>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> After a two-year epic run in tech, 2022 has been an epically bad year. Through yesterday, The NASDAQ composite is down 30%. The S$P 500 is off 21%. And the Dow Jones Industrial average 16% down. And the poor holders at Bitcoin have had to endure a nearly 60% decline year to date. But judging by the attendance and enthusiasm, in major in-person tech events this spring. You'd never know that tech was in the tank. Moreover, walking around the streets of Las Vegas, where most tech conferences are held these days. One can't help but notice that the good folks of Main Street, don't seem the least bit concerned that the economy is headed for a recession. Hello, and welcome to this weeks Wiki Bond Cube Insights powered by ETR. In this Breaking Analysis we'll share our main takeaways from the first half of 2022. And talk about the outlook for tech going forward, and why despite some pretty concerning headwinds we remain sanguine about tech generally, but especially enterprise tech. Look, here's the bumper sticker on why many folks are really bearish at the moment. Of course, inflation is high, other than last year, the previous inflation high this century was in July of 2008, it was 5.6%. Inflation has proven to be very, very hard to tame. You got gas at $7 dollars a gallon. Energy prices they're not going to suddenly drop. Interest rates are climbing, which will eventually damage housing. Going to have that ripple effect, no doubt. We're seeing layoffs at companies like Tesla and the crypto names are also trimming staff. Workers, however are still in short supply. So wages are going up. Companies in retail are really struggling with the right inventory, and they can't even accurately guide on their earnings. We've seen a version of this movie before. Now, as it pertains to tech, Crawford Del Prete, who's the CEO of IDC explained this on theCUBE this very week. And I thought he did a really good job. He said the following, >> Matt, you have a great statistic that 80% of companies used COVID as their point to pivot into digital transformation. And to invest in a different way. And so what we saw now is that tech is now where I think companies need to focus. They need to invest in tech. They need to make people more productive with tech and it played out in the numbers. Now so this year what's fascinating is we're looking at two vastly different markets. We got gasoline at $7 a gallon. We've got that affecting food prices. Interesting fun fact recently it now costs over $1,000 to fill an 18 wheeler. All right, based on, I mean, this just kind of can't continue. So you think about it. >> Don't put the boat in the water. >> Yeah, yeah, yeah. Good luck if ya, yeah exactly. So a family has kind of this bag of money, and that bag of money goes up by maybe three, 4% every year, depending upon earnings. So that is sort of sloshing around. So if food and fuel and rent is taking up more, gadgets and consumer tech are not, you're going to use that iPhone a little longer. You're going to use that Android phone a little longer. You're going to use that TV a little longer. So consumer tech is getting crushed, really it's very, very, and you saw it immediately in ad spending. You've seen it in Meta, you've seen it in Facebook. Consumer tech is doing very, very, it is tough. Enterprise tech, we haven't been in the office for two and a half years. We haven't upgraded whether that be campus wifi, whether that be servers, whether that be commercial PCs as much as we would have. So enterprise tech, we're seeing double digit order rates. We're seeing strong, strong demand. We have combined that with a component shortage, and you're seeing some enterprise companies with a quarter of backlog, I mean that's really unheard of. >> And higher prices, which also profit. >> And therefore that drives up the prices. >> And this is a theme that we've heard this year at major tech events, they've really come roaring back. Last year, theCUBE had a huge presence at AWS Reinvent. The first Reinvent since 2019, it was really well attended. Now this was before the effects of the omicron variant, before they were really well understood. And in the first quarter of 2022, things were pretty quiet as far as tech events go But theCUBE'a been really busy this spring and early into the summer. We did 12 physical events as we're showing here in the slide. Coupa, did Women in Data Science at Stanford, Coupa Inspire was in Las Vegas. Now these are both smaller events, but they were well attended and beat expectations. San Francisco Summit, the AWS San Francisco Summit was a bit off, frankly 'cause of the COVID concerns. They were on the rise, then we hit Dell Tech World which was packed, it had probably around 7,000 attendees. Now Dockercon was virtual, but we decided to include it here because it was a huge global event with watch parties and many, many tens of thousands of people attending. Now the Red Hat Summit was really interesting. The choice that Red Hat made this year. It was purposefully scaled down and turned into a smaller VIP event in Boston at the Western, a couple thousand people only. It was very intimate with a much larger virtual presence. VeeamON was very well attended, not as large as previous VeeamON events, but again beat expectations. KubeCon and Cloud Native Con was really successful in Spain, Valencia, Spain. PagerDuty Summit was again a smaller intimate event in San Francisco. And then MongoDB World was at the new Javits Center and really well attended over the three day period. There were lots of developers there, lots of business people, lots of ecosystem partners. And then the Snowflake summit in Las Vegas, it was the most vibrant from the standpoint of the ecosystem with nearly 10,000 attendees. And I'll come back to that in a moment. Amazon re:Mars is the Amazon AI robotic event, it's smaller but very, very cool, a lot of innovation. And just last week we were at HPE Discover. They had around 8,000 people attending which was really good. Now I've been to over a dozen HPE or HPE Discover events, within Europe and the United States over the past decade. And this was by far the most vibrant, lot of action. HPE had a little spring in its step because the company's much more focused now but people was really well attended and people were excited to be there, not only to be back at physical events, but also to hear about some of the new innovations that are coming and HPE has a long way to go in terms of building out that ecosystem, but it's starting to form. So we saw that last week. So tech events are back, but they are smaller. And of course now a virtual overlay, they're hybrid. And just to give you some context, theCUBE did, as I said 12 physical events in the first half of 2022. Just to compare that in 2019, through June of that year we had done 35 physical events. Yeah, 35. And what's perhaps more interesting is we had our largest first half ever in our 12 year history because we're doing so much hybrid and virtual to compliment the physical. So that's the new format is CUBE plus digital or sometimes just digital but that's really what's happening in our business. So I think it's a reflection of what's happening in the broader tech community. So everyone's still trying to figure that out but it's clear that events are back and there's no replacing face to face. Or as I like to say, belly to belly, because deals are done at physical events. All these events we've been to, the sales people are so excited. They're saying we're closing business. Pipelines coming out of these events are much stronger, than they are out of the virtual events but the post virtual event continues to deliver that long tail effect. So that's not going to go away. The bottom line is hybrid is the new model. Okay let's look at some of the big themes that we've taken away from the first half of 2022. Now of course, this is all happening under the umbrella of digital transformation. I'm not going to talk about that too much, you've had plenty of DX Kool-Aid injected into your veins over the last 27 months. But one of the first observations I'll share is that the so-called big data ecosystem that was forming during the hoop and around, the hadoop infrastructure days and years. then remember it dispersed, right when the cloud came in and kind of you know, not wiped out but definitely dampened the hadoop enthusiasm for on-prem, the ecosystem dispersed, but now it's reforming. There are large pockets that are obviously seen in the various clouds. And we definitely see a ecosystem forming around MongoDB and the open source community gathering in the data bricks ecosystem. But the most notable momentum is within the Snowflake ecosystem. Snowflake is moving fast to win the day in the data ecosystem. They're providing a single platform that's bringing different data types together. Live data from systems of record, systems of engagement together with so-called systems of insight. These are converging and while others notably, Oracle are architecting for this new reality, Snowflake is leading with the ecosystem momentum and a new stack is emerging that comprises cloud infrastructure at the bottom layer. Data PaaS layer for app dev and is enabling an ecosystem of partners to build data products and data services that can be monetized. That's the key, that's the top of the stack. So let's dig into that further in a moment but you're seeing machine intelligence and data being driven into applications and the data and application stacks they're coming together to support the acceleration of physical into digital. It's happening right before our eyes in every industry. We're also seeing the evolution of cloud. It started with the SaaS-ification of the enterprise where organizations realized that they didn't have to run their own software on-prem and it made sense to move to SaaS for CRM or HR, certainly email and collaboration and certain parts of ERP and early IS was really about getting out of the data center infrastructure management business called that cloud 1.0, and then 2.0 was really about changing the operating model. And now we're seeing that operating model spill into on-prem workloads finally. We're talking about here about initiatives like HPE's Green Lake, which we heard a lot about last week at Discover and Dell's Apex, which we heard about in May, in Las Vegas. John Furrier had a really interesting observation that basically this is HPE's and Dell's version of outposts. And I found that interesting because outpost was kind of a wake up call in 2018 and a shot across the bow at the legacy enterprise infrastructure players. And they initially responded with these flexible financial schemes, but finally we're seeing real platforms emerge. Again, we saw this at Discover and at Dell Tech World, early implementations of the cloud operating model on-prem. I mean, honestly, you're seeing things like consoles and billing, similar to AWS circa 2014, but players like Dell and HPE they have a distinct advantage with respect to their customer bases, their service organizations, their very large portfolios, especially in the case of Dell and the fact that they have more mature stacks and knowhow to run mission critical enterprise applications on-prem. So John's comment was quite interesting that these firms are basically building their own version of outposts. Outposts obviously came into their wheelhouse and now they've finally responded. And this is setting up cloud 3.0 or Supercloud, as we like to call it, an abstraction layer, that sits above the clouds that serves as a unifying experience across a continuum of on-prem across clouds, whether it's AWS, Azure, or Google. And out to both the near and far edge, near edge being a Lowes or a Home Depot, but far edge could be space. And that edge again is fragmented. You've got the examples like the retail stores at the near edge. Outer space maybe is the far edge and IOT devices is perhaps the tiny edge. No one really knows how the tiny edge is going to play out but it's pretty clear that it's not going to comprise traditional X86 systems with a cool name tossed out to the edge. Rather, it's likely going to require a new low cost, low power, high performance architecture, most likely RM based that will enable things like realtime AI inferencing at that edge. Now we've talked about this a lot on Breaking Analysis, so I'm not going to double click on it. But suffice to say that it's very possible that new innovations are going to emerge from the tiny edge that could really disrupt the enterprise in terms of price performance. Okay, two other quick observations. One is that data protection is becoming a much closer cohort to the security stack where data immutability and air gaps and fast recovery are increasingly becoming a fundamental component of the security strategy to combat ransomware and recover from other potential hacks or disasters. And I got to say from our observation, Veeam is leading the pack here. It's now claiming the number one revenue spot in a statistical dead heat with the Dell's data protection business. That's according to Veeam, according to IDC. And so that space continues to be of interest. And finally, Broadcom's acquisition of Dell. It's going to have ripple effects throughout the enterprise technology business. And there of course, there are a lot of questions that remain, but the one other thing that John Furrier and I were discussing last night John looked at me and said, "Dave imagine if VMware runs better on Broadcom components and OEMs that use Broadcom run VMware better, maybe Broadcom doesn't even have to raise prices on on VMware licenses. Maybe they'll just raise prices on the OEMs and let them raise prices to the end customer." Interesting thought, I think because Broadcom is so P&L focused that it's probably not going to be the prevailing model but we'll see what happens to some of the strategic projects rather like Monterey and Capitola and Thunder. We've talked a lot about project Monterey, the others we'll see if they can make the cut. That's one of the big concerns because it's how OEMs like the ones that are building their versions of outposts are going to compete with the cloud vendors, namely AWS in the future. I want to come back to the comment on the data stack for a moment that we were talking about earlier, we talked about how the big data ecosystem that was once coalescing around hadoop dispersed. Well, the data value chain is reforming and we think it looks something like this picture, where cloud infrastructure lives at the bottom. We've said many times the cloud is expanding and evolving. And if companies like Dell and HPE can truly build a super cloud infrastructure experience then they will be in a position to capture more of the data value. If not, then it's going to go to the cloud players. And there's a live data layer that is increasingly being converged into platforms that not only simplify the movement in ELTing of data but also allow organizations to compress the time to value. Now there's a layer above that, we sometimes call it the super PaaS layer if you will, that must comprise open source tooling, partners are going to write applications and leverage platform APIs and build data products and services that can be monetized at the top of the stack. So when you observe the battle for the data future it's unlikely that any one company is going to be able to do this all on their own, which is why I often joke that the 2020s version of a sweaty Steve Bomber running around the stage, screaming, developers, developers developers, and getting the whole audience into it is now about ecosystem ecosystem ecosystem. Because when you need to fill gaps and accelerate features and provide optionality a list of capabilities on the left hand side of this chart, that's going to come from a variety of different companies and places, we're talking about catalogs and AI tools and data science capabilities, data quality, governance tools and it should be of no surprise to followers of Breaking Analysis that on the right hand side of this chart we're including the four principles of data mesh, which of course were popularized by Zhamak Dehghani. So decentralized data ownership, data as products, self-serve platform and automated or computational governance. Now whether this vision becomes a reality via a proprietary platform like Snowflake or somehow is replicated by an open source remains to be seen but history generally shows that a defacto standard for more complex problems like this is often going to emerge prior to an open source alternative. And that would be where I would place my bets. Although even that proprietary platform has to include open source optionality. But it's not a winner take all market. It's plenty of room for multiple players and ecosystem innovators, but winner will definitely take more in my opinion. Okay, let's close with some ETR data that looks at some of those major platform plays who talk a lot about digital transformation and world changing impactful missions. And they have the resources really to compete. This is an XY graphic. It's a view that we often show, it's got net score on the vertical access. That's a measure of spending momentum, and overlap or presence in the ETR survey. That red, that's the horizontal access. The red dotted line at 40% indicates that the platform is among the highest in terms of spending velocity. Which is why I always point out how impressive that makes AWS and Azure because not only are they large on the horizontal axis, the spending momentum on those two platforms rivals even that of Snowflake which continues to lead all on the vertical access. Now, while Google has momentum, given its goals and resources, it's well behind the two leaders. We've added Service Now and Salesforce, two platform names that have become the next great software companies. Joining likes of Oracle, which we show here and SAP not shown along with IBM, you can see them on this chart. We've also plotted MongoDB, which we think has real momentum as a company generally but also with Atlas, it's managed cloud database as a service specifically and Red Hat with trying to become the standard for app dev in Kubernetes environments, which is the hottest trend right now in application development and application modernization. Everybody's doing something with Kubernetes and of course, Red Hat with OpenShift wants to make that a better experience than do it yourself. The DYI brings a lot more complexity. And finally, we've got HPE and Dell both of which we've talked about pretty extensively here and VMware and Cisco. Now Cisco is executing on its portfolio strategy. It's got a lot of diverse components to its company. And it's coming at the cloud of course from a networking and security perspective. And that's their position of strength. And VMware is a staple of the enterprise. Yes, there's some uncertainty with regards to the Broadcom acquisition, but one thing is clear vSphere isn't going anywhere. It's entrenched and will continue to run lots of IT for years to come because it's the best platform on the planet. Now, of course, these are just some of the players in the mix. We expect that numerous non-traditional technology companies this is important to emerge as new cloud players. We've put a lot of emphasis on the data ecosystem because to us that's really going to be the main spring of digital, i.e., a digital company is a data company and that means an ecosystem of data partners that can advance outcomes like better healthcare, faster drug discovery, less fraud, cleaner energy, autonomous vehicles that are safer, smarter, more efficient grids and factories, better government and virtually endless litany of societal improvements that can be addressed. And these companies will be building innovations on top of cloud platforms creating their own super clouds, if you will. And they'll come from non-traditional places, industries, finance that take their data, their software, their tooling bring them to their customers and run them on various clouds. Okay, that's it for today. Thanks to Alex Myerson, who is on production and does the podcast for Breaking Analysis, Kristin Martin and Cheryl Knight, they help get the word out. And Rob Hoofe is our editor and chief over at Silicon Angle who helps edit our posts. Remember all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can email me directly at david.vellante@siliconangle.com or DM me at dvellante, or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE's Insights powered by ETR. Thanks for watching be well. And we'll see you next time on Breaking Analysis. (upbeat music)

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's day one coverage of HPE discover 22 live from the Venetian in Las Vegas. I got a power panel here, Lisa Martin, with Dave Valante, John furrier, Holger Mueller also joins us. We are gonna wrap this, like you've never seen a rap before guys. Lot of momentum today, lot, lot of excitement, about 8,000 or so customers, partners, HPE leaders here. Holger. Let's go ahead and start with you. What are some of the things that you heard felt saw observed today on day one? >>Yeah, it's great to be back in person. Right? 8,000 people events are rare. Uh, I'm not sure. Have you been to more than 8,000? <laugh> yeah, yeah. Okay. This year, this year. I mean, historically, yes, but, um, >>Snowflake was 10. Yeah. >>So, oh, wow. Okay. So 8,000 was my, >>Cisco was, they said 15, >>But is my, my 8,000, my record, I let us down with 7,000 kind of like, but it's in the Florida swarm. It's not nicely. Like, and there's >>Usually what SFI, there's usually >>20, 20, 30, 40, 50. I remember 50 in the nineties. Right. That was a different time. But yeah. Interesting. Yeah. Interesting what people do and it depends how much time there is to come. Right. And know that it happens. Right. But yeah, no, I think it's interesting. We, we had a good two analyst track today. Um, interesting. Like HPE is kind of like back not being your grandfather's HPE to a certain point. One of the key stats. I know Dave always for the stats, right. Is what I found really interesting that over two third of GreenLake revenue is software and services. Now a love to know how much of that services, how much of that software. But I mean, I, I, I, provocate some, one to ones, the HP executives saying, Hey, you're a hardware company. Right. And they didn't even come back. Right. But Antonio said, no, two thirds is, uh, software and services. Right. That's interesting. They passed the one exabyte, uh, being managed, uh, as a, as a hallmark. Right. I was surprised only 120,000 users if I had to remember the number. Right, right. So that doesn't seem a terrible high amount of number of users. Right. So, but that's, that's, that's promising. >>So what software is in there, cuz it's gotta be mostly services. >>Right? Well it's the 70 plus cloud services, right. That everybody's talking about where the added eight of them shockingly back up and recovery, I thought that was done at launch. Right. >>Still who >>Keep recycling storage and you back. But now it's real. Yeah. >>But the company who knows the enterprise, right. HPE, what I've been doing before with no backup and recovery GreenLake. So that was kind of like, okay, we really want to do this now and nearly, and then say like, oh, by the way, we've been doing this all the time. Yeah. >>Oh, what's your take on the installed base of HP. We had that conversation, the, uh, kickoff or on who's their target, what's the target audience environment look like. It certainly is changing. Right? If it's software and services, GreenLake is resonating. Yeah. Um, ecosystems responding. What's their customers cuz managed services are up too Kubernetes, all the managed services what's what's it like what's their it transformation base look like >>Much of it is of course install base, right? The trusted 20, 30 plus year old HP customer. Who's keeping doing stuff of HP. Right. And call it GreenLake. They've been for so many name changes. It doesn't really matter. And it's kind of like nice that you get the consume pain only what you consume. Right. I get the cloud broad to me then the general markets, of course, people who still need to run stuff on premises. Right. And there's three reasons of doing this performance, right. Because we know the speed of light is relative. If you're in the Southern hemisphere and even your email servers in Northern hemisphere, it takes a moment for your email to arrive. It's a very different user experience. Um, local legislation for data, residency privacy. And then, I mean Charles Phillips who we all know, right. Former president of uh, info nicely always said, Hey, if the CIOs over 50, I don't have to sell qu. Right. So there is not invented. I'm not gonna do cloud here. And now I've kind of like clouded with something like HP GreenLake. That's the customers. And then of course procurement is a big friend, right? Yeah. Because when you do hardware refresh, right. You have to have two or three competitors who are the two or three competitors left. Right. There's Dell. Yeah. And then maybe Lenovo. Right? So, so like a >>Little bit channels, the strength, the procurement physicians of strength, of course install base question. Do you think they have a Microsoft opportunity where, what 365 was Microsoft had office before 365, but they brought in the cloud and then everything changed. Does HP have that same opportunity with kind of the GreenLake, you know, model with their existing stuff. >>It has a GreenLake opportunity, but there's not much software left. It's a very different situation like Microsoft. Right? So, uh, which green, which HP could bring along to say, now run it with us better in the cloud because they've been selling much of it. Most of it, of their software portfolio, which they bought as an HP in the past. Right. So I don't see that happening so much, but GreenLake as a platform itself course interesting because enterprise need a modern container based platform. >>I want, I want to double click on this a little bit because the way I see it is HP is going to its installed base. I think you guys are right on say, this is how we're doing business now. Yeah. You know, come on along. But my sense is, some customers don't want to do the consumption model. There are actually some customers that say, Hey, of course I got, I don't have a cash port problem. I wanna pay for it up front and leave me alone. >>I've been doing this since 50 years. Nice. As I changed it, now <laugh> two know >>Money's wants to do it. And I don't wanna rent because rental's more expensive and blah, blah, blah. So do you see that in the customer base that, that some are pushing back? >>Of course, look, I have a German accent, right? So I go there regularly and uh, the Germans are like worried about doing anything in the cloud. And if you go to a board in Germany and say, Hey, we can pay our usual hardware, refresh, CapEx as usual, or should we bug consumption? And they might know what we are running. <laugh> so not whole, no offense against the Germans out. The German parts are there, but many of them will say, Hey, so this is change with COVID. Right. Which is super interesting. Right? So the, the traditional boards non-technical have been hearing about this cloud variable cost OPEX to CapEx and all of a sudden there's so much CapEx, right. Office buildings, which are not being used truck fleets. So there's a whole new sensitivity by traditional non-technical boards towards CapEx, which now the light bulb went on and say, oh, that's the cloud thing about also. So we have to find a way to get our cost structure, to ramp up and ramp down as our business might be ramping up through COVID through now inflation fears, recession, fears, and so on. >>So, okay. HP's, HP's made the statement that anything you can do in the cloud you can do in GreenLake. Yes. And I've said you can't run on snowflake. You can't run Mongo Atlas, you can't run data bricks, but that's okay. That's fine. Let's be, I think they're talking about, there's >>A short list of things. I think they're talking about the, their >>Stuff, their, >>The operating experience. So we've got single sign on through a URL, right. Uh, you've got, you know, some level of consistency in terms of policy. It's unclear exactly what that is. You've got storage backup. Dr. What, some other services, seven other services. If you had to sort of take your best guess as to where HP is now and peg it toward where Amazon was in which year? >>20 14, 20 14. >>Yeah. Where they had their first conference or the second we invent here with 3000 people and they were thinking, Hey, we're big. Yeah. >>Yeah. And I think GreenLake is the building blocks. So they quite that's the >>Building. Right? I mean similar. >>Okay. Well, I mean they had E C, Q and S3 and SQS, right. That was the core. And then the rest of those services were, I mean, base stock was one of that first came in behind and >>In fairness, the industry has advanced since then, Kubernetes is further along. And so HPE can take advantage of that. But in terms of just the basic platform, I, I would agree. I think it's >>Well, I mean, I think, I mean the software, question's a big one. I wanna bring up because the question is, is that software is getting the world. Hardware is really software scales, everything, data, the edge story. I love their story. I think HP story is wonderful Aruba, you know, hybrid cloud, good story, edge edge. But if you look under the covers, it's weak, right? It's like, it's not software. They don't have enough software juice, but the ecosystem opportunity to me is where you plug and play. So HP knows that game. But if you look historically over the past 25 years, HP now HPE, they understand plug and play interoperability. So the question is, can they thread the needle >>Right. >>Between filling the gaps on the software? Yeah. With partners, >>Can they get the partners? Right. And which have been long, long time. Right. For a long time, HP has been the number one platform under ICP, right? Same thing. You get certified for running this. Right. I know from my own history, uh, I joined Oracle last century and the big thing was, let's get your eBusiness suite certified on HP. Right? Like as if somebody would buy H Oracle work for them, right. This 20 years ago, server >>The original exit data was HP. Oracle. >>Exactly. Exactly. So there's this thinking that's there. But I think the key thing is we know that all modern forget about the hardware form in the platforms, right? All modern software has to move to containers and snowflake runs in containers. You mentioned that, right? Yeah. If customers force snowflake and HPE to the table, right, there will be a way to make it work. Right. And which will help HPE to be the partner open part will bring the software. >>I, I think it's, I think that's an opportunity because that changes the game and agility and speed. If HP plays their differentiation, right. Which we asked on their opening segment, what's their differentiation. They got size scale channel, >>What to the enterprise. And then the big benefit is this workload portability thing. Right? You understand what is run in the public cloud? I need to run it local. For whatever reason, performance, local residency of data. I can move that. There that's the big benefit to the ISVs, the sales vendors as well. >>But they have to have a stronger data platform story in my that's right. Opinion. I mean, you can run Oracle and HPE, but there's no reason they shouldn't be able to do a deal with, with snowflake. I mean, we saw it with Dell. Yep. We saw it with, with, with pure and I, if our HPE I'd be saying, Hey, because the way the snowflake deal worked, you probably know this is your reading data into the cloud. The compute actually occurs in the cloud viral HB going snowflake saying we can separate compute and storage. Right. And we have GreenLake. We have on demand. Why don't we run the compute on-prem and make it a full class, first class citizen, right. For all of our customers data. And that would be really innovative. And I think Mongo would be another, they've got OnPrem. >>And the question is, how many, how many snowflake customers are telling snowflake? Can I run you on premise? And how much defo open years will they hear from that? Right? This is >>Why would they deal Dell? That >>Deal though, with that, they did a deal. >>I think they did that deal because the customer came to them and said, you don't exactly that deal. We're gonna spend the >>Snowflake >>Customers think crazy things happen, right? Even, even put an Oracle database in a Microsoft Azure data center, right. Would off who, what as >>Possible snowflake, >>Oracle. So on, Aw, the >>Snow, the snowflakes in the world have to make a decision. Dave on, is it all snowflake all the time? Because what the reality is, and I think, again, this comes back down to the, the track that HP could go up or down is gonna be about software. Open source is now the software industry. There's no such thing as proprietary software, in my opinion, relatively speaking, cloud scale and integrated, integrated integration software is proprietary. The workflows are proprietary. So if they can get that right with the partners, I would focus on that. I think they can tap open source, look at Amazon with open source. They sucked it up and they integrated it in. No, no. So integration is the deal, not >>Software first, but Snowflake's made the call. You were there, Lisa. They basically saying it's we have, you have to be in snowflake in order to get the governance and the scalability, all that other wonderful stuff. Oh, but we we'll do Apache iceberg. We'll we'll open it up. We'll do Python. Yeah. >>But you can't do it data clean room unless you are in snowflake. Exactly. Snowflake on snowflake. >>Exactly. >>But got it. Isn't that? What you heard from AWS all the time till they came out outposts, right? I mean, snowflake is a market leader for what they're doing. Right. So that they want to change their platform. I mean, kudos to them. They don't need to change the platform. They will be the last to change their platform to a ne to anything on premises. Right. But I think the trend already shows that it's going that way. >>Well, if you look at outpost is an signal, Dave, the success of outpost launched what four years ago, they announced it. >>What >>EKS is beating, what outpost is doing. Outpost is there. There's not a lot of buzz and talk to the insiders and the open source community, uh, EKS and containers. To your point mm-hmm <affirmative> is moving faster on, I won't say commodity hardware, but like could be white box or HP, Dell, whatever it's gonna be that scale differentiation and the edge story is, is a good one. And I think with what we're seeing in the market now it's the industrial edge. The back office was gen one cloud back office data center. Now it's hybrid. The focus will be industrial edge machine learning and AI, and they have it here. And there's some, some early conversations with, uh, I heard it from, uh, this morning, you guys interviewed, uh, uh, John Schultz, right? With the world economic 4k birth Butterfield. She was amazing. And then you had Justin bring up a Hoar, bring up quantum. Yes. That is a differentiator. >>HP. >>Yes. Yeah. You, they have the computing shops. They had the R and D can they bring it to the table >>As, as HPC, right. To what they Schultz for of uh, the frontier system. Right. So very impressed. >>So the ecosystem is the key for them is because that's how they're gonna fill the gaps. They can't, they can't only, >>They could, they could high HPC edge piece. I wouldn't count 'em out of that game yet. If you co-locate a box, I'll use the word box, particularly at a telco tower. That's a data center. Yep. Right. If done properly. Yep. So, you know, what outpost was supposed to do actually is a hybrid opportunity. Aruba >>Gives them a unique, >>But the key thing is right. It's a yin and yang, right? It's the ecosystem it's partners to bring those software workload. Absolutely. Right. But HPE has to keep the platform attractive enough. Right. And the key thing there is that you have this workload capability thing that you can bring things, which you've built yourself. I mean, look at the telcos right. Network function, visualization, thousands of man, years into these projects. Right. So if I can't bring it to your edge box, no, I'm not trying to get to your Xbox. Right. >>Hold I gotta ask you since in the Dave too, since you guys both here and Lisa, you know, I said on the opening, they have serious customers and those customers have serious problems, cyber security, ransomware. So yeah. I teach transformation now. Industrial transformation machine learning, check, check, check. Oh, sounds good. But at the end of the day, their customers have some serious problems. Right? Cyber, this is, this is high stakes poker. Yeah. What do you think HP's position for in the security? You mentioned containers, you got all this stuff, you got open source, supply chain, you have to left supply chain issues. What is their position with security? Cuz that's the big one. >>I, I think they have to have a mature attitude that customers expect from HPE. Right? I don't have to educate HP on security. So they have to have the partner offerings again. We're back at the ecosystem to have what probably you have. So bring your own security apart from what they have to have out of the box to do business with them. This is why the shocker this morning was back up in recovery coming. <laugh> it's kind like important for that. Right? Well >>That's, that's, that's more ransomware and the >>More skeleton skeletons in the closet there, which customers should check of course. But I think the expectations HP understands that and brings it along either from partner or natively. >>I, I think it's, I think it's services. I think point next is the point of integration for their security. That's why two thirds is software and services. A lot of that is services, right? You know, you need security, we'll help you get there. We people trust HP >>Here, but we have nothing against point next or any professional service. They're all hardworking. But if I will have to rely on humans for my cyber security strategy on a daily level, I'm getting gray hair and I little gray hair >>Red. Okay. I that's, >>But >>I think, but I do think that's the camera strategy. I mean, I'm sure there's a lot of that stuff that's beginning to be designed in, but I, my guess is a lot of it is services. >>Well, you got the Aruba. Part of the booth was packed. Aruba's there. You mentioned that earlier. Is that good enough? Because the word zero trust is kicked around a lot. On one hand, on the other hand, other conversations, it's all about trust. So supply chain and software is trusting trust, trust and verified. So you got this whole mentality of perimeter gone mentality. It's zero trust. And if you've got software trust, interesting thoughts there, how do you reconcile zero trust? And then I need trust. What's what's you? What are you seeing older on that? Because I ask people all the time, they're like, uh, I'm zero trust or is it trust? >>Yeah. The middle ground. Right? Trusted. The meantime people are man manipulating what's happening in your runtime containers. Right? So, uh, drift control is a new password there that you check what's in your runtime containers, which supposedly impenetrable, but people finding ways to hack them. So we'll see this cat and mouse game going on all the time. Yeah. Yeah. There's always gonna be the need for being in a secure, good environment from that perspective. Absolutely. But the key is edge has to be more than Aruba, right? If yeah. HV goes away and says, oh yeah, we can manage your edge with our Aruba devices. That's not enough. It's the virtual probability. And you said the important thing before it's about the data, right? Because the dirty secret of containers is yeah, I move the code, but what enterprise code works without data, right? You can't say as enterprise, okay, we're done for the day check tomorrow. We didn't persist your data, auditor customer. We don't have your data anymore. So filling a way to transport the data. And there just one last thought, right? They have a super interesting asset. They want break lands for the venerable map R right. Which wrote their own storage drivers and gives you the chance to potentially do something in that area, which I'm personally excited about. But we'll see what happens. >>I mean, I think the holy grail is can I, can I put my data into a cloud who's ever, you know, call it a super cloud and can I, is it secure? Is it governed? Can I share it and be confident that it's discoverable and that the, the person I give it to has the right to use it. Yeah. And, and it's the correct data. There's not like a zillion copies running. That's the holy grail. And I, I think the answer today is no, you can, you can do that maybe inside of AWS or maybe inside of Azure, look maybe certainly inside of snowflake, can you do that inside a GreenLake? Well, you probably can inside a GreenLake, but then when you put it into the cloud, is it cross cloud? Is it really out to the edge? And that's where it starts to break down, but that's where the work is to be done. That's >>The one Exide is in there already. Right. So men being men. Yeah. >>But okay. But it it's in there. Yeah. Okay. What do you do with it? Can you share that data? What can you actually automate governance? Right? Uh, is that data discoverable? Are there multiple copies of that data? What's the, you know, master copy. Here's >>A question. You guys, here's a question for you guys analyst, what do you think the psychology is of the CIO or CSO when HP comes into town with GreenLake, uh, and they say, what's your relationship with the hyperscalers? Cause I'm a CIO. I got my environment. I might be CapEx centric or Hey, I'm open model. Open-minded to an operating model. Every one of these enterprises has a cloud relationship. Yeah. Yeah. What's the dynamic. What do you think the psychology is of the CIO when they're rationalizing their, their trajectory, their architecture, cloud, native scale integration with HPE GreenLake or >>HP service. I think she or he hears defensiveness from HPE. I think she hears HPE or he hears HPE coming in and saying, you don't need to go to the cloud. You know, you could keep it right here. I, I don't think that's the right posture. I think it should be. We are your cloud. And we can manage whether it's OnPrem hybrid in AWS, Azure, Google, across those clouds. And we have an edge story that should be the vision that they put forth. That's the super cloud vision, but I don't hear it >>From these guys. What do you think psycho, do you agree with that? >>I'm totally to make, sorry to be boring, but I totally agree with, uh, Dave on that. Right? So the, the, the multi-cloud capability from a trusted large company has worked for anybody up and down the stack. Right? You can look historically for, uh, past layers with cloud Foundry, right? It's history vulnerable. You can look for DevOps of Hashi coop. You can look for database with MongoDB right now. So if HPE provides that data access, right, with all the problems of data gravity and egres cost and the workability, they will be doing really, really well, but we need to hear it more, right. We didn't hear much software today in the keynote. Right. >>Do they have a competitive offering vis-a-vis or Azure? >>The question is, will it be an HPE offering or will, or the software platform, one of the offerings and you as customer can plug and play, right. Will software be a differentiator for HP, right. And will be close, proprietary to the point to again, be open enough for it, or will they get that R and D format that, or will they just say, okay, ES MES here on the side, your choice, and you can use OpenShift or whatever, we don't matter. That's >>The, that's the key question. That's the key question. Is it because it is a competitive strategy? Is it highly differentiated? Oracle is a highly differentiated strategy, right? Is Dell highly differentiated? Eh, Dell differentiates based on its breadth. What? >>Right. Well, let's try for the control plane too. Dell wants to be an, >>Their, their vision is differentiated. Okay. But their execution today is not >>High. All right. Let me throw, let me throw this out at you then. I'm I'm, I'm sorry. I'm I'm HPE. I wanna be the glue layer. Is that, does that fly? >>What >>Do you mean? The group glue layer? I'll I wanna be, you can do Amazon, but I wanna be the glue layer between the clouds and our GreenLake will. >>What's the, what's the incremental value that, that glue provides, >>Provides comfort and reliability and control for the single pane of glass for AWS >>And comes back to the data. In my opinion. Yeah. >>There, there there's glue levels on the data level. Yeah. And there's glue levels on API level. Right. And there's different vendors in the different spaces. Right. Um, I think HPE will want to play on the data side. We heard lots of data stuff. We >>Hear that, >>But we have to see it. Exactly. >>Yeah. But it's, it's lacking today. And so, Hey, you know, you guys know better than I APIs can be fragile and they can be, there's a lot of diversity in terms of the quality of APIs and the documentation, how they work, how mature they are, what, how, what kind of performance they can provide and recoverability. And so just saying, oh wow. We are living the API economy. You know, the it's gonna take time to brew, chime in here. Hi. >><laugh> oh, so guys, you've all been covering HPE for a long time. You know, when Antonio stood up on stage three years ago and said by 2022, and here we are, we're gonna be delivering everything as a service. He's saying we've, we've done it, but, and we're a new company. Do you guys agree with that? >>Definitely. >>I, yes. Yes. With the caveat, I think, yes. The COVID pandemic slowed them down a lot because, um, that gave a tailwind to the hyperscalers, um, because of the, the force of massive O under forecasting working at home. I mean, everyone I talked to was like, no one forecasted a hundred percent work at home, the, um, the CapEx investments. So I think that was an opportunity that they'd be much farther along if there's no COVID people >>Thought it wasn't impossible. Yeah. But so we had the old work from home thing right. Where people trying to get people fired at IBM and Yahoo. Right. So I would've this question covering the HR side and my other hat on. Right. And I would ask CHS let's assume, because I didn't know about COVID shame on me. Right. I said, big California, earthquake breaks. Right. Nobody gets hurt, but all the buildings have to be retrofitted and checked for seism logic down. So everybody's working from home, ask CHS, what kind of productivity gap hit would you get by forcing everybody working from home with the office unsafe? So one, one gentleman, I won't know him, his name, he said 20% and the other one's going ha you're smoking. It's 40 50%. We need to be in the office. We need to meet it first night. And now we went for this exercise. Luckily not with the California. Right. Well, through the price of COVID and we've seen what it can do to, to productivity well, >>The productivity, but also the impact. So like with all the, um, stories we've done over two years, the people that want came out ahead were the ones that had good cloud action. They were already in the cloud. So I, I think they're definitely in different company in the sense of they, I give 'em a pass. I think they're definitely a new company and I'm not gonna judge 'em on. I think they're doing great. But I think pandemic definitely slowed 'em down that about >>It. So I have a different take on this. I think. So we've go back a little history. I mean, you' said this, I steal your line. Meg Whitman took one for the Silicon valley team. Right. She came in. I don't think she ever was excited that I, that you said, you said that, and I think you wrote >>Up, get tape on that one. She >>Had to figure out how do I deal with this mess? I have EDS. I got PC. >>She never should have spun off the PC, but >>Okay. But >>Me, >>Yeah, you can, you certainly could listen. Maybe, maybe Gerstner never should have gone all in on services and IBM would dominate something other than mainframes. They had think pads even for a while, but, but, but so she had that mess to deal with. She dealt with it and however, they dealt with it, Antonio came in, he, he, and he said, all right, we're gonna focus the company. And we're gonna focus the mission on not the machine. Remember those yeah. Presentations, but you just make your eyes glaze over. We're going all in on Azure service >>And edge. He was all on. >>We're gonna build our own cloud. We acquired Aruba. He made some acquisitions in HPC to help differentiate. Yep. And they are definitely a much more focused company now. And unfortunately I wish Antonio would CEO in 2015, cuz that's really when this should have started. >>Yeah. And then, and if you remember back then, Dave, we were interviewing Docker with DevOps teams. They had composability, they were on hybrid really early. I think they might have even coined the term hybrid before VMware tri-state credit for it. But they were first on hybrid. They had DevOps, they had infrastructure risk code. >>HPE had an HP had an awesome cloud team. Yeah. But, and then, and then they tried to go public cloud. Yeah. You know, and then, you know, just made them, I mean, it was just a mess. The focus >>Is there. I give them huge props. And I think, I think the GreenLake to me is exciting here because it's much better than it was two years ago. When, when we talked to, when we started, it's >>Starting to get real. >>It's, it's a real thing. And I think the, the tell will be partners. If they make that right, can pull their different >>Ecosystem, >>Their scale and their customers and fill the software gas with partners mm-hmm <affirmative> and then create that integration opportunity. It's gonna be a home run if they don't do that, they're gonna miss the operating, >>But they have to have their own to your point. They have to have their own software innovation. >>They have to good infrastructure ways to build applications. I don't wanna build with somebody else. I don't wanna take a Microsoft stack on open source stack. I'm not sure if it's gonna work with HP. So they have to have an app dev answer. I absolutely agree with that. And the, the big thing for the partners is, which is a good thing, right? Yep. HPE will not move into applications. Right? You don't have to have the fear of where Microsoft is with their vocal large. Right. If AWS kind of like comes up with APIs and manufacturing, right. Google the same thing with their vertical push. Right. So HPE will not have the CapEx, but >>Application, >>As I SV making them, the partner, the bonus of being able to on premise is an attractive >>Part. That's a great point. >>Hold. So that's an inflection point for next 12 months to watch what we see absolutely running on GreenLake. >>Yeah. And I think one of the things that came out of the, the last couple events this past year, and I'll bring this up, we'll table it and we'll watch it. And it's early in this, I think this is like even, not even the first inning, the machine learning AI impact to the industrial piece. I think we're gonna see a, a brand new era of accelerated digital transformation on the industrial physical world, back office, cloud data center, accounting, all the stuff. That's applications, the app, the real world from space to like robotics. I think that HP edge opportunity is gonna be visible and different. >>So guys, Antonio Neri is on tomorrow. This is only day one. If you can imagine this power panel on day one, can you imagine tomorrow? What is your last question for each of you? What is your, what, what question would you want to ask him tomorrow? Hold start with you. >>How is HPE winning in the long run? Because we know their on premise market will shrink, right? And they can out execute Dell. They can out execute Lenovo. They can out Cisco and get a bigger share of the shrinking market. But that's the long term strategy, right? So why should I buy HPE stock now and have a good return put in the, in the safe and forget about it and have a great return 20 years from now? What's the really long term strategy might be unfair because they, they ran in survival mode to a certain point out of the mass post equipment situation. But what is really the long term strategy? Is it more on the hardware side? Is it gonna go on the HPE, the frontier side? It's gonna be a DNA question, which I would ask Antonio. >>John, >>I would ask him what relative to the macro conditions relative to their customer base, I'd say, cuz the customers are the scoreboard. Can they create a value proposition with their, I use the Microsoft 365 example how they kind of went to the cloud. So my question would be Antonio, what is your core value proposition to CIOs out there who want to transform and take a step function, increase for value with HPE? Tell me that story. I wanna hear. And I don't want to hear, oh, we got a portfolio and no, what value are you enabling your customers to do? >>What and what should that value be? >>I think it's gonna be what we were kind of riffing on, which is you have to provide either what their product market fit needs are, which is, are you solving a problem? Is it a pain point is a growth driver. Uh, and what's the, what's that tailwind. And it's obviously we know at cloud we know edge. The story is great, but what's the value proposition. But by going with HPE, you get X, Y, and Z. If they can explain that clearly with real, so qualitative and quantitative data it's home >>Run. He had a great line of the analyst summit today where somebody asking questions, I'm just listening to the customer. So be ready for this Steve jobs photo, listening to the customer. You can't build something great listening to the customer. You'll be good for the next quarter. The next exponential >>Say, what are the customers saying? <laugh> >>So I would make an observation. And my question would, so my observation would be cloud is growing collectively at 35%. It's, you know, it's approaching 200 billion with a big, big four. If you include Alibaba, IBM has actually said, Hey, we're gonna gr they've promised 6% growth. Uh, Cisco I think is at eight or 9% growth. Dow's growing in double digits. Antonio and HPE have promised three to 4% growth. So what do you have to do to actually accelerate growth? Because three to 4%, my view, not enough to answer Holger's question is why should I buy HPE stock? Well, >>If they have product, if they have customer and there's demand and traction to me, that's going to drive the growth numbers. And I think the weak side of the forecast means that they don't have that fit yet. >>Yeah. So what has to happen for them to get above five, 6% growth? >>That's what we're gonna analyze. I mean, I, I mean, I don't have an answer for that. I wish I had a better answer. I'd tell them <laugh> but I feel, it feels, it feels like, you know, HP has an opportunity to say here's the new HPE. Yeah. Okay. And this is what we stand for. And here's the one thing that we're going to do that consistently drives value for you, the customer. And that's gonna have to come into some, either architectural cloud shift or a data thing, or we are your store for blank. >>All of the above. >>I guess the other question is, would, would you know, he won't answer a rude question, would suspending things like dividends and stock buybacks and putting it into R and D. I would definitely, if you have confidence in the market and you know what to do, why wouldn't you just accelerate R and D and put the money there? IBM, since 2007, IBM spent is the last stat. And I'm looking go in 2007, IBM way, outspent, Google, and Amazon and R and D and, and CapEx two, by the way. Yep. Subsequent to that, they've spent, I believe it's the numbers close to 200 billion on stock buyback and dividends. They could have owned cloud. And so look at this business, the technology business by and large is driven by innovation. Yeah. And so how do you innovate if >>You have I'm buying, I'm buying HP because they're reliable high quality and they have the outcomes that I want. Oh, >>Buy their products and services. I'm not sure I'd buy the stock. Yeah. >>Yeah. But she has to answer ultimately, because a public company. Right. So >>Right. It's this job. Yeah. >>Never a dull moment with the three of you around <laugh> guys. Thank you so much for sharing your insights, your, an analysis from day one. I can't imagine what day two is gonna bring tomorrow. Debut and I are gonna be anchoring here. We've got a jam packed day, lots going on, hearing from the ecosystem from leadership. As we mentioned, Antonio is gonna be Tony >>Alma Russo. I'm dying. Dr. >>EDMA as well as on the CTO gonna be another action pack day. I'm excited for it, guys. Thanks so much for sharing your insights and for letting me join this power panel. >>Great. Great to be here. >>Power panel plus me. All right. For Holger, John and Dave, I'm Lisa, you're watching the cube our day one coverage of HPE discover wraps right now. Don't go anywhere, cuz we'll see you tomorrow for day two, live from Vegas, have a good night.

(upbeat music) >> Once a staid and boring business dominated by IBM, Oracle, and at the time newcomer Microsoft, along with a handful of wannabes, the database business has exploded in the past decade and has become a staple of financial excellence, customer experience, analytic advantage, competitive strategy, growth initiatives, visualizations, not to mention compliance, security, privacy and dozens of other important use cases and initiatives. And on the vendor's side of the house, we've seen the rapid ascendancy of cloud databases. Most notably from Snowflake, whose massive raises leading up to its IPO in late 2020 sparked a spate of interest and VC investment in the separation of compute and storage and all that elastic resource stuff in the cloud. The company joined AWS, Azure and Google to popularize cloud databases, which have become a linchpin of competitive strategies for technology suppliers. And if I get you to put your data in my database and in my cloud, and I keep innovating, I'm going to build a moat and achieve a hugely attractive lifetime customer value in a really amazing marginal economics dynamic that is going to fund my future. And I'll be able to sell other adjacent services, not just compute and storage, but machine learning and inference and training and all kinds of stuff, dozens of lucrative cloud offerings. Meanwhile, the database leader, Oracle has invested massive amounts of money to maintain its lead. It's building on its position as the king of mission critical workloads and making typical Oracle like claims against the competition. Most were recently just yesterday with another announcement around MySQL HeatWave. An extension of MySQL that is compatible with on-premises MySQLs and is setting new standards in price performance. We're seeing a dramatic divergence in strategies across the database spectrum. On the far left, we see Amazon with more than a dozen database offerings each with its own API and primitives. AWS is taking a right tool for the right job approach, often building on open source platforms and creating services that it offers to customers to solve very specific problems for developers. And on the other side of the line, we see Oracle, which is taking the Swiss Army Knife approach, converging database functionality, enabling analytic and transactional workloads to run in the same data store, eliminating the need to ETL, at the same time adding capabilities into its platform like automation and machine learning. Welcome to this database Power Panel. My name is Dave Vellante, and I'm so excited to bring together some of the most respected industry analyst in the community. Today we're going to assess what's happening in the market. We're going to dig into the competitive landscape and explore the future of database and database platforms and decode what it means to customers. Let me take a moment to welcome our guest analyst today. Matt Kimball is a vice president and principal analysts at Moor Insights and Strategy, Matt. He knows products, he knows industry, he's got real world IT expertise, and he's got all the angles 25 plus years of experience in all kinds of great background. Matt, welcome. Thanks very much for coming on theCUBE. Holgar Mueller, friend of theCUBE, vice president and principal analyst at Constellation Research in depth knowledge on applications, application development, knows developers. He's worked at SAP and Oracle. And then Bob Evans is Chief Content Officer and co-founder of the Acceleration Economy, founder and principle of Cloud Wars. Covers all kinds of industry topics and great insights. He's got awesome videos, these three minute hits. If you haven't seen 'em, checking them out, knows cloud companies, his Cloud Wars minutes are fantastic. And then of course, Marc Staimer is the founder of Dragon Slayer Research. A frequent contributor and guest analyst at Wikibon. He's got a wide ranging knowledge across IT products, knows technology really well, can go deep. And then of course, Ron Westfall, Senior Analyst and Director Research Director at Futurum Research, great all around product trends knowledge. Can take, you know, technical dives and really understands competitive angles, knows Redshift, Snowflake, and many others. Gents, thanks so much for taking the time to join us in theCube today. It's great to have you on, good to see you. >> Good to be here, thanks for having us. >> Thanks, Dave. >> All right, let's start with an around the horn and briefly, if each of you would describe, you know, anything I missed in your areas of expertise and then you answer the following question, how would you describe the state of the database, state of platform market today? Matt Kimball, please start. >> Oh, I hate going first, but that it's okay. How would I describe the world today? I would just in one sentence, I would say, I'm glad I'm not in IT anymore, right? So, you know, it is a complex and dangerous world out there. And I don't envy IT folks I'd have to support, you know, these modernization and transformation efforts that are going on within the enterprise. It used to be, you mentioned it, Dave, you would argue about IBM versus Oracle versus this newcomer in the database space called Microsoft. And don't forget Sybase back in the day, but you know, now it's not just, which SQL vendor am I going to go with? It's all of these different, divergent data types that have to be taken, they have to be merged together, synthesized. And somehow I have to do that cleanly and use this to drive strategic decisions for my business. That is not easy. So, you know, you have to look at it from the perspective of the business user. It's great for them because as a DevOps person, or as an analyst, I have so much flexibility and I have this thing called the cloud now where I can go get services immediately. As an IT person or a DBA, I am calling up prevention hotlines 24 hours a day, because I don't know how I'm going to be able to support the business. And as an Oracle or as an Oracle or a Microsoft or some of the cloud providers and cloud databases out there, I'm licking my chops because, you know, my market is expanding and expanding every day. >> Great, thank you for that, Matt. Holgar, how do you see the world these days? You always have a good perspective on things, share with us. >> Well, I think it's the best time to be in IT, I'm not sure what Matt is talking about. (laughing) It's easier than ever, right? The direction is going to cloud. Kubernetes has won, Google has the best AI for now, right? So things are easier than ever before. You made commitments for five plus years on hardware, networking and so on premise, and I got gray hair about worrying it was the wrong decision. No, just kidding. But you kind of both sides, just to be controversial, make it interesting, right. So yeah, no, I think the interesting thing specifically with databases, right? We have this big suite versus best of breed, right? Obviously innovation, like you mentioned with Snowflake and others happening in the cloud, the cloud vendors server, where to save of their databases. And then we have one of the few survivors of the old guard as Evans likes to call them is Oracle who's doing well, both their traditional database. And now, which is really interesting, remarkable from that because Oracle it was always the power of one, have one database, add more to it, make it what I call the universal database. And now this new HeatWave offering is coming and MySQL open source side. So they're getting the second (indistinct) right? So it's interesting that older players, traditional players who still are in the market are diversifying their offerings. Something we don't see so much from the traditional tools from Oracle on the Microsoft side or the IBM side these days. >> Great, thank you Holgar. Bob Evans, you've covered this business for a while. You've worked at, you know, a number of different outlets and companies and you cover the competition, how do you see things? >> Dave, you know, the other angle to look at this from is from the customer side, right? You got now CEOs who are any sort of business across all sorts of industries, and they understand that their future success is going to be dependent on their ability to become a digital company, to understand data, to use it the right way. So as you outline Dave, I think in your intro there, it is a fantastic time to be in the database business. And I think we've got a lot of new buyers and influencers coming in. They don't know all this history about IBM and Microsoft and Oracle and you know, whoever else. So I think they're going to take a long, hard look, Dave, at some of these results and who is able to help these companies not serve up the best technology, but who's going to be able to help their business move into the digital future. So it's a fascinating time now from every perspective. >> Great points, Bob. I mean, digital transformation has gone from buzzword to imperative. Mr. Staimer, how do you see things? >> I see things a little bit differently than my peers here in that I see the database market being segmented. There's all the different kinds of databases that people are looking at for different kinds of data, and then there is databases in the cloud. And so database as cloud service, I view very differently than databases because the traditional way of implementing a database is changing and it's changing rapidly. So one of the premises that you stated earlier on was that you viewed Oracle as a database company. I don't view Oracle as a database company anymore. I view Oracle as a cloud company that happens to have a significant expertise and specialty in databases, and they still sell database software in the traditional way, but ultimately they're a cloud company. So database cloud services from my point of view is a very distinct market from databases. >> Okay, well, you gave us some good meat on the bone to talk about that. Last but not least-- >> Dave did Marc, just say Oracle's a cloud company? >> Yeah. (laughing) Take away the database, it would be interesting to have that discussion, but let's let Ron jump in here. Ron, give us your take. >> That's a great segue. I think it's truly the era of the cloud database, that's something that's rising. And the key trends that come with it include for example, elastic scaling. That is the ability to scale on demand, to right size workloads according to customer requirements. And also I think it's going to increase the prioritization for high availability. That is the player who can provide the highest availability is going to have, I think, a great deal of success in this emerging market. And also I anticipate that there will be more consolidation across platforms in order to enable cost savings for customers, and that's something that's always going to be important. And I think we'll see more of that over the horizon. And then finally security, security will be more important than ever. We've seen a spike (indistinct), we certainly have seen geopolitical originated cybersecurity concerns. And as a result, I see database security becoming all the more important. >> Great, thank you. Okay, let me share some data with you guys. I'm going to throw this at you and see what you think. We have this awesome data partner called Enterprise Technology Research, ETR. They do these quarterly surveys and each period with dozens of industry segments, they track clients spending, customer spending. And this is the database, data warehouse sector okay so it's taxonomy, so it's not perfect, but it's a big kind of chunk. They essentially ask customers within a category and buy a specific vendor, you're spending more or less on the platform? And then they subtract the lesses from the mores and they derive a metric called net score. It's like NPS, it's a measure of spending velocity. It's more complicated and granular than that, but that's the basis and that's the vertical axis. The horizontal axis is what they call market share, it's not like IDC market share, it's just pervasiveness in the data set. And so there are a couple of things that stand out here and that we can use as reference point. The first is the momentum of Snowflake. They've been off the charts for many, many, for over two years now, anything above that dotted red line, that 40%, is considered by ETR to be highly elevated and Snowflake's even way above that. And I think it's probably not sustainable. We're going to see in the next April survey, next month from those guys, when it comes out. And then you see AWS and Microsoft, they're really pervasive on the horizontal axis and highly elevated, Google falls behind them. And then you got a number of well funded players. You got Cockroach Labs, Mongo, Redis, MariaDB, which of course is a fork on MySQL started almost as protest at Oracle when they acquired Sun and they got MySQL and you can see the number of others. Now Oracle who's the leading database player, despite what Marc Staimer says, we know, (laughs) and they're a cloud player (laughing) who happens to be a leading database player. They dominate in the mission critical space, we know that they're the king of that sector, but you can see here that they're kind of legacy, right? They've been around a long time, they get a big install base. So they don't have the spending momentum on the vertical axis. Now remember this is, just really this doesn't capture spending levels, so that understates Oracle but nonetheless. So it's not a complete picture like SAP for instance is not in here, no Hana. I think people are actually buying it, but it doesn't show up here, (laughs) but it does give an indication of momentum and presence. So Bob Evans, I'm going to start with you. You've commented on many of these companies, you know, what does this data tell you? >> Yeah, you know, Dave, I think all these compilations of things like that are interesting, and that folks at ETR do some good work, but I think as you said, it's a snapshot sort of a two-dimensional thing of a rapidly changing, three dimensional world. You know, the incidents at which some of these companies are mentioned versus the volume that happens. I think it's, you know, with Oracle and I'm not going to declare my religious affiliation, either as cloud company or database company, you know, they're all of those things and more, and I think some of our old language of how we classify companies is just not relevant anymore. But I want to ask too something in here, the autonomous database from Oracle, nobody else has done that. So either Oracle is crazy, they've tried out a technology that nobody other than them is interested in, or they're onto something that nobody else can match. So to me, Dave, within Oracle, trying to identify how they're doing there, I would watch autonomous database growth too, because right, it's either going to be a big plan and it breaks through, or it's going to be caught behind. And the Snowflake phenomenon as you mentioned, that is a rare, rare bird who comes up and can grow 100% at a billion dollar revenue level like that. So now they've had a chance to come in, scare the crap out of everybody, rock the market with something totally new, the data cloud. Will the bigger companies be able to catch up and offer a compelling alternative, or is Snowflake going to continue to be this outlier. It's a fascinating time. >> Really, interesting points there. Holgar, I want to ask you, I mean, I've talked to certainly I'm sure you guys have too, the founders of Snowflake that came out of Oracle and they actually, they don't apologize. They say, "Hey, we not going to do all that complicated stuff that Oracle does, we were trying to keep it real simple." But at the same time, you know, they don't do sophisticated workload management. They don't do complex joints. They're kind of relying on the ecosystems. So when you look at the data like this and the various momentums, and we talked about the diverging strategies, what does this say to you? >> Well, it is a great point. And I think Snowflake is an example how the cloud can turbo charge a well understood concept in this case, the data warehouse, right? You move that and you find steroids and you see like for some players who've been big in data warehouse, like Sentara Data, as an example, here in San Diego, what could have been for them right in that part. The interesting thing, the problem though is the cloud hides a lot of complexity too, which you can scale really well as you attract lots of customers to go there. And you don't have to build things like what Bob said, right? One of the fascinating things, right, nobody's answering Oracle on the autonomous database. I don't think is that they cannot, they just have different priorities or the database is not such a priority. I would dare to say that it's for IBM and Microsoft right now at the moment. And the cloud vendors, you just hide that right through scripts and through scale because you support thousands of customers and you can deal with a little more complexity, right? It's not against them. Whereas if you have to run it yourself, very different story, right? You want to have the autonomous parts, you want to have the powerful tools to do things. >> Thank you. And so Matt, I want to go to you, you've set up front, you know, it's just complicated if you're in IT, it's a complicated situation and you've been on the customer side. And if you're a buyer, it's obviously, it's like Holgar said, "Cloud's supposed to make this stuff easier, but the simpler it gets the more complicated gets." So where do you place your bets? Or I guess more importantly, how do you decide where to place your bets? >> Yeah, it's a good question. And to what Bob and Holgar said, you know, the around autonomous database, I think, you know, part of, as I, you know, play kind of armchair psychologist, if you will, corporate psychologists, I look at what Oracle is doing and, you know, databases where they've made their mark and it's kind of, that's their strong position, right? So it makes sense if you're making an entry into this cloud and you really want to kind of build momentum, you go with what you're good at, right? So that's kind of the strength of Oracle. Let's put a lot of focus on that. They do a lot more than database, don't get me wrong, but you know, I'm going to short my strength and then kind of pivot from there. With regards to, you know, what IT looks at and what I would look at you know as an IT director or somebody who is, you know, trying to consume services from these different cloud providers. First and foremost, I go with what I know, right? Let's not forget IT is a conservative group. And when we look at, you know, all the different permutations of database types out there, SQL, NoSQL, all the different types of NoSQL, those are largely being deployed by business users that are looking for agility or businesses that are looking for agility. You know, the reason why MongoDB is so popular is because of DevOps, right? It's a great platform to develop on and that's where it kind of gained its traction. But as an IT person, I want to go with what I know, where my muscle memory is, and that's my first position. And so as I evaluate different cloud service providers and cloud databases, I look for, you know, what I know and what I've invested in and where my muscle memory is. Is there enough there and do I have enough belief that that company or that service is going to be able to take me to, you know, where I see my organization in five years from a data management perspective, from a business perspective, are they going to be there? And if they are, then I'm a little bit more willing to make that investment, but it is, you know, if I'm kind of going in this blind or if I'm cloud native, you know, that's where the Snowflakes of the world become very attractive to me. >> Thank you. So Marc, I asked Andy Jackson in theCube one time, you have all these, you know, data stores and different APIs and primitives and you know, very granular, what's the strategy there? And he said, "Hey, that allows us as the market changes, it allows us to be more flexible. If we start building abstractions layers, it's harder for us." I think also it was not a good time to market advantage, but let me ask you, I described earlier on that spectrum from AWS to Oracle. We just saw yesterday, Oracle announced, I think the third major enhancement in like 15 months to MySQL HeatWave, what do you make of that announcement? How do you think it impacts the competitive landscape, particularly as it relates to, you know, converging transaction and analytics, eliminating ELT, I know you have some thoughts on this. >> So let me back up for a second and defend my cloud statement about Oracle for a moment. (laughing) AWS did a great job in developing the cloud market in general and everything in the cloud market. I mean, I give them lots of kudos on that. And a lot of what they did is they took open source software and they rent it to people who use their cloud. So I give 'em lots of credit, they dominate the market. Oracle was late to the cloud market. In fact, they actually poo-pooed it initially, if you look at some of Larry Ellison's statements, they said, "Oh, it's never going to take off." And then they did 180 turn, and they said, "Oh, we're going to embrace the cloud." And they really have, but when you're late to a market, you've got to be compelling. And this ties into the announcement yesterday, but let's deal with this compelling. To be compelling from a user point of view, you got to be twice as fast, offer twice as much functionality, at half the cost. That's generally what compelling is that you're going to capture market share from the leaders who established the market. It's very difficult to capture market share in a new market for yourself. And you're right. I mean, Bob was correct on this and Holgar and Matt in which you look at Oracle, and they did a great job of leveraging their database to move into this market, give 'em lots of kudos for that too. But yesterday they announced, as you said, the third innovation release and the pace is just amazing of what they're doing on these releases on HeatWave that ties together initially MySQL with an integrated builtin analytics engine, so a data warehouse built in. And then they added automation with autopilot, and now they've added machine learning to it, and it's all in the same service. It's not something you can buy and put on your premise unless you buy their cloud customers stuff. But generally it's a cloud offering, so it's compellingly better as far as the integration. You don't buy multiple services, you buy one and it's lower cost than any of the other services, but more importantly, it's faster, which again, give 'em credit for, they have more integration of a product. They can tie things together in a way that nobody else does. There's no additional services, ETL services like Glue and AWS. So from that perspective, they're getting better performance, fewer services, lower cost. Hmm, they're aiming at the compelling side again. So from a customer point of view it's compelling. Matt, you wanted to say something there. >> Yeah, I want to kind of, on what you just said there Marc, and this is something I've found really interesting, you know. The traditional way that you look at software and, you know, purchasing software and IT is, you look at either best of breed solutions and you have to work on the backend to integrate them all and make them all work well. And generally, you know, the big hit against the, you know, we have one integrated offering is that, you lose capability or you lose depth of features, right. And to what you were saying, you know, that's the thing I found interesting about what Oracle is doing is they're building in depth as they kind of, you know, build that service. It's not like you're losing a lot of capabilities, because you're going to one integrated service versus having to use A versus B versus C, and I love that idea. >> You're right. Yeah, not only you're not losing, but you're gaining functionality that you can't get by integrating a lot of these. I mean, I can take Snowflake and integrate it in with machine learning, but I also have to integrate in with a transactional database. So I've got to have connectors between all of this, which means I'm adding time. And what it comes down to at the end of the day is expertise, effort, time, and cost. And so what I see the difference from the Oracle announcements is they're aiming at reducing all of that by increasing performance as well. Correct me if I'm wrong on that but that's what I saw at the announcement yesterday. >> You know, Marc, one thing though Marc, it's funny you say that because I started out saying, you know, I'm glad I'm not 19 anymore. And the reason is because of exactly what you said, it's almost like there's a pseudo level of witchcraft that's required to support the modern data environment right in the enterprise. And I need simpler faster, better. That's what I need, you know, I am no longer wearing pocket protectors. I have turned from, you know, break, fix kind of person, to you know, business consultant. And I need that point and click simplicity, but I can't sacrifice, you know, a depth of features of functionality on the backend as I play that consultancy role. >> So, Ron, I want to bring in Ron, you know, it's funny. So Matt, you mentioned Mongo, I often and say, if Oracle mentions you, you're on the map. We saw them yesterday Ron, (laughing) they hammered RedShifts auto ML, they took swipes at Snowflake, a little bit of BigQuery. What were your thoughts on that? Do you agree with what these guys are saying in terms of HeatWaves capabilities? >> Yes, Dave, I think that's an excellent question. And fundamentally I do agree. And the question is why, and I think it's important to know that all of the Oracle data is backed by the fact that they're using benchmarks. For example, all of the ML and all of the TPC benchmarks, including all the scripts, all the configs and all the detail are posted on GitHub. So anybody can look at these results and they're fully transparent and replicate themselves. If you don't agree with this data, then by all means challenge it. And we have not really seen that in all of the new updates in HeatWave over the last 15 months. And as a result, when it comes to these, you know, fundamentals in looking at the competitive landscape, which I think gives validity to outcomes such as Oracle being able to deliver 4.8 times better price performance than Redshift. As well as for example, 14.4 better price performance than Snowflake, and also 12.9 better price performance than BigQuery. And so that is, you know, looking at the quantitative side of things. But again, I think, you know, to Marc's point and to Matt's point, there are also qualitative aspects that clearly differentiate the Oracle proposition, from my perspective. For example now the MySQL HeatWave ML capabilities are native, they're built in, and they also support things such as completion criteria. And as a result, that enables them to show that hey, when you're using Redshift ML for example, you're having to also use their SageMaker tool and it's running on a meter. And so, you know, nobody really wants to be running on a meter when, you know, executing these incredibly complex tasks. And likewise, when it comes to Snowflake, they have to use a third party capability. They don't have the built in, it's not native. So the user, to the point that he's having to spend more time and it increases complexity to use auto ML capabilities across the Snowflake platform. And also, I think it also applies to other important features such as data sampling, for example, with the HeatWave ML, it's intelligent sampling that's being implemented. Whereas in contrast, we're seeing Redshift using random sampling. And again, Snowflake, you're having to use a third party library in order to achieve the same capabilities. So I think the differentiation is crystal clear. I think it definitely is refreshing. It's showing that this is where true value can be assigned. And if you don't agree with it, by all means challenge the data. >> Yeah, I want to come to the benchmarks in a minute. By the way, you know, the gentleman who's the Oracle's architect, he did a great job on the call yesterday explaining what you have to do. I thought that was quite impressive. But Bob, I know you follow the financials pretty closely and on the earnings call earlier this month, Ellison said that, "We're going to see HeatWave on AWS." And the skeptic in me said, oh, they must not be getting people to come to OCI. And then they, you remember this chart they showed yesterday that showed the growth of HeatWave on OCI. But of course there was no data on there, it was just sort of, you know, lines up and to the right. So what do you guys think of that? (Marc laughs) Does it signal Bob, desperation by Oracle that they can't get traction on OCI, or is it just really a smart tame expansion move? What do you think? >> Yeah, Dave, that's a great question. You know, along the way there, and you know, just inside of that was something that said Ellison said on earnings call that spoke to a different sort of philosophy or mindset, almost Marc, where he said, "We're going to make this multicloud," right? With a lot of their other cloud stuff, if you wanted to use any of Oracle's cloud software, you had to use Oracle's infrastructure, OCI, there was no other way out of it. But this one, but I thought it was a classic Ellison line. He said, "Well, we're making this available on AWS. We're making this available, you know, on Snowflake because we're going after those users. And once they see what can be done here." So he's looking at it, I guess you could say, it's a concession to customers because they want multi-cloud. The other way to look at it, it's a hunting expedition and it's one of those uniquely I think Oracle ways. He said up front, right, he doesn't say, "Well, there's a big market, there's a lot for everybody, we just want on our slice." Said, "No, we are going after Amazon, we're going after Redshift, we're going after Aurora. We're going after these users of Snowflake and so on." And I think it's really fairly refreshing these days to hear somebody say that, because now if I'm a buyer, I can look at that and say, you know, to Marc's point, "Do they measure up, do they crack that threshold ceiling? Or is this just going to be more pain than a few dollars savings is worth?" But you look at those numbers that Ron pointed out and that we all saw in that chart. I've never seen Dave, anything like that. In a substantive market, a new player coming in here, and being able to establish differences that are four, seven, eight, 10, 12 times better than competition. And as new buyers look at that, they're going to say, "What the hell are we doing paying, you know, five times more to get a poor result? What's going on here?" So I think this is going to rattle people and force a harder, closer look at what these alternatives are. >> I wonder if the guy, thank you. Let's just skip ahead of the benchmarks guys, bring up the next slide, let's skip ahead a little bit here, which talks to the benchmarks and the benchmarking if we can. You know, David Floyer, the sort of semiretired, you know, Wikibon analyst said, "Dave, this is going to force Amazon and others, Snowflake," he said, "To rethink actually how they architect databases." And this is kind of a compilation of some of the data that they shared. They went after Redshift mostly, (laughs) but also, you know, as I say, Snowflake, BigQuery. And, like I said, you can always tell which companies are doing well, 'cause Oracle will come after you, but they're on the radar here. (laughing) Holgar should we take this stuff seriously? I mean, or is it, you know, a grain salt? What are your thoughts here? >> I think you have to take it seriously. I mean, that's a great question, great point on that. Because like Ron said, "If there's a flaw in a benchmark, we know this database traditionally, right?" If anybody came up that, everybody will be, "Oh, you put the wrong benchmark, it wasn't audited right, let us do it again," and so on. We don't see this happening, right? So kudos to Oracle to be aggressive, differentiated, and seem to having impeccable benchmarks. But what we really see, I think in my view is that the classic and we can talk about this in 100 years, right? Is the suite versus best of breed, right? And the key question of the suite, because the suite's always slower, right? No matter at which level of the stack, you have the suite, then the best of breed that will come up with something new, use a cloud, put the data warehouse on steroids and so on. The important thing is that you have to assess as a buyer what is the speed of my suite vendor. And that's what you guys mentioned before as well, right? Marc said that and so on, "Like, this is a third release in one year of the HeatWave team, right?" So everybody in the database open source Marc, and there's so many MySQL spinoffs to certain point is put on shine on the speed of (indistinct) team, putting out fundamental changes. And the beauty of that is right, is so inherent to the Oracle value proposition. Larry's vision of building the IBM of the 21st century, right from the Silicon, from the chip all the way across the seven stacks to the click of the user. And that what makes the database what Rob was saying, "Tied to the OCI infrastructure," because designed for that, it runs uniquely better for that, that's why we see the cross connect to Microsoft. HeatWave so it's different, right? Because HeatWave runs on cheap hardware, right? Which is the breadth and butter 886 scale of any cloud provider, right? So Oracle probably needs it to scale OCI in a different category, not the expensive side, but also allow us to do what we said before, the multicloud capability, which ultimately CIOs really want, because data gravity is real, you want to operate where that is. If you have a fast, innovative offering, which gives you more functionality and the R and D speed is really impressive for the space, puts away bad results, then it's a good bet to look at. >> Yeah, so you're saying, that we versus best of breed. I just want to sort of play back then Marc a comment. That suite versus best of breed, there's always been that trade off. If I understand you Holgar you're saying that somehow Oracle has magically cut through that trade off and they're giving you the best of both. >> It's the developing velocity, right? The provision of important features, which matter to buyers of the suite vendor, eclipses the best of breed vendor, then the best of breed vendor is in the hell of a potential job. >> Yeah, go ahead Marc. >> Yeah and I want to add on what Holgar just said there. I mean the worst job in the data center is data movement, moving the data sucks. I don't care who you are, nobody likes it. You never get any kudos for doing it well, and you always get the ah craps, when things go wrong. So it's in- >> In the data center Marc all the time across data centers, across cloud. That's where the bleeding comes. >> It's right, you get beat up all the time. So nobody likes to move data, ever. So what you're looking at with what they announce with HeatWave and what I love about HeatWave is it doesn't matter when you started with it, you get all the additional features they announce it's part of the service, all the time. But they don't have to move any of the data. You want to analyze the data that's in your transactional, MySQL database, it's there. You want to do machine learning models, it's there, there's no data movement. The data movement is the key thing, and they just eliminate that, in so many ways. And the other thing I wanted to talk about is on the benchmarks. As great as those benchmarks are, they're really conservative 'cause they're underestimating the cost of that data movement. The ETLs, the other services, everything's left out. It's just comparing HeatWave, MySQL cloud service with HeatWave versus Redshift, not Redshift and Aurora and Glue, Redshift and Redshift ML and SageMaker, it's just Redshift. >> Yeah, so what you're saying is what Oracle's doing is saying, "Okay, we're going to run MySQL HeatWave benchmarks on analytics against Redshift, and then we're going to run 'em in transaction against Aurora." >> Right. >> But if you really had to look at what you would have to do with the ETL, you'd have to buy two different data stores and all the infrastructure around that, and that goes away so. >> Due to the nature of the competition, they're running narrow best of breed benchmarks. There is no suite level benchmark (Dave laughs) because they created something new. >> Well that's you're the earlier point they're beating best of breed with a suite. So that's, I guess to Floyer's earlier point, "That's going to shake things up." But I want to come back to Bob Evans, 'cause I want to tap your Cloud Wars mojo before we wrap. And line up the horses, you got AWS, you got Microsoft, Google and Oracle. Now they all own their own cloud. Snowflake, Mongo, Couchbase, Redis, Cockroach by the way they're all doing very well. They run in the cloud as do many others. I think you guys all saw the Andreessen, you know, commentary from Sarah Wang and company, to talk about the cost of goods sold impact of cloud. So owning your own cloud has to be an advantage because other guys like Snowflake have to pay cloud vendors and negotiate down versus having the whole enchilada, Safra Catz's dream. Bob, how do you think this is going to impact the market long term? >> Well, Dave, that's a great question about, you know, how this is all going to play out. If I could mention three things, one, Frank Slootman has done a fantastic job with Snowflake. Really good company before he got there, but since he's been there, the growth mindset, the discipline, the rigor and the phenomenon of what Snowflake has done has forced all these bigger companies to really accelerate what they're doing. And again, it's an example of how this intense competition makes all the different cloud vendors better and it provides enormous value to customers. Second thing I wanted to mention here was look at the Adam Selipsky effect at AWS, took over in the middle of May, and in Q2, Q3, Q4, AWS's growth rate accelerated. And in each of those three quotas, they grew faster than Microsoft's cloud, which has not happened in two or three years, so they're closing the gap on Microsoft. The third thing, Dave, in this, you know, incredibly intense competitive nature here, look at Larry Ellison, right? He's got his, you know, the product that for the last two or three years, he said, "It's going to help determine the future of the company, autonomous database." You would think he's the last person in the world who's going to bring in, you know, in some ways another database to think about there, but he has put, you know, his whole effort and energy behind this. The investments Oracle's made, he's riding this horse really hard. So it's not just a technology achievement, but it's also an investment priority for Oracle going forward. And I think it's going to form a lot of how they position themselves to this new breed of buyer with a new type of need and expectations from IT. So I just think the next two or three years are going to be fantastic for people who are lucky enough to get to do the sorts of things that we do. >> You know, it's a great point you made about AWS. Back in 2018 Q3, they were doing about 7.4 billion a quarter and they were growing in the mid forties. They dropped down to like 29% Q4, 2020, I'm looking at the data now. They popped back up last quarter, last reported quarter to 40%, that is 17.8 billion, so they more doubled and they accelerated their growth rate. (laughs) So maybe that pretends, people are concerned about Snowflake right now decelerating growth. You know, maybe that's going to be different. By the way, I think Snowflake has a different strategy, the whole data cloud thing, data sharing. They're not trying to necessarily take Oracle head on, which is going to make this next 10 years, really interesting. All right, we got to go, last question. 30 seconds or less, what can we expect from the future of data platforms? Matt, please start. >> I have to go first again? You're killing me, Dave. (laughing) In the next few years, I think you're going to see the major players continue to meet customers where they are, right. Every organization, every environment is, you know, kind of, we use these words bespoke in Snowflake, pardon the pun, but Snowflakes, right. But you know, they're all opinionated and unique and what's great as an IT person is, you know, there is a service for me regardless of where I am on my journey, in my data management journey. I think you're going to continue to see with regards specifically to Oracle, I think you're going to see the company continue along this path of being all things to all people, if you will, or all organizations without sacrificing, you know, kind of richness of features and sacrificing who they are, right. Look, they are the data kings, right? I mean, they've been a database leader for an awful long time. I don't see that going away any time soon and I love the innovative spirit they've brought in with HeatWave. >> All right, great thank you. Okay, 30 seconds, Holgar go. >> Yeah, I mean, the interesting thing that we see is really that trend to autonomous as Oracle calls or self-driving software, right? So the database will have to do more things than just store the data and support the DVA. It will have to show it can wide insights, the whole upside, it will be able to show to one machine learning. We haven't really talked about that. How in just exciting what kind of use case we can get of machine learning running real time on data as it changes, right? So, which is part of the E5 announcement, right? So we'll see more of that self-driving nature in the database space. And because you said we can promote it, right. Check out my report about HeatWave latest release where I post in oracle.com. >> Great, thank you for that. And Bob Evans, please. You're great at quick hits, hit us. >> Dave, thanks. I really enjoyed getting to hear everybody's opinion here today and I think what's going to happen too. I think there's a new generation of buyers, a new set of CXO influencers in here. And I think what Oracle's done with this, MySQL HeatWave, those benchmarks that Ron talked about so eloquently here that is going to become something that forces other companies, not just try to get incrementally better. I think we're going to see a massive new wave of innovation to try to play catch up. So I really take my hat off to Oracle's achievement from going to, push everybody to be better. >> Excellent. Marc Staimer, what do you say? >> Sure, I'm going to leverage off of something Matt said earlier, "Those companies that are going to develop faster, cheaper, simpler products that are going to solve customer problems, IT problems are the ones that are going to succeed, or the ones who are going to grow. The one who are just focused on the technology are going to fall by the wayside." So those who can solve more problems, do it more elegantly and do it for less money are going to do great. So Oracle's going down that path today, Snowflake's going down that path. They're trying to do more integration with third party, but as a result, aiming at that simpler, faster, cheaper mentality is where you're going to continue to see this market go. >> Amen brother Marc. >> Thank you, Ron Westfall, we'll give you the last word, bring us home. >> Well, thank you. And I'm loving it. I see a wave of innovation across the entire cloud database ecosystem and Oracle is fueling it. We are seeing it, with the native integration of auto ML capabilities, elastic scaling, lower entry price points, et cetera. And this is just going to be great news for buyers, but also developers and increased use of open APIs. And so I think that is really the key takeaways. Just we're going to see a lot of great innovation on the horizon here. >> Guys, fantastic insights, one of the best power panel as I've ever done. Love to have you back. Thanks so much for coming on today. >> Great job, Dave, thank you. >> All right, and thank you for watching. This is Dave Vellante for theCube and we'll see you next time. (soft music)

>>no real filter and that kind of stuff. But you're also an entrepreneur, right? And you know the business, you've been in software, you detect business. I'm instructing you get a lot of pictures, this entertainment business on our show, we're a bubble. We don't do a lot of tech deals that were talking because it's boring tv tech people love tech consumers love the benefit of text. No consumer opens up their iphone and says, oh my gosh, I love the technology behind my, what's it been like being on the shark tank? You know, filming is fun and hang out just fun and it's fun to be a celebrity at first your head gets really big and you get a really good tables at restaurants and who says texas has got a little possessed more skin in the game today in charge of his destiny. Great robert Herjavec. No, these two stars cube alumni >>welcome back to the cubes coverage of A W. S. Public sector seven. I'm john for your host of the cube got a great segment here on healthcare startup accelerators of course. Sandy carter is co hosting media. This one Vice President Aws. She's awesome on the cuBA and jennifer Blumenthal co founder and C of one record entrepreneur, very successful. Thanks for coming on jennifer. Thank good to see you. Sandy thanks for joining me again. You >>are most welcome, >>jennifer. Before we get into the whole accelerated dynamic, just take a minute to explain what you guys do. One record. >>Sure. So one record is a digital health company that enables users to access aggregate and share their healthcare information. So what that means is we help you as a person get your data and then we also help companies who would like to have workflows were consumers in the loop to get their data. So whether they're sharing it with a provider, researcher payer. >>So, Sandy, we've talked about this amazon web services, healthcare accelerator cohort batches. What do you call cohort batches? Cohorts explain what's going on with the healthcare accelerator? >>Yeah. So, um, we decided that we would launch and partner an accelerator program and accelerator program just provides to a start up a little bit extra technical help. A little bit extra subject matter expertise and introductions to funders. And so we decided we were going to start one for health care. It's one of the biggest disruptive industries in public sector. Um, and so we weren't sure how it's gonna go. We partnered with Kids X. Kids X is part of the Los Angeles system for medical. And so we put out a call for startups and we had 427 startups, we were told on average and accelerating it's 50-100. So we were blown away 31 different countries. So it was really amazing. And then what we've been doing is down selecting and selecting that Top 10 for our first cohort. So we're going from 427 down to 10. And so obviously we looked at the founders themselves to see the quality of the leadership of the company, um the strength of their technology and the fit of the technology into the broader overall healthcare and healthcare ecosystem. And so we were thrilled that jennifer and one record was one of the top 10 start ups in this space that we chose to be in the, in the cohort. And so now we're going to take it to the six weeks intensive where we'll do training, helping them with AWS, provide them A W. S. Credits and then Kid X will also provide some of the health care uh subject matter >>expertise as well. Can I get some of those credits over here to maybe? >>Yes, you can actually, you can talk to me don you can't >>Talk to me, Jennifer, I gotta ask you. So you're an entrepreneur. So doing start doing cos it's like a roller coaster. So now to make the top 10 but also be in the area of his accelerator, it's a partnership, right? You're making a bet. What's your take on all this? >>Well, we've always been partners with a W. S. We started building on AWS in the very beginning. So when I was setting up the company a huge decision early on with infrastructure and when I saw the launch of the accelerator, I had to apply because we're at the point in the company that we're growing and part of growing is growing with the VW. So I was really excited to take advantage of that opportunity and now in the accelerator, it's more of thinking about things that we weren't thinking about the services that we can leverage to fill in the gaps within our platform so we can meet our customers where they are >>using award winning MSP cloud status city, your partners, great relationship with the ecosystem. So congratulations Sandi. What's the disruption for the healthcare? Because right now education and health care, the two top areas we're seeing and we're reporting on where cloud scale developed two point or whatever buzzword digital transformation you want to use is impacting heavily healthcare industry. There's some new realities. What's your, what's your vision, what's your view? >>Hey john before she does that, I have to give a plug to Claudius city because they just made premier partner as well, which is a huge deal. Uh and they're also serving public sector. So I just wanted to make sure that you knew that too. So you can congratulate. Go ahead, jennifer >>Well, so if I zoom in, I think about a P. I. S. Every day, that's what I think about and I think about microservices. So for me and for one record, what we think about is legislation. So 21st century Cures act says that you as a consumer have to be able to access your healthcare data from both your providers and from your players and not just your providers, but also the underlying technology vendors and H. I. E. S. H. I am and it's probably gonna extend to really anyone who plays within the healthcare ecosystem. So you're just going to see this explosion of A. P. I. S. And we're just your one of that. I mean for the payers that we went into effect on july 1st. So I mean when you think about the decentralization of healthcare where healthcare is being delivered plus an api economy, you're just going to have a whole new model developing and then throwing price transparency and you've got a whole new cake. >>I'm smiling because I love the peacocks. In fact, last night I shouldn't have tweeted this but there's a little tweet flames going on around A. P. Is being brittle and all this stuff and I said, hey developer experience about building great software apps are there for you. It's not a glue layer by itself. You got to build software around the so kind of a little preaching to the younger generation. But this health care thing is huge because think about like old school health care, it was anti ap I was also siloed. So what's your take on has the culture is changing health care because the user experience, I want my records, I want my privacy, I want to maintain everything confidential but access. That's hard. >>I think well health care to be used to just be paper was forget about a. P. I. Is it was just paper records. I think uh to me you think about uh patient journey, like a patient journey starts with booking an appointment and then everything after that is essentially an api call. So that's how I think about it is to all these micro transactions that are happening all the time and you want your data to go to your health care provider so they can give you the proper care, you want your data to go to your pair so they can pay for your care and then those two stakeholders want your data so that they can provide the right services at the right time to the right channel. And that is just a series of api calls that literally sits on a platform. >>What's interesting, I'd love to get your take on the where you think the progress bar is in the industry because Fintech has shown the way you got defy now behind a decentralized finance, health care seems to be moving on in a very accelerated rate towards that kind of concept of cloud, scale, decentralization, privacy. >>Yeah, I mean, that's a big question, what's interesting to me around that is how healthcare stakeholders are thinking about where they're providing care. So as they're buying up practices primary care specialty care and they're moving more and more outside of the brick and mortar of the health care system or partnering with your startups. That's really where I think you're going to see a larger ecosystem development, you could just look at CVS and walmart or the dollar store if they're going to be moving into health care, what does that look like? And then if you're seeking care in those settings, but then you're going to Mayo clinic or Kaiser permanente, there's so many new relationships that are part of your hair circle >>delivery is just what does that even mean now, delivery of health >>care. It's wherever you it's like the app economy you want to ride right now, you want a doctor right now, that's where we're heading its ease of use. >>This is this exciting startups, changing the game. Yes, I love it. I mean, this is what it's all about this health >>Care, this is what it's all about. And if you look at the funding right now from VCS, we're seeing so much funding pour into health care, we were just looking at some numbers and in the second quarter alone, the funding went up almost 700%. And the amount of funding that is pouring into companies like jennifer's company to really transform healthcare, 30% of it is going into telehealth. So when you talked about, you know, kind of ai at the edge, getting the right doctor the right expert at the right time, we're seeing that as a big trend in healthcare to >>well jennifer, I think the funding dynamics aside the opportunity for market total addressable market is massive when the application is being decomposed, you got front end, whether it's telemedicine, you got the different building blocks of healthcare being radically reconfigured. It's a re factoring of healthcare. Yeah, >>I think if you just think about where we're sitting today, you had to use an app to prove proof of vaccination. So this is not just national, this is a global thing to have that covid wallet. We at one record have a covid wallet. But just a couple years from now, I need more than just by covid vaccination. I need all my vaccinations. I need all my lab results. I need all my beds. It's opening the door for a new consumer behavior pattern, which is the first step to adoption for any technology. >>So somebody else covid wallet. So I need >>that was California. Did the, did a version of we just have a pen and it's pretty cool. Very handy. I should save it to my drive. But my phone, but I don't jennifer, what's the coolest thing you're working on right now because you're in the middle of all the action. >>I get very excited about the payer app is that we're working on. So I think by the end of the month we will be connected to almost to all the blues in the United States. So I'm very excited when a user comes into the one record and they're able to get their clinical data from the provider organization and then their clinical financial and formulary data from their payers because then you're getting a complete view, You're getting the records for someone who gave you care and you're getting the records from someone who paid for your care. And that's an interesting thing that's really moving towards a complete picture. So from a personal perspective that gets exciting. And then from a professional perspective, it's really working with our partners as they're using our API s to build out workflows and their applications. >>It's an api economy. I'd like to ask you to on the impact side to the patient. I hear a lot of people complaining that hey, I want to bring my records to the doctor and I want to have my own control of my own stuff. A lot of times, some doctors don't even know other historical data points about a patient that could open up a diagnosis and, or care >>or they can't even refer you to a doctor. Most doctors really only refer within a network of people that they know having a provider directory that allows doctors refer, having the data from different doctors outside of their, you know, I didn't really allows people to start thinking beyond just their little box. >>Cool. Well, great to have you on and congratulations on being in the top 10 saying this is a wonderful example of how the ecosystem where you got cloud city, your MSP. You mentioned the shout out to them jerry Miller and his team by working together the cloud gives you advantages. So I have to ask, we look at amazon cloud as an entrepreneur. It's kind of a loaded question, but I'm going to ask it. I love it. >>You always do it >>when you look at amazon, what do you see as opportunities as an entrepreneur? Because I'll see the easy ones. They have computing everything else. But like what's the, what does cloud do for you as an entrepreneur? What does it, what does it make you do? >>Yeah. So for been working with jerry since the beginning for me when I think about it, it's really the growth of our company. So when we start building, we really just thinking about it from a monolithic build and we move to microservices and amazon has been there every step of the way to support us as that. And now, you know, the things that I'm interested in are specifically health lake and anything that's NLP related that we could plug into our solution for when we get data from different sources that are coming in really unstructured formats and making it structured so that it's searchable for people and amazon does that for us with their services that we can add into the applications. >>Yeah, we announced that data health like and july it has a whole set of templates for analytics, focused on health care as well as hip hop compliance out of the box as well. >>The I think I think that's what's important is people used to think application first. Now it's creating essentially a data lake, then analytics and then what applications you build on top of that. And that's how our partners think about it and that's how we try and service them using amazon as our problem. So >>you're honing in on the value of the data and how that conflicts and then work within the whatever application requests might come >>in. Yes, >>it's interesting. You know, we had an event last month and jerry Chen from Greylock partners came on and gave a talk called castles in the cloud. He's gonna be cute before. He's a, he's a veces, they talk about moats and competitive manage so having a moat, The old school perimeter moz how cloud destroyed that. He's like, no, now the castles are in the cloud, he pointed snowflake basically data warehouse in the cloud red shifts there too. But they can be successful. And that's how the cloud, you could actually build value, sustainable value in the cloud. If you think that way of re factoring not just hosting a huge, huge, huge thing. >>I think the only thing he, this was customer service because health care is still very personal. So it's always about how you interact with the end user and how you can help me get to where they need to be going >>and what do you see that going? Because that's, that's a good point. >>I think that is a huge opportunity for any new company that wants to enter healthcare, customer service as a service in health care for all the different places that health care is going to be delivered. Maybe there's a company that I don't know about, but when they come out, I'd like to meet them. >>Yeah, I mean, I can't think of one cover that can think of right now. This is what I would say is great customer service for health care. >>And if there is one out there contacted me because I want to talk to you about AWS. >>Yeah. And you need the app from one record that make it all >>happen. That's where Omni channel customer service across all health care entities. Yeah, that's >>a great billion dollar idea for someone listening to our show right now. >>Right, alright. So saying they had to give you the opportunity to talk more because this is a great example of how the world's very agile. What's the next step for the AWS Healthcare accelerator? Are there more accelerators? Do you do it by vertical? >>What happens next? So, with the healthcare accelerator, this was our first go at the accelerator. So, this is our first set of cohorts, Of course, all 427 companies are going to get some help from a W. S. as well. We also you'll love this john We also did a space accelerator. Make sure you ask Clint about that. So we have startups that are synthesizing oxygen on mars to sending an outpost box to the moon. I mean, it's crazy what these startups are doing. Um, and then the third accelerator we started was around clean energy. So sustainability, we sold that one out to, we had folks from 66 different countries participate in that one. So these have been really successful for us. So it reinvent. When we talk again, we'll be announcing a couple of others. So right now we've got healthcare, space, clean energy and we'll be announcing a couple other accelerators moving forward. >>You know, it's interesting, jennifer the pandemic has changed even our ability to get stories. Just more stories out there now. So you're seeing kind of remote hybrid connections, ap ideas, whether it's software or remote interviews or remote connections. There's more stories being told out there with digital transformation. I mean there wasn't that many before pandemic has changed the landscape because let's face it, people were hiding some really bad projects behind metrics. But when you pull the pandemic back and you go, hey, everyone's kind of emperors got no clothes on. Those are bad projects. Those are good projects that cloud investment worked or I didn't have a cloud investment. They were pretty much screwed at that point. So this is now a new reality of like value, you can't show me value. >>It's crazy to me when I meet people who tell me like we want to move to the cloud of like, why are you not on the cloud? Like this really just blows my life. Like I don't understand why you have on prem or while you did start on the cloud, this is more for larger organizations, but younger organizations, you know, the first thing you have to do, it's set up that environment. >>Yeah. And then now with the migration plans and seeing here, uh whereas education or health care or other verticals, you've got, now you've got containers to give you that compatibility and then you've got kubernetes and you've got microservices, you've got land. Uh I mean, come on, that's the perfect storm innovation. There's no excuses in my opinion. So, you know, if you're out there and you're not leveraging it, then you're probably gonna be out of business. That's my philosophy. Thank you for coming up. Okay. Sandy, thank you. Thank you, john Okay. Any of his coverage here, summit here in D. C. I'm john ferrier. Thanks for watching. Mm >>mm mm mhm. I have been in the software and technology industry for over 12 years now, so I've had >>the opportunity

>>10 years ago, a group of industry storage veterans formed a company called Infinidat. The DNA of the company was steeped in the heritage of its founder, Moshe Yanai, who had a reputation for relentlessly innovating on three main areas, the highest performance, rock solid availability, and the lowest possible cost. Now these elements have historically represented the superpower triumvirate of a successful storage platform. Now, as Infinidat evolved, landed on a fourth vector, that has been a key differentiator and its value proposition, and that is petabyte scale. Hello everyone. And welcome to this Qube conversation. My name is Dave Vellante and I'm pleased to welcome in two longtime friends of theCube. Phil Bullinger is newly minted CEO of Infinidat and of course, Lee Caswell, VMware's VP of Marketing for the cloud platform business unit. Gents, welcome. >>Great to be here. Always good to see you guys. Phil, so you're joining at the 10 year anniversary mark. Congratulations on the appointment. What attracted you to the company? >>You know I spent a long time in my career at enterprise storage and, and enjoying many of the opportunities, you know, through a number of companies. Last fall when I became aware of the Infinidat opportunity and it immediately captured my attention because of frankly my respect for the product through several opportunities I've had with enterprise customers in selling cycles of different products, if they happened to be customers of Infinidat, , they were not bashful about talking about their satisfaction with the product, their level of delight with it. And so I think from, from the sidelines, I've always had a lot of respect for the Infinidat platform, the implementation of the product quality and reliability that it's kind of legendary for. And so when the opportunity came along, it really captured my interest in of course behind a great product is almost always a great team. >>And as I got to know the company and the board, and, you know, some of the leaders, and learned about the momentum and the business, it was just a very, very compelling opportunity for me. And I'll have to say just, you know, 60 days into the job. Everything I hoped for is here, not only a warm welcome to the company, but an exciting opportunity with respect to where Infinidat is at today with the growth of the business. The company has achieved a level of consistent growth through 2020, cashflow positive, EBITDA positive. And now it's a matter of scaling, scaling the business and it's something that I have had success with several times in my career and really, really enjoying the opportunity here at Infinidat to do that. >>That's great. Thanks for that. Now, of course, Lee, VMware was founded nearly a quarter century ago and carved out a major piece of the enterprise pie and predominantly that's been on prem, but the data center's evolving the cloud is evolving, and this universe is expanding. How do you see the future of that on-prem data center? >>No, I think Satya recently said, right, that, that we've reached max consolidation almost right. You pointed that out earlier. I thought that was really interesting, right. You know, we believe in the distributed hybrid cloud and you know, the reasons for that actually turn out to be storage led in there and in, in the real thinking about it, because we're going to have distributed environments and, you know, one of the things that we're doing with Infinidat here today, right, is we're showing how customers can invest intelligently and responsibly on prem and have bridges in across the hybrid cloud. We do that through something called the VMware Cloud Foundation. That's a full stack offering that, uh, an interesting here, right? It started off with a HCI element, but it's expanded into storage and storage at scale, you know, because storage is going to exist... We have very powerful storage value propositions, and you're seeing customers go and deploy both. We're really excited about seeing Infinidat lean into the VMware Cloud Foundation and vVols actually as a way to match the pace of change in today's application world. >>These trends, I mean, building bridges is what we called it. And so that takes a lot of hard work, especially when you're doing from on-prem into hybrid, across clouds, eventually the edge, you know, that's a, that's a non-trivial task. How do you see this playing out in market trends? >>Yeah. You know, we're, we're in the middle of this every day as, as you know, Dave, uh, and certainly Lee, uh, data center architectures ebb and flow from centralized to decentralized, but clearly data locality, I think, is driving a lot of the growth of the distributed data center architecture, the edge data centers, but core is still very significant for, for most enterprise. Uh, and it's, it's, it has, it has a lot to do with the fact that most enterprises want to own their own cloud. You know, when a Fortune 15 or a Fortune 50 or Fortune 100 customer, when they talk about their cloud, they don't want to talk about, you know, the AWS cloud or the GCP cloud or the Azure cloud. They want to talk about their cloud. And almost always, these are hybrid architectures with a large on-prem or colo footprint. >>Uh, the reason for that number of reasons, right? Data sovereignty is a big deal, uh, among the highest priorities for enterprise today. The control of the security, the, the ability to recover quickly from ransomware attacks, et cetera. These, these are the things that are just fundamentally important, uh, to the business continuity and enterprise risk management plan for these companies. But I think one thing that has changed the on prem data center is the fact that it's the core operating characteristics have to take on kind of that public cloud characteristic. It has to be a transparent, seamless scalability. I think the days of, of CIO's  you know, even tolerating people showing up in their data centers with, with disk trays under their arms to add capacity is, is over. Um, they want to seamlessly add capacity. They want nonstop operation, a hundred percent uptime is the bar. >>Now it has to be a consolidation. Massive consolidation is clearly the play for TCO and efficiency. They don't want to have any compromises between scale and availability and performance. You know, the, the very characteristics that you talked about upfront, Dave, that make Infinidat unique, I think are fundamentally the characteristics that enterprises are looking for when they build their cloud on prem. Uh, I, I think our architecture also really does provide a, a set it and forget it, uh, kind of experience. Um, when we install a new Infinidat frame in an enterprise data center, our intentions are we're, we're not going to come back. We don't intend to come back, uh, to, to help fiddle with the bits or, uh, you know, tweak the configuration as applications and, and multitenant users are added. And then of course, flexible economic models. I mean, everybody takes this for granted, but you really, really do have to be completely flexible between the two rails, the CapEx rail and the OpEx rail and every, uh, every step in between. And importantly, when a customer, when an enterprise customer needs to add capacity, they don't have a sales conversation. They just want to have it right. They're already running in their data center. And that's the experience that we provide. >>Yeah. You guys are aligned in that vision, that layer, that abstracts the complexity from the underlying wherever cloud on prem, et cetera. Right. Let's talk about the VMware and Infinidat relationship. I mean, every, every year at VMworld, up until last year, thank you COVID, Infinidat would host this awesome dinner. You'd have the top customers there. Very nice Vegas steak restaurant. I, of course, I always made a point to stop by not just for the food. I mean, I was able to meet some customers and I've talked to many dozens over the years, Phil, and I can echo that sentiment, but, you know, why is the VMware ecosystem so important to Infinidat? And I guess the question there is, is, is petabyte scale that really that prominent in the VMware customer base? >>It's a, it's a very, very important point. VMware is the longest standing Alliance partner of Infinidat. It goes back to really, almost the foundation of the company, certainly starting with the release one, the very first commercial release of Infinidat VMware and a very tight integration with the VMware was a core part of that. Uh, we, we have a capability. We call the Host PowerTools, which drives a consistent best practices implementation around our, our VMware, uh, integration and, and how it's actually used in the data center. And we built on that through the years through just a deep level of integration. And, um, our customers typically are, are at scale petabyte scale or average deployment as a petabyte and up, um, and over 90% of our customers use VMware. So you would say, I, I think I can safely say we're we serve the VMware environment for some of VMware's largest enterprise footprints, uh, in the market. >>I know it's like children, you got, you love all your partners, but is there anything about Infinidat that, that stands out to you a particular area where, where they shine that from your perspective? >>Yeah, I think so. You know, the, the best partnerships, one are ones that are customer driven. It turns out right. And the idea that we have joint customers at large scale and listen storage is a tough business to get, right, right. It takes time to go and mature to harden a code base. Right. And particularly when you're talking about petabyte scale, right now, you've basically got customers buying in for the largest systems. And what we're seeing overall is customers are trying to do more things with fewer component elements, makes sense, right? And so the scale here is important because it's not just scale in terms of like capacity, right. It's scale in terms of performance as well. And so, as you see customers trying to expand the number of different types of applications, this is one of the things we're seeing, right. Is new applications, which could be container-based Kubernetes orchestrated our Tanzu portfolio helps with that. >>Right. If you see what we're doing with Nvidia, for example, we announced some AI work, right. Uh, this week with vSphere. And so what you're starting to see is like the changing nature of applications and the fast pace of applications is really helping customers save us. And I want to go and find solutions that can meet the majority of my needs. And that's one of the things that we're seeing. And particularly with the vVols integration at scale, that we just haven't seen before, uh, and Infinidat has set the bar and is really setting a new, a new record for that. >>Yeah. Let me, let me comment on that a little bit, Dave, we've been a core part of the VMware Cloud Solutions Lab, which is a very, very exciting engaging, investment that VMware has made. A lot of people have contributed to in the industry, but in the, in the VMware Cloud Solutions Lab, we recently demonstrated on a single Infinidat frame over 200,000 vVols on a single system. And I think that not only edges up the bar, I think it completely redefines what, what scale means when you're talking about a vVols implementation. >>So not to geek out here, but vVols, they're kind of a game changer because instead of admins, having to manually allocate storage to performance tiers. An array, that is VASA certified, VASA is VMware, or actually vStorage API for, for storage awareness, VASA, anyway, with vVols, you can dynamically provision storage that matches the way I say it as a match as device attributes to the data and the application requirements of the VM. So Phil,  it seems like so much in VMware land hearkens back to the way mainframes used to solve problems in a modern way. Right. And vVols is a real breakthrough in that regard in terms of storage. So, so how do you guys see it? I, I presume you're, you're sort of vVols certified based on what you just said in the lab. >>Yeah. We recently announced our vVols release and we're not the first to market with the vVols, but from, from the start of the engineering project, we wanted to do it. We wanted to do it the way we think. We think at scale in everything we do, and our customers were very prescriptive about the kind of scale and performance and availability that they wanted to experience in vVols. And we're now seeing quite a bit of customer interest with traction in it. Uh, as I said, we, we redefined the bar for vVols scalability. We support on a single array now, um, a thousand storage containers. Uh, and I think most of our competition is like at one or maybe 10 or 13 or something like that. So, uh, our customers are, again at scale, they said, if you're going to do vVols, we want it... We want it at scale. We want it to embody the characteristics of your, of your platform. We really liked vVols because it, it helps, it helps separate kind of the roles and responsibilities between the VI administrator and the storage system administrator. If you're going to put a majority of your most critical bits on Infinidat in your data center, you're going to want to, you're going to want to have control over how that resource is used, but yet the vVols mplementation and the tools that we provide with that deep level of integration, give the VI, the VI administrator, all of the flexibility they need to manage applications. And vVols of course gives the VI administrator the native use of our snapshot technology. And so it makes it incredibly easy for them to administrate the platform without having to worry about the physical infrastructure, but yet the people worried about the physical infrastructure still have control over that resource. So it's, it's a game changer as far as we're concerned. >>Yeah. Storage has come a long way. Hasn't it, Lee? I'm wondering if you could add some color here, it seems in talking to ... Uh, so that's interesting. You've had, you had a hand in the growth of vSAN and it was very successful product, but he chose Infinidat for that higher end application. It seems like vVols are a key innovation in that regard. How's the vVols uptake going from your perspective. >>Yeah, I think we you know, we're in the second phase of vVols adoption, right? First phase was, Hey, technically interesting, intriguing. Um, but adoption was relatively low, I think because, you know, up until five years ago, um, applications, weren't actually changing that fast. I mean, think about it, right? The applications, ERP systems, CRM systems, you weren't changing those at the pace of what we're doing today. Now what's happening is every business is a software business. Every business, when you work, when you interact with your healthcare provider right now, it's about the apps. Like, can you go and get your schedules online? Can you email your doctors? Right? Can you go and get your labs? Right? The pace of new application development, we have some data showing that there will be more apps developed in the next five years, and then the past 40 years of computing combined. >>And so when you think about that, what's changed now is trying to manage that all from the kind of storage hardware side was just actually getting in the way you want to organize around the fastest beat rate in your infrastructure today. That's the application. So what vVols has helped you do is it allows the vSphere administrator, who's managing VMs and looking at the apps and the changing pace, and be able to basically select storage attributes, including QoS, capacity, IOPS, and do that from the vCenter console, and then be able to rectify things and manage them right from the console right next to the apps. And that provides a really integrated way. So when you have a close interaction, like what we're talking about today, or, you know, integration, um, that the Infinidat has provided now, you've got this ability to have a faster moving activity. And, you know, consolidation is one of the themes you've heard from time to time from VMware, we're consolidating the management so that the vSphere administrator can now go and manage more things. What traditional VMs yes. VMs across HCI. Sure. Plus now, plus storage and into the hybrid cloud and into like containers. It's that consolidated management, which is getting us speed and basically a consumer like experience for infrastructure deployments. >>Yeah. Now Phil mentioned the solutions lab. We've got a huge ecosystem. Several years ago, you launched this, this via the VMware. I think it's called the VMware Cloud Solutions Lab is the official name. What, explain what it does for collaboration and joint solutions development. And then Phil, I want you to go into more detail about what your participation is, but Lee, why don't you explain it? >>Yeah. You know, we don't take just any products that, because listen, there's a mixing. What we take is things that really expand that innovation frontier. And that's what we saw with Infinidat was expanding the frontier on like large capacity for many, many different mixed workloads and a commitment, right. To go and bring in, not just vVols support, of course, all the things we do for just a normal interaction with vSphere. But, uh, bringing vVols in was certainly important in showing how we operate at scale. And then importantly, as we expanded the VCF, VMware Cloud Foundation, to include storagee systems for a customer, for example, right, who has storage and HCI, right? And it looks for how to go and use them. And that's an individual choice at a customer level. We think this is strategically important. Now, as we expand a multicloud experience, that's different from the hyperscalers. Hyperscalers are coming in with two kind of issues, maybe, right? So one is it's single cloud. And the other one is there's a potential competitive aspect or from some right around the ongoing, underlying business and a hyperscaler business model. And so what VMware uniquely is doing is extending a common control plane across storage systems and HCI, and doing that in a way that basically gives customers choice. And we love that the cloud lab is really designed to go and make that a reality for customers strip out perceived and real risk. >>Yeah. To Lee's point of, it's not like there's not dozens and dozens and dozens of logos on the slide for the lab. I think there's like, you know, 10 or 12 from what I saw and Infinidat is one of them. Maybe you could talk a little bit more about your participation in the program and what it does for customers. >>Yeah, absolutely. And I would agree it's I, we liked the lab because it's not just supposed to be one of everything eye candy it's a purpose-built lab to do real things. And we like it because we can really explore, you know, some of the most contemporary, workloads in that environment, as well as solutions to what I considered some of the most contemporary industry problems. We're participating in a couple of ways. I believe we're the only petabyte scale storage solution in the Cloud Solutions Lab at VMware. One of the projects we're working on with VMware is their machine learning platform.  That's one of the first cloud solutions lab projects that we worked on at Infinidat. And we're also a core part of, of what VMware is driving from a data for good initiative. This was inspired by the idea that that tech can be used as a force for good in the world. And right now it's focused on the technology needs of nonprofits. And so we're closely working in, in the cloud solutions lab with, the VMware cloud foundation layers, as well as, their Tanzu and Kubernetes environments and learning a lot and proving a lot. And it's also a great way to demonstrate the capabilities of our platform. >>Yeah. So, yeah, it was just the other day I was on the VMware analyst meeting virtually of course in Zane and Sanjay and a number of other execs were giving the update. And, and just to sort of emphasize what we've been talking about here, this expansion of on-prem the cloud experience, the data from, especially from our survey data, we have a partner UTR that did great surveys on a regular quarterly basis, the VMware cloud on AWS, doing great for sure, but the VMware Cloud Foundation, the on-prem cloud, the hybrid cloud is really exploding and resonating with customers. And that's a good example of this sort of equilibrium that we're seeing between the public and private coming together >>Well on the VMware Cloud Foundation right now with, uh, you know, over a thousand customers, but importantly over 400 of the global 2000, it's the largest customers. And that's actually where the Venn diagram between the work that VMware Cloud Foundation is doing and Infinidat right, you know, this large scale, actually the, you know, interesting crossover, right. And, you know, listen for customers to go and take on a new store system. We always know that it's a high bar, right. So they have to see some really unique value, like how is this going to help? Right. And today that value is I want to spend less time looking down at the storage and more time looking up at the apps, that's how we're working together. Right. And how vVols fits into that, you know, with the VMware Cloud Foundation, it's the hype that hybrid cloud offering really gives customers that future-proofing right. And the degrees of freedom they're most likely to exercise. >>Right. Well, let's close with a, kind of a glimpse of the future. What do you see as the future of the data center specifically, and also your, your collaborations Lee? Why don't you start? >>I think what we hope to be true is turning out to be true. So, you know, if you've looked at the, you know, what's happening in the cloud, not everything is migrating in the cloud, but the public cloud, for example, and I'm talking about public cloud there. The public cloud offers some really interesting, unique value and VMware is doing really interesting things about like DR as a service and other things, right? So we're helping customers tap into that at the same time. Right. We're seeing that the on-prem investment is not stalling at all because of data sovereignty because of bandwidth limitations. Right. And because of really the economics of what it means to rent versus buy. And so, you know, partnering with  leaders on, in storage, right, is a core part of our strategy going forward. And we're looking forward to doing more right with Infinidat, as we see VCF evolve, as we see new applications, including container based applications running on our platform, lots of futures, right. As the pace of application change, you know, doesn't slow down. >>So what do you see for the next 10 years for Infinidat? >>Yeah, well, um, we, I appreciated your introduction because of this speak to sort of the core characteristics of Infinidat. And I think a company like us and at our, at our juncture of evolution, it's important to know exactly who you are. And we clearly are focused in that on-prem hybrid data center environment. We want to be the storage tier that companies use to build their clouds. And, uh, the partnership with VMware, uh, we talked about the Venn diagram. I think it just could not be more complimentary. And so we're certainly going to continue to focus on VMware as our largest and most consequential Alliance partner for our business going forward. Um, I'm excited about, about the data center landscape going forward. I think it's going to continue to ebb and flow. We'll see growth in distributed architectures. We'll see growth at the edge in the core data center. >>I think the, the old, the old days where customers would buy a storage system for a application environment, um, those days are over, it's all about consolidating multiple apps and thousands of users on a single platform. And to do that, you have to be really good at, uh, at a lot of things that we are very good at. Our, our strategy going forward is to evolve as media evolves, but never stray far from what has made Infinidat unique and special and highly differentiated in the marketplace. I think the work that VMware is doing and in Kubernetes >>Is very exciting. We're starting to see that really pick up in our business as well. So as we think about, um, uh, you know, not only staying relevant, but keeping very contemporary with application workloads, you know, we have some very small amount of customers that still do some bare metal, but predominantly as I said, 90% or above is VMware infrastructure. Uh, but we also see, uh, Kubernetes, our CSI driver works well with the VMware suite above it. Uh, so that, that complimentary relationship we see extending forward as, as the application environment evolves. Great, thank you. You know, many years ago when I attended my first, uh, VMworld, the practitioners that were there, you talked to them, half the conversations, they were complaining about storage and how it was so complicated and you needed guys in lab coats to solve problems. And, you know, VMware really has done a great job, publishing the APIs and encouraging the ecosystem. And so if you're a practitioner you're interested in how vVols and Infinidat and VMware were kind of raising the bar and on petabyte scale, there's some good blogs out there. Check out the Virtual Blocks blog for more information, guys. Thanks so much great to have you in the program. Really appreciate it. Thanks so much. Thank you for watching this Cube conversation, Dave Vellante. We'll see you next time.

(upbeat music) >> 10 years ago, a group of industry storage veterans formed a company called INFINIDAT. The DNA of the company was steeped in the heritage of its founder, Moshe Yanai who had a reputation for relentlessly innovating on three main areas, the highest performance, rock solid availability and the lowest possible cost. Now these elements have historically represented the superpower triumvirate of a successful storage platform. Now as INFINIDAT evolved it landed on a fourth vector that has been a key differentiator in its value proposition and that is petabyte scale. Hello everyone and welcome to this Cube Conversation. My name is Dave Vellante and I'm pleased to welcome in two long time friends of the cube, Phil Bullinger is newly minted CEO of INFINIDAT and of course, Lee Caswell, VMware's VP of marketing for the cloud platform business unit. Gents welcome. >> Thank you so much. Yeah. Great to be here Dave. >> Yeah. Great to be here Dave. Thanks. >> Always good to see you guys. Phil, so you're joining at the 10 year anniversary, Mark, congratulations on the appointment. What attracted you to the company? >> Yeah that's a great question Dave. I spent a long time in my career at enterprise storage and enjoyed many of the opportunities through a number of companies. Last fall when I became aware of the INFINIDAT opportunity and immediately captured my attention because of frankly my respect for the product. Through several opportunities I've had with enterprise customers in selling cycles of different products, if they happen to be customers of INFINIDAT they were not bashful about talking about their satisfaction with the product, their level of delight with it. And so I think from the sidelines I have always had a lot of respect for the INFINIDAT platform, the implementation of the product quality and reliability that it's kind of legendary for. And so when the opportunity came along it really captured my interest and of course behind a great product is almost always a great team and as I got to know the company and the board and some of the leaders and learned about the momentum and the business it was just a very, very compelling opportunity for me. And I'll have to say just 60 days into the job everything I hoped for is here not only a warm welcome to the company but an exciting opportunity with respect to where INFINIDAT is at today with growth of the business, the company has achieved a level of consistent growth through 2020 cashflow, positive, even thought positive and now it's a matter of scaling the business and it's something that I have had success with at several times in my career and I'm really, really enjoying the opportunity here at INFINIDAT to do that. >> That's great. Thanks for that. Now, of course Lee, VMware was founded nearly a quarter century ago and carved out a major piece of the enterprise pie and predominantly that's been on prem but the data centers evolving, the cloud is evolving and this universe is expanding. How do you see the future of that on-prem data center? >> I think Satya recently said, right? That we've reached max consolidation almost right. You pointed that out earlier. I thought that was really interesting, right? We believe in the distributed hybrid cloud and the reasons for that actually turn out to be storage led in there and in the real thinking about it because we're going to have distributed environments. And one of the things that we're doing with INFINIDAT here today, right? Is we're showing how customers can invest intelligently and responsibly on prem and have bridges in across the hybrid cloud. We do that through something called the VMware Cloud Foundation. That's a full stack offering that... And interesting here, right? It started off with a HCI element but it's expanded into storage and storage at scale. Because storage is going to exist we have very powerful storage value propositions and you're seeing customers go and deploy both. We're really excited about seeing INFINIDAT lean into the VMware Cloud Foundation and VVol has actually a way to match the pace of change in today's application world. >> Yes, so Phil you see these trends, I mean building bridges is what we called it. And so that takes a lot of hard work especially when you're doing from on-prem into hybrid, across clouds, eventually the edge, that's a non-trivial task. How do you see this playing out in market trends? >> We're in the middle of this every day and as you know Dave and certainly Lee, data center architecture is urban flow from centralized to decentralized but clearly data locality I think is driving a lot of the growth of the distributed data center architecture, the edge data centers but core is still very significant for most enterprise. And it has a lot to do with the fact that most enterprises want to own their own cloud when a Fortune 15 or a Fortune 50 or a Fortune 100 customer, when they talk about their cloud they don't want to talk about the AWS cloud or the GCP cloud or the Azure cloud. They want to talk about their cloud and almost always these are hybrid architectures with a large on-prem or colo footprint. The reason for that number of reasons, right? Data sovereignty is a big deal among the highest priorities for enterprise today. The control, the security, the ability to recover quickly from ransomware attacks, et cetera. These are the things that are just fundamentally important to the business continuity and enterprise risk management plan for these companies. But I think one thing that has changed the on-prem data center is the fact that it's the core operating characteristics have to take on kind of that public cloud characteristic, it has to be a transparent seamless scalability. I think the days of CIOs even tolerating people showing up in their data centers with disk trays under their arms to add capacity is over. They want to seamlessly add capacity, they want nonstop operation, a hundred percent uptime is the bar now it has to be a consolidation, massive consolidation, is clearly the play for TCO and efficiency. They don't want to have any compromises between scale and availability and performance. The very characteristics that you talked about upfront Dave, that make INFINIDAT unique I think are fundamentally the characteristics that enterprises are looking for when they build their cloud on prem. I think our architecture also really does provide a set it and forget it kind of experience when we install a new INFINIDAT frame in an enterprise data center, our intentions are we're not going to come back. We don't intend to come back to help fiddle with the bits or tweak the configuration and as applications and multi tenant users are added. And then of course, flexible economic models. I mean, everybody takes this for granted but you really really do have to be completely flexible between the two rails, the cap X rail and the objects rail and every step in between. And importantly when an enterprise customer needs to add capacity they don't have a sales conversation. They just want to have it right there already running in their data center. And that's the experience that we provide. >> Yeah. You guys are aligned in that vision, that layer that abstracts the complexity from the underlying wherever cloud on prem, et cetera. >> Right? >> Let's talk about VMware and INFINIDAT their relationship, I mean, every year at VMworld up until last year, thank you COVID, INFINIDAT would host this awesome dinner, you'd have his top customers there, very nice Vegas steak restaurant. I of course, I always made a point to stop by not just for the food. I mean, I was able to meet some customers and I've talked to many dozens over the years Phil, and I can echo that sentiment, why is the VMware ecosystem so important to INFINIDAT? And I guess the question there is, is petabyte scale really that prominent in the VMware customer base? >> It's a very, very important point. VMware is the longest standing alliance partner of INFINIDAT. It goes back to really almost the foundation of the company certainly starting with the release one, the very first commercial release of INFINIDAT, VMware and a very tight integration where VMware was a core part of that. We have a capability we call the host power tools which drives a consistent best practices implementation around our VMware integration and how it's actually used in the data center. And we built on that through the years through just a deep level of integration and our customers typically are at scale, petabyte scale or average deployment as a petabyte and up and over 90% of our customers use VMware. I think I can safely say we serve the VMware environment for some of VMware's largest enterprise footprints in the market. >> So Lee It's like children, you love all your partners but is there anything about INFINIDAT that stands out to you, a particular area where they shine from your perspective? >> Yeah, I think so. The best partnerships won are ones that are customer driven it turns out, right? And the idea that we have joint customers at large-scale, I must say storage is a tough business to go, right? Right, it takes time to go and mature to harden a code base, right? And particularly when you talk about petabyte scale right now, you've basically got customers buying in for the largest systems. And what we're seeing overall is customers are trying to do more things with fewer component elements. Makes sense, right? And so the scale here is important because it's not just scale in terms of like capacity, right? It's scale in terms of performance as well. And so, as you see customers trying to expand the number of different types of applications and this is one of the things we're seeing, right? Is new applications which could be container-based, Kubernetes orchestrated, our Tansu portfolio helps with that, right? If you see what we're doing with Nvidia, for example we announced some AI work, right? This week with vSphere. And so what you're starting to see is like the changing nature of applications and the fast pace of applications is really helping customers say, listen I want to go and find solutions that can meet the majority of my needs. And that's one of the things that we're seeing and particularly with the VVol'sintegration at scale that we just haven't seen before, INFINIDAT is setting the bar and really setting a new record for that. >> Yeah. Let me comment on that a little bit, Dave. We've been a core part of the VMware Cloud Solutions Lab, which is a very very exciting engaging investment that VMware has made. A lot of people have contributed to in the industry but in the VMware Cloud Solutions Lab we recently demonstrated on a single INFINIDAT frame over 200,000 VVols on a single system. And I think that not only edges up the bar I think it completely redefines what scale means when you're talking about a VVol implementation >> So lets talk about both those things. Not to geek out here but VVols they're kind of a game changer because instead of admins having to manually allocate storage to performance tiers, an array that is VASA certified, VASA is VMware or actually the storage API for storage awareness, VASA, anyway with VVols you can dynamically provision storage that matches, the way I say it as matches device attributes to the data and the application requirements of the VM. So Phil, it seems like so much in VMware land harkens back to the way mainframes used to solve problems in a modern way, right? And VVol is a real breakthrough in that regard in terms of simplifying storage. So how do you guys see it? I presume you're sort of VVol certified based on what you just said in the lab. >> Yeah. We recently announced our VVols release and we're not the first to market with VVols but from the start of the engineering project we wanted to do it. We wanted to do it the way we think. We think at scale in everything we do and our customers were very prescriptive and the kind of scale and performance and availability that they wanted to experience in VVols. And we're now seeing quite a bit of customer interest with traction in it. As I said, we redefined the bar for VVol scalability. We support on a single array now a thousand storage containers. And I think most of our competition is like at one or maybe 10 or 13 or something like that. So our customers are again at scale, they said if you're going to do VVols we want it at scale. We want it to embody the characteristics of your platform. We really liked VVols because it helps separate kind of the roles and responsibilities between the BI administrator and the storage system administrator. If you're going to put the majority of your most critical bits on INFINIDAT in your data center you're going to want to have control over how that resource is used, the at the VVols in rotation and the tools that we provide with that deep level of integration give the BI administrator all of the flexibility they need to manage applications and VVols of course gives the BI administrator the native use of our in minute snapshot technology. And so it makes it incredibly easy for them to administrate the platform without having to worry about the physical infrastructure but yet the people worried about the physical infrastructure still have control over that resource. So it's a game changer as far as we're concerned. >> Yeah. Storage has come a long way hasn't it Lee? If you could add some color here it seems in talking needs so VASA that's interesting you had a hand in the growth of VASA and very successful product but he chose INFINIDAT for that higher end application. It seemed like VVols are a key innovation in that regard. How's the VVol uptake going from your perspective. >> Yeah, I think we're in the second phase of VVol adoption, right? First phase was, hey, it technically interesting, intriguing but adoption was relatively low I think because you know up until five years ago applications weren't actually changing that fast. I mean, think about it, right? The applications, ERP systems, CRM systems, you weren't changing those at the pace of what we're doing today. Now what's happening is every business is a software business. Every business when you work, when you interact with your healthcare provider right now it's about the apps. Like, can you go and get your schedules online? Can you email your doctors, right? Can you go and get your labs, right? The pace of new application development, we have some data showing that there will be more apps developed in the next five years and then the past 40 years of computing combined. And so when you think about that what's changed now is trying to manage that all from the kind of storage hardware side was just actually getting in the way you want to organize around the fastest beat rate in your infrastructure, today that's the application. So what VVOls helps you do is it allows the vSphere administrator who's managing VMs and looking at the apps and the changing pace and be able to basically select storage attributes including QoS, capacity, IOPS and do that from the V center console and then be able to rectify things and manage them, right? From the console right next to the apps. And that provides a really integrated way. So when you have a close interaction like what we're talking about today or integration that the INFINIDAT has provided now you've got this ability to have a faster moving activity. And consolidation is one of the themes you've heard from time to time from VMware, we're consolidating the management so that the vSphere administrator can now go and manage more things. What traditional VMs, yes, VMs across HI sure put now plus storage and into the hybrid cloud and into like containers, it's that consolidated management which is getting us speed and basically a consumer like experience for infrastructure deployments. >> Yeah. Now Phil mentioned the solutions lab. We've got a huge ecosystem. Several years ago you launched this, the VMware, I think it's called the VMware Cloud Solutions Lab is the official name. Explain what it does for collaboration and joint solutions development. And then Phil, I want you to go in more detail about what your participation has been but Lee why don't you explain it? >> Yeah. We don't take just any products that because listen there's a mixing, what we take is things that really expand that innovation frontier. And that's what we saw with INFINIDAT was expanding the frontier on like large capacity for many many different mixed workloads and a commitment, right? To go and bring in not just VVol support, of course all the things we do for just normal interaction with vSphere but bringing VVOls in was certainly important in showing how we operate at scale. And then importantly as we expanded the vSphere or cloud foundation to include store systems, fair customer for example, right? Who has storage and HCI, right? And it looks for how to go and use them. And that's an individual choice at a customer level. We think this is strategically important now as we expand a multi-cloud experience that's different from the hyperscalers, right? Hyperscalers are coming in with two kind of issues, maybe, right? So one is it's single cloud. And the other one is there's a potential competitive aspect from some right around the ongoing underlying business and a hyperscaler business model. And so what VMware uniquely is doing is extending a common control plane across storage systems and HCI and doing that in a way that basically gives customers choice. And we love that the cloud lab is really designed to go and make that a reality for customers strip out perceived and real risk. >> Yeah. Phil to Lee's point, it's not dozens and dozens and dozens of logos on the slide for the lab. I think there's like 10 or 12 from what I saw and INFINIDAT is one of them. Maybe you could talk a little bit more about your participation in the program and what it does for customers. >> Yeah, absolutely. And I would agree it's, we like the lab because it's not just supposed to be one of everything I can do it, it's a purpose-built lab to do real things. And we like it because we can really explore some of the most contemporary workloads in that environment as well as solutions to what I centered as some of the most contemporary industry problems we're participating in a couple of ways. I believe we're the only petabyte scale storage solution in the cloud solutions lab at VMware. One of the projects we're working on with VMware is their machine learning platform. That's one of the first cloud solutions lab projects that we worked on with INFINIDAT. And we're also a core part of what VMware is driving from at but we call it data for good initiative. This was inspired by the idea that tech can be used as a force for good in the world. And right now it's focused on the technology needs of nonprofits. And so we're closely working in the cloud solutions lab with the VMware Cloud Foundation layers as well as the Tansu and Kubernetes environments and learning a lot and proving a lot. And it's also a great way to demonstrate the capabilities of our platform. >> Yeah. So Lee, I was just the other day I was under VMware analyst meeting virtually of course and Zane and Sanjay and a number of other execs were given the update. And just to sort of emphasize what we've been talking about here this expansion of on-prem, the cloud experience, the data especially from our survey data we have a partner at ETR they do great surveys on quarterly basis. The VMware cloud on AWS do great for sure but the VMware Cloud Foundation, the on-prem cloud, the hybrid cloud is really exploding and resonating with customers. And that's a good example of this sort of equilibrium that we're seeing between the public and private coming together. >> Well, VMware Cloud Foundation right now with over a thousand customers but importantly over 400 of the global 2000, right? It's the largest customers. And that's actually where the Venn diagram between the work that VMware Cloud Foundation is doing and INFINIDAT, right? This large scale actually the interesting crossover, right? And listen for customers to go and take on a new storage system we always know that it's a high bar, right? So they have to see some really unique value, like how is this going to help, right? And today that value is I want to spend less time looking down at the storage and more time looking up at the apps, that's how we're working together, right? And how VVols fits into that with the VMware Cloud Foundation, it's that hybrid cloud offering really gives customers that future-proofing, right? And the degrees of freedom they're most likely to exercise. >> Right. Well, let's close with a kind of a glimpse of the future. What do you two see as the future of the data center specifically and also your collaborations Lee? Why don't you start? >> So I think what we hope to be true is turning out to be true. So, if you've looked at what's happening in the cloud not everything is migrating in the cloud but the public cloud for example and I'm talking about public cloud there, the public cloud offers some really interesting unique value. And VMware is doing really interesting things about like Dr as a service and other things, right? So we're helping customers tap into that at the same time, right? We're seeing that the on-prem investment is not stalling at all because of data sovereignty because of bandwidth limitations, right? And because of really the economics of what it means to rent versus buy. And so partnering with leaders in storage, right? Is a core part of our strategy going forward. And we're looking forward to doing more, right? With INFINIDAT as we see VCF evolve, as we see new applications including container-based applications running on our platform, lots of futures, right? As the pace of application change doesn't slow down. >> So Phil, what do you see for the next 10 years for INFINIDAT? >> Yeah, well, I appreciated your introduction because it does speak to sort of the core characteristics of INFINIDAT. And I think a company like us and at our juncture of evolution it's important to know exactly who you are. And we clearly are focused in that on-prem hybrid data center environment. We want to be the storage tier that companies use to build their clouds. The partnership with VMware we talked about the Venn diagram, I think it just could not be more complimentary. And so we're certainly going to continue to focus on VMware as our largest and most consequential alliance partner for our business going forward. I'm excited about the data center landscape going forward. I think it's going to continue to ebb and flow. We'll see growth and distributed architectures, we'll see growth at the edge. In the core data center I think the old days where customers would buy a storage system for a application environment, those days are over it's all about consolidating multiple apps and thousands of users on a single platform. And to do that you have to be really good at a lot of things that we are very good at. Our strategy going forward is to evolve as media evolves but never stray far from what has made INFINIDAT unique and special and highly differentiated in the marketplace. I think the work that VMware is doing in Kubernetes is very exciting. We're starting to see that really pick up in our business as well. So as we think about not only staying relevant but keeping very contemporary with application workloads, we have some very small amount of customers that still do some bare metal but predominantly as I said 90% or above is a VMware infrastructure. But we also see Kubernetes, our CSI driver works well with the VMware suite above it. So that that complimentary relationship we see extending forward as the application environment evolves. >> It's great. Thank you. Many years ago when I attended my first VMworld the practitioners that were there you talked to them, half the conversations they were complaining about storage and how it was so complicated and you needed guys in lab coats to solve problems. And VMware really has done a great job publishing the APIs and encouraging the ecosystem. And so if you're a practitioner you're interested in in how VVols and INFINIDAT and VMware, we're kind of raising the bar and on petabyte scale there's some good blogs out there. Check out the virtual blocks blog for more information. Guys thanks so much. Great to have you in the program. Really appreciate it. >> Thanks so much, Dave. >> All right. Thank you for watching this cute conversation, Dave Vellante, we'll see you next time. (upbeat music)

>>from around the globe. It's the cue with digital coverage of active EO data driven 2020. Brought to you by activity. We're back. This is the cubes coverage. Our ongoing coverage of active FiOS data driven. Of course, we've gone virtual this year. Ash. Ashutosh is here. He's the founder, president and CEO of Active Eo. Great to see you again. >>Likewise, They always always good to see you. >>We have We're in a little meet up, You and I in Boston. I always enjoy our conversations. Little did we know that, You know, a few months later, we would only be talking at this type of distance and, uh and of course, it's sad. I mean, a data driven is one of our favorite events is intimate, its customer content driven. The theme this year is you call it the next normal. Some people call it the new abnormal, the next normal. What's that all about? >>I think it's pretty pretty fascinating to see when we walked in in March, all of us were shocked by the effect of this pandemic. And for a while we all scrambled around trying to figure out How do you react to this one, and everybody reacted very differently. But most people have this tendency to think that this is going to be a pretty broom environment with lots of unknown variables, and it is important for us to try to figure out how to get a get our hands on this. By the time we came on. For six weeks into that, almost all of us have figured out this is Ah, this is not something you fight again. This is not something you wait, what, it to go away? But this is one. Did you figure out how to live in and you figured out how to work around it? And that, we believe, is the next long. It's not about trying to create a new abnormal. It's not about creating a new normal, but it's truly one that basically says that is it. That is a way, perhaps packed forward. There's a is a way to create this next normal, and you just figured out how to live with the environment, behalf and the normal outcomes of companies that have done remarkably well as a result of these actions. Fact. If you're being one of them, >>it's quite amazing isn't it? I mean, I've talked to a lot of tech companies, CEOs and their customers, and it's almost like they feel the first reaction was course they cared about their there, their employees and their broader families. Number one number two was many companies, as you know, saw a tailwind, and it initially didn't want to be seen as ambulance chasing. And then, of course, the entrepreneurial spirit kicked in and they said, Okay, we can only control what we can control and tech companies in particular just exceedingly Well, I don't think anybody really predicted that early >>on. Yeah, I, um I think of the heart, We're all human beings, and the first reaction was to take it off. Four constituencies, right? One. Take care of your family. Take it off your community, take care of your employees, take care of your customers. And that was the hardest part. The first 4 to 6 weeks was to figure out How do you do each of those four. Once you figured that part out or you figured out ways to get around to making sure you can take it off those you really found the next mom, you really start forgetting our out of continue to innovate Could, you know to support each of those four constituencies and people have done different things. I know it's amazing how, um, Cuba continues to operate As far as a user is concerned, they're all watching anymore. Yes, we don't have the wonderful desk, and we all get to chat and look in the eye. But the content of the messages asked powerful as what it waas a few months ago. So I'm sure this is how we're all going to figure out how to make through this new next normal >>and digital transformation kind of went from from push to pull. I mean, every conference you go to, they say, Well, look at uber, you know, look at Airbnb and it put up the examples you have to do this to, and then all of sudden the industry dragged you along. Some Curis esta is toe. How and and I guess the other point there is digital means data. We've said that many, many times. If you didn't have a digital strategy during the height of the lock down, you couldn't transact business and still many restaurants is still trying to figure this out, But so how did it affect you and your customers? >>Yeah, it's very interesting. And I we spend a lot of time with several of our customers were managing some of the largest I T organizations. We talk about very interesting phenomena that happened some better beginning of this year. About 20 years ago, we used to worry about this thing called the Digital Divide, those who have access the network and Internet and those who don't. And now there is this beta divide, the divide between organizations that know how to leverage, exploit and absolutely excellent the business using data and those adorable. I think we're seeing this effect so very clearly among organizations that unable to come back and address some of this stuff. And it's fascinating. Yes, we all have the examples off the lights off. People are doing delivery. People are doing retailing, but there are so many little things you're seeing organizations. And just the other day, he had a video from Century Days Is Central Data System, which is helping accelerate Cohen 19 research because it will get copies of the data faster than they would get access to data so that these are just much, much faster. Sometimes you know, several days to a few minutes. It's that that level of effect, it's not just down to some seven. You know, you almost think of it as nice to have, but it's must have life threatening stuff. Essential stuff or just addressing. Korea was running a very pretty in a wonderful article about this supercomputer in That's Doing an Aristo covert 19 and how it's figured out most of these symptoms they're able to figure out by just crunching a ton of data. And almost every one of those symptoms that the computer has predicted Supercomputer is predicted has being accurate. It's about data. It is absolutely about data, which is why I think this is a phenomenal time for companies. Toe Absolutely go change. Make this information about data exploration, data leverage, exploitation. And there's a ton of it all over all around us. >>Yeah, and and part of that digital transformation, the mandate is to really put data at the core. I mean, we've we've certainly seen this with the top market cap companies. They've got dated at the core, and and now, as they say it's it's become a A mandate. And, you know, there's been several things that we've clearly noticed. I mean, you saw the work from home required laptops and, you know, endpoint security and things of that. VD. I made a comeback, and certainly Cloud was there. But I've been struck by the reality of multi Cloud. I was kind of a multi cloud skeptic early on. >>Yeah, >>I said many times I thought it was more of a symptom than it was a strategy, but it's that's completely flipped. Ah, recently in r e t r surveys, we saw multi cloud popping up all over the place. I wonder what you're seeing when you talk to your customers and other CEOs. >>Yeah, So fascinating, though really is the first flower part of sometime in 2018. End of 2018 >>Go right, Yeah, >>the act if you'll go on world, which is a phenomenal way to completely change the way you think about the using object storage in the flower for two years that we saw about 20% of our business. By the end of two years, the beginning of this year, 20% of our business was built on never it in the cloud since March. So that was end of our almost ended the Q one. So now we just limit left you three in six months. We added 12 more percent of the business literally weeded in six months. What we did not do before for 18 months before that, right? Significantly more than what we did for a year and a half before that. And there are really three reasons and we see this old nor again, we have a large customer. We closed in January. Ironically, were deploying out of UK, a very large marketing organization. Got everything deployed, running the they're back up and beyond and a separate data center. And they had a practical problem of not being able to access the second sight literally in the middle of deployment. Mystere that customer, Did you see me Google Cloud? Because they were simply no way for them to continue protecting their data, being able to develop new applications with that data that simply had no access. So there was. This was the number one reason the inability for already physically access, but put their their employees at rest and have before the plow would be the infrastructure. That's number one, so that first of all, drove the reason for the cloud. And then there's a second reason there are practical reasons. And why some clerk platforms that good one working the other ones are not. So where, uh, some other more fuels. And so if I'm an organization that has that spans everything, I've got no power PC and X 86 machine A vm I got container platforms. I got Oracle. They got a C P. There is no single cloud platform that supports all my work loaders efficiently. It's available in all the agents I want. So inevitably I have to go at our different about barefoot. So that's a second practical visa. And then there's a strategic reason. No, when no customer what's really locked into anyone card back at least two. You're gonna go pear more likely? Three. So those are the reasons. And then, interestingly enough, have you were on a panel with as global Cee Io's and in addition to just the usual cloud providers of you all know and love inside the U. S. Across the world, in Europe, in Asia, there's a rise off the regional flower fire. See you take all this factor. So have you got absolute physical necessity? You got practical constraints of what can the club provided support the strategic reasons on why either Because I don't want to be locked into a part for better or because there is a rise off data nationalism that's going on, that people want to keep their data within the country bombs all of these reasons. But the foundations or why multiplier is almost becoming a de facto. It's impossible. What a decent size organization to assume. They were just different on one car ready. >>The big trend we're seeing, I wonder if you could comment. Is this this notion of the data life cycle of the data pipeline? It's a very complex situation for a lot of organizations, their data siloed. We hear that a lot. They have data scientists, data engineers, developers, data quality engineers, just a lot of different constituencies and lines of business. And it's kind of a mess. And so what they're trying to do is bring that together. So they've done that data. Scientists complain they spend all their time wrangling data, but but ultimately the ones that are succeeding to putting data at the core is, we've just been discussing are seeing amazing outcomes by being able to have a single version of the truth, have confidence in that data, create self serve for their for their lines of business and actually reduce the end and cycle times. It's driving your major monetization, whether that's cost cutting or revenue. And I'm curious as to what you're seeing. You guys do a lot of work. Heavy work in Dev ops and hard core database those air key components of that data Lifecycle. Yeah, you're seeing in that regard regarding that data pipeline. >>Yeah, it's a It's a phenomenal point if you really want to go back and exploit data within an organization. If you really want to be a data driven organization, the very first thing you have to do is break down the silos. Ironically, every organization has all the data required to make the decisions they want to. They just can't either get to it or it's so hard to make the silos. That is just not what trying to make it happen. And 10 years ago we set out on this mission rather than keep this individual silos of data. Why don't we flip it open and making it a pipeline, which looks like a data cloud where essentially anybody who's consuming it has access to it based on the governance rules based on the security rules that the operations people have said and based on the kind of format they want to see data. Not everyone even want to see the data in a database. Former, maybe you want the database for my convert CSP for my before you don't analytics And this idea of making data, the new infrastructure, this idea of having the operations people provide this new layer for data, it's finally come to roost. I mean, it's it's fascinating. I was the numbers last quarter. We just finished up. You do now. 45% of our customer base is uses activity or for reuse is the back of data for things that excellent. The business things that make the business move faster, more productive or you will survive. That was the mission. That was what we set out to do 10 years ago. We were talking to an analyst this morning, and now this is question off. You know, it looks like there's a team of backup data being reused, said Yeah, that's kind of what we've been saying for 10 years. Backup cannot be an insurance back up in order to your destination. It has to be something that you could use as an asset and that I think it's finally coming to the point with you can use back up a single source of truth only if you designed it right from the beginning. For that purpose, you cannot just lots of lots of ways to fake it. Make it try to pretend like you're doing it. But that was a trooper was off making date of the new infrastructure, making it a cloud, making it something that is truly an ask. And it's fascinating to see our businesses. You take any of our larger counts and the way they've gone about transforming not just basic backup. India. Yes, we are the world's glasses back up in most Kayla will be our solution. That's that's a starting point. But do we will be used after Devil applications 8, 10 times faster? Ron Analytics, 100 ex pastor. The more data you have, the more people who use data you have, the better this return makeups. >>You know, that is interesting to hear you talk about that because that has been the holy Grail of backup. Was toe go beyond insurance to actually create business value. And you're actually seeing some underlying trends We talked about that data pipeline in one of the areas that is the most interesting is in database, which was so boring for so many years. Ah, and you're seeing new workloads emerge. Take the data warehouse beyond your reporting. Never really lived up to its Ah, it's promise of 360 degree view. You mentioned analytics. That's really starting toe happen. Ah, and it's all about data John, for Used to say that your data is that is the new development kit. You call it the new infrastructure, and it's sort of the same same type of theme. So maybe some of the trends you're seeing in ah in database enoughto talk about that for a little bit and then pick your brains and some other tech like object storage is another one that we've really seen takeoff? >>Yeah. So I think our journey with object story began in 16 4017 as we started or Doctor Cloud platform in response to the user requirements, Uh, we did more like most companies have done and unfortunately continue to do to take the in print product. And then it's smooth under the cloud. And one of the things we saw was there was a fundamental difference off how the design points of flower engineering is all about what they're designed it for object story, that one of those one of those primitives fundamental stories, primitives that the cloud providers actually produced that we know really exploited. There was. It was used as a graveyard for data. It's a replacement for me, please, where data goes to die. And then we look at it really closely and say, Well, this is actually a massively scalable, very low cost storage, but it has some problems. It has an interface that you cannot use with traditional servers. Uh, it has some issues around not being able to read, modify right the data. So it feels like a consuming a lot of stories. So we're going to solve those problems because a good two years to come back with something on world that fundamentally creeds objects the lady like this massive use capable high performer disk? Yes, except it is ridiculously low cost and optimize the capacity. So this finger on world that patented has really become the foundation of how everything in our works without using CPU Ray, that is simply nothing at a lower PCO that if you wanted to basic backup, the, uh, more importantly, use that to do this a massive analytics and you don't know more data warehouse data leaks. It is not a good deal of Lake House aladi. All of these are still silent. All of these are people trying to take some data from somewhere put into one of the new construct and have it being controlled by somebody else. This is artist thing. It's just you just move the silos from some place to another place instead of creating a pipeline. If you want to really create a pipeline object story has been integral part of the pipeline, not a separate bucket by itself. And that's what we did. And same thing with databases, you know, most business, most of the critical business and I was on a daily basis, and the ability to find a way to leverage those. Move them on our leverage in terms of whichever format databases access. Which location or Saxes doesn't know how big it is. Lots of work has gone into trying to figure figure that one out. And we we had some very, very good partners in some of the largest customers who help take the journey with us. I'm pretty much all of the global 2000 accounts you see across the board, but an integral part of a process. >>You mentioned the word journey and triggered a thought. Is your discussion with Robbie, the CEO of of Seeing >>A. It was a customer years. >>Ah, and what he said. I liked what he said. He course he used the term journey. We all do. But he said, You know what? I kind of don't like that term because I want to inject the sense of urgency essentially what he was saying. I want speed, you know, journeys like Okay, kids get in the car, were in a drive across country. We're gonna make some stops. And so, while there's a journey, he also was was really trying to push the organization hard and he talked about culture. Ah, as some of the most difficult things and it goes like many. See, I said, Now the technology is almost the easy part. It's true when it works. Oh, I thought that was a great discussion that you had. What were some of your takeaways >>with thinking? Robbie's is very astute. Ah, I t executive was being around the block for so long and one of the fascinating things, but a asking this question about what's the biggest challenge was just gone through this a couple of times. What is the biggest challenge? Taking an organization as vulnerable as well known A C gate is. I mean, this is a data company. This is This is the heart of the Oliver Half the world's data is on seeing stuff. How are you today was, or company has been around for long in the middle of Silicon Valley and make it into ah into a fast growing transformation company that's responding to the newer challenges. And I thought he was going to come back with Well, you know, I gotta go to the abuses. I picked this technology that techno in. Surely that is exactly what I expected he would end up with. There's nothing through technology in this day and age when you can have an Elon Musk and send a card of Mars. It's not many technologies that we can really solve many covered 19 ism. Next one Do we gotta go solve? Well, frankly, he kid upon the one thing that matters to every company. It is the fundamental culture to create a biased of action. It's a fundamental culture where you have to come back and have a deliverable that moves the ball forward every day, every month, every quarter, as opposed to have this CDs off. Like you said, a journey that say's and we all know this right? People talk about, we're going to do this in face one. We're gonna do this and face to and good food release and face three nothing and what happens Invasive. Nobody gets a number feast. I think he did a great job of saying I fundamentally had to go change the culture that was my biggest take away, and this I've heard this so many times the most effective I D execs wait a transformation. It actually shows in the people that they have. It's not the technology, it's the people. And some. This history is replete with organizations that have done remarkably well, not by leveraging the heck out of the technology, but truly by leveraging the change in the people's mindset. And, of course, that at that point that leverages technology where a proper here. But Robbie's a insightful person, always such a They lied to talk them, said they like for him to have chosen us as a its information technology for him to go pull his data warehouses and completely transformed how I was doing manufacturing across the globe. >>You know, I want to have some color of what you just said because some key keep takeaways that from what you just said, ashes is You know, you're right when you look back at the history of the computer industry used to be very well known processes, but the technology was the big mystery and the and the big risk and you think about with Cove it were it not for Technology Way didn't know what was coming. We were inventing new processes literally every day, every week, every month. It's so technology was pretty well understood. It and enabled that. And when you when you think when we talked earlier about putting data at the core, it was interesting to hear Robbie. He basically said, Yeah, we had a big data team in the U. S. A big tainted TV in Europe. We actually organized around silos and and so you guys played a role you were very respectful about, you know, touting active video with him. You did ask him, You know what role you play, But it is interesting to hear and talk about how he had to address that both culturally. And of course, there's technology underneath to enable that unification of data that silo busting, if you will. And you guys played a role in that. >>Yeah, I always enjoy, um, conversation with folks who have taken a problem, identified what needs to be done and then just get it done. And its That's more fascinating than you. Of course, I video plays a small part in a lot of things, and we're proud to have played a small part in his big initiative, and that's true of know the thousands of customers we talk about. But it's such a fascinating story to have leaders who come back and make this transformation happen, and to understand how they went about making those decisions, how they identified where the problem with these are so hard. We all see them in our own life, right? We see there is a there's a problem, but sometimes it takes a wider don't understand. How do you identify them and what do you have to do and more importantly, actually do it? And so whenever use, whenever I get an opportunity with people like Robbie, I think understanding that there's a way to help, uh, we always make sure that we play our own small part, and we're privileged to be a part of those kinds of journeys. >>Well, I think what's interesting about activity on the company that you created is essentially that. We're talking about the democratisation of data, that whole data pipeline, that discussion, that we had the self service of that data to the lines of business and, you know, you guys clearly play a role there. The multi cloud discussion fits into that. I mean that these air all trends that are tail winds for companies that can that can help sort of you know, flattened the data globe. If you if you will, your final thoughts. >>Yeah, I know you said something that is so much at the heart of every idea Exactly that you're talking to, if they truly is. The fundamental asset that I finally end up with is an organization. The democratization of data. Where I do not lock this into another silo, another platform, another ploughed. Another application has to be part of my foundation design and therefore my ability to use each of this cloud platform for the services they provide. While I and they were to move the data to where I needed to be. That is so critical. So you almost start to think about the one possession and organization now has. And we talked about this with a group of CEOs. They might be some pretty soon. Not too far off, but data stolen asset. I might actually have our data mark data market, just like you. I was stopped working, but I can start to sell my data. You know, imagine a coup in 19. There's so many organization that have so much data, and many of them have contributed to this research because this is an existence of issue. But you can see this turning into a next level. So, yes, we've got activities, will move the data toe one level higher where it's become a foundation construct for the organization. The next part is gonna actually done. This is the one asset would actually monetize someone stuff. And it will be not too long when you need to talk about how there's this new exchange and what's the rate of data for this company? Was, is that company in the future trading options? Who knows is gonna be really interesting. >>Well, I think you're right on this notion of a data. Marketplaces is coming, and it's not not that far away, Blash. It's always great to talk to you. I hope next year a data driven weaken we could be face to face. But I mean, look, this has been we we've dealt with it. It's it's actually created opportunities for us toe to reinvent ourselves. So congratulations on the success that you've had and ah, and thank you for coming on the Cube. >>No, thank you for hosting us and always a big fan off Cube. You guys, you engage with you since early days, and it is fascinating to see how this company has grown. And it's probably many people don't even know how much you've grown behind the seats, technologies and culture that you created yourself. So it's hopefully one day we'll strict the table that I would be another side and asking of our transformation. Digital transformation of Cuban cell >>I would love to. I'd love to do that index again. And thank you, everybody for watching our continuous coverage of active fio data driven keeper Right there. We'll be back with our next guest right after this short break. >>Thank you.

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 over the past 150 days virtually everybody that i know in the technology industry has become an expert on covid in some way shape or form we've all lived the reality that covet 19 has accelerated by at least two years many trends that were in motion well before the virus hit the cyber security sector is no exception and one of the best examples where we have witnessed the accelerated change hello everyone and welcome to this week's episode of wikibon cube insights powered by etr in this breaking analysis we'll update you on the all-important security sector which remains one of the top spending priorities for organizations and i want to give you a shout out to my colleague eric bradley from etr who gave me some really good data and some macro insights as well as some anecdotal data from csos for this episode let's take a look at the big picture first now for many years we've talked about the shifting patterns in networking moving from what's often referred to as a north-south architecture meaning a hierarchical network that supports you know age-old organizational structures well today the network is flattening into what they often refer to as an east-west model and the moat or perimeter it's been vaporized the perimeter is now wherever the user is and users are at home or they're at their beach houses thanks to kovid now this is a bad actor's dream as the threat surfaced has expanded by orders of magnitude and as we've said in the past the adversary is well funded extremely capable and highly motivated because the roi of infiltration and exfiltration is outstanding the cso's job quite simply stated is to lower that return on investment now the other big trend that we see is that the cloud and sas are reducing reliance on hardware-based solutions like traditional firewalls because so many workers are now at home they're in their accessing sensitive data identity and endpoint security are exploding xdr or extended detection and response and zero trust networks are on the rise organizations are increasingly relying on analytics and automation to detect and remediate threats you know alerts just don't cut it anymore i need action and so to do so they're turning to a number of best of breed point products that have the potential to become the next great security platforms and this is setting up an epic battle between hot startups that are growing very very quickly and entrenched incumbents that really aren't going to go down without a fight finally while security is clearly a top spending priority customers and their cfos continue to be somewhat circumspect with respect to how much they allocate toward security budgets especially in the context of a shrinking i.t spending climate that we have said is dropping between five and eight percent in 2020. now security is critical but even in these times spending is governed by these tight budgets well cyber remains a top category in the etr taxonomy in terms of its presence in the data set what this chart tells us is that cios and i.t buyers have other priorities that they have to fund this data shows a comparison of net scores over three survey dates october of last year april and july net score remember is an indicator of momentum which is calculated by subtracting the percent of customers spending less on the technology from those spending more it's more complicated than that but that's that's the basics and you can see that at a 29 net score the security sector is just one of many priorities that i.t buyers face now remember this is the july survey and it's asking customers are you planning to spend more or less in the second half of 2020 relative to the first half and it's a forward-looking metric so what may be happening here is that the height of the lockdown and in the u.s anyway and the pivot to work from home organizations were spending heavily and are now fine-tuning those investments and maybe addressing other digital priorities let's look back and do some pre and post-covet assessments of various players within the etr data set i'm gonna go fairly quickly through these next slides but i want to give you a perspective as to how the security landscape and the vendor momentum has changed in the past eight months first i'm going to take you back to the january data set we actually originally did this exercise last year and then we updated it right at the beginning of 2020. the chart shows the top-ranked cyber security companies based on two metrics the left-hand side sorts the data and ranks companies based on net score or spending momentum and the right-hand side shows the ranking by shared n which is a measure of the pervasiveness of a company in the data set i.e the number of mentions that they get in the sector and what we did is we gave four stars to those companies that showed up in the top of both of those rankings and two stars to those that were close so you can see that microsoft splunk palo alto and proofpoint as well as octa and crowdstrike and then we added z scalar in january as new and then cyber arc software all got four stars then we gave cisco and fortinet two stars now this next chart shows the same thing at the height of the u.s lockdown now you may say okay what's the difference there's still microsoft palo alto proof point octa cyber arc z scaler and crowdstrike at four stars with cisco and fortnite having two star stars splunk fell off but that's it well what's different is instead of making the cut the top 22 which we did last time we narrowed it down to the top ten in order for a company to make that grade so if we had done that in january octa crowdstrike zscaler and cyberark they wouldn't have made the cut but in april they did as their presence in the dataset grew and we strongly believe this is a direct result of the work from home pivot crowdstrike endpoint octa identity access management z-scaler cloud security and they're disrupting traditional appliance-based firewalls now just to note we placed dell emc which was rsa and ibm in the list just for context now let's take a look at the most recent july survey now a lot of i'm out on a limb a little bit here because many of these companies they haven't reported yet so we don't have full visibility on their business outlook but we show the same data for the most recent survey the red line that you see there is the top 10 cutoff point and you can see splunk which didn't make the cut in april is back on the four-star list it's very possible buyers took a pause last quarter and focused attention on work from home but splunk continues to impress as it shifts toward the subscription model that we've talked about in the past splunk has a very strong hold on the sim space but everyone wants a piece of splunk especially some of the traditional firewall companies who they're seeing their hardware business dying so we're watching the competition from these players but also some other players like tennable now proof point fell off the four-star list because its net score didn't make the top ten crowdstrike cyber arc and zscaler also fell back because they dropped below the top 10 in shared in but we still really like these companies and expect them to continue to do well you know it could be some anomalies in the survey but we're trying to be as transparent as possible with you share the data listen to it interpret it and really adjust our models accordingly each quarter now let me make a few points and try to interpret what might be happening here first i want to point out octa pops to the top of the net score ranking overtaking crowdstrike's momentum from the last survey now one customer in the financial services sector told eric bradley on a recent then we're seeing amazing things from octa but the traditional firewall companies are stepping into identity they may not be best of breed but they have a level of integration and that's appealing to this individual this person also specifically called out palo alto and fortinet is trying to encroach on that space so keep your eyes on that now crowdstrike has declined noticeably which surprised us z z scalar is actually showing more momentum relative to the last survey so that's a positive palo alto and microsoft are consistently holding serve and continue to be leaders proof point and cyber arc are showing a bit of a velocity drop and sales point and tenable are also catching our attention in this survey and of course sales sale point which is identity management had a great quarter and reinstituted its guidance giving us the benefit of hindsight on its performance so it was actually pretty easy to give them two stars now just a side note by the way we've cut the data here with those companies that have more than 50 mentions in the sector we didn't do that the first time we did this we allowed companies with less than 50. so we're trying to tighten that up a bit so we still maintain strongly that you're seeing cloud endpoint and identity as the big security themes here csos need tools to be responsive they don't want to just get an alert secops pros would rather immediately shut off access and risk pissing off a user than getting hacked and companies are increasingly turning to ai to detect and they're relying on automation to remediate or protect and fence off critical resources let's now look at the two players or players in our two-dimensional view followers of this program know that we like to plot vendors within a sector across two of our favorite metrics net score or spending momentum which is a simple metric that tracks those spending more versus less on the technology and market share which measu measures a vendor's pervasiveness in the data set and it's calculated by taking the number of mentions a vendor gets within a sector divided by the total responses what we show here are the key security players that we've highlighted over the last several quarters let me start with microsoft microsoft has consistently performed well in the security sector as well as other parts of the etr taxonomy as you know they have a huge presence in the survey which is indicated on the horizontal axis and you can see they have a very solid net score which is shown on the y-axis impressive for a company their size now one interesting thing is you don't see aws in this chart and it's because aws and microsoft at least so far have somewhat different strategies with respect to security microsoft with its long application software history and sas presence across office 365 and sharepoint etc with active directory has been really focused on selling security solutions to directly protect its apps they have offerings like defender atp which is advanced threat protection sentinel which is microsoft sim cloud offering azure identity access management and the company's really going hard after this space now aws of course prioritizes security but they don't show an etr data set the same way microsoft does it's almost like aws is hiding in plain sight look aws has always put a great deal of emphasis on security and securing its infrastructure like the s3 buckets and it's you know it announced iam for ec2 way back in 2012. and last year at its reinforced conference you saw an impressive focus on security in a burgeoning security ecosystem in fact when you think of getting started in aws you really think about three things ec2 s3 and iam so i'd expect to see aws really become more prominent over time in the data set now i'll spend a minute talking about octa for the first time since we've been analyzing the security space with etr data octa has the highest net score at 58 percent it had consistently been crowdstrike with this moniker and the momentum lead the company though is dropped in this quarter survey and that's something that we're watching and by the way we're not implying that octa and crowdstrike are direct competitors they're not now as you can see nonetheless that crowdstrike z scalar and sales point sale sale point show very elevated net scores and we've plotted tenable here which is also showing some strength so you can see the respective positions of proof point and fortinet these are more mature companies they were founded in the early part of the century so you'd expect them to have somewhat lower net scores given their history and maturity and then there's cisco they've got a huge presence in the data and big in security cisco's doing really well in that space it consistently grows its security business in the double digits each quarter and it's a real feather in the cisco portfolio cap this is important because cisco's traditional hardware business continues to come under pressure splunk we talked about a lot and it's no surprise at their leadership position but i want to talk a little bit more about palo alto networks here's a company that we've talked about quite a bit in the past they are a tier one player in security they got great service csos want to work with them because they are thought leaders they're like a gold standard and have an impressive portfolio of great solutions but their traditional firewall business is coming under pressure for the reasons that we discussed earlier now palo alto has expanded its portfolio into the cloud and with prisma the company's suite of security services it will maintain a leadership position in our view but palo alto networks as we've discussed had some missteps with its product transition its sales execution and some of some challenges with its pricing models and it hurt their stock price but we've always said that they would work through these issues and that that was a buying opportunity the other thing about palo alto is you know they're considered the expensive choice you got to pay for that gold standard but that's what customers you know will tell us and so you're paying up for those top tier offerings but that's a sort of two-edged sword for palo alto here's an example why people often compare fortinet to palo alto and as we've shared in previous segments the valuation divergence between palo alto and fortinet where the the latter was making a smoother transition to its future and people often tell us that fortinet well you know maybe it's considered not as elite as palo alto they are a value choice their stuff just works and fortinet is a great alternative to palo alto and that has served them very well now let's take a closer look at the valuations of some of these companies we started off this segment by saying that the pandemic has affected every sector and especially cyber security so the next chart that we're showing here is the progression of key valuation metrics since earlier this year what we show are the valuations of nine of the companies in the sector since mid-february the data tracks their respective valuations their revenue multiples their growth rates in both value and revenue revenue growth is shown in the last column for the most recent quarterly report now the companies in red have yet to report the report any day now so he said i'm flying a little bit blind here and we'll have to take a look after the earnings to see how the survey data aligns with the actual results but let me make a few points here first here's the s p in nasdaq performance you see it in february in june and august pandemic recession what are you talking about you'd never know it looking at this data the nasdaq especially is up 14 said since mid february which is quite astounding next i want to come back to the discussion about palo alto and fortinet fortinet already has reported this quarter and palo alto has not but you can see based on the revenue multiples highlighted in red that the valuation divergence is starting to shrink a little bit and we'll see if that holds up after palo alto reports now the big eye popper in this chart is the valuation increases from february to august for octa crowdstrike and z scalar 52 67 and 104 percent increase respectively now you can't say we didn't warn you that these companies were all well positioned when we reported last year and in our january episode but i did say actually to be honest in the last episode that these three i thought were getting a little expensive that was a couple months ago and since then they've continued to run up so if you've been waiting for an entry point based on my advice well i'm sorry for that but look at the revenue multiples look at the expansion in the orange octa goes from 34x to 52x crowdstrike from 39x to 66x z scalar 25x to 43x i mean wow let's see what happens after these three report by this time i would have hoped that they'd taken a little breather maybe over the summer and you could have jumped in to these stocks but they just keep going up and despite the decline in net score for crowdstrike i still really like all three of these companies and feel that they're very well positioned from a product standpoint and customer feedback perspective and finally i want to mention sale point which we said last time was one to watch sale point crushed its quarter bringing in some large deals and providing forward guidance nearly a 50 percent valuation increase since february in a revenue multiple expansion from last quarter where the street last quarter wasn't really thrilled with their numbers but identity management is hot and so now is sales point from the streets perspective the last thing i'll say here is watch the growth rates expectations are very high for some of these companies and the street will cream any of them that misses now that may be your opportunity to jump in because i like these companies i think they're disruptors but as always do your research and watch out for the big whales trying to freeze the markets on these guys all right let's wrap up we've covered a lot of ground today and surf the landscape a little bit so look the trend is plain as day the move to sas is entrenched and by the way this isn't necessarily all good news for buyers cios and cfos tell me that the dark side of capex to opex is unpredictable bills but the flexibility and business value gained is outweighing the downside and every vendor in this space is transitioning into a sas and annual recurring revenue model we believe the remote work trend is here to stay organizations are re-architecting their business around work from home and we think that they're seeing some real benefits they've made investments and it's driving new modes of work and productivity they're not just going to throw away those investments why should they what just to go back to the old way it's not going to happen and if we as we've said previously look the internet it's like the new private network so you've got a question vpns and sd-wan they start to look like stop gaps and of course you know the cloud endpoint security cloud-based iam they are clearly winning in the marketplace you know we're also seeing new security regimes emerge where the cso and the secops team are not this island we we've seen even some csos falling back under the cio which used to be taboo he used to be thought of that's like the fox guarding the hen house but this idea of shared responsibility is not just between the cloud providers and the secops teams because security is a board level priority everyone in the business is becoming more aware more attuned and despite the millennials fascination with and undotted courage when it comes to tick tock i digress now the last two points are interesting i remember reading a post by john oltzek who was an esg security analyst and he predicted last year that integrated suites would win out over the buffet of point products on the market and you know generally i i agreed with that assessment but look at least in the near term and probably mid-term that doesn't seem to be happening as we we've seen these hot companies really take off the ones that we've highlighted now these companies have ambitions beyond selling products and they would bristle at me lumping them into point products their boards are going after platform plays so they're on a collision course with each other and the big guys this should be fun to watch because the big integrated companies are well funded they got great cash flow they got large customer bases and and i've said they're not going down without a fight so i would expect eventually there's going to be more of an equilibrium to what seems to be right now a bifurcated and unbalanced market today so you're going to see more m a activity expect that however at these valuations some of these companies that we've highlighted they're becoming acquisition proof as such they'd better keep innovating or they're going to be in big trouble all right that's it for today remember these episodes are all available as podcasts wherever you listen so please subscribe i publish weekly on wikibon.com we've added in the wikibon menu bar a breaking analysis link that has all the episodes in there i also publish on siliconangle.com so check that out and please do comment on my linkedin posts don't forget to check out etr.plus for all the survey action get in touch on twitter i'm at d vellante or email me at david.vellante at siliconangle.com this is dave vellante for the cube insights powered by etr thanks for watching everybody be well and we'll see you next time [Music] you

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. (audience applauding) (upbeat music) ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ba do ♪

>> Live from Washington DC, it's theCUBE. Covering AWS Public Sector Summit 2018. Brought to you by Amazon Web Services and it's ecosystem partners. >> Hey, welcome back everyone. This is theCUBE's exclusive coverage live in Washington DC at Amazon Web Services AWS Public Sector Summit. I mean, it's so jam-packed you can't even move. This is like the re:Invent for Public Sector even though it's a summit for Amazon Web Services. I'm here with Dave Vellante, my co-host. Our next guest is John Wood, Chairman and CEO of Telos, and Rick Tracy, Chief Security Officer and the co-inventor of Xacta, it's hot technology. John, great to see you, welcome to theCUBE. >> Thanks guys. >> Thanks for having us. >> I love to get the brain trust here, John you're, like, probably one of the most experienced cyber security gurus in the DC area still standing. (laughing) As we said last time on theCUBE. >> Always, always. >> Okay. (laughing) And you've got some patents here, with some core technology, so first of all, I want to, before we get into some of the cool features of the products, talk about the dynamic of public sector, because Amazon has these summits, and they're kind of like a recycled re:Invent. Small scale, still packed. Talk about what Public Sector Summit is, because this is a completely different ballgame in this world. >> Sure, it's a perfect age for the cloud, and what this summit does, is it provides a great venue for people to come, learn about what works, get best practices, find use cases and just see what the ecosystem's all about in terms of how to make it work with the cloud. >> Rick, so what's your take? >> Well, if there's any doubt about it, what, is it double the size of last year? I think there were 7,000 people here last year and Teresa said today 14,500. So, yeah, I mean, it suits us perfectly because this is our sweet spot. >> So, Dave and I are always amazed by Amazon in general, the slew of announcements, Teresa Carlson picking the reins up where Andy Jassy does that Amazon re:Invent which is just tons of content, so many new announcements. What's your guys take on the hot news for you guys, because you guys are a major sponsor and you're in the ecosystem, you've been doing a lot of business with Amazon. >> Sure. >> What's going on in the business? What's happening with Telos? Why is it so booming right now for you guys? >> Well, I think people realize that there is a way to use automation where security can help drive cloud adoption. So, Rick and I co-authored an article back in 2011 that talked about why the cloud was more secure and it went over kind of like a lead ballon. And then back in 2014 the agency made the decision, the CIA made the decision, arguably the most security conscious organization in the world, to go to the cloud. And so that was a big, big, big, deal. But what we do is we help drive the security automation and orchestration stuff so you can reduce the time it takes to get what's called your authority to operate. And so I think that's a big deal now. The use of automation is being used to enhance the mission, so that the mission owners can get to their mission using the cloud, much more quickly. >> And we heard from the most powerful sentence in the keynote this morning was, "The cloud on it's weakest day is more secure than Client Service Solutions." This is a practitioner saying that, a leader of an agency saying that, not Amazon or not Telos. >> Absolutely. >> And it's because of that automation, right? I mean, that's really a key factor. >> It's because of the automation. It's also because the cloud providers are making sure that they lock down their physical infrastructure. Guards, gates, guns. All of the physical infrastructure and the virtual infrastructure, they do a really good job of that. If you think about it, the US government, unfortunately, 80% of their spend is around maintaining old systems. Well, the cloud providers are keeping modern. Those old systems have a lot of weaknesses from a standpoint of cyber security flaws. So, with a modern technology like the cloud, there's a lot more you can do around automation to lock down much more quickly. >> And the standardization that you get with a cloud makes it's easier as well, because there's not so many variations of things that you have to figure out how to protect. So, the standardized services that everything's built on really helps. >> Yeah, and people are adopting cloud in kind of different ways, which makes it harder, too. But you get the benefits of scale and speed, certainly. But I got to just pick up on some big news that's happened just last night and today. Microsoft Azure suffered an 11 hour downtime across Europe. 11 hours Azure's down, Microsoft Azure. This is a huge concern. Downtime, security, these are issues, I mean, this is just like, so, what's going on with this? >> Well, the truth of the matter is, if you think about where Amazon is today, Amazon is light years ahead of the rest of the cloud guys. The reason for that is they made the decision early on to take the risk around cloud. As a result of that, they have so many lessons learned that are beyond all of the other cloud providers, that that wouldn't happen to Amazon today, because they'll be able to back up, replication and duplication if they have, and their environments. >> How big do you think that lead is? You know, there's a lot of debate in the industry that other guys are catching up. The other side of the coin is, no, actually the flywheel effect is a lot like Secretariat in the stretch run of the Belmont, you were talking about racing before. What's your sense of that lead, even subjectively. >> I think it's between 5 and 10 years. There was a, it was crickets in this world, in the public sector world for cloud up until, literally, the agency decided to adopt. So the CIA made that decision, that was, sort of, the shot heard around the world as it relates to cloud adoption. Not just for public sector but for commercial as well, 'cause if you look at Amazon's ramp up, right after that decision was made, their ramp up has been amazing. >> That was a watershed event, for sure. >> It was, and it was very well documented, I mean, I read the judges ruling on that when IBM tried to stop them and the judge eviscerated IBM. And of course IBM had no cloud at the time, they had to go out and spend two billion dollars on software. John has lots of opinions on that, but okay, so that leaves-- >> I'm on the right side of history on that call. >> I think you are, it was a pretty good call. What about, what should be practitioners be thinking about? You talked about the standardization. Where should they be focused? Is it on response, is it on analytics, is it on training? What should it be? >> Well, from our perspective it is, a lot of the focus is on analytics, right? So, a lot of data that we've helped our customers collect over time for this ATO process that John previously mentioned, our goal with IO, Xacta IO, is to help organizations leverage that data to do more through analytics, so there's this dashboard with ad hoc reporting and analytic capability that's going to allow them to blend asset data with risk-to-threat data, with other sorts of data that they're collecting for ATO, specifically for the ATO process, that they can use now for more robust cyber risk management. So, for me, analytics is huge moving forward. >> And that's a prioritization tool so they can focus on the things that matter, or maybe double-click on that? >> It could be, it could be a prioritization tool, but it could also be a tool that you use to anticipate what might happen, right? So, some analytics will help you determine this asset is vulnerable for these variety of reasons, therefore it has to go to the top of the sack for remediation. But also, using that data over time might help you understand that this plus this plus this is an indication that this bad thing is going to happen. And so, analytics, I think, falls into both categories. Probably it's more the forecasting and predictive is something that's going to come later but as you unmask more data and understand how to apply rules to that data, it will naturally come. So, Rick and I have worked together for many, many years and, over a quarter of a century, so the way I would say it is like this. Xacta 360 helps you to accelerate your authority to operate, but that's a point in time. The holy grail for us as security practitioners is all around continuous monitoring of your underlying risk. So, the data analytics that he's talking about, is where we come about and looking at Xacta IO. So, Xacta IO helps fulfill that mission of continuous compliance, which means that the ATO is no longer just relevant at that moment in time because we can do continuous monitoring now at scale, in hybrid environments, in the cloud, on prem. 'Cause our clients are huge, so they're going to be a combination of environments that they're sitting in, and they need to understand their underlying risk posture. They need to have, they're going to have all kinds of scanners, so we don't really care, we can ingest any kind of scanner that you have with Exact IO. As a result of that, the security professional can spend their time on the analysis and not the pedestrian stuff that's just kind of wasting time, like documentation and all that stuff. >> Yeah, for us, data's a means to an end, right? It's either to get an ATO or to help you understand where you need to be focusing your resources to remediate issues. So, for us, leveraging the data that's produced by many companies that are at this show. Their data is a means to help us get our job done. >> Were you able to have, one follow up, if I may, were you able to have an impact, to me, even, again, subjectively, on that number, whatever that number is, that we get infiltrated, the customer gets infiltrated, it's 300 days before they even realize it. Are you seeing an impact on that as a result of analytics, or is it too early days? >> I would say it's still early. But it's reasonable to expect that there will be benefits in terms of faster detection. And maybe it's not even detection at some point, hopefully, it's anticipating so that you're not detecting something bad already happened, it's avoiding it before it happens. >> Yeah, and let me say it this way, too. You know, if you listen to John Edwards, the CIO from the CIA, he talks about how the reason he loves the cloud is because it used to take the agency about a year to provision a server, now it's a few minutes, right? Well that's great, but if you can't get your authority to operate, 'cause that can take another 18 months, you're not going to get the benefit of the cloud, right? So what we do, is we help accelerate how fast you can get to that ATO so that guys like the agency and anybody else that wants to use the cloud can use it much more quickly, right? >> Yeah, and the continuous integration and all that monitoring is great for security but I've got to ask you a question. Analytics are super important, we all know data analysis now is in the center of the value proposition across the board, horizontally. Not just data warehousing, analytics that are used as instrumentation and variables into critical things like security. So, with that being said, if you believe that, the question is, how does that shape the architecture, if I'm in an agency or I'm a customer, I want to build a cloud architecture that's going to scale and do all those things, be up, not go down, and have security. How does the architecture change with the cloud formula for the decision maker? Because right now they're like, "Oh, should I do multi-cloud, should I just Amazon" So, the data is a critical architectural decision point. How do you guys see that shaping, what's your advice to practitioners around designing the cloud architecture for data in mind. Just use Amazon? (laughs) >> Well, yes. (laughs) Just use Amazon. I mean, all the tools that you need exist here, right, and so-- >> If all the tools you need in the cloud exist here. >> Alright, so rephrase another way. >> But John, the issue is you're not going to have all your stuff in the cloud if you're the air force or if you're the army, because you have 75 years of data that you got to push in. So over the next 10 years there's going to be this "hybrid" environment where you'll have some stuff in the cloud, some stuff in a hybrid world, some stuff on prem, right? >> How I secured that, so that's a great point. So, data's everywhere, so that means you're going to need to collect it and then measure certain things. What's the best way to secure it and then is that where Xacta fits in? I'm trying to put that together if I'm going to design my architecture and then go to procurement, whether it's on premise or multi-cloud. >> Well, there are lots of security products that people use to secure, whether you're on prem or whether you're in the cloud and our platform leverages that information to determine whether things are secure enough. So there's a distinction between cyber risk management and actually securing a database, right? So, there's so many granular point products that exist for different points along the security chain, lifecycle chain, if you will, that our objective is to ingest as much of that information and purpose it in a way that allows someone to understand whether they're actually secure or not. And so it's understanding your security posture, transforming that security information to risk so that you can prioritize, as you were talking about before. >> You're taking a platform mentality as opposed to a point product. >> We're taking an enterprise view of risk. So, the enterprise is, remember, it's on prem, and hybrid and cloud. If all your stuff is in the cloud, Amazon has the answer for you. None of our customers are in that situation. If you're a start up, Amazon's the way to go, period. But all of our customers have legacy. As a result of that it's an enterprise view of risk. That's why companies like Telos partner so well with Amazon because they're all about being close to the customer, they're all about using automation. We are as well. >> Alright, talk about the news you guys have, Xacta IO, you're the co-inventor of it, Jack. Talk about this product. What's the keys, what does it do, where's it applied to, you mentioned a little bit of getting past the authority time point there. What's the product about? The product is about ingesting massive amounts of information to facilitate the ATO process, one, but managing cyber risk more generically because not everybody has an ATO requirement. So, you asked a few seconds ago about, so you're taking a platform approach. Yes, we're blending three separate products that we currently have, taking that functionality and putting it on a very, very, robust platform that can exist on prem, it can exist in the cloud. To enable organizations to manage their cyber risk and if they choose, or they have a requirement, to deal with things like FedRAMP and risk management framework and cyber security framework and iso certification and things of that nature. The point is, not everyone has an ATO requirement but everyone has a need to manage their risk posture. So we're using our ability to ingest lots and lots of data from lots and lots of different sources. We're organizing that data in ways that allow an organization to understand compliance and/or risk and/or security, and visualize all that through some dashboard with ad hoc reporting that let's them blend that data across each other to get better insights about risk posture. >> And to visualize it in a way that makes sense to the user. >> Yes, so, if you're the CEO, you're going to want to see it a certain way. If you're the IT manager, you're going to want to see it a certain way. If you're a risk assessor, you're going to want to see it a different way. So that's kind of what we're talking about. >> I got to ask you one question, I know we got to go, but, a hardcore security practitioner once said to me that hardcore security practitioners, like you guys, when they were kids they used to dream about saving the world. So, I want to know, who's your favorite superhero? >> Superman. >> Superman? >> Spiderman. >> Alright, awesome. (laughing) >> That was a basic question for you guys. >> Thank you very much >> Yeah, that's the hardest question, see they're fast, they know. Star Trek or Star Wars? (laughing) >> Depends on the generation. >> We won't go there. theCUBE have 15 more minutes today. Okay, final question, what's this going to do for your business now you have new, opened up new windows with the new product integration. How's that going to change Telos, what does it do for you guys from a capabilities standpoint? >> Well, the big thing I'd suggest your listeners and your watchers to consider is, there's a new case study that just came out, it's published jointly by the CIA, Amazon and Telos, talking about why working together is really, really, really groundbreaking in terms of this movement to the cloud. 'Cause your public sector listeners and viewers are going to want to know about that because this ATO thing is really a problem. So this addresses a massive issue inside of the public sector. >> And final question, while you're here, just to get your thoughts, obviously there's a big change of the guard, if you will, from old guard to new guard, that's an Amazon term Andy Jassy uses. Also, we all saw the DOD deal, JEDI's right there on the table, a lot of people jockeying, kind of old school policy, lobbying, sales is changing. How is the landscape, from a vendor-supplies to the agencies changed and/or changing with this notion of how things were done in the past and the new school? So, three points, legislatively there's top cover, they understand the need to modernize, which is great. The executive branch understands the need to modernize through the IT modernization act as well as the cyber security executive order. And then lastly, there are use cases now that can show the way forward. Here's the problem. The IT infrastructure out there, the IT guys out there that do business in the government, many of them are not paid to be efficient, they're paid cost plus, they're paid time and material, that's no way to modernize. So, fundamentally, I think our customers understand that and they're going to revolutionize the move forward. >> And the rules are changing big time. Sole source, multi-source, I mean, Amazon's on record, I've got Teresa on record saying, "Look, if we don't want a sole source requirement, let everyone bid fairly." Let's see who wins. Who can bring a secret cloud to the table? No one else has that. >> In terms of past performance and customer use cases they're pretty much in the head, for sure. >> Great, Amazon kicking butt here, Telos, congratulations for a great event, thanks for coming on. >> Thanks a lot guys. >> I appreciate it. >> Alright, CUBE coverage here in DC, this is theCUBE, I'm John Furrier with Dave Vellante. Stay with us, we have more great interviews stacked up all day and all day tomorrow. Actually you have half day tomorrow until two 'o clock Eastern. Stay with us for more, we'll be right back. (upbeat music)

Live from San Juan, Puerto Rico. It's the Cube. Covering Blockchain Unbound. Brought to you by, Blockchain Industries. >> Welcome back everyone. This is the Cube's exclusive coverage in Puerto Rico. I'm John Furrier, the co-host of the Cube, co-founder of SiliconANGLE Media. In Puerto Rico for Blockchain Unbound, this is a global conference. Going to the next level in industry migration up and growth, and blockchain, decentralized internet and obviously cryptocurrency, changing the world up and down the stack. I have an industry veteran here. My next guest Salim is founding CEO, Singularity University and author of the best-selling book, Exponential Organizations. He's seen many waves, friend, known him for years. Haven't seen you in a while, you look great. You haven't changed. >> (laughs) The hair has changed a lot. >> (laughs) I've still got mine. Hey great to see you. Bumping into you in Puerto Rico is really compelling because you have a nose for the future, and I've always respected that about you. You have the ability to understand at the root level what's going on but also pull back and see the big picture. Puerto Rico is the center of all the action because the killer wrap in this is money. So money is driving a lot of change, but there's some fundamental infrastructure, stack upgrades going on. Blockchain has been highly discussed, crypto is highly hyped, ICO's are-- Scammers out there but now some legits. What's your take? What's your view right now on the current situation? >> Well I think what's happening with a place like Puerto Rico is. When you get kind of wiped out of the old, you have the chance to leap-frog. When you think about any of our traditional environments, laying down Blockchain technologies, et cetera. It's really, really hard because you have to get the Supreme Court, the Constitution to approve blockchain based land titles, and then you build a stack there from a legal perspective. Here they can basically start from scratch and do it completely from the ground up. Which is what's exciting for everybody here. >> The top story that we've been reporting here is that Puerto Rico is rebooting. The hurricane obviously, I won't say a forcing function, but in general when you get wiped out, that is certainly an opportunity to rebuild. If there's any kind of silver lining in that. >> There's a long history of that. Japan got wiped out during World War II, so did Germany and they rebounded incredibly. We've seen that recently with Rwanda. We do a lot of work in Medillin, in Colombia, and that's just been one of the worst cities in the world, is now the most innovative city in the world. So this is the transition that we've seen a pattern for. >> One of the things I'm really excited about decentralization and blockchain is all the conversations have the same pattern. Efficiency is getting wired into things. So if you see slack in the system or inefficiencies, entrepreneurs are feeling the void. The entrepreneurial eye of the tiger goes that to that opportunity to reset, reduce steps, save time and make things easier. Classic value proposition in these new markets. You run a great university but also author of Exponential Organizations. A lot of people are scared, they're like, "Whoa, hold on. Slow down, this is bullshit, "we're not going to prove it." And then the other half saying, "No this is the future." So you have two competing forces colliding. You have the new guard saying, "We got to do this, this is the future." Old guard saying, "Blocks, Road blocks, blockers" You covered this in your book in a way, so how do you win, who wins? How do you create a win win? >> You can create a win win. What you have to do is leap-frog to the newest, fast as possible. The only question is, how can you get to the new? And the problem that you have is, as you rightly pointed out is. When you try disruptive innovation in any large organization or institution, the immune system attacks. I saw this at Yahoo running Brickhouse. Yahoo is supposedly a super advanced organization, and yet the minute you try to do something really radical, you spend all your time fighting the mother ship. So I've been focusing a lot of time the last few years focused on that particular problem, and we're pretty excited, we believe we've cracked it. >> How does someone crack that code? If I'm Puerto Rico, obviously the government officials are here at Blockchain Unbound. This is not just a tech conference. It's like a tech conference, investor conference, kind of world economic form rolled into one. >> Sure >> There's some serious players here. What's your advice to them? >> So what we do, and let me describe what we do in the private sector and what we do in the public sector. A couple of years ago, the global CI of Procter & Gamble came to me and said, "Hey, we'd like to work with you." And what we typically see is, some executive from a big company will come to Singularity. They'll go back headquarters with their hair on fire going, "Oh my god!" If they're from BMW for example. They go back going, "Drones, autonomous cars, hyperloop, VR." Back in Munich, they'll be given a white coat and some medicine and be put in a corner. "You're too crazy, now stand over there." And that's the tension that you are talking about. And then somebody else will come six months later then they'll do the Silicon Valley tour, then they'll have one of our people go over there, and it takes about three years for the big company to get up to speed, just the C-Suite to get up to speed. Forget transmitting that down. So I was talking to Linda Clement-Holmes and I said, "Look we're about to start this three year dance "I've been thinking about this, "let's shrink it to 10 weeks." So we designed what we now call an ExO Sprint. Which is how you get a leadership, culture and management thinking of a legacy organization, three years ahead in a 10 week process. And the way we do it is, we're in an opening workshop, that's really shock and awe. Freaks out all the incumbent management. And then young leaders and future lieutenants of the business do the thinking of what should come next. And they report back. Some thing about that opening workshop suppresses the immune system, and when the new ideas arrive they don't attack them in the same way. >> It's like a transplant if you will. >> It's like when you do a kidney transplant. You suppress the immune system, right? It's that same idea. So we've now run that like a dozen times. We just finished TD Ameritrade, HP, Visa, Black & Decker, et cetera. We're open-sourcing it. We're writing a manual on how to do it so that anybody can self-provision that process and run it. Because, every one of the Global 5000 has to go through that process with or without us. So then we said, "Okay, could we apply it to the public sector?" Where the existing policy is the immune system. You try and update transportation and you're fighting the taxis. Or education and you're fighting the teacher's unions. We have a 16 week process that we run in cities. We do it through a non-profit called the Fastrack Institute based out of Miami. We've run it four times in Medillin, in Colombia and we just finished four months with the mayor of Miami on the future of transportation. We're talking to the officials here about running a similar process here in Puerto Rico. >> Are they serious about that? Because they throw money at projects, it kind of sits on the vine, dies on the vine. Because there is an accelerated movement right now. I mean, exponential change is here. I'll give you an example. We're seeing and reporting that this digital nation trend is on fire. Suddenly everyone wants digital cities, IoT is out there. But now what cryptocurrency, the money being the killer app. It's flowing everywhere, out of Colombia, out of everywhere. Every country is moving money around with crypto it's easier, faster. So everyone is trying to be the crypto, ICO city. Saw it on Telegram today, France wants to be, Paris wants to be the ICO city. Puerto Rico, Bahrain, Armenia, Estonia. U.K. just signed a deal with Coinbase. What the hell is going on? How do you rationalize this and what do you see as a future of state here? >> Well I think, couple of thoughts. And you're hitting into some of the things I've been thinking about a lot recently. Number one is, that when you have a regulatory blockage, it's a huge economic developing opportunity for anybody that can leap-frog it. Nevada authorized autonomous cars early and now a lot of testing is done there. So the cities that have appreciated-- >> So you're saying regulatory is an opportunity to have a competitive advantage? >> Huge, because look at Zug in Switzerland. Nobody had ever heard of the place. You pass through there on the way to Zermatt. But now it's like a destination that everybody needs to get to because they were earlier. This is the traditional advantage of places like Hong Kong or Dubai or whatever. They're open and they're hungry. So we're going to see a lot of that going on. I think there's a bigger trend though, which is that we're seeing more and more action happen at the city level and very, very little happen at the national or global level. The world is moving too fast today for a big country to keep up. It's all going to happen this next century at the city level. >> Or smaller countries. >> Or small countries. >> So what's going on here at Blockchain Unbound for you? Why are you here? What are you doing? What's your story? >> I have this kind of sprint that we run in the private sector and in the public sector and then a community of about 200 consultants. And I have to pay 200 people in 40 countries and it's and unholy mess. Withholding taxes and concerns around money transfer costs-- >> It's a hassle. >> It's a nightmare. And so I've been thinking about an internal cryptocurrency just to pay our network. All of a sudden now, three or four countries have said, "Hey we want to buy that thing, "to have access to your network." So I've got all this demand over here, and I need to figure out how to design this thing properly. So I've been working with some of the folks like Brock and DNA and others to help think through it. But what I'm really excited about here is that, there's a-- You know what I love is the spectrum of dress. You got the radical, Burning Man, hippie guy, all the way to a three-piece suit. And that diversity is very, very rich and really, real creativity comes from it. This feels like the web in '96, '95. It's just starting, people know there's something really magical. They don't quite know what to do. >> Well what I'm impressed about is that there's no real bad vibe from either sets of groups. There's definitely some posturing, I've noticed some things. Obviously I'm wearing a jacket, so those guys aren't giving me hugs like they're giving Brock a hug. I get that, but the thing is, the coexistence is impressive. I'm not seeing any real mud-slinging, again I didn't like how Brock got handled with John Oliver. I thought that was unacceptable because he's done a lot of good work. I don't know him personally, I've never met him, but I like what he's doing, I like his message. His keynote here, at d10e, was awesome. Really the right messaging, I thought. That's something that I want to get behind and I think everyone should. But he just got trashed. Outside of that, welcoming culture. And they're like, "Hey if you don't like it, "just go somewhere else." They're not giving people a lot of shit for what they do. It's really accepting on all sides. >> Here's my take on the whole decentralization thing. We run the world today on a series of very top down hierarchical structures. The corporation, the military industrial complex, Judeo-Christian religions, et cetera. That are very hierarchical-- Designed for managing scarcity, right? We're moving the world very, very quickly to abundance. We now have an abundance of information, we'll soon have an abundance of energy, we'll soon have an abundance of money, et cetera. And when you do these new structures, you need very decentralized structures. Burning Man, the maker movement, the open-source movement, et cetera. It's a very nurturing, participatory, female type of archetype and we're moving very quickly to that. What we're seeing in the world today is the tension going from A to B. >> And also when you have that next level, you usually have entrepreneurs and sponsorships. People who sponsor entrepreneurs the promotion side of it, PR and that starts the industry. Then when it hits that level it's like, "Wow it's going to the next level." Then it gets capital markets to come in. Then you have new stake holders coming in now with government officials. This thing is just rocket-shipping big time. >> Yes >> And so, that's going to change the dynamics. Your thoughts and reaction to that dynamic. >> Completely, for example... When we do these public sprints we end up usually with a decentralized architecture that needs to built. For example, we're working with the justice system in Colombia. And the Supreme Court has asked us to come in and re-do the entire justice system. Now you think about all the court filings and court dates, and briefs, and papers all should be digitized and put on a blockchain type structure because it's all public filing. We have an opportunity to completely re-do that stack and then make that available to the rest of the world. I think that trend is irreversible for anything that previously had centered-- I mean, most government services are yes, ratifying this and ratifying that. They all disappear. >> Well Salim, I want to tap your brain for a second. Since you're here, get it out there, I want to throw a problem at you, quick real time riff with you. So one of the things that I've been thinking about is obviously look at what cloud computing did, no one saw Amazon web services early, except some of the insiders like us. Who saw it's easy to host and build a data center. "I have no money, I'm a start-up or whatever." You use AWS, EC2 and S3... They were misunderstood, now it's clear what they're doing. But that generated the DevOps movement. So question for you is, I want to riff with you on is, "Okay that created programmable infrastructure, "the notion of server-less now going mainstream." Meaning, I don't have to talk about the server, I need resource so I can just make software, make it happen. That's flipped around the old model, where it used to be the network would dictate to the applications what they could do. How is that DevOps ethos, certainly it's driven by open-source, get applied to this cryptocurrency? Because now you have blockchain, cryptocurrency, ICO is kind of an application if you will, capital market. How does that model get flipped? Is there a DevOps model, a blockchain ops model, where the decentralized apps are programming the blockchain? Because the plumbing is the moving chain right now. You got, Hashgraph's got traction, then you got Etherium, Lightning's just got 2.5 million dollars. I mean, anyone who's technical knows it's a moving train in the plumbing. But the business logic is pretty well-defined. I'm like, "I want to innovate this process. "I'm going to eliminate the efficiency." So this dynamic. Does the business model drive infrastructure? Does the plumbing drive the business model? Your thoughts on this new dynamic and how that plays out. >> I suspect you and in violent agreement here. It's always going to be lead by the business model because you need something to act as the power of pull to pull the thing along, right? The real reason for the success of Etherium right now is all the ICOs and it was a money driven thing. Today we're going to see these new stacks, now we're on version three of these new types of stacks coming along, and I think they're all looking for a business model. Once we find some new killer ops for this decentralized structure, then you'll see things happen. But the business model is where it's at. >> So basically I agree with you. I think we're on the same page here. But then advice would be to the entrepreneurs, don't fret about the infrastructure, just nail your business model because the switching cost might not be as high as you think. Where in the old days, when we grew up, you made a bad technical assess and you're out of business. So it's kind of flipped around. >> Yeah, just hearing about this term, atomic swaps. Where you can just, essentially once you have a tokenized structure, you can just move it to something else pretty quickly. Therefore, all the effort should be on that. I think finding the really compelling use cases for this world is going to be fascinating to see. >> So software-defined money, software-defined business, software defined society is coming. >> Yes >> Okay, software defined, that's the world Salim thanks for coming on, sharing your awesome expert opinon. Congratulations on your awesome book. How many countries is your book, Exponential Organizations-- >> It's now about a quarter of a million copies in 15 languages. >> Required reading in all MBA programs, and the C-Suite. Congratulations, it's like the TANEx Engineering that Mark Dandriso put out. A whole new paradigm of management is happening. Digital transformation. >> We now have the ability to scale an organization structure as fast as we can scale technology. >> Blockchain you know, the nature of the firm was all about having people in one spot. So centralized, you can manage stuff. Now with blockchain you have a decentralized organization. That's your new book, the Decentralized Organization. >> Although, I'm not sure I have another book in me. >> There's a book out there for somebody, Decentralized Organizations. Salim, thank you for joining us. The Cube here, I'm John Furrier the co-host. Day two coverage of Blockchain Unbound more coverage after this short break. (electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering AWS Reinvent 2017, presented by AWS, intel, and our ecosystem of partners. >> Well, welcome back. We're live here in Las Vegas at Reinvent. AWS putting on it's annual show, and you might notice the volume's gone up a little bit around here. Well, it's 5 o'clock reception time here, so the show floor has a little different vibe to it, you might say, right now. Justin Warren, John Walls, you kind of feel it, don't you, right now? >> Oh yeah, there is an energy just sort of vibrating around. I can feel the energy lifting as the booze starts to flow some more. >> Energy's a good way to put it. >> Yeah. >> Right. We're with W. Curtis Preston, who is the chief technical architect at Druva, and Curtis, thanks for being with us. >> Glad to be here. >> Do you feel a vibe, too? >> I feel the vibe. I feel the vibe standing out in the big line to get in here. >> Yeah, in here. >> And now we're in here, it's yeah, it's a lot of people. >> By the way, for those of you at home not familiar, you were named, this year, on the Deloitte Technology Fast 500 list, 175. >> Hey. >> Quite an honor. >> I assume you're talking about Druva, not me personally. >> Well, yeah, Druva, not you. Although maybe you did, I don't know. >> Yeah, I don't think. >> But that's quite an accomplishment, though. And quite an honor for the company. I mean, tell a little bit about that, about that process, and what do you think that means? What's that stamp of approval for what you guys are doing? >> Well, I think it's just, you know, like a lot of those lists, it's a recognition of the position that we're holding, right? I mean, Druva historically is really well known for their protection of endpoints and SaaS applications. They're expanding into data center and Cloud protection, but I think they're absolutely recognized as the leader in the protection of endpoints. >> Okay, so characterize the Cloud work you guys are doing. Like you said, this is a new move for you, I mean relatively new move, but the market's driving that way, right? >> Yeah. >> People starting to nod their head, and they're thinking, yep, this is where we need to be. >> Yeah, yep. >> So, what has been your strategy then, as far as facilitating what's no longer a trend, it's a way of life. >> Yeah, so I'd say first off, we are definitely unlike a lot of other players. We are a Cloud first company, in that, it's not a strategy, it's a way of life, so our entire application is built in and for the cloud, and by that I mean that it takes advantage of everything that the Cloud offers, right? And when you look at specifically AWS, a lot of backup software products use, well, they all use some kind of database, some kind of catalog to keep track of all the backups. And all of those catalogs, all of those databases, whether it's SQL Server or Db2 or Oracle, they all have scalability limits. We chose to use DynamoDB, which is an incredibly scalable no-SQL database. It's built and available in Amazon as a service, and then all of our products all run in Amazon, right? And so, we can scale both up and down to meet the requirements of a customer. So if we get a new customer. We had a customer that I can't mention by name, but they're a large company that started out with what we consider a small installation of about 10,000 laptops. And that was nice. And then it went well. And then there was a ransomware scare, and so they said, you know what, we're gonna go ahead and do everything. And so suddenly we needed to do 10 times as many laptops. Well, because of the way AWS is, we could scale both the database, the compute, and the storage all instantly to meet the demands of that client. And then once that's done, scale it back down to get back to a state of normal, right? So, for us the Cloud is sort of the core of who we are, and then the only expansion for us is actually protecting the Cloud. So, we've always used the Cloud as our destination, but now our newest offering, Apollo, actually is designed to protect starting with AWS and then expanding into the rest of the Amazon, well, I should say starting with EC2, and then eventually expanding into the rest of the AWS world. >> All right, so, with the tradition of endpoint protection and... >> You're gonna have to speak up, it's really loud in here. >> It's really loud, I'll make sure I'm yelling. So with the heritage that you've got of backing up endpoints and being able to protect endpoints, and now you're moving to protect Cloud workloads, as you say, you've got this Cloud heritage, but you're now looking at protecting workloads that live in the Cloud, what are some of the things that Druva's bringing from that endpoint knowledge that applies to those Cloud type workloads? >> Well I think the idea is that, you know, one of the things about the Cloud, people sort of view, I think there's steps of people using AWS, right? They sort of experiment, and they try out this and that, but once somebody really understands like we did, the things you can do when you can scale your VMs instantly and limitlessly, and your storage and your compute and your databases, once they go down that route, I think the fact that we, it's not necessarily the history of the endpoint itself, but the infrastructure that we built in order to protect those endpoints is already totally scalable and ready to meet the needs of however big of a workload that you'll put in AWS. >> Yeah, I often like to say that Cloud is a state of mind, so if you've already got that state of mind that I want to run my workloads in a Cloud-like way, well I want to be able to protect them in a Cloud-like way, and it sounds like that's really what you're trying to nail there. >> Yeah, and it's a big, because any like, I can look out and see multiple backup products available, and there's a lot of good backup products here. And any of them can run in the cloud, right? You can create a Linux VM or a Windows VM and install your backup software, but it's not going to magically become more scalable because you're running it in Amazon, right? So, designing the product for Amazon and that scalable way of doing things, that's why we talk about being Cloud native. >> Yeah, so how are you attracting customers who would have traditionally thought of you as an endpoint company. It's like, now you're actually saying, look we have these different offerings. So how are you starting to talk to those different kind of customers. How are you finding them and what is it that you're finding resonates with them as compared to some of the other options that they might have? >> Yeah, so as you probably know, I've been in the backup space now for, quarter of a century... (clearing throat) >> Literally wrote the book. >> Literally wrote the book, right? It's on O'Reilly. (laughing) Oreilly.com. >> We'll give you a plug later. >> Don't worry. >> Yeah, yeah, yeah. >> Literally wrote the book. >> Yeah, one thing I can say, there's a couple of things I can say about backups in general, in the average data center. One is, everybody hates their backup software. Right? Like, nobody likes it because it's so hard, right? It's so hard to configure, and using disc as a mechanism instead of tape as a primary mechanism, it's made things better, but it hasn't really solved it, right? It's still this really difficult to manage. There's this massive amount of infrastructure that has to be put in place to do all of that. And because that's so hard and it's so error prone and you're invisible or you're in trouble. No one cares about the millions of backups you get right, only the one restore you got wrong. And so what that translates into is the other truth, which is nobody wants to be the backup guy, right? >> I mean the way I got my first job in backups 24 years ago was a guy named Ron Rodriguez did not want to be the backup guy. >> Curtis, you're it. >> Yeah, you're it. And I within two months, had my first major failure as the backup guy for a 35 billion dollar company, and I thought I was done, I thought I was fired, like so many other backup people, and somehow just accidentally I ended up staying, and so what happens is, it's so hard. So, to go to your question, well what if it was simple, right? The situation is, the current system's not scalable. You're always buying another media server, you're always buying another tape driver, you're always buying another dedupe box. You know, you're always out of something, right? I remember having to go to my boss and being out of tapes. This is, you know, back when tapes were a thing. And I remember saying, "hey, I'm out of tapes." and she was like, we don't have budget." She's like, "what are our choices?" and I go, well, I can stop the backups. She's like, "that's not funny." I'm like, "that's our choice." Right? >> I have so much capacity here. >> These are our choices, right? And so she gave me the tapes that I needed, right? And so it's not scalable, the current system. You're always in need of some piece. It's also super expensive, right? And it's super hard. So we try to be the opposite of that. We try to be scalable, simple, and you know save people money. Right? I know we have a 4S thing. >> It's right there on the tip of your tongue. >> It's right there on the tip of my tongue. But basically we try to be the opposite of everything that backups are. So the big thing is, it's way easier. Just put a piece of software and magic happens, right? And if you're large enough data center that you need to do what we call seeding, where you have to use sneakernet to get the data to us, we have a system for that. If you have a large enough system where the RTO is not going to be able to be met by a copy that's on the other side of the internet, then we have a caching appliance that goes onsite to provide fast recovery. So it's like it's super simple, way less expensive. And I do mean way less expensive. I've seen some TCOs where we compete against other companies, we're even less expensive than renewing what you have, let alone going and buying. >> John: Replacing. >> And replacing it with something, because that happens all the times. Because people are always swapping their backup software, because the problem has got to be the backup software. Right? And I think in the end, it is, right? But, it's because that core architecture, that core way we've done backups, has essentially stayed the same since before I started. All we did was we changed tape to disc, right? And we introduced dedupe, which was great, but there's this technology that we call dedupe, that is really hard when you do it on the backend. You know, there's a company here who makes a lot of money on selling those appliances, right? Except it's really hard to do that, and so it's really expensive to do that. And then you gotta pay for one here and you gotta pay for one over there. With us, you don't buy that. You just go straight to us, and then because we're in AWS, it's already in three locations, right? And it's already offsite. >> Well Curtis, they said 24 years ago it wasn't gonna last, but it did. You made it, congratulations. >> Thanks. >> We appreciate the time here. >> Thanks. >> Thanks for being here with us on theCUBE and onto 175th. Next year who knows where you're going, right? >> Who knows where we're going. >> Excellent, Curtis Preston, joining us here from Druva. Back with more live from Las Vegas. We are at Reinvent at AWS. Back with more in a bit.

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Hey, welcome back everybody. Jeff Rick here with the Cube. We are live in downtown San Jose at the historic Pagoda Lounge, part of Big Data SV, which is part of Strata + Hadoop Conference, which is part of Big Data Week because everything big data is pretty much in San Jose this week. So we're excited to be here. We're here with George Gilbert, our big data analyst from Wikibon, and a great guest, Gaurav Dhillon, Chairman and CEO of SnapLogic. Gaurav, great to see you. >> Pleasure to be here, Jeff. Thank you for having me. George, good to see you. >> You guys have been very busy since we last saw you about a year ago. >> We have. We had a pretty epic year. >> Yeah, give us an update, funding, and customers, and you guys have a little momentum. >> It's a good thing. It's a good thing, you know. A friend and a real mentor to us, Dan Wormenhoven, the Founder and CEO of NetApp for a very long time, longtime CEO of NetApp, he always likes to joke that growth cures all startup problems. And you know what, that's the truth. >> Jeff: Yes. >> So we had a scorching year, you know. 2016 was a year of continuing to strengthen our products, getting a bunch more customers. We got about 300 new customers. >> Jeff: 300 new customers? >> Yes, and as you know, we don't sell to small business. We sell to the enterprise. >> Right, right. >> So, this is the who's who of pharmaceuticals, continued strength in high-tech, continued strength in retail. You know, all the way from Subway Sandwich to folks like AstraZeneca and Amgen and Bristol-Myers Squibb. >> Right. >> So, some phenomenal growth for the company. But, you know, we look at it very simply. We want to double our company every year. We want to do it in a responsible way. In other words, we are growing our business in such a way that we can sail over to cash flow break-even at anytime. So responsibly doubling your business is a wonderful thing. >> So when you look at it, obviously, you guys are executing, you've got good products, people are buying. But what are some of the macro-trends that you're seeing talking to all these customers that are really helping push you guys along? >> Right, right. So what we see is, and it used to be the majority of our business. It's now getting to be 50/50. But still I would say, historically, the primary driver for 2016 of our business was a digital transformation at a boardroom level causing a rethinking of the appscape and people bringing in cloud applications like Workday. So, one of the big drivers of our growth is helping fit Workday into the new fabric in many enterprises: Vassar College, into Capital One, into finance and various other sectors. Where people bring in Workday, they want to make that work with what they have and what they're going to buy in the future, whether it's more applications or new types of data strategies. And that is the primary driver for growth. In the past, it was probably a secondary driver, this new world of data warehousing. We like to think of it as a post-modern era in the use of data and the use of analytics. But this year, it's trending to be probably 50/50 between apps and data. And that is a shift towards people deploying in the same way that they moved from on-premise apps to SAS apps, a move towards looking at data platforms in the cloud for all the benefits of racking and stacking and having the capability rather than being in the air-conditioning, HVAC, and power consumption business. And that has been phenomenal. We've seen great growth with some of the work from Microsoft Azure with the Insights products, AWS's Redshift is a fantastic growth area for us. And these sorts of technologies, we think are going to be of significant impact to the everyday, the work clothing types of analytics. Maybe the more exotic stuff will stay on prem, but a lot of the regular business-like stuff, you know, stuff in suits and ties is moving into the cloud at a rapid pace. >> And we just came off the Google Next show last week. And Google really is helping continue to push kind of ML and AI out front. And so, maybe it's not the blue suit analytics. >> Gaurav: Indeed, yes. >> But it does drive expectations. And you know, the expectations of what we can get, what we should get, what we should be moving towards is rapidly changing. >> Rapidly changing, for example, we saw at The New York Times, which as many of Google's flagship enterprise customers are media-related. >> Jeff: Right. >> No accident, they're so proficient themselves being in the consumer internet space. So as we encountered in places like The New York Times, is there's a shift away from a legacy data warehouse, which people like me and others in the last century, back in my time in Informatica, might have sold them towards a cloud-first strategy of using, in their case, Google products, Bigtable, et cetera. And also, they're doing that because they aspirationally want to get at consumer prices without having to have a campus and the expense of Google's big brain. They want to benefit from some of those things like TensorFlow, et cetera, through the machine learning and other developer capabilities that are now coming along with that in the cloud. And by the way, Microsoft has amazing machine learning capability in its Azure for Microsoft Research as well. >> So Gaurav, it's interesting to hear sort of the two drivers. We know PeopleSoft took off starting with HR first and then would add on financials and stumble a little bit with manufacturing. So, when someone wants to bring in Workday, is it purely an efficiency value prop? And then, how are you helping them tie into the existing fabric of applications? >> Look, I think you have to ask Dave or Aneel or ask them together more about that dynamic. What I know, as a friend of the firm and as somebody we collaborate with, and, you know, this is an interesting statistic, 20 percent of Workday's financial customers are using SnapLogic, 20 percent. Now, it's a nascent business for them and you and I were around in the last century of ERP. We saw the evolution of functional winners. Some made it into suites and some didn't. Siebel never did. PeopleSoft at least made a significant impact on a variety of other things. Yes, there was Bonn and other things that prevented their domination of manufacturing and, of course, the small company in Walldorf did a very good job on it too. But that said, what we find is it's very typical, in a sense, how people using TIBCO and Informatica in the last century are looking at SnapLogic. And it's no accident because we saw Workdays go to market motion, and in a sense, are following, trying to do the same thing Dave and Aneel have done, but we're trying to do the same thing, being a bunch of ex-Informatica guys. So here's what it is. When you look at your legacy installation, and you want to modernize it, what are your choices? You can do a big old upgrade because it's on-premise software. Or you can say, "You know what? "For 20% more, I could just get the new thing." And guess what? A lot of people want to get the new thing. And that's what you're going to see all the time. And that's what's happening with companies like SnapLogic and Workday is, you know, someone. Right here locally, Adobe, it's an icon in technology and certainly in San Jose that logo is very big. A few years ago, they decided to make the jump from legacy middleware, TIBCO, Informatica, WebMethods, and they've replaced everything globally with SnapLogic. So in that same way, instead of trying to upgrade this version and that version and what about what we do in Japan, what do we do in Sweden, why don't you just find a platform as a service that lets you elevate your success and go towards a better product, more of a self-service better UX, millennial-friendly type of product? So that's what's happening out there. >> But even that three-letter company from Walldorf was on-stage last week. You can now get SAP on the Google Cloud Platform which I thought was pretty amazing. And the other piece I just love but there's still a few doubters out there on the SAS platform is now there's a really visual representation. >> Gaurav: There is. >> Of the dominance of that style going up in downtown San Francisco. It's 60 stories high, and it's taken over the landscape. So if there's ever any a doubt of enterprise adaptation of SAS, and if anything, I would wonder if kind of the proliferation of apps now within the SAS environment inside the enterprise starts to become a problem in and of its own self. Because now you have so many different apps that you're working on and working. God help if the internet goes down, right? >> It's true, and you know, and how do you make e pluribus unim, out of many one, right? So it's hilarious. It is almost at proliferation at this point. You know, our CFO tapped me the other day. He said, "Hey, you've got to check this out." "They're using a SAS application which they got "from a law firm to track stock options "inside the company." I'm like, "Wow, that is a job title and a vertical." So only high growth private venture backed companies need this, and typically it's high tech. And you have very capable SAS, even in the small grid squares in the enterprise. >> Jeff: Right, right. >> So, a sign, and I think that's probably another way to think about the work that we do at SnapLogic and others. >> Jeff: Right, right. >> Other people in the marketplace like us. What we do essentially is we give you the ERP of one. Because if you could choose things that make sense for you and they could work together in a very good way to give you very good fabric for your purposes, you've essentially bought a bespoke suit at rack prices. Right? Without that nine times multiplier of the last century of having to have just consultants without end, darkened the sky with consultants to make that happen. You know? So that, yes, SAS proliferation is happening. That is the opportunity, also the problem. For us, it's an opportunity where that glass is half-full we come in with SnapLogic and knit it together for you to give you fabric back. And people love that because the businesses can buy what they want, and the enterprise gets a comprehensive solution. >> Jeff: Right, right. >> Well, at the risk of taking a very short tangent, that comment about darkening the skies, if I recall, was the battle of the Persians threatening the 300 Greeks at the battle of Thermopylae. >> Gaurav: Yes. >> And they said, "We'll darken the skies with our arrows." And so the Greek. >> Gaurav: Come and get 'em. >> No, no. >> The famous line was, he said, "Give us your weapons." And the guy says, "Come and get 'em." (laughs) >> We got to that point, the Greek general says, "Well, we'll fight in the shade." (all laughing) But I wanted to ask you. >> This is the movie 300 as well, right? >> Yes. >> The famous line is, "Give us your weapons." He said, "Come and get 'em." (all laughing) >> But I'm thinking also of the use case where a customer brings in Workday and you help essentially instrument it so it can be a good citizen. So what does that make, or connect it so it can be a good citizen. How much easier does that mean or does that make fitting in other SAS apps or any other app into the fabric, application fabric? >> Right, right. Look, George. As you and I know, we both had some wonderful runs in the last century, and here we are doing version 2.0 in many ways, again, very similar to the Workday management. The enterprise is hip to the fact that there is a Switzerland nature to making things work together. So they want amazing products like Workday. They want amazing products like the SAP Cloud Suite, now with Concur, SuccessFactors in there. Some very cool things happening in the analytics world which you'll see at Sapphire and so on. So some very, very capable products coming from, I mean, Oracle's bought 80 SAS companies or 87 SAS companies. And so, what you're seeing is the enterprise understands that there's going to be red versus blue and a couple other stripes and colors and that they want their businesspeople to buy whatever works for them. But they want to make them work together. All right? So there is a natural sort of geographic or structural nature to this business where there is a need for Switzerland and there is a need for amazing technology, some of which can only come from large companies with big balance sheets and vertical understanding and a legacy of success. But if a customer like an AstraZeneca where you have a CIO like Dave Smoley who transformed Flextronics, is now doing the same thing at AstraZeneca bringing cloud apps, is able to use companies like SnapLogic and then deploy Workday appropriately, SAP appropriately, have his own custom development, some domestic, some overseas, all over the world, then you've got the ability again to get something very custom, and you can do that at a fraction of the cost of overconsulting or darkening the skies in the way that things were done in the last century. >> So, then tell us about maybe the convergence of the new age data warehousing, the data science pipeline, and then this bespoke collection of applications, not bespoke the way Oracle tried it 20 years ago where you had to upgrade every app tied into every other app on prem, but perhaps the integration, more from many to one because they're in the cloud. There's only one version of each. How do you tie those two worlds together? >> You know, it's like that old bromide, "Know when to hold 'em. "Know when to fold them." There is a tendency when programming becomes more approachable, you have more millennials who are able to pick up technology in a way. I mean, it's astounding what my children can do. So what you want to do is as a enterprise, you want to very carefully build those things that you want to build, make sure you don't overbuild. Or, say, if you have a development capability, then every problem looks like a development nail and you have a hammer called development. "Let's hire more Java programmers." That's not the answer. Conversely, you don't want to lose sight of the fact that to really be successful in this millennium, you have to have a core competence around technology. So you want to carefully assemble and build your capability. Now, nobody should ever outsource management. That's a bad idea. (chuckles) But what you want to do is you want to think about those things that you want to buy as a package. Is that a core competence? So, there are excellent products for finance, for human capital management, for travel expense management. Coupa just announced today their for managing your spend. Some of the work at Ariba, now the Ariba Cloud at SAP, are excellent products to help you do certain job titles really well. So you really shouldn't be building those things. But what you should be doing is doing the right element of build and buy. So now, what does that mean for the world of analytics? In my view, people building data platforms or using a lot of open source and a lot of DevOps labor and virtualization engineering and all that stuff may be less valuable over time because where the puck is going is where a lot of people should skate to is there is a nature of developing certain machine language and certain kind of AI capabilities that I think are going to be transformational for almost every industry. It is hard to imagine anything in a more mechanized back office, moving paper, manufacturing, that cannot go through a quantum of improvement through AI. There are obviously moral and certain humanity dystopia issues around that to be dealt with. But what people should be doing is I think building out the AI capabilities because those are very custom to that business. Those have to do with the business's core competence, its milieu of markets and competitors. But there should be, in a sense, stroking a purchase order in the direction of a SAS provider, a cloud data provider like Microsoft Azure or Redshift, and shrinking down their lift-and-shift bill and their data center bill by doing that. >> It's fascinating how long it took enterprises to figure out that. Just like they've been leveraging ADP for God knows how many years, you know, there's a lot of other SAS applications you can use to do your non-differentiated heavy lifting, but they're clearly all in now. So Gaurav, we're running low on time. I just want to say, when we get you here next year, what's top of your plate? What's top of priorities for 2017? Cause obviously you guys are knocking down things left and right. >> Thank you, Jeff. Look, priority for us is growth. We're a growth company. We grow responsibly. We've seen a return to quality on the part of investors, on the part of public and private investors. And you know, you'll see us continue to sort of go at that growth opportunity in a manner consistent with our core values of building product with incredible success. 99% of our customers are new to our products last quarter. >> Jeff: Ninety-nine percent? >> Yes sir. >> That says it all. >> And in the world of enterprise software where there's a lot of snake oil, I'm proud to say that we are building new product with old-fashioned values, and that's what you see from us. >> Well 99% customer retention, you can't beat that. >> Gaurav: Hard to beat! There's no way but down from there, right? (laughing) >> Exactly. Alright Gaurav, well, thanks. >> Pleasure. >> For taking a few minutes out of your busy day. >> Thank you, Jeff. >> And I really appreciate the time. >> Thank you, Jeff, thank you, George. >> Alright, he's George Gilbert. I'm Jeff Rick. You're watching the Cube from the historic Pagoda Lounge in downtown San Jose. Thanks for watching.

>> Announcer: Live from San Jose, California, it's the Cube, covering Big Data Silicon Valley 2017. (upbeat music) >> Hello and welcome to the Cube's special coverage of Big Data SV, Big Data in Silicon Valley in conjunction with Strata + Hadoop. I'm John Furrier with George Gilbert, with Mickey Bonn and Peter Burns as well. We'll be doing interviews all day today and tomorrow, here in Silicon Valley in San Jose. Our next guest is Amit Walia who's the Executive Vice President and Chief Product Officer of Informatica. Kicking of the day one of our coverage. Great to see you. Thanks for joining us on our kick off. >> Good to be here with you, John. >> So obviously big data. this is like the eighth year of us covering, what was once Hadoop World, now it's Strata + Hadoop, Big Data SV. We also do Big Data NYC with the Cube and it's been an interesting transformation over the past eight years. This year has been really really hot with you're starting to see Big Data starting to get a clear line of sight of where it's going. So I want to get your thoughts, Amit, on where the view of the marketplace is from your standpoint. Obviously Informatica's got a big place in the enterprise. And the real trends on how the enterprises are taking analytics and specifically with the cloud. You got the AI looming, all buzzed up on AI. That really seized, people had to get their arms around that. And you see IoT. Intel announced an acquisition, $15 billion for autonomous vehicles, which is essentially data. What's your views? >> Amit: Well I think it's a great question. 10 years have happened since Hadoop started right? I think what has happened as we see is that today what enterprises are trying to encapsulate is what they call digital transformation. What does it mean? I mean think about it, digital transformation for enterprises, it means three unique things. They're transforming their business models to serve their customers better, they're transforming their operational models for their own execution internally, if I'm a manufacturing or an execution-oriented company. The third one is basically making sure that their offerings are also tailored to their customers. And in that context, if you think about it, it's all a data-driven world. Because it's data that helps customers be more insightful, be more actionable, and be a lot more prepared for the future. And that covers the things that you said. Look, that's where Hadoop came into play with big data. But today the three things that organizations are catered around big data is just a lot of data right? How do I bring actionable insights out of it? So in that context, ML and AI are going to play a meaningful role. Because to me as you talk about IoT, IoT is the big game changer of big data becoming big or huge data if I may for a minute. So machine learning, AI, self-service analytics is a part of that, and the third one would be big data and Hadoop going to cloud. That's going to be very fast. >> John: And so the enterprises now are also transforming, so this digital transformation, as you point out, is absolutely real, it's happening. And you start to see a lot more focus on the business models of companies where it's not just analytics as a IT function, it's been talked about for a while, but now it's really more relevant because you're starting to see impactful applications. >> Exactly. >> So with cloud and (chuckles) the new IoT stuff you start to say okay apps matter. And so the data becomes super important. How is that changing the enterprises' readiness in terms of how they're consuming cloud and data and what not? What's you're view on that? Because you guys are deep in this. >> Amit: Yep. >> What's the enterprises' orientation these days? >> So slight nuance to that, as an answer. I think what organizations have realized is that today two things happened that never happened in the last 20 years. Massive fragmentation of the persistence layer, you see Hadoop itself fragmented the whole database layer. And a massive fragmentation of the app layer. So there are 3,000 enterprise size apps today. So just think about it, you're not restricted to one app. So what customers and enterprises are realizing is that, the data layer is where you need to organize yourself. So you need to own the data layer, you cannot just be in the app layer and the database layer because you got to be understanding your data. Because you could be anywhere and everywhere. And the best example I give in the world of cloud is, you don't own anything, you rent it. So what do you own? You own the darn data. So in that context, enterprise readiness as you came to, becomes very important. So understanding and owning your data is the critical secret sauce. And that's where companies are getting disrupted. So the new guys are leveraging data, which by the way the legacy companies had, but they couldn't figure it out. >> What is that? This is important. I want to just double-click on that. Because you mentioned the data layer, what's the playbook? Because that's like the number one question that I get. >> Mm-hmm. >> On Cube interviews or off camera is that okay, I want to have a data strategy. Now that's empty in its statement, but what is the playbook? I mean, is it architecture? Because the data is the strategic advantage. >> Amit: Yes. >> What are they doing? What's the architecture? What are some of the things that enterprises do? Now obviously they care about service level agreements and having potentially multicloud, for instance, as a key thing. But what is that playbook for this data layer? >> That's a very good question, sir. Enterprise readiness has a couple of dimensions. One you said is that there will be hybrid doesn't mean a ground cloud multicloud. I mean you're going to be in multi SAS apps, multi platform apps, multi databases in the cloud. So there is a hybrid world over there. Second is that organizations need to figure out a data platform of their own. Because ultimately what they care for is that, do I have a full view of my customer? Do I have a full view of the products that I'm selling and how they are servicing my customers? That can only happen if you have what I call a meta-data driven data platform. Third one is, boy oh boy, you talked about self-service analytics, you need to know answers today. Having analytics be more self-serving for the business user, not necessarily the IT user, and then leveraging AI to make all these things a lot more powerful. Otherwise, you're going to be spending, what? Hours and hours doing statistical analysis, and you won't be able to get to it given the scale and size of data models. And SLAs will play a big role in the world of cloud. >> Just to follow up on that, so it sounds like you've got the self-service analytics to help essentially explore and visualize. >> Amit: Mm-hmm. >> You've got the data governance and cataloging and lineage to make sure it is high quality and navigable, and then you want to operationalize it once you've built the models. But there's this tension between I want what made the data lake great, which was just dump it all in there so we have this one central place, but all the governance stuff on top of that is sort of just well, we got to organize it anyway. >> Yeah. >> How do you resolve that tension? >> That is a very good question. And that's where enterprises kind of woke up to. So a good example I'll give you, what everybody wanted to make a data lake. I mean if you remember two years ago, 80% of the data lakes fell apart and the reason was for the fact that you just said is that people made the data lake a data swamp if I may. Just dump a lot of data into my loop cluster, and life will be great. But the thing is that, and what customers of large enterprises realized is they became system integrators of their own. I got to bring data, catalog it, prepare it, surface it. So the belief of customers now is that, I need a place to go where basically it can easily bring in all the data, meta-data driven catalog, so I can use AI and ML to surface that data. So it's very easy at the preparation layer for my analysts to go around and play with data and then I can visualize anything. But it's all integrated out of the box, then each layer, each component being self-integrated, then it falls apart very quickly when you want to, to your question, at an enterprise level operationalize it. Large enterprises care about two things. Is it operationalizable? And is it scalable? That's where this could fall apart. And that's what our belief is. And that's where governance happens behind the scenes. You're not doing anything. Security of your data, governance of their data is driven through the catalog. You don't even feel it. It's there. >> I never liked the data lakes term. Dave Vellante knows I've always been kind of against, even from day one, 'cause data's more fluid, I call it a data ocean, but to your point, I want to get on that point because I think data lakes is one dimension, right? >> Yeah. >> And we talked about this at Informatica World, last year I think. And this year it's May 15th. >> Yes. >> I think your event is coming up, but you guys introduced meta-data intelligence. >> Yep. >> So there was, the old model was throw it centralized, do some data governance, data management, fence it out, call, make some queries, get some reports. I'm over simplifying but it was like, it was like a side function. You're getting at now is making that data valuable. >> Amit: Yep. >> So if it's in a lake or it's stored, you never know when the data's going to be relevant, so you have to have it addressable. Could you just talk about where this meta-data intelligence is going? Because you mentioned machine learning and AI. 'Cause this seems to be what everyone is talking about. In real time, how do I make the data really valuable when I need it? And what's the secret sauce that you guys have, specifically, to make that happen? >> So that, to contextualize that question, think about it. So if you. What you don't want to do is keep make everything manual. Our belief is that the intelligence around data has to be at the meta-data level, right? Across the enterprise, which is why, when we invested in the catalog, I used the word, "It's the google of data for the enterprise." No place in an enterprise you can go search for all your data, and given that the fast, rapid-changing sources of data, think about IoT, as you talked about, John. Or think about your customer data, for you and me may come from a new source tomorrow. Do you want the analyst to figure out where the data is coming from? Or the machine learning or AI to contextualize and tell you, you know what, I just discovered a great new source for where John is going to go shop. Do you want to put that as a part of analytics to give him an offer? That's where the organizing principle for data sits. The catalog and all the meta-data, which is where ML and AI will converge to give the analyst self-discovery of data sets, recommendations like in Amazon environment, recommendations like Facebook, find other people or other common data that's like a Facebook or a LinkedIn, that is where everything is going, and that's why we are putting all our efforts on AI. >> So you're saying, you want to abstract the way the complexity of where the data sits? So that the analyst or app can interface with that? >> That's exactly right. Because to me, those are the areas that are changing so rapidly, let that be. You can pick whatever data sets based on what you want, you can pick whichever app you want to use, wherever you want to go, or wherever your business wants to go. You can pick whichever analytical tool you like, but you want to be able to take all of those tools but be able to figure out what data is there, and that should change all the time. >> I'm trying to ask you a lot while you're here. What's going to be the theme this year at Informatica World? How do you take it to the next level? Can you just give us a teaser of what we might expect this year? 'Cause this seems to be the hottest trend. >> This is, so first, at Informatica World this year, we will be unveiling our whole new strategy, branding, and messaging, there's a whole amount of push on that one. But the two things that will be focused a lot on is, one is around that intelligent data platform. Which is basically what I'm talking about. The organizing principle of every enterprise for the next decade, and within that, where AI is going to play a meaningful role for people to spring forward, discover things, self-service, and be able to create sense from this mountains of data that's going to sit around us. But we won't even know what to do. >> All right, so what do you guys have in the product, just want to drill into this dynamic you just mentioned, which is new data sources. With IoT, this is going to completely make it more complex. You never know what data's going to be coming off the cars, the wearables, the smart cities. You have all these new killer use-cases that are going to be transformational. How do you guys handle that, and what's the secret sauce of? 'Cause that seems to be the big challenge, okay, I'm used to dealing with data, its structure, whether it's schemas, now we got unstructured. So okay, now I got new data coming in very fast, I don't even know when or where it's going to come in, so I have to be ready for these new data. What is the Informatica solution there? >> So in terms of taking data from any source, that's never been a challenge for us, because Informatica, one of the bread and butter for us is that we connect and bring data from any potential source on the planet, that's what we do. >> John: And you automate that? >> We automate that process, so any potential new source of data, whether it's IoT, unstructured, semi-structured, log, we connect to that. What I think the key is, where we are heavily invested, once you've brought all that. By the way, you can use Kafka Cues for that, you can use back-streaming, all of that stuff you could do. Question is, how do you make sense out of it? I can get all the data, dump it in a Kafka Cue, and then I take it to do some processing on Spark. But the intelligence is where all the Informatica secret sauce is, right? The meta-data, the transformations, that's what we are invested in, but in terms of connecting anything to everything? That we do for a living, we have done that for one quarter of a century, and we keep doing it. >> I mean, I love having a chat with you, Amit, you're a product guy, and we love product guys, 'cause they can give us a little teaser on the roadmap, but I got to ask you the question, with all this automation, you know, the big buzz out in the world is, "Oh machine learning and AI is replacing jobs." So where is the shift going to be, because you can almost connect the dots and say, "Okay, you're going to put some people out of work, "some developer, some automation, "maybe the systems management layer or wherever." Where are those jobs shifting to? Because you could almost say, "Okay, if you're going to abstract away and automate, "who loses their job?" Who gets shifted and what are those new opportunities, because you could almost say that if you automate in, that should create a new developer class. So one gets replaced, one gets created possibly. Your thoughts on this personnel transformation? >> Yeah, I think, I think what we see is that value creation will change. So the jobs will go to the new value. New areas where value is created. A great example of that is, look at developers today, right. Absolutely, I think they did a terrific job in making sure that the Hadoop ecosystem got legitimized, right? But in my opinion, where enterprise scalability comes, enterprises don't want lots of different things to be integrated and just plumbed together. They want things to work out of the box, which is why, you know, software works for them. But what happens is that they want that development community to go work on what I call value-added areas of the stack. So think about it, in connected car, they're working with lots of customers on the connected car issue, right? They don't want developers to work on the plumbing. They want us to kind of give that out of the box, because SLA is operational scale, and enterprise scalability matters, but in terms of the top-layer analytics, to make sure we can make sense out of it, that's what they're, that's where they want innovation. So what you will see is that, I don't think the jobs will go in vapor, but I do think the jobs will get migrated to a different part of the stack, which today it has not been, but that's, you know, we live in Silicon Valley, that's a natural evolution we see, so I think that will happen. In general in the larger industry, again I'd say, look, driverless cars, I don't think they've driven away jobs. What they've done is created a new class of people who work. So I do think that will be a big change. >> Yeah there's a fallacy there. I mean with the ATM argument was ATM's are going to replace tellers, yet more branches opened up. >> That's exactly it. >> So therefore creating new jobs. I want to get to the quick question, I know George has a question, but I want to get on the cost of ownership, because one of the things that's been criticized in some of these emerging areas, like Hadoop and Open Stack, for instance, just to pick two random examples. It's great, looks good, you know, all peace and love. An industry's being created, legitimized, but the cost of ownership has been critical to get that done, it's been expensive, talent, to find talent and deploying it was hard. We heard that on the Cube many times. How does the cost of ownership equation change? As you go after these more value, as developers and businesses go after these more value-creating activities in the Stack? >> See look, I always say, there is no free lunch. Nothing is free. And customers realize that, that open source, if you completely wanted to, to your point, as enterprises wanted to completely scale out and create an end-to-end operational infrastructure, open source ends up being pretty expensive. For all the reasons, right, because you throw in a lot of developers, and it's not necessarily scalable, so what we're seeing right now is that enterprises, as they have figured that this works for me, but when they want to go scale it out, they want to go back to what I call a software provider, who has the scale, who has the supportability, who also has the ability to react to changes and also for them to make sure that they get the comfort that it will work. So to me, that's where they find it cheaper. Just building it, experimenting with that, it's cheaper here, but scaling it out is cheaper with a software provider, so we see a lot of our customers when we start a little bit experimenting to developers, downloading something, works great, but would I really want to take it across Nordstrom or a JP Morgan or a Morgan Stanley. I need security, I need scalability, I need somebody to call to, at that point on those equations become very important. >> And that's where the out of box experience comes in, where you have the automation, that kind of. >> Exactly. >> Does that ease up some of the cost of ownership? >> Exactly, and the talent is a big issue, right? See we live in Silicon Valley, so we. By the way, Silicon Valley hiring talent is hard. Just think about it, if you go to Kansas City, hiring a scholar developer, that's a rare breed. So just, when I go around the globe and talk to customers, they don't see that talent at all that we here just somehow take for granted. They don't, so it's hard for them to kind of put their energy behind it. >> Let me ask. More on the meta-data layer. There's an analogy that's come up from the IIoT world where they're building these digital twins, and it's not just GE. IBM's talking about it, and actually, we've seen more and more vendors where the digital twin is this, it's a digital representation now of some physical object. But you could think of it as meta-data, you know, for a physical object, and it gets richer over time. So my question is, meta-data in the old data warehouse world, was we want one representation of the customer. But now it's, there's a customer representation for a prospect, and one for an account, and one for, you know, in warranty, and one for field service. Is that, how does that change what you offer? >> That's a very very good question. Because that's where the meta-data becomes so much more important because its manifestation is changing. I'll give you a great example, take Transamerica, Transamerica is a customer of ours leveraging big data at scale, and what they're doing is that, to your question, they have existing customers who have insurance through them. But they're looking for white space analysis, who could be potential opportunities? Two distinct ones, and within that, they're looking at relationships. I know you, John, you have Transamerica, could you be an influencer with me? Or within your family, extended family. I'm a friend, but what about a family member that you've declared out there on social media? So they are doing all that stuff in the context of a data lake. How are they doing it? So in that context, think about that complexity of the job, pumping data into a lake won't solve it for them, but that's a necessary first step. The second step is where all of that meta-data through ML and AI, starts giving them that relationship graph. To say, you know what, John in itself has this white space opportunity for you, but John is related to me in one way, him and me are connected on Facebook. John's related to you a little bit more differently, he has a stronger bond with you, and within his family, he has different strong bonds. So that's John's relationship graph. Leverage him, if he has been a good customer of yours. All of that stuff is now at the meta-data level, not just the monolithic meta-data, relationship graph. His relationship graph of what he has bought from you, so that you can just see that discovery becomes a very important element. Do you want to do that in different places? You want to do that in one place. I may be in a cloud environment, I may be on prem, so that's where when I say that meta-data becomes the organized principle, that's where it becomes real. >> Just a quick follow-up on that, then. It doesn't seem obvious that every end customer of yours, not the consumer but the buyer of the software, would have enough data to start building that graph. >> I don't think, to me, what happened was, the word big data, I thought got massively abused. A lot of Hadoop customers are not necessarily big data customers. I know a lot of banking customers, enterprise banking, whose data volumes will surprise you, but they're using Hadoop. What they want is intelligence. That's why I keep saying that the meta-data part, they are more interested in a deeper understanding of the data. A great example is, if John. I had a customer, who basically had a big bank. Rich net worth customer. In their will, the daughter was listed. When the daughter went to school, by the way, went to the bank branch in that city, she had no idea, she walked up, she basically wanted to open an account. Three more friends in the line. Manager comes out because at that point, the teller said, "This is somebody you should take special care of." Boom, she goes in a special cabin, the other friends are standing in a line. Think of the customer service perception, you just created a new millennia right? That's important. >> Well this brings up the interesting comment. The whole graph thing, we love, but this brings back the neural network trend. Which is a concept that's been around for a long long time, but now it's front and center. I remember talking to Diane Green who runs Google Cloud, she was saying that you couldn't hire neural network, they couldn't get jobs 15 years ago. Now you can't hire enough of them. So that brings up the ML conversation. So, I want to take that to a question and ask about the data lake, 'cause you guys have announced a new cloud data lake. >> Yes. >> So it sounds like, from what you're saying, is you're going beyond the data lake. So talk about what that is. Because data lake, people get, you throw stuff into a lake. And hopefully it doesn't become a swamp. How are you guys going beyond just the basic concept of a data lake with your new cloud data lake? >> Yeah, so, data lake. If you remember last year, actually at Strata San Jose we chatted, and we had announced the data lake because we realized customers, to your point John, as you said, were struggling on how to even build a data lake, and they were all over the place, and they were failing. And we announced the first data lake there, and then in Strata New York, basically we brought the meta-data ML part to the data lake. And then obviously right now we're taking it to the cloud, and what we see in the world of data lake is that customers ask for three things. First, they want the prebuilt integrated solution. Data can come in, but I want the intelligence of meta-data and I want data preparation baked in. I don't want to have three different tools that I will go around, so out of the box. But we also saw, as they become successful with our customers, they want to scale up, scale down. Cloud is just a great place to go. You can basically put a data lake out there, by the way in the context of data, a lot of new data sources are in the cloud, so it's easy for them to scale in and out in the cloud, experiment there and all that stuff. Also you know Amazon, we supported Amazon Kinesis, all of these new sources and technologies in the world of cloud are allowing experimentation in the data lake, so that allowed our customers to basically get ahead of the curve very quickly. So in some ways, cloud allowed customers to do things a lot faster, better, and cheaper. So that's what we basically put in the hands of our customers. Now that they are feeling comfortable, they can do a secured and governed data lake without feeling that it's still not self-served. They want to put it in the cloud and be a lot more faster and cheaper about it. >> John: And more analytics on it. >> More analytics. And now, because our ML, our AI, the meta-data part, connects cloud, ground, everything. So they have an organizing principle, whatever they put wherever, they can still get intelligence out of it. >> Amit, we got to break, but I want to get one final comment for you to kind of end the segment, and it's been fun watching you guys work over the past couple years. And I want to get your perspective because the product decisions always have kind of a time table to them, it's not like you made this up last night because it's trendy, but you guys have made some good product choices. It seems like the wind's at your back right now at Informatica. What, specifically, are bets that you guys made a couple years ago that are now bearing fruit? Can you just take a minute to end the segment, share some of those product bets. Because it's not always that obvious to make those product bets years earlier, seems to be a tail wind for you. You agree, and can you share some of those bets? >> I think you said it rightly, product bets are hard, right? Because you got to see three, four years ahead. The one big bet that we made is that we saw, as I said to you, the decoupling of the data layer. So we realized that, look, the app layer's getting fragmented. The cloud platforms are getting fragmented. Databases are getting fragmented. That that whole old monolithic architecture is getting fundamentally blown up, and the customers will be in a multi, multi, multi spread out hybrid world. Data is the organizing principle, so three years ago, we bet on the intelligent data platform. And we said that the intelligent data platform will be intelligent because of the meta-data driven layer, and at that point, AI was nowhere in sight. We put ML in that picture, and obviously, AI has moved, so the bet on the data platform. Second bet that, in that data platform, it'll all be AI, ML driven meta-data intelligence. And the third one is, we bet big on cloud. Big data we had already bet big on, by the way. >> John: You were already there. >> We knew the cloud. Big data will move to the cloud far more rapidly than the old technology moved to the cloud. So we saw that coming. We saw the (mumbles) wave coming. We worked so closely with AWS and the Azure team. With Google now, as well. So we saw three things, and that's what we bet. And you can see the rich offerings we have, the rich partnerships we have, and the rich customers that are live in those platforms. >> And the market's right on your doorstep. I mean, AI is hot, ML, you're seeing all this stuff converge with IoT. >> So those were, I think, forward-looking bets that paid out for us. (chuckles) And but there's so much more to do, and so much more upside for all of us right now. >> A lot more work to do. Amit, thank you for coming on, sharing your insight. Again, you guys got in good pole position in the market, and again it's right on your doorstep, so congratulations. This is the Cube, I'm John Furrier with George Gilbert. With more coverage in Silicon Valley for Big Data SV and Strata + Hadoop after this short break.

but cute at servicenow knowledge 14 is sponsored by service now here are your hosts Dave vellante and Jeff Creek we're back hi everybody this is Dave vellante with Jeff Frick we're here live at knowledge 14 this is service now it's big customer event about 6,600 people up from about four thousand last year as we've been saying it's kind of tracking the growth of service now which has been pretty meteoric we heard from Mike scarpelli the CFO Frank's loot men they're really doubling down and it's exciting to see we're here in San Francisco where all the action is Jeffrey Moore is here author consultant pundits all-around smart guy cube alum greatly again thank you here so um so you're speaking at the CIO decisions i love the fact that they got so many CIOs here who real CIA a lot of times these conferences you get to you know the infrastructure guys but so what's the vibe like over there well you know it's kind of cool because if you think about service now and you go back to say 10 years this was all about how to make IT more productive around the ITIL model and you know and you'd use these automated services to do this stuff what's happening and Frank nailed it in the keynote he said look this infrastructure can be turned inside out and you can service enable the entire enterprise not just IT need a service enterprise you know HR you can decision a marketing eight-day any other shared service you can turn into a bunch of services that you can sort of call in and use service now as a platform so so the cios it was all about well that that's a different that's a different vision and so how do we map from the old way of sort of thinking about this is an internal productivity facility to this new way of saying no this is an enterprise enablement platform that's a big that's a big move a little bit like Salesforce going to force calm that same flavor yes sir frank's keynote was talking about how the CIO has to become you know more business savvy and of course we've heard that a lot for years and years and years but in fact a number of the folks that we've had on here at the service now are actually of that hill maybe they came from the business but most CIOs didn't necessarily come from the business they weren't P&L managers they weren't running sales do you see that changing yeah I think what happened in the 20th century was IT was sufficiently complex that frankly you had to be a technical person to do it it just it was just really hard and and yes you needed business consultants but the end of the day you needed ten percent business consultants and ninety percent technical people I think we've come a long way since then in the next generation of stuff is more around systems of engagement these things that that communicate with each other as opposed to systems of record and so the profile the winning IT strategy is migrating from help us run information about our business in the back office to help us actually re-engineer the dynamics of our business in the world in the present and that's like going from from data to behavior them it's a big we call it going from systems of record to systems of engagement it's a big show and is that that transition in your mind is very disruptive so what happens to all those purveyors and buyers of systems of engagement to they morph into obsessive record do they morph into systems of engagement do they just get blown away no it's interesting so so so first of all you're never going to get rid of your systems of record but at the margin we've probably extracted most of the lifetime value from that investment already so you need to maintain them and so the industry is consolidating a round of an anchor set of vendors who we trust to do that but the growth is going to be like if you look at systems of engagement we might have gotten five percent of the lifetime value there so at their margin if you have a dollar to spend people want to spend it in there so the challenge of being an incumbent is I'm not going to lose my base but man the growth is happening over here so the real challenge for that for the incumbent vendors is how can i participate in the new world and still maintain my relationships in the old world whereas the new guys are just coming and saying i don't i'll leave the old world of you guys i just want to play over here i can get your take on the structure of the IT business is you've observed as have i sort of these disruptions and these changes over time so obviously we went from being framed at pc you saw that the competitive line started to get more disintegrated yes i could use that that term a competition occurred on those I see that Intel's ascendancy in Microsoft and Oracle the best database companies the emc was the storage company and everything was sort of you know siloed and but leadership the leadership matrix has largely stayed intact I mean even IBM and okay HP said its ends up and down but it's largely stayed intact do you see the cloud changing that fundamentally changing the economic yes I think yes I think what happened is so in the client server error we did we built the stack what you're just described and every layer of the stack had a leader now I think since 2000 y2k that stack is being compressed meaning there are fewer and fewer vendors that are still in the in that in that leadership cadre and as we go to like cloud and computing the service you start saying well yeah i still have cisco in there i still have IBM in there but maybe i'm buying them as a service rather than as a set of equipment so you kind of can feel that world just I think compressing this look is the right word and where is the experimentation the opportunity to sort of find new places to go to it's very much in this world outboard of the IT data center where it it is about engaging engaging with your customer engaging with your employee engaging with your supply chain and using mobile things and social and you know analytics and cloud and all these new technologies the freedom to do that is is actually outboard of the of the old style I show you what you described as sort of an oligopoly and you've got these big whales and I've always asking you know guys who follow this it are we going to see somebody to disrupt that Amazon is the obvious you have to go to them a three billion dollar you know company growing at sixty percent a year with marginal economics of services that look like software yep but at the same time it's okay they've got this huge lead but it doesn't just make sense to me that it's sustainable I mean because hardware economics never will go to 0 so you would think that somebody was almost like the IBM early pc days remember IBM heavily yep we're domin to play that's kind of what kind of way amazon is now do you do you see that you see more competition from amazon why is it that they don't have direct competition so the less of the last book i wrote in the last the thing i've been working on most recently is around why is it so hard for the established incumbents to catch the next wave and the problem is so you look at why amazon's why is Amazon so unopposed in many of its initiatives well their business model in the economic model is completely divorced from the incumbent model and so you look at the incumbent in there going it's not that I don't see what the guys are doing I get what they're doing I just don't see how I can get my investors or my my whole infrastructure on to that new place in my example that was code at so you know Antonio Perez came from HP he knew what he was getting into he understood digital everybody at Kodak understood digital but they couldn't get to the other place so in this it would call it escape velocity how do you free yourself from your own paths and you you really do have to take a pretty dramatic approach to it and I think by the way i think i'm looking at microsoft in particular i think it I think Microsoft's going to give a very very big run at doing it and but I think that they're still more the exception than the rule you would wish that every one of those vendors would say look you know because every CIO here if any of those vendors came to him and said hey we're going to really try to play here will you help they'd say yes they don't want to change their relationships but but we get trapped in these business models and then you sort of grind and you grind and grind and after a while it's like well man you've just ground yourself to do I owe the classic label Christensen right individuals dilemma and it also makes a question is d said David's been the same characters kind of changing companies had not Jeff Bezos and Amazon come in with a completely different model to drive cloud with the other people who still has to transfer so they want to give credit to you want to bet it to be so so you want to give credit to Benioff by the way Benioff has been has been the kind of prow of a ship that brings in the illusory at work day brings in netsuite brings in service service now you know so the software-as-a-service thing is coming in at one level and remember if you were an on-premise guy it's very very how many years did did SI p commit an enormous amount of money to say we're going to have a great cloud offering and it just it's so hard so so it is so and then you're looking now at this sort of this next layer of collaborative IT and you're seeing box and octant hang all these cool thing and analytics and splunk consumer logic and all these companies going really I mean I you know I mean if your fear of my age is like okay you have a t-shirt they got love to you think I'm a teacher but but but the point is this free space and they're saying there's these cool problems to solve we're not encumbered by any of the legacy we're going to race ahead and so if you're a CIO well we spent most of our time with the cios today was ok i have established set of relationships here i'm not going to abandon them but at the margin i need them to help me think about the future I thought these really start sparkly new startups some i'm sure not going to exist next year but some are going to be the leaders so how play that game right now and and the pressure it's putting on the IT organization is the people I know that are good at this are not the people that are good at this and so how do I so we had to talk about talent and how do you manage and how do you create career paths and and is it or do you have a infrastructure officer vs an innovation office I'm it was all around that same prob right and then oh by the way there's Hadoop and mobile and big data and some of these other just open source innovations that are being just thrown all these guys played it is so from a technology plate from a technology play if you're technologists it's like bring it on right but I think the interesting thing is and most of my career aighty was about the business so you ran a business and you had IT systems which gave you information about your business what's happened in the last 15 years is that more and more sectors of the economy i T is becoming the business so you saw what happened the newspapers in facilitate with IT isn't about the newspaper business IT is displacing the newspaper business Google is displaced in the media business amazon is displacing retail you know mobile banking is displacing banking Airbnb uber I mean this so there we have the taxi guys are worried them it and so you start saying it isn't IT isn't about the business it's a digital world and and so all of us and that was it i think that was probably at the core of the discussion so which cio am i what do I have permission to be would do my colleagues get this you know am I competent to do it if they do I mean you've talked about this a lot and you've given a number of examples so so was nicked car just dead wrong in 2003 or just to a narrow it is to keep what he was saying I believe is that systems of record okay are dead I think at that time by the way it wasn't obvious there was anything else because it no serious i can remember to you know the whole venture community kind of abandoned itv4 about researcher ivan on 101 yeah it was and even in the end even in the physical infrastructure there's still the idea is the basis of the competitive and about the reporting system yeah and i think this issue about so i think there's still a few businesses we're really IT still is about the business and you know what you can kind of stick with whatever you were doing you'll be okay but if your business is under an existential threat meaning the new IT model eviscerates your business model which arguably you could say all those both those incumbent stack vendors you know I mean cloud does eviscerate the on-premise hardware data center business model which was the fundamental foundation of IT as I knew it for all my business career and now all this it's like holy how do i how do i how do I deal with it so we talk about Amazon as a potential you know new you know big whale Salesforce is obviously he's got it but they've been around since 99 there's going to be exception mm-hmm proves the rule I don't maybe a service now or a workday you know we'll see if this market is big enough it looks like it it might be what often happens is they these guys let's get gobbled up or Larry Ellison writes a check you say these to denigrate people who write write checks not code I think the biggest matter and they got such mass never was afraid to reinvent himself change the game change the dynamics of the industry so do you think we will see a another big player and where will that comfort will it be the SAS guys will it be the sum of the guys out of the hadoop world what I don't think it will so here here's what I don't think will work I don't think you can be an established incumbent vendor under this compression power and write a check and get yourself back I think what happens when you write a check if you just bring a hot property into cold molecules and it loses its exactly exactly so I don't think that will work I think if you want to be one of these incumbents and succeed over here you have to actually pull part of your own DNA and capability and we literally just jump and then I think you can acquire it to it to build a thing there but what Larry did was he consolidate he basically was the first guy to figure out Nick Carr is right I need to buy up all the properties yep and brother George ball and run a maintenance business which by the way came to read and Georgia computer associates had that play up in the eighties it's the same play with this is a different plan well I love what you say in emc is an interesting one to watch the way to chi is setting up this Federation with pivotal and VMware you know who see we'll see what happens with the quarry NC and I think VI 3 of 8 yeah I think that that is I mean VMware's one of the wonderful examples of think we're a company did not cause the hot molecules become the cold molecules the thing you wonder there though is it feels a little bit like a like a holding company if you will and so and by the way vmware is in a curious tweener right like they kind of were the most they made the old stack incredibly productive so in some sense they can feel like they're part of the old world right they're probably the newest kid on the old world but then you think well yeah but I want to look at their plan now they want to be into software-defined networks they wanted me to software-defined data centers they definitely want to play over here and what it's in this case so state partners Wow one could argue that that was it because of what big in the cloud virtualize computing absolutely absolutely so what're you working on these days that's exciting well so that I think this issue of working with management teams to say okay look this is a self-imposed exile that we're putting ourselves under you know we get it i'll call it the Kodak problem because I don't want to talk about anybody in high tech specifically at the moment but the point is every management team in the established vendor group puts itself on a self imposed discipline to make you know certain kinds of eps things certain kinds of growth you know whatever it is the expectations of their investors and you look at the situation you say guys that is a slope glide path to extinction we all know that and by the way off the record they know it's no it's not that that is this is not a failure of it like this is a failure of will so then the question is well so how do you negotiate a different path and part of it is you have to make you have you have to be able to tell a story of your investors part of it is you have to negotiate a different operating model inside the company and what they've done so far is they said well okay we've got our established businesses and we've got our innovative businesses and we know enough to keep them apart so that part is not the problem and they actually come up with cool stuff the the moment of truth is when can you scale any of these innovative businesses to compete to actually be a material part of your historical portfolio meaning in my terminology at least ten percent of your total revenue going to twenty percent in what happens in that journey is it a key point you have to draw on the resources of your established business and all the people that make their living and they're compensated on getting the next quarter in the next quarter go guys I can't make the quarter and do this and you've got it you've got to find a way to say you know if we don't figure out a way to pull some of that resource over here and play our next hand will invent everything in the world but we'll never get it to scale and so there's there's a bunch of stuff around business model planning and then Investor Relations organizational development it's all around saying and the key there's two key ideas idea number one is it's a go-to-market problem not an RD problem you do not have an innovation problem you can't get your thing to market and the second cool idea is you can only do one of the time and everybody says well but give have the risk to so high you got a three or four or five of these things maybe want to work it's like know the sacrifice is so great if you put two or more horses in the race people people won't even run so the other one that's a focus and don't it's ok not to make the quarter that's like on American looking like michael dunn right i mean that's obsessively what he's hoping to be able to do and i think one of the reasons you see people go private is to say i can't play this game bye-bye normal public company protocol i mean i like to but i can't get there from here now i actually don't think every company ought to have to go private to do this but i think they do have to change their playboys all right Jeff we have to leave it there hey great to see you thank you very much me feel smarter just hanging out with you right there buddy we'll be right back after this is the cube you