCs are kind of undersupply right now so it's helping margins. There's a big focus in Jeff Clarke's organization on VMware integration. To me this makes a lot of sense. To the extent that you can take the VMware platform and make Dell hardware run VMware better, that's something that is an advantage for Dell, obviously. And at the same time, VMware has to walk the fine line with the ecosystem. But certainly it's earned the presence in the market now that it can basically do what I just said, tightly integrate with Dell and at the same time serve the ecosystem, 'cause frankly, the ecosystem has no choice. It must serve VMware customers. The strategy, essentially, is to, as I say, capitalize on vendor consolidation, leverage value across the portfolio, so whether it's pivotal, VMware integration, the security portfolio, try to leverage that and then differentiate with scale. And Dell really has the number one supply chain in the tech business. Something that Dave Donatelli at HP, when he was at HP, used to talk about. HPE doesn't really talk about that supply chain advantage anymore 'cause essentially it doesn't have it. Dell does. So Jeff Clarke's reorganization, he came in, he streamlined the organization, really from the focus on R and D to product to collaboration across the organization and the VMware integration. I actually was quite impressed with when I first met Jeff Clarke I guess two years ago now, what he and the organization have accomplished since then. No BS kind of person. And you can see it's starting to take effect. So we'll keep an eye on that. The next slide I want to show you, I want to bring in the ETR data. We've been sharing with you the ETR spending intention surveys for the last couple of weeks and months. ETR, enterprise technology research, they have a data platform that comprises 4,500 practitioners that share spending data with them. CIOs, IT managers, et cetera. What I'm showing here is a cut off of the server sector. So I'm going to drill down into server and storage. So these are spending intentions from the July survey asking about the second half of 2019 relative to the first half of 2019. And this is a drill-down into the giant public and private firms. Why do I do that? Because in meeting the ETR, this is the best indicator. So it's big, big public companies and big private companies. Think Uber. Private companies that spend a ton of dough on IT. UPS before it went public, for example. So those companies are in here. And they're, according to ETR, the best indicators. What this chart shows, so the bars show, and I've shared this with you a number of times, the lime green is we're adding, we're new to this platform, we're new adoption. The evergreen is we're spending more, the gray is we're spending the same, the light red or pink is we're spending less, and the dark red is we're leaving the platform. So if you subtract the red from the green you get what's called a net score, and that's that blue line. And this is the overall server spending intentions from that July survey. The end is about 525 respondents out of the 4,500. And this is, again, those that just answered the question on server. So you can see the net score on server spend is dropping. And you can see the market share on server is dropping. The takeaway here is that servers, as a percentage of overall IT spend, are on a downward slope, and have been for quite some time. Back to the January '16 survey. Okay, so that's going to serve us. Let's take a look at the same data for storage. So if, Alex, if you bring up the storage sector slide, You can see kind of a similar trend. And I would argue what's happening here, a couple of things. You've got the CLOB effect, I'll talk about that some more, and you've also got, in this case, the flash, all-flash array effect. What happened was you had all-flash arrays and flash come into the data center, and that gave performance a huge headroom. Remember, spinning disk was the last bastion of mechanical movement and it was the main bottleneck in terms of overall application performance. IO was the problem. Well you put a bunch of flash into the system and it gives a lot of headroom. People used to over-provision capacity just for performance reasons. So flash has had the effect of customers saying, hey, my performance is good, I don't need to over-provision anymore, I don't need to buy so much. So that combined with cloud, I think, has put down the pressure on the storage business as well. Now the next slide, Alex, that I want you to bring up is the vendor net scores, the server spending intentions. And what I've done is I've highlighted Dell EMC. Now what's happening here in the slide, and I realize it's an eye chart, but basically where you want to be in this chart is in the left-hand side. What it shows is the spending intentions and the momentum from the October '18, which is the gray, the April '19, which is the blue, and then the July '19 which is the most recent one. Again, the end is 525 in the servers for the July '19 survey. And you can see Dell's kind of in the middle of the pack. You'd love to be in the left-hand side, you know, Docker, Microsoft, VMware, Intel, Ubuntu. And you don't want to be on the right-hand side, you know, Fujitsu, IBM, is sort of below the line. Dell's kind of in the middle there, Dell EMC. The next slide I want to show you is that same slide for storage. And again, you can see here is that on-- So this is vendor net scores, the storage spending intentions. On the left-hand side it's all the high growth companies. Rubrik, Cohesity, Nutanix, Pure, VMware with vSAN, Veeam. You see Dell EMC's VxRail. On the right-hand side, you see the guys that are losing momentum. Veritas, Iron Mountain, Barracuda, HitachiHDS, Fusion-io still comes up in the survey after the acquisition by Western Digital. Again, you see Dell EMC kind of holding serve in the middle there. Not great, not bad. Okay, so that's kind of just some other ETR data that I wanted to share. All right, next thing we're going to talk about is the macros market summary. And Alex, I've got some bullet points on this, so if you bring up that slide, let me talk about that a little bit. So five points here. First, cloud continues to eat away at on-prem, despite all this talk about repatriation, which I know does happen. People try to throw everything to the cloud and they go, whoa! Look at my Amazon bill, yeah, I get that. That's at the margin. The main trend is that cloud continues to grow. That whole repatriation thing is not moving the on-prem market. On-prem is kind of steady eddy. Storage is still working through that AFA injection. Got a lot of headroom from performance standpoint. So people don't need to buy as much as they used to because you had that step function in performance. Now eventually the market will catch up, all this digital transformation is happening, all this data is flowing through the system and it will catch up, and the storage market is elastic. As NAN prices fall, people will, I predict, will buy more storage. But there's been somewhat of a lull in the overall storage market. It's not a great market right now, frankly, at the macro level. Now ETR does these surveys on a quarterly basis. They're just about to release the October survey, and they put out a little glimpse on Friday about this survey. And I'll share some bullet points there. Overall IT spending clearly is softening. We kind of know that, everybody kind of realizes that. Here's the nuance. New adoptions are reverting to pre-2018 levels, and the replacements are rising. What does this mean? So the number of respondents that said, oh yes, we're adopting this platform for the first time is declining, and the replacements are actually accelerating. Why is that? Well I was at ETR last week and we were talking about this and one of the theories, and I think it's a good one, is that 2016, 2017 was kind of experimentation around digital transformation. 2018, people started to put things into production or closer to production, they were running systems in parallel, and now they're making their bets, they're saying, hey, this test worked, let's put this heavy into production in 2019, and now we're going to start replacing. So we're not going to adopt as much stuff 'cause we're not doing as much experimentation. We're going to now focus and narrow in on those things that are going to drive our business, and we're going to replace those things that aren't going to drive our business. We're going to start unplugging them. So that's some of what's happening. Another big trend is Microsoft. Microsoft is extending its presence throughout. They're goin' after collaboration, you saw the impact that they had on Slack and Slack stock recently. So Slack Box, Dropbox, are kind of exposed there. They're goin' after security, they've just announced a SIM product. So Splunk and IBM, they're kind of goin' after that base. The application performance management vendors. For instance, New Relic. Microsoft goin' after them. Obviously they got a huge presence in cloud. Their Windows 10 cycle is a little slower this time around, but they've got other businesses that are really starting to click. So Microsoft is one of the few vendors that really is showing accelerated spending momentum in the ETR data. Financial services and telcos, which are always leading spender indicators, are actually very weak right now. That's having a spillover effect into Europe, which is over-banked, if I can use that term. Banking heavy, if you will. So right now it's not a pretty picture, but it's not a disaster. I don't want to necessarily suggest this as like going back to 2007, 2008, it's not. It's really just a matter of things are softening and it's, you know, maybe taking a little breath. Okay, so let me summarize the meeting overall. Again, it was a very well-run meeting. Started at 9:00, ended at 12:00, bagged lunch, go home. Nice and crisp. So these guys are very well-prepared. I think, again, Dell is a extremely well-managed company. They laid out a much clearer vision for Wall Street of its strategy, where it's headed. As they say, they're going after IT complexity. I want to make a comment on this. You think about Legacy EMC. Legacy EMC was not the company that you would expect to deal with complexity. In fact, they were the culprit of complexity. One of the things that Jeff Clarke did when he came in, he said, this portfolio's too complex, needs to be simplified. Joe Tucci used to say, overlap is better than gaps. Jeff Clarke said we got too much overlap. We don't have a lot of gaps so let's streamline that portfolio. Taking advantage of vendor consolidation, this is an interesting one. Ever since I've been in this business, which has been quite a long time now, I've been hearing that buyers want to consolidate the number of vendors that they have. They've really not succeeded in doing that. Now can they do that now 'cause there are less vendors? Well, in a sense, yes, there are less sort of on-prem big vendors. EMC's no longer in the market, you don't have companies like Sun and Digital anymore, Compact is gone. HP split in two, but still. You're not seeing a huge number of new vendors, at scale, come into the market. Except you've got AWS and Google as new players there. So I think that injects sort of a new dynamic that a lot of people like to put cloud aside and kind of ignore it and talk about the old on-prem business, but I think that you're going to see a lot of experimentations and workload ins and outs, particularly with AWS and Google and of course Azure, which is in itself, their cloud is almost a separate force. So we'll see how that shakes up. As I say, servers right now, Dell's got a very tough compare. I think Dell will be fine in the server space. Storage, it's all about simplifying the portfolio, they've got a refreshed portfolio focused on regaining share. They've rebranded everything Power, so their whole line is going to be Power by, if it's not already, by May of next year, Dell Technologies World. It's a much more scalable portfolio. And I think Dell's got a lot of valuation levers. They're a $92 billion company, they've got their current operations, their current P and L, their share gains, their cross-company synergies, particularly with VMware, they can expand their TAM into cloud with partnerships like they're doing with AWS and others, Google, Microsoft. The Edge is a TAM expansion opportunity to them. And also corporate structure. You've seen them. VMware acquired Pivotal. They're cleaning that up. I'm sure they could potentially make some other moves. Secureworks is out there, for example. Maybe they'll do some things with RSA. So they got that knob to turn and they can delever. Paying down the debt to the extent that they can get back to investment grade, that will lower their interest rates, that'll drop right to the bottom line, and they'll be able to reinvest that. And Tom Sweet said, within 18 months, we'll be able to get there with that two X ratio relative to EBITA, and that's when they're going to start having conversations with the rating agencies to talk about you know, hey, maybe we can get a better rating and lower our interest expense. Bottom line, did Wall Street buy the story? Yes. But I don't think it's going to necessarily change anything in the near term. This is a show me from Missouri, prove it, execute, and then I think Dell will get rewarded. Okay, so this is Dave Vellante, thanks for watching this Cube Insights powered by ETR. We'll see ya next time. (electronic music)

>> lie from San Francisco celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back to the cubes. Live coverage here in San Francisco, California, for VM World 2019. I'm Jeff Davis Davis, our 10th year, 10 years covering the M world. Quite a run. Got a great stories. More stories coming, Emma days. A lot of organic growth. A lot of typos in the startup scene. Our next two guests Mike Adams, CIA Director bm wear and Ziv Kalman. Oh, vich product line manager here. Welcome to the Cube. Great to see you. Yes, Curtsy to you guys. Got a lot of activity happening around bit fusion. A lot of news to share. Exciting. I mean, in the M. And a story has been high on VM. Where we talking back? Elsie earlier. Continue to fill in on the strategy. >> Yeah, absolutely. Give us the update. Yeah, I think the key thing for us is we really want to become a key player in the A. I am l space and say that those workloads should come on visa. And with this acquisition we think, provides a great framework for a lot of the hardware accelerator devices. The best of you known of those his GP use. But we think there's four coming market with PG A's and also custom a six. So we're super excited about that. >> For the folks that don't know much about the acquisition, what was the motivation? What was the company's core product? What was the interest? Yeah, the >> company had a product called Flex Direct, and that particular product was really focused on taking, ah, similar concept that a lot of V m writes No, which was, Hey, we knew that computes space. We were trying to take these isolated islands and pull them together. Same type of thing. Here you had these expensive devices that people were buying and they were isolated. And now if we could take a single server, it's got a bunch of GP use on it. Why don't we share it? You see all these papers that come out around machine learning at the very end. It says she's I'm amazed that thes GP user so underutilized even when we're actually using them. It's kind of like buying a car and then using the radio only right? Doesn't. It just doesn't make sense. I >> got this trend of alternative processors just sort of exploding all over the place. I mean, obviously in video, sort of people know what's going on there, but but you've got arm. Now you've got the edge coming in, you know, Intel. Still dominant in the server space. But even even storage devices today use different type, not in the not Intel processors in there. It's a combination of our mo are Sometimes you know, G. P uses you say F g a Z, even though they're sort of a narrow use case. You're seeing a six make a comeback. So you got all this additional processing power, you know, going. So that's a tailwind. Absolutely, guys, and it's sort of the intersection of those to maybe talk about some of the trends you see in that regard and how you're taking advantage of them. >> Yeah, it reminds me of many moons ago when we had new chips that were coming out. We said, Jesus, hardware, flurry here, right? And now we're in a really similar spot. Ziv and I see a lot of different types of devices and acceleration devices, whether it's computer network or storage. And in this particular case, right, we just see a hotbed of all these customers that air seeing the same problem, right? And we've got great partnerships with Intel you mentioned in video and and many others. And we just want to really leverage those for these devices because you look at V sphere and say, OK, your traditional workloads. We've done those very, very well. But as we get into containers, KUBERNETES, machine Learning and I, we want these newer cloud native and newer workloads to come our way. And taking advantage of these new capabilities really helps accelerate that in a big way. >> Could you >> explain Maur on the the sphere impact? Because, you know, first of all, of'em, where community you get the feedback right away on Twitter and a lot of things. But sometimes you gotta dig in and find out what people are thinking and where there might. I think that could be future up opportunities or because it meets skepticism. Well, the the sphere native having a eye on the sphere, that's just mind blowing to me. But I mean, I can see I can see a data processor kind of vibe going on here where data needs to be processed. That seems to be a trend. What is it going on with the sphere with this? Is there what's the what's to customers? No. >> Well, I think the first thing to clarify here is that, you know, some often there is this question. Why would Iran m Ellery I work? Look specifically envy. Sphere is a platform. But then customers do run Emily and workers and public clouds. And those layers are not that different than the spirits virtualization layer, and they're running it in virtual machines. So the whole idea would be fusion specifically, is it? Actually, we can make it even more efficient to run these workloads on top of the sphere because the underlying infrastructure that you two actually, you have to accelerate these workloads there. Today they are mostly GP use, obviously, but in the futures, Michael so mentioned you a six are coming in and effigies are coming in. We are going to make those as well. That's the plan using the B fusion framework. Be more efficient to use. A lot >> of people are skeptical around running machine learning on these are not skeptical because, I mean, it's great for any time you have the opportunity to automate something or used software to make something go away. That's not the difference. You're undifferentiated, so it makes sense. But I just can't figure out where, specifically, within these fears of being targeted to use >> where envy sphere as in, Well, >> if I'm operating the sphere on top operator, I got Debs kicking around the corner. I got a cloud Mom reclaiming. Where's this fit in? Where >> this fits into essentially any place for a visa is running. It doesn't matter if it would run on via MacLeod and for any other for cloud partnerships or on the the edge of our Vesey runs. This is a core capability of the sphere, so it doesn't matter. You know where physically or infrastructure is, we would be able to expose this technology. The idea is also that you mentioned the trends in the A six as they're coming into the enterprise. There's an architectural changes also coming in, and in the server perspective, it's just it's the servers are actually getting more dense there, in there, in there in the accelerator infrastructure that they have in them. So you're seeing four to a GP using a single server. Those are very powerful machines. You can just move oil, represent a single machine again. That brings us back to be fusion and descend. The segregated model affects territory used, which is very similar by the way to centralize stories use. >> You guys are on something really big here. I think that hardware assists off load anything. Hardware system, harbor off load is gonna be a more of a bigger trend. And we saw it happen big time and hyper converge just for storage and everything. But I think as you want to stack where kubernetes gonna flourish? Yeah. I mean, imagine all the service is that he turned on Turned off. I mean, that's not I mean, men even know when it gets turned on or off. >> Absolutely offload for awhile with things like a raise, right, trying to push processing off to a bigger ray that you've got there. And then one other thing you said that I think was really important is the audience, right? If you look at a i n m l, we have traditionally haven't talked to the data, scientists of the machine learning folks. And we need to get to the I t. Folks that air supporting those workloads saying similar to some other workloads that were new and saying these were gonna come your way. And so we need to be prepared and you need to be able to leverage. So >> what's the What's the pitch to those folks? What's that? What's what you guys saying to them? Because it is a benefit for Debs and Dev Ops is to have an ops right. You got the ops down. Okay, see that and this change happening. But a dev, What's the pitch? But how do you get their attention? What's the value proposition? >> The the Actually, that's the beauty of it. It's exactly the same bottle proposition that the sphere in Vienna, where the Vienna state provides the developers and the only thing is that now we are letting the the office people to actually provide this doing this infrastructure as well in the same efficient manner. So it's your transformation. Basically, it's giving the exact same value proposition. >> Talk about the multi cloud tie in here. We've heard a lot about multi cloud and I think multi cloud in part anyway, is being able to run any application and workload anywhere. And one of things about your technology is the ability to not have to rewrite the application to take advantage of acceleration. Does it fit into multi cloud? And if so, how? >> Yeah, when we made the bet Fusion acquisition, if you look at their story, they had the any any any story as well, just like we do. And so, you know, we made announcement this week within video and eight of us and VM, where it's definitely possible of the technology that we have to extend that even further. And so, you know, the only thing I know with users going forward is they're gonna have more than one cloud, and so we just need to prepare for that and make sure that it works. And it works well across the board and the common layer. When you look at our multi cloud strategy is vey sphere is going to be at each of those layers. So if it's ties in disease here, it should be pretty easy to make it work in each of those environments. >> What was that What was the announcement you made you share? The big >> one was being able to use in video in the context of cloud in AWS. So's GPU capabilities and bring it to the service as we do on Prem. And so that was a big piece. And then we also obviously, in making that announcement talking about Hey, you know, this is a critical area for us because not only are we doing this, but we're also saying that your bit fusion will help enhance this because we think in video and bit fusion work very well together as well. >> And is that a product of service? Ah, go to market initiative. >> In the case of the coordinated us, it would be offered as part of the service. So when you can consume the compute, you know you want a GPU, it'll be there for you to help run that workload in the cloud. >> And that's available. When >> that's an nvidia in AWS kind of question. When they are making that infrastructure available, it's essentially going to be a nun. In another instance, type that the ember cloud in AWS will offer okay, I >> mean, it's a tech preview. >> What if some of the things that people should know about because again, in the pattern I'm seeing here of'em world is as in love to stack with kubernetes being that abstraction layer that guessing eyes promoting heavily on rightfully so. We're big fans communes with that for the beginning is that you're gonna have this this purpose built, um, native capability so that when you guys got this native vibe going on native to hype the sphere native TSX native, what does that actually mean? Native like Cooper, naked native on I. But what does it native mean? Explain to the audience what that actually means. >> I'll start up. Sure. You could >> elaborate 30 minutes if you want. But what is that >> true native native? The idea >> for us was used kubernetes really two ways. You know, most of the time when we were talking about Cooper Naser Containers, it's running that on top of these Fair right? What happens if you could take the DNA of that and put it actually inside of east here? Right, so not only you could run these clusters and native pods, but you could also leverage some of the value and one of the things that Cubans does really well is it handles workloads really well. So if we take an example where we have 145 e ems and they make up your app, right, normally you'd have to go to each one of those and figure out OK, let's make some changes in tweaks. And now what I can do is I can treat all of those is one workload and I can move them. I could do really interesting things with that. And that's the power one of the powers that you have with Kubernetes. >> And that's where the differentiation. Then you don't think that there's a >> Yeah, exactly. I mean you are essentially getting There are a lot of benefits our customers, our values value that the customer is getting today from V Sphere, generically speaking, and our longtime customers are familiar with the value propositions. And what we are saying is that when you're getting something as a native capability is that essentially ties into all the other capabilities that you already were know very well and you will be able to get those. But with on top on, sometimes on top orbit in conjunction with what >> is that gonna enable? Now let's talk about the enablement. >> So let's go back all the way. If you go all the way back to be fusion, for example, if you enable it is a native technology, then if you're running containers or viens on the sphere natively they can consume to be fusion technology. If you have cool, it is. It can orchestrate natively, the PM's and containers that are using the confusion to collision. Excited. Oh, so this is the whole thing, >> more efficient platform standpoint, >> and it's easier to manage as well, because you don't have to install a bunch of stuff on top of each other because it's needed. It's part of the first. >> A lot of hassle go away that people might >> take it in and you're gonna have to guess tomorrow they're going to go deep into it with >> you. Great, we're excited. So we're hearing a lot, obviously, but kubernetes at this event and and but most of the audience, they're not developers. So how can you use the sort of bit Fusion mojo to attract developers for some of these new workloads, that air come into the marketplace? >> Yeah, I mean it's all about acquiring new audiences in a case of infusions. More the data scientists. In the case of the communities, it's more around the developer. But I think let's use the kubernetes examples as a good one and what we announced with Project Pacific. Basically, the way it looks, the technology looks to them. It'll look like the kubernetes, a p I with a little bit of east for goodness from the operator perspective, the people that we know the 20,000 that are here, it looks to them like the sphere was from kubernetes Goodness. So that's the right mix is you've got to get it. So it looks exactly the smells and feels just like what they're used to. And I think that's a that's a key aspect. And then for the data Scientists with fusion, we really need to say Okay, you know you want to run these workloads, but she's you're paying really a lot of money for these expensive, isolated devices, and you could get more value by kind of grouping them up and making sure that they're used kind of in aggregate, right? >> So there's more leverage on the data science side So if I'm say hiring someone I know I'm or more to work with with >> exactly, essentially, it's it's the same story. They don't need to change their applications, their framework. Their models use the same could interface, which is the GPU interface for for the GPS computer. >> So So let's talk about that. So data scientist, you know, they always complain that most their time is spent wrangling data That's their, you know, bugaboo. And then there's a collaboration between data scientists and developers, which probably doesn't happen enough. What are you seeing in terms of the trends from the data science role? And can you help solve some of those problems? >> Well, what we are about to solve is really access access to infrastructure for them. Easy access to the infrastructure in their software stack. And the way to get there is to make the data engineers that serve these data scientists and the application administrators that surges data scientist to get easy access to the infrastructure Dany to provide the software, and that's where the sphere eventually comes in. So it's not the Celia direct relationship with the end users. It's more enabling the entire organization that actually served these end users and let them use as much infrastructure as your partners. And >> that and that and user organization. The buffer >> guys last question share what the plans are. What's next? What's your goals for the next 6 to 12 months? I'll see. Get the acquisition under your belt. Native in these fear, a lot of other cool things. I mean that I could talk about >> customers and maybe you can talk about product from a customer perspective. You know, we want engage in proof of concepts. So we want to bring them in, let them test out the software. It already works with the beast here, so I'll be running with multiple proof of concepts across the globe. We >> use cases in the U. S. Case or what? >> Yeah, I mean, it's it's pretty simple at the moment. It seems to be most people that are using GP use around ml. We have a great demo down the floor that shows people trying to run inception, three year resident 50 And how can we actually help those v EMs that are running that? So that's gonna be my focus. The next six >> years you want get some use cases come over here, bring him up to Mike. >> And from that perspective, I mean, obviously, we acquired occasion in an early stage. The technology works well. It works well enough to be product eyes. However, Veum, wherein the sphere has very high enterprise software stone standards in terms of security and management and governance. All this capabilities so that's going to be are focused on the next, you know, even almost a year to make sure that we bring it up to a level where we can confidently provide it and sell. It is a product >> you gotta engineering hye bar there absolutely thanks to Russia coming on keeping the update, the end world coverage Breaking it down. 2019. It's the Cuba job for David. Thanks for watching Be back with more after this short break.