>> From theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante >> In its early days, robotic process automation emerged from rudimentary screen scraping, macros and workflow automation software. Once a script heavy and limited tool that largely was used to eliminate mundane tasks for individual users, and by the way still is, RPA's evolved into an enterprise-wide mega trend that puts automation at the center of digital business initiatives. Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this breaking analysis, we present our quarterly update of the trends in RPA and automation and share the latest survey data from enterprise technology research. RPA has grown quite rapidly and the acronym is becoming a convenient misnomer in a way. I mean the real action in RPA has evolved into enterprise-wide automation initiatives. Once exclusively focused really on back office automation and areas such as finance, RPA has now become an enterprise initiative as many larger organizations especially, move well beyond cost savings and outside of the CFO's purview. We predicted in early "Breaking Analysis" episodes that productivity declines in the US and Europe especially, would require automation to solve some of the world's most pressing problems. And that's what's happening. Automation today is attacking not only the labor shortage but it's supporting optimizations in ESG, supply chain, helping with inflation challenges, improving capital allocation. For example, the supply chain issues today, think about what they require. Somebody's got to do research, they got to figure out inventory management, they got to go into different systems, do prioritizations, do price matching, and perform a number of other complex tasks. These are time consuming processes. Now the combination of RPA and machine intelligence is helping managers compress the time to value and optimize decision making. Organizations are realizing that a digital business goes beyond cloud and SaaS, and puts data, AI and automation at the core leveraging cloud and SaaS but reimagining entire workflows and customer experiences. Moreover, low code solutions are taking off and dramatically expanding the ability of organizations to make changes to their processes. We're also seeing adjacencies to RPA becoming folded into enterprise automation initiatives. And that trend will continue for example Legacy software testing tools. This is especially important as companies SaaSify their business and look for modern testing tools that can keep pace with their transformations. So the bottom line is, RPA or intelligent automation has become a strategic priority for many companies. And that means you got to get the CIO involved to ensure that the governance and compliance edicts of the organization are appropriately met. And that alignment occurs across the technology and business lines. A couple of years ago, when we saw that RPA could be much much more than what it was at the time, we revisited our total available market or TAM analysis. And in doing so, we felt there would be a confluence of automation, AI, and data and that the front and back office schism would converge. That is shown here. This is our updated TAM chart, which we shared a while back with a dramatically larger scope. We were interested that, just a few days ago by the way Forrester put out a new report, picked up by Digital Nation, that the RPA market would reach 22 billion by 2025. Now, as we said at the time our TAM includes the entire ecosystem including professional services as does Forrester's recent report and the projections they're in. So see that little dotted red line there, that's about at the 22 billion mark. We're a few years away but we definitely feel as though this is taking shape the way we had previously envisioned. That is to say a progression from back office blending with customer facing processes becoming a core element of digital transformations and eventually entering the realm of automated systems of agency where automations are reliable enough and trusted enough to make realtime decisions at scale for a much, much wider scope of enterprise activities. So we see this evolving over the 2020s or the balance of this decade and becoming a massive multi hundred billion dollar market. Now, unfortunately for later investors, this enthusiasm that I'm sharing around automation has not translated into price momentum for the stocks in this sector. Here are the charts, the stock charts for four RPA related players with market values inserted in each graphic. We've set the cross hairs approximately at the timing of UiPath's IPO. And that's where we'll start. UiPath IPOed last April and you can see the steady decline in its price. UiPath's Series F investors got in at $30 billion valuation, so that's been halved, more than half. But UiPath is the leader in this sector as we'll see in a moment. So investors are just going to have to be patient. Now, you know the problem with these hot tech companies is the cat gets let out of the bag before the IPO because they raise so much private money, it hits the headlines and then, at the time you had zero interest rates, you had the tech stock boom during the pandemic, so you're just going to have to wait it out to get a nice return if you got in sort of post IPO. You know, which... I think this business will deliver over the long term. Now, Blue Prism is interesting because it's being bought by SS&C Technologies after a bidding war with Vista. So that's why their stock has held up pretty reasonably. Vista's PE firm, which owns TIBCO and was going to mash it, Blue Prism that is, together with TIBCO. That was a play I always liked because RPA is going to be integrated across the board. And TIBCO is an integration company, and I felt it was in a good position to do that. But SS&C obvious said, "Hey, we can do that too." And look, they're getting a proven RPA tech stack for 10% of the value of UiPath. Might be a sharp move, we'll see. Or maybe they'll jack prices and squeeze the cashflow, I honestly have no idea. And we shelled the other two players here who really aren't RPA specialists. Appian is a low code business process development platform and Pegasystems of course, we've reported on them extensively. They're a longtime business process player that has done pretty well. But both stocks have suffered pretty dramatically since last April. So let's take a look at the customer survey data and see what it tells us. The ETR survey data shows a pretty robust picture frankly. This chart depicts the net score or customer spending momentum on that vertical axis and market share or pervasiveness relative to other companies and technologies in the ETR dataset, that's on the horizontal. That red dotted line at the 40% mark, that indicates an elevated spending level for the company within this technology. The chart insert you see there shows how the company positions are plotted using net score and market share or Ns. And ETR's tool has a couple of cool features. We can click on the dot and it allows you to track the progression over time, in this case going back to January, 2020 that's the lines that we've inserted here. So we'll start with Microsoft and we'll get that over with. Microsoft acquired a company called Softomotive for a reported a hundred million dollars thereabout, it's a little more than that. So pretty much a lunch money for Mr. Softy. So Microsoft bought the company in May and look at the gray line where it started showing up in the October ETR surveys at a very highly elevated level, typical Microsoft, right? I mean, a lot of spending momentum and they're pretty much ubiquitous. And it just stayed there and it's gone up and to the right, just really a dominant picture. But Microsoft Power Automate is really kind of a personal productivity tool not super feature rich like some of the others that we're going to talk about, it's just part of the giant Microsoft software estate. And there's a substantial amount of overlap between, for example, UiPath's and Automation Anywhere's customer bases and Power Automate users. And it's speaking with the number of customers. They'll say, "Yeah, we use Power Automate," but they see enterprise automation platforms as much more feature rich and capable and they see a role for both. But it's something to watch out for because Microsoft can obviously take a bite out of virtually any platform and moderate the enthusiasm for it. But nonetheless, these other firms that we're mentioning here, the two leaders, they really stand out, UiPath and Automation Anywhere. Both are elevated well above that 40% line with a meaningful presence in the data set. And you can see the path that they took to get to where they are today. Now we had predicted in 2021 in our predictions post that Automation Anywhere would IPO in 2021. So we predicted that in December of 2020 but it hasn't happened yet. The company obviously wasn't ready, and it brought in new management. We reported on that, Chris Riley as the Chief Revenue Officer, and it made other moves to show up their business. Now let me say this about Riley. I've known him him for years, he's a world class sales leader, one of the best in the tech business. And he knows how to build a world class go to market team, I guarantee that's what he's doing. I have no doubt he's completely reinventing his sales team, the alliances, he's got a lot of experience of that when he was at EMC and Dell and HPE, and he knows the channel really well. So I have a great deal of confidence that if Automation Anywhere's product is any good, which the ETR data clearly shows that it is, then the company is going to do very well. Now, as for the timing of an IPO, look, with the market choppiness, who knows? Automation Anywhere, they raised a ton of dough and it was last valued around... In 2019, it was just north of 7 billion. And so if UiPath is valued at 15 billion, you could speculate that Automation Anywhere can't be valued at much more than 10 billion, maybe a little under, maybe a little over. And so they might wait for the market volatility to chill out a little bit before they do the IPO or maybe they've got some further cleanup to do and they want to get their metrics better, but we'll see. Now to the point earlier about Blue Prism, look at its position on the vertical axis, very respectable. Just a finer point on Pega. We've always said that they're not an RPA specialist but they have an RPA offering and a presence in the ETR data set in this sector. And they got a sizeable market cap so we'd like to include them. Now here's another look at the net score data. The way net score works is ETR asks customers, are you adopting a platform for the first time? That's that lime green there. Are you accelerating spending on the platform by 6% or more relative to last year, or sometimes relative to some other point in time, this is relative to last year. That's the forest green. Is your spending flat or is it, that's the gray, or is it decreasing by 6% or worse? Or are you churning? That's that bright red. You subtract the reds from the greens and you get net score which is shown for each company on the right along with the Ns in the survey. So other than Pega, every company shown here has new adoptions in the double digits, not a lot of churn. UiPath and and Automation Anywhere have net scores well over that 40% mark. Now, some other data points on those two, ETR did a little peeling of the onion in their data set and I found a couple of interesting nuggets. UiPath in the Fortune 500 has a 91% net score and a 77% net score in the Global 2000. So significantly higher than its overall average. This speaks to the company's strong presence in larger companies and the adoption and how larger companies are leaning in. Although UiPath's actually still solid in smaller firms as well by the way but... Now the other piece of information is, when asked why they buy UiPath over alternatives customers said a robust feature set, technical lead and compatibility with their existing environment. Now to Automation Anywhere. They have a 72% net score in the Fortune 500, well above its average across the survey, but 46% only in the Global 2000 below its overall average shown here of 54. So we'd like to see a wider aperture in the Global 2000. Again, this is a survey set, who knows, but oftentimes these surveys are indicative. So maybe Automation Anywhere just working that out, more time, figuring out the go to market in the Global 2000 beyond those larger customers. Now, when asked why they buy from Automation Anywhere versus the competition customers cited a robust feature set, just like UiPath, technological lead, just like UiPath, and fast ROI. Now I really believe that both for Automation Anywhere and UiPath, the time to value is much compressed relative to most technology projects. So I would highlight that as well. And I think that's a fundamental reason, one of the reasons why RPA has taken off. All right let's wrap up. The bottom line is this space is moving and it's evolving quickly, and will keep on a fast pace given the customer poll, the funding levels that have been poured into the space, and, of course, the competitive climate. We're seeing a new transformation agenda emerge. Pre COVID, the catalyst was back office efficiency. During the pandemic, we saw an acceleration and organizations are taking the lessons learned from that forced March experience, the digital I sometimes call it, and they're realizing a couple things. One, they can attack much more complex problems than previously envisioned. And two, in order to cloudify and SaaSify their businesses, they need to put automation along with data and AI at the core to completely transform into a digital entity. Now we're moving well beyond automating bespoke tasks and paving the cow path as I sometimes like to say. And we're seeing much more integration across systems like ERP and HR and finance and logistics et cetera, collaboration, customer experience, and importantly, this has to extend into broader ecosystems. We're also seeing a rise in semantic workflows to tackle more complex problems. We're talking here about going beyond a linear process of automation. Like for instance, read this, click on that, copy that, put it here, join it with that, drag and drop it over here and send it over there. It's evolving into a much more interpreter of actions using machine intelligence to watch, to learn, to infer, and then ultimately act as well as discover other process automation opportunities. So think about the way work is done today. Going into various applications, you grab data, you trombone back out, you do it again, in and out, in and out, in and out of these systems, et cetera, NASM, and replacing that sequence with a much more intelligent process. We're also seeing a lot more involvement from C-level executives, especially the CIO, but also the chief digital officer, the chief data officer, with low code solutions enabling lines of business to be much more involved in the game. So look, it's still early here. This sector, in my view, hasn't even hit that steep part of the S-curve yet, it's still building momentum with larger firms leading the innovation, investing in things like centers of excellence and training, digging in to find new ways of doing things. It's a huge priority because the efficiencies that large companies get, they drop right to the bottom line and the big ER the more money that drops. We see that in the adoption data and we think it's just getting started. So keep an eye on this space. It's not a fad, it's here to stay. Okay, that's it for now. Thanks to my colleagues, Stephanie Chan who helped research this week's topics and Alex Myerson on the production team who also manages the Breaking Analysis Podcast, Kristen Martin and Cheryl Knight, helped get the word out on social. Thanks guys. Your great teamwork, really appreciate that. Now remember, these episodes, they're all available as podcasts, wherever you listen just search "Breaking Analysis Podcast". Check out ETR's website at etr.ai. And we also publish a full report every week on wikibon.com and siliconangle.com. You can get in touch with me directly, david.vellante@siliconangle.com is my email. You can DM me @dvellante or comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights, powered by ETR. Have a great week, stay safe, be well, and we'll see you next time. (outro music)

>>From the cube studios in Palo Alto, in Boston, bringing you data-driven insights from the cube and ETR. This is breaking analysis with Dave Vellante >>UI path has always been an unconventional company. You know, it started with humble beginnings. It was essentially a software development shop. And then it caught lightning in a bottle with its computer vision technology. And it's really it's simplification mantra. And it created a very easy to deploy software robot system for bespoke departments. So they could automate mundane tasks. You know, you know, the story, the company grew rapidly was able to go public early this year. Now consistent with its out of the ordinary approach. While other firms are shutting down travel and physical events, UI path is moving ahead with forward for its annual user conference next week with a live audience there at the Bellagio in Las Vegas, it's also fast-forwarding as a company determined to lead the charge beyond RPA and execute on a more all encompassing enterprise automation agenda. Hello everyone. And welcome to this week's Wiki bond Cuban sites powered by ETR in this breaking analysis and a head of forward four we'll update you in the RPA market. >>The progress that UI path has made since its IPO and bringing some ETR customer survey data to contextualize the company's position in the overall market and relative to the competition. Here's a quick rundown of today's agenda. First, I want to tell you the cube is going to be at forward for, at the Bellagio next week, UI paths. This is their big customer event. It's live. It's a physical event. It's primarily outdoors. You have to be vaccinated to attend. Now it's not completely out of the ordinary John furrier and the cube. We're at AWS public sector this past week. And we were at mobile world Congress and one of the first big hybrid events of the year at Barcelona. And we thought that event would kick off the fall event season live event in earnest, but the COVID crisis has caused many tech firms. Most tech firms actually to hit the pause button, not UI path. >>They're moving ahead, they're going forward. And we see a growing trend for smaller VIP events with a virtual component topic, maybe for another day. Now we've talked extensively about the productivity challenges and the automation mandate. The pandemic has thrust upon us. Now we've seen pretty dramatic productivity improvements as remote work kicked in, but it's brought new stresses. For example, according to Qualtrics, 32% of working moms said their mental health has declined since the pandemic hit. 15% of working dads said the same by the way. So one has to question the sustainability of this perpetual Workday, and we're seeing a continuum of automation solutions emerging. And we'll talk about that today. We're seeing tons of MNA, M and a as well, but now in that continuum on the left side of the spectrum, there's Microsoft who in some ways they stand alone and that Azure is becoming ubiquitous as a SAS cloud collaboration and productivity platform. >>Microsoft is everywhere and in virtually every market with their video conferencing security database, cloud CRM, analytics, you name it, Microsoft is pretty much there. And RPA is no different with the acquisition of soft emotive. Last year, Microsoft entered the RTA market in earnest and is penetrating very deeply into the space, particularly as it pertains to personal approach, personal productivity building on its software state. Now in the middle of that spectrum, if you will, we're seeing more M and a, and that's defined really by the big software giants. Think of this domain as integrated software plays SAP, they acquired contexture, uh, uh, they also acquired a company called process insight service now acquired Intella bought Salesforce service trace. We see in for entering the fray. And I, I would put even Pega Pega systems in this camp, software companies focused on integrating RPA into their broader workflows into their software platforms. >>And this is important because these platforms are entrenched. They're walled gardens of sorts and complicated with lots of touchpoints and integration points. And frankly, they're much harder to automate because of their entrenched legacy. Now on the far side of that, spectrum are the horizontal automation players and that's being led by UI path with automate automation anywhere as the number two player in this domain. And I didn't even put blue prism prism in there more M and a recently announced, uh, that Vista is going to acquire them. Vista also owns TIBCO. They're going to merge those two companies, you know, tip goes kind of an integration play. And so again, I'm, I might, I would put them in that, you know, horizontal piece of the spectrum. So with that as background, we're going to look at how UI path has performed since we last covered them at IPO. >>And then we'll bring in some ETR survey data to get the spending view from customers. And then we'll wrap up now just to emphasize the importance of, of automation and the automation mandate mandate. We talk about it all the time in this program, we use this ETR chart. It's a two dimensional view with net score, which is a measure of spending momentum on the vertical axis and market share, which is a proxy for pervasiveness in the dataset. That's on the horizontal axis. Now note that red dotted line at signifies companies with an elevated position on the net score, vertical axis, anything over that is considered pretty good, very good. Now this shows every spending segment within the ETR taxonomy and the four spending categories with the greatest velocity are AI cloud containers and RPA. And they've topped the charts for quite a while. Now they're the only four categories which have sustained above that 40% line consistently throughout the pandemic. >>And even before now, the impressive thing about cloud of course, is it has a spending has both spending momentum on the vertical axis at a very large share of the, of the market share of presence in the dataset. The point is RPA is nascent still. It has an affinity with AI as a means of more intelligently identifying and streamlining process improvements. And so we expect those to, to remain elevated and grow to the right together, UI path pegs it's Tam, total available market at 60 billion. And the reality is that could be understated. Okay. As we reported from the UI path S one analysis, we did pre IPO. The company at that time had an AR annual recurring revenue of $580 million and was growing at 65% annually at nearly 8,000 customers at the time, a thousand of which had an ARR in excess of a hundred K and a net revenue retention, the company had with 145%. >>So let's take a look at the picture six months forward. We mentioned the $60 billion Tam ARR now up over 725 million on its way to a billion ARR holding pretty steady at 60% growth as is an RR net revenue retention, and more than a thousand new customers in 200 more with over a hundred thousand in ARR and a small operating profit, which by the way, exceeded the consensus pretty substantially. Profitability is not shown here and no one seems to care anyway, these days it's all about growing into that Tam. Well, that's a pretty good looking picture. Isn't it? The company had a beat and a raise for the quarter early this month. So looking good, right? Well, you ask how come the stock's not doing better. That's an interesting question. So let's first look at the stocks performance on a relative basis. Here, we show you I pass performance against Pega systems and blue prism. >>The other two publicly traded automation, pure plays, you know, sort of in the case of Pega. So UI path outperformed post its IPO, but since the early summer Pega has been the big winner. Well, UI path slowly decelerated, you see blue prism was the laggard until it was announced. It was in an acquisition talks with a couple of PE firms and the prospects of a bidding war sent that yellow line up. As you can see UI path, as you can see on the inset has a much higher valuation than Pega and way higher than blue prison. Pega. Interestingly is growing revenues nicely at around 40%. And I think what's happening is the street simply wants more, even though UI path beat and raised wall street, still getting comfortable with which is new to the public market game. And the company just needs to demonstrate a track record and build trust. >>There's also some education around billings and multi-year contracts that the company addressed on its last earnings call, but the street was concerned about ARR from new logos. It appears to be slowing down sequentially in a notable decline in billings momentum, which UI pass CEO, CFO addressed on the earnings call saying, look, they don't need to trade margin for prepaid multi-year deals, given the strong cash position while I give anything up. And even though I said, nobody cares about profitability. Well, I guess that's true until you guide for an operating loss. When you've been showing a small profit in recent recent quarters, which you AIPAC did, then all of a sudden people care. So UI path, isn't a bit of an unknown territory to the street and it has a valuation that's pretty rich, very rich, actually at 30 times, a revenue multiple greater than 30 times revenue, multiple. >>So that's why in, in my view, investors are being cautious, but I want to address a dynamic that we've seen with these high growth rocket ship companies, something we talked about with snowflake. And I think you're seeing some of that here with UI paths, different model in the sense that snowflake is pure cloud, but I'm talking about concerns around ARR from new logos and in that growth on a sequential basis. And here's what's happening in my view with UI path, you have a company that started within departments with a small average contract size in ACV, maybe 25,000, maybe 50,000, but not deep six figure deals that wasn't UI paths play it because the company focused so heavily on simplicity and made it really easy to adopt customer saw really fast ROI. I mean breakeven in months. So you very quickly saw expansion into other departments. >>So when ACV started to rise and installations expanded within each customer UI path realized it had to move beyond being a point product. And it started thinking about a platform and making acquisitions like process gold and others, and this marked a much deeper expansion into the customer base. And you can see that here in this UI path, a chart that they shared at their investor deck customers that bought in 2016 and 2017 expanded their they've expanded their spend 15, 13, 15, 18 20 X. So the LTV, the lifetime value of the customer is growing dramatically. And because UI path has focused on simplicity, it has a very facile freemium model, much easier to try before you buy than its competitors. It's CAC, it's customer acquisition costs are likely much lower than some of its peers. And that's a key dynamic. So don't get freaked out by some of those concerns that we raised earlier, because just like snowflake what's happening is the company for sure is gaining new customers. >>Maybe just not at the same rate, but don't miss the forest through the trees. I E they're getting more money from their existing customers, which means retention, loyalty and growth. Speaking of forests, this chart is the dynamic I'm talking about. It's an ETR graphic that shows the components of net score or against spending momentum net score breaks down into five areas that lime green at the top is new additions. Okay? So that's only 11% of the customer mentions by the way, we're talking about more than 125 responses for UI path. So it's meaningful. It's, it's actually larger in this survey, uh, or certainly comparable to Microsoft. So that says something right there. The next bar is the forest green forest. Green is where I want you to focus. That's customer spending 6% or more in the second half of the year, relative to the first half. >>The gray is flat spending, which is quite large, the pink or light red that's spending customer spending 6% or worse. That's a 4% number, but look at the bottom bar. There is no bar that's churn. 0% of the respondents in the survey are churning and churn is the silent killer of SAS companies, 0% defections. So you've got 46% spending, more nobody leaving. That's the dynamic that is powering UI path right now. And I would take this picture any day over a larger lime green and a smaller forest green and a bigger churn number. Okay. So it's pretty good. It's not snowflake good, but it's solid. So how does this picture compare to UI pass peers? Well, let's take a look at that. So this is ETR data, same data showing the granularity net score for Microsoft power, automate UI path automation, anywhere blue prism and Pega. >>So as we said before, Microsoft is ubiquitous. What can we say about that? But UI path is right there with a more robust platform, not to overlook Microsoft. You can't, but UI path, it'll tell you that they don't compete head to head for enterprise automation deals with Microsoft. Now, maybe they will over time. They do however, compete head to head with automation anywhere. And their picture is quite strong. As you can see here, it has this blue Prism's picture and even Pega, although blue prism, automation, anywhere UI path and power automate all have net scores on this chart. As you can see the table in the upper right over 40% Pega does not. But again, we don't see Pega as a pure play RPA vendor. It's a little bit of sort of apples and oranges there, but they do sell RPA and ETR captures in their taxonomy. >>So why not include them also note that UI path has, as I said before, more mentions in the survey than power automate, which is actually quite interesting, given the ubiquity of Microsoft. Now, one other notable notable note is the bright red that's defections and only UI path is showing zero defections. Everybody else has at least even of the slim, some defections. Okay. So take that as you will, but it's another data 0.1. That's powerful, not only for UI path, but really for the entire sector. Now, the last ETR data point that we want to share is our famous two dimensional view. Like the sector chart we showed earlier, this graphic shows net score on the vertical axis. That's against spending velocity and market share or pervasiveness on the horizontal axis. So as we said earlier, UI path actually has greater presence in the survey than the ever-present Microsoft. >>Remember, this is the July survey. We don't have full results from the September, October survey yet. And we can't release them until ETR is out of its quiet period. But I expect the entire sector, like everything is going to be slightly down because as we reported last week, tech spending is moderated slightly in the second half of this year, but we don't expect the picture to change dramatically. UI path and power automate, we think are going to lead and market presence in those two plus automation anywhere are going to show strength and spending momentum as well. Most of the sector. And we'll see who comes in above the 40% line. Okay. What to watch at forward four. So in summary, I'll be looking for a few things. One UI path has hinted toward a big platform announcement that will deepen its capabilities to go beyond being an RPA point tool into much more of an enterprise automation platform rewriting a lot of the code Linux cloud, better automation of the UI. >>You're going to hear all kinds of new product announcements that are coming. So I'll be listening for those details. I want to hear more from customers to further confirm what I've been hearing from them over the last couple of years and get more data, especially on that ROI on that land and expand. I want to understand that dynamic and that true enterprise automation. It's going to be good to get an update face to face and test some of our assumptions here and see where the gaps are and where UI path can improve. Third. I want to talk to ecosystem players to see where they are in participating in the value chain here. What kind of partner has UI path become since it's IPO? Are they investing more in the ecosystem? How to partners fit into that flywheel fourth, I want to hear from UI path management, Daniel DNAs, and other UI path leaders, they're exiting toddler Ville and coming into an adolescent phase or early adulthood. >>And what does that progression look like? How does it feel? What's the vibe at the show. And finally, I'm very excited to participate in a live in-person event to see what's working, see how a hybrid events are evolving. We got a good glimpse at mobile world Congress and this week, and, uh, in DC and public sector summit, here's, you know, the cube has been doing hybrid events for years, and we intend to continue to lead in this regard and bring you the best, real time information as possible. Okay. That's it for today. Remember, these episodes are all available as podcasts, wherever you listen. All you do is search braking analysis podcast. We publish each week on Wiki bond.com and siliconangle.com. And you can always connect on twitter@devolanteoremailmeatdaviddotvolanteatsiliconangle.com. Appreciate the comments on LinkedIn. And don't forget to check out E T r.plus for all the survey data. This is Dave Volante for the cube insights powered by ETR be well, and we'll see you next time.

>>From the Bellagio hotel in Las Vegas, it's the cube covering UI path forward for brought to you by, >>Hey, welcome to the cubes coverage of forward for UI path forward for live from the Bellagio in Las Vegas. I'm Lisa Martin with David. David's great to be back sitting at an anchor desk. >>Yeah, good to see. This is my first show. Since June, we were at mobile world Congress and I've been, I've been doing a number of shows where they'll they'll the host myself would be there with some guests as a pre-record to some simulive show, but this is real live awesome to be working with you again. So we did live last week at a DC public sector summit for AWS next week's cube con. So it's three in a row. So maybe it's a trend. It we'll see. >>Well, the thing that was really surprising was that we were in the keynote briefly this morning. It was standing room only. There are a lot of people at this conference. They think they were expecting about 2000. And to me it looked like there were at least out, if not more >>Funny leases, most companies, if not virtually all of them, except for a handful are canceling physical events. And because they're saying their customers aren't traveling, but I've talked to over a dozen customers. I just got here yesterday afternoon. I've talked about 10 or 12 customers who are here. They're flying, they're traveling. And we're going to dig into a lot of that. Today. We have Uber coming on the program. We have applied materials coming on, blue cross blue shield. I'm really happy that you AIPAC decided to, to put a number of customers on the cubes so we can test what we're hearing, you know, in the marketing. >>Well, one of the first things that they said in the keynote this morning was we want to hear from our customers, what are we doing? Right? What are we not doing enough of? What do you want more? They've got eight over 8,000 customers. You mentioned some of the ones that are going to be on the program this week, including Chevron and Merck who are on today. And 70% of their revenue comes from existing customers. This is a company that has, is really kind of a use case in land and expand. Yeah. >>And I think you're going to see this trend. You know what it's like with COVID it's day to day, month to month, quarter to quarter, you're trying to figure out, okay, what's the right model. Clearly hybrid is the, is the new abnormal, if you will. And I think we're going to see is, is you're going to have VIP events. And this is kind of a VIP event. It's not, you know, 5,000 people, it's kind of 1500, 2000, but there are a lot of VIP customers here. Obviously the partners here. So what they did before the show is they had a partner summit. It was packed. You talked about standing room only. They had a healthcare summit, it was packed. And so they have these little VIP sections, little events within the event, and then they broadcast it out to a wider audience. And I think that's going to be the normal one. I think you're going to see CEO's in a room, maybe in a hotel and wherever in Manhattan or, or San Francisco. And then they'll broadcast out to that wider audience. I think people are learning how to build better hybrid events, but by the way, this is all new. As I said, hybrid events, I meant virtual events. And now they're learning to learn how to build hybrid events. And that's a whole nother new process. >>It is. But it's also exciting to see the traction, the momentum that is here from, you know, they and they IPO at about what six months ago, you covered that your breaking analysis that you did right before the IPO and the breaking analysis that you did last last week, I believe really fascinating. Interesting acceleration is a theme. We're going to talk about the acceleration of automation and the momentum that the pandemic is driving. But this is a company that's accelerated everything. As you said on your breaking analysis, lightning in a bottle, this is a company that went global very quickly. We're seeing them as some of the leading companies. We can probably count on one hand who are actually coming back to these hybrid events and say, we want to be with our customers again and learn from you what you're doing, what's going on. And we've got a lot of news to share. >>Yeah, we've been covering UI path since 2015. And the piece we wrote back at IPO was, uh, you, you bypass long, strange trip to IPO and it, and it was strange. And that they kind of hung out as a software development shop for the better part of a decade. And then just listening and learning, writing code, they were kind of gigs writing code and loved it. And then they realized, wow, we have something here we can. And they, their uniqueness is they have a computer vision technology. They have the ability to sort of infer what a form looks like and then actually populated. And the thing that UI path did that was different was they made it sound, sounds crazy. They made the product really simple to use, and we know simplicity works. We see that with best example in storage storage, a complicated business, pure storage, right? >>They pop it in. You kind of Veeam is another one. It just works. And so they, they created a freemium model. It made it easy for departments to start small, you know, maybe for 15, 20, 20 $5,000, you could get a software robot and then it would do things like whatever it, it would pull data out of one spreadsheet, put it into another pull date out of one, SAS populated and people then realize, wow, I am saving a ton of time. I can do some other things I'm more productive. And then other people looking over her shoulder would say, Hey, what is that you're using? Can I get that? And then all of a sudden, like you said, lightning in a bottle and it exploded, not a conventional Silicon valley, you know, funded company, even though they got a lot of funding, they got, they raised, I think, close to a billion dollars before they went public. Um, and now they're public went public in April. The stock has been sort of trending downward for the last four or five months, a little bit off on sympathy, but you know, >>What do you think that is? They had such momentum going into it. They clearly have a lot of momentum here. 8,000 plus customers. They've got over 1200 customers with an ARR above a hundred thousand. Why do you think the stock is? >>So I think a couple of things, at least, I think first of all, the street doesn't fully understand this company. You know, Daniel DNAs has never been the CEO of a public company. He's not from Silicon valley. He's, you know, from, from, uh, Eastern Europe and they don't know him that well, uh, they've got, you know, the very, very capable, and so they're educating the streets. So there's a comfort level there. They're looking at their growth and they're inferring from their billings that their growth is, is declining. The new growth from new customers in particular. But there, the ARR is still growing at 60% annually. They also guided a little bit conservatively for the street. And the other thing is they've been profitable. I'm not if a cashflow basis. And then they guided that they would actually be, be somewhat unprofitable in the coming quarter. >>People didn't like that. They don't care about profits until you're somewhat profitable. And then you say, Hey, we're going to be a little less profitable, but of course they get events like this. So that, that, I think it's just a matter of the street getting to understand them. And I will say this, and you know, this, they're getting a lot of business from their existing customers. We saw this with snowflake, uh, Cleveland research, put out a note saying, oh, Snowflake's new customer growth is slowing. We published research from our friends at ETR that showed well, they're getting a lot of business from existing customers that sort of fat middle is really where they're starting to mind. And you can see this with UI path. The lifetime value of the customers is just growing and growing and growing. And so I'm not as concerned. The stocks, you know, we don't, we don't, we're not the stock advisors, but the stock is just over 50. >>Now it wasn't 90 at one point. So it's got a valuation of somewhere around 26 billion, which was closer to 50 billion. So who knows, maybe this is a buying opportunity. There's not a lot of data. So the technical analyst are saying, well, we really don't know where it's going to cook it down to 30. It could go, could go rock it up from here. I think the point Lisa is, this is a marathon. It's not a sprint, it's a long-term play. And these guys are the leaders. And they're, I think moving away from the pack. And the last thing is this concern about competition from Microsoft who bought a company last year to really in earnest, get into this business. And everybody's afraid of Microsoft. >>Well, one thing that we know that's growing considerably is the total addressable market pre pandemic. It was about 30 billion. It's now north of 60 billion. We've seen the pandemic accelerate a lot of things. Talk to me a little bit about automation as its role in digital transformation from your side. >>Yeah, I think, you know, this is again, it's a really good question because when you look at these total available market numbers, the way that companies virtually all companies, whether it's Dell or Cisco or UI path or anybody, they take data from like Gartner and IDC and they say, okay, these are the markets that we kind of play in, and this is how it's growing. What's really happening leases. All these markets are converging because of digital. So to your question, it's a di what's a digital business. A digital business is a data business and they differentiate by the way in which they use data. And if you're not a digital business during the pandemic, you're out of business. So all of these markets, cloud machine intelligence, AI automation, orchestra, uh, container orchestration, container platforms, they're all coming together as one, it's all being built in as one. >>So 60 billion, you know, up from 30 billion, I think it could be a hundred billion. I think, you know, they threw out a stat today that 2% of processes are automated says to me that, I mean, anything digital is going to be automated. So that is hundreds of billions of dollars of, of market opportunity, right? And so there's no shortage of market opportunity for this company. And that's why, by the way, everybody's entering it. We saw SAP make some acquisitions. We S we see in for talking about it, uh, uh, Salesforce, uh, service now, and these SAS companies are all saying, Hey, we can own the automation piece within our stack, what UI path is doing. And the reason why I liked their strategy better is they're a specialist in automation horizontally across all these software stacks. And that's really why they're Tam, I think is, >>And that gives them quite a big differentiator that horizontal play >>It does. I think I see. So I don't see, I think there's a continuum and I think you got Microsoft over here with Azure and personal productivity in their cloud. And then you've got the pure plays, which are really focusing on a broader automation agenda. That's UI path, that's automation, anywhere I would put blue prism in that category blueprints. And by the way, he's getting, getting acquired by Vista, and they're gonna merge them with TIBCO company that, you know, quite a bit about, and that's an integration play. So that's kind of interesting. I would put them as more of a horizontal play. And then in the fat middle, you've got SAP and in four and, you know, IBM is getting to the game. Although they, I think they OEM from a lot of different companies and all those other companies I mentioned before, they're kind of the walled gardens. >>And so I think that UI path is less of a head-to-head competitor with, with Microsoft today anyway, than it is for instance, with automation anywhere. And it's, and it's growing faster than automation, anywhere from what we can tell. And it's, it's still a leader in that horizontal play. You know, you never discount Microsoft, but I think just like for instance, Okta is a specialist in, in, in access identity, access management and privileged, privileged access management and access government, they compete with Microsoft's single sign on, right. But they're a horizontal play. So there's plenty of room for, for both in my view. Anyway, >>Some of the things that you can you think that we're going to hear, you know, seem to be at this inflection point where UI path wants to move away from being an RPA point solution to an enterprise automation platform they made, they made some announcements about vision a couple of years ago at the last in-person event. What are some of the things you think that are going to be announced in the next couple? >>That's a really good question. I'm glad you picked up on that because they started as a point tool essentially. And then they realized, wow, if we're really going to grow as a company, we have to expand that. So they made acquisite, they've been making acquisitions. One of the key acquisitions they made was a company called process gold. So it's funny when we've done previous, uh, RPA events, I've said RPA in its early days was kind of scripts paving the cow path, meaning you're taking existing processes of saying, okay, we're just going to automate them where UI path is headed in others is they're looking across the enterprise and how do we go end to end? How do we take a broader automation agenda and drive automation throughout the entire organization? And I think that's a lot of what we're going to hear from today. We heard that from executives, APAR, co Kaylon, and, um, and, and, and Ted Coomer talked about their engineering and their product vision. And I think you iPad has to show that that's actually what's happening with customers and they have the portfolio to deliver >>Well, those two executives that you just mentioned, and a lot of others are going to be on the program. The next couple of days jam packed. Dave, I'm looking forward to unpacking what UI path is doing. The acceleration in the automation market. We're going to have a fun >>Couple of days. Thanks for coming on here for David >>Lante. I'm Lisa Martin. We're going to be back live from Las Vegas at UI path forward for in just a minute.

>> 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. >> UiPath has always been an unconventional company. You know it started with humble beginnings. It's essentially a software development shop. Then it caught lightning in a bottle with its computer vision technology. It's really, it's simplification mantra and it created a very easy to deploy software robot system for bespoke departments so they could automate mundane tasks. You know the story. The company grew rapidly, was able to go public early this year. Now consistent with its out-of-the-ordinary approach, while other firms are shutting down travel and physical events, UiPath is moving ahead with Forward IV, it's annual user conference next week with a live audience there at the Bellagio in Las Vegas. It's also fast forwarding as a company, determined to lead the charge beyond RPA and execute on a more all-encompassing Enterprise automation agenda. Hello everyone and welcome to this week's Wikibond Cube Insights powered by ETR. In this breaking analysis and ahead of Forward IV, we'll update you in the RPA market the progress that UiPath has made since its IPO and bringing some ETR customer survey data that's contextualized the company's position in the overall market and relative to the competition. Here's a quick rundown of today's agenda. First I want to tell you theCube is going to be at Forward IV at the Bellagio next week. UiPath, this is their big customer event. It's live, it's a physical event. It's primarily outdoors. You have to be vaccinated to attend. Now, this not completely out of the ordinary. John Furrier and theCube were at AWS Public Sector this past week and we were at Mobile World Congress in one of the first big hybrid events of the year at Barcelona. We thought that event would kick of the fall event season, live event in earnest but the COVID crisis has caused many tech firms, most tech firms actually, to hit pause button. Not UiPath, they're moving ahead. They're going forward and we see a growing trend for smaller VIP events with a virtual component, topic maybe for another day. Now we've talked extensively about the productivity challenges and the automation mandate the pandemic has thrust upon us. Now, we've seen pretty dramatic productivity improvements as remote work kicked in but its brought new stresses. For example, according to Qualtrics, 32% of working moms said their mental health has declined since the pandemic hit. 15% of working dads said the same by the way. So, one has to question the sustainability of this perpetual workday. And we're seeing a continuum of automation solutions emerging and we'll talk about that today. We're seeing tons of M&A as well but now, in that continuum, on the left-side of the spectrum, there's Microsoft who in some ways, they stand alone and their Azure is becoming ubiquitous as a SaaS-Cloud collaboration and productivity platform. Microsoft is everywhere and in virtually every market, whether video conferencing, security, database, cloud, CRM, analytics, you name it. Microsoft is pretty much there and RPA is no different. With the acquisition of Softomotive last year, Microsoft entered the RTA market in earnest and is penetrating very deeply into the space, particularly as it pertains to personal productivity building on its software stake. Now in the middle of that spectrum if you will, we're seeing more M&A and that's defined really by the big software giants. Think of this domain as integrated software place. SAP, they acquired Contextere. They also acquired a company called Process Insights, Service now acquired Inttellebot. Salesforce acquired Servicetrace, we see Infor entering the frame and I would put even Pega, Pega systems in this camp. Software companies focused on integrating RPA into their broader workflows, into their software platforms and this is important because these platforms are entrenched Their well guardants of thoughts and complicated with lots of touchpoints and integration points and frankly they are much harder to automate because of their entrenched legacy. Now, on the far side of that spectrum, are the horizontal automation players and that's been let by UiPath with automation anywhere as the number two player in this domain. And I even put a blue prism in there more M&A recently announced that Vista is going to acquire them Vista also owns Tibco, they are going to merge those two companies. You know Tibco is come up with the integration play. So again I would put them in that you know, horizontal piece of the spectrum. So with that as background, we're going to look at how UiPath has performed since we last covered them and IPO and I'm going to bring in some ETR survey data to get the spending view from customers and we'll wrap up. Now, just to emphasize the importance of automation and the automation mandate, we talk about it all the time in this program. We use this ETR chart. It's a two dimensional view with net score which is the measure of spending momentum on the vertical axis and market share which is a proxy for pervasiveness in the data set that's on the horizontal axis. Now note that red dotted line, it signifies companies within elevated position on the net score vertical axis anything over that is considered pretty good. Very good. Now this shows every spending segment within the ETR taxonomy. And the four spending categories with the greatest velocity are AI, cloud, containers and RPA. And they have topped the charts for quite a while now. They are the only 4 categories which have sustained above that 40% line consistently throughout the pandemic and even before. Now the impressive thing about cloud of course is it has both spending momentum on the vertical axis and a very large market share or presence in the data set. The point is RPA is nascent still. It has an affinity with AI as a means of more intelligently identifying and streamlining process improvements. And so we expect those two to remain elevated and grow to the right together. UiPath pegs its TAM, total available market at 60 billion. And the reality is that could be understated. Okay, as we reported from the UiPath S1 analysis we did pre IPO, the company at that time had an ARR annual recurring revenue of $580 million and it was growing at 65% annually. And nearly 8000 customers at the time, a 1000 of which had an ARR in excess of a 100k. And the net revenue retention the company had was over 145%. So let's take a look at the pictures 6 months forward. We mentioned the $60 billion TAM, ARR now up over $726.5 million on its way to a billion ARR holding pretty steady at 60% growth as is NRR, net revenue retention and more then a 1000 new customers and 200 more with over a 100000 in ARR and a small operating profit which by the way exceeded the consensuses pretty substantially. Profitability is not shown here and no one seems to care anyway these days. It's all about growing into that TAM. Well that's a pretty good looking picture, isn't it? The company had a beat and a raise for the quarter earlier this month, so looking good right. Well you ask how come the stock is not doing better. That's an interesting question. So let's first look at the stocks performance on a relative basis. Here we show UiPath performance against Pega systems and blue prism, the other two publicly traded automation. Pure plays sort of in the case of Pega. So UiPath outperformed post its IPO but since the early summer Pega is been the big winner while UiPath slowly decelerated. You see Blue prism was at the lag until it was announced that it was in an acquisition talks with a couple of PE firms and the prospects of a bidding war sent that yellow line up as you can see. UiPath as you can see on the inset, has a much higher valuation than Pega and way higher than blue Prism. Pega interestingly is growing revenues nicely at around 40%. And I think what's happening is that the street simply wants more. Even though UiPath beat and raised, Wallstreet is still getting comfortable with management which is new to the public market game and the company just needs to demonstrate a track record and build trust. There's also some education around billings and multi-year contracts that the company addressed on its last earnings call. But the street was concerned about ARR for new logos. It appears to be slowing down sequentially and a notable decline in billings momentum which UiPath CFO addressed on the earnings call saying look they don't need the trade margin for prepaid multi year deals, given the strong cash position. Why give anything up. And even though I said nobody cares about profitability well, I guess that's true until you guide for an operating loss when you've been showing small profit in recent quarters what UiPath did. Then, obviously people start to care. So UiPath is in bit of an unknown territory to the street and it has a valuation, it's pretty rich. Very rich actually at 30 times revenue multiple or greater than 30 times revenue multiple. So that's why in my view, investors are being cautious. But I want to address a dynamic that we have seen with this high growth rocket chip companies. Something we talked about Snowflake and I think you are seeing some of that here with UiPath. Different model in the sense that Snowflake is pure cloud but I'm talking about concerns around ARR and from new logos and that growth in a sequential basis. And here's what's happening in my view with UiPath. You have a company that started within departments with a smaller average contract size, ACV maybe 25000, may be 50000 but not deep six figure deals. That wasn't UiPath's play. And because the company focused so heavily on simplicity and made it really easy to adapt, customers saw really fast ROI. I mean break-even in months. So we very quickly saw expansion into other departments. So when ACV started to rise and installations expanded within each customer, UiPath realized it had to move beyond a point product and it started thing about a platform and making acquisitions like Processgold and others and this marked a much deeper expansion into the customer base. And you can see that here in this UiPath chart that they shared at their investor deck, customers that bought in 2016 and 2017 expanded their spend 13, 15, 18, 20x So the LTV, life time value of the customer is growing dramatically and because UiPath is focused on simplicity, and has a very facile premium model much easier to try before you buy than its competitors it's CAC, Customer acquisition cost are likely much lower than some of its peers. And that's a key dynamic. So don't get freaked out by some of those concerns that we raised earlier because just like Snowflake what's happening is that the company for sure is gaining new customers, may be just not at the same rate but don't miss the forest through the trees I.e getting more money from their existing customers which means retention, loyalty and growth. Now speaking of forest, this chart is the dynamic I'm talking about, its an ETR graphic that shows the components of net score against spending momentum. Net score breaks down into 5 areas. That lime green at the top is new additions. Okay, so that's only 11% of the customer mentions. By the way we are talking about more than a 125 responses for UiPath. So it's meaningful, it's actually larger in this survey or certainly comparable to Microsoft. So that's just something right there. The next bar is the forest green. Forest green is what I want you to focus. That's customer spending 6% or more in the second half of the year relative to the first half. The gray is flat spending which is quite large. The pink or light red, that's spending customers spending 6% or worse, that's a 4% number. But look at the bottom bar. There is no bar, that's churn. 0% of the responders in the survey are churning. And Churn is the silent killer of SaaS companies. 0% defections. So you've got 46% spending more, nobody leaving. That's the dynamic powering UiPath right now and I would take this picture any day over a larger lime green and a smaller forest green and a bigger churn number. Okay, it's pretty good, not Snowflake good but it's solid. So how does this picture compare to UiPath's peers. Let's take a look at that. So this is ETR data, same data showing the granularity net score for Microsoft power automate, UiPath automation anywhere, Blue Prism and Pega. So as we said before, Microsoft is ubiquitous. What can we say about that. But UiPath is right there with a more robust platform. Not to overlook Microsoft, you can't but UiPath will you that the don't compete head to head for enterprise automation deals with Microsoft and may be they will over time. They do however compete head to head with automation anywhere. And their picture is quite strong as you can see here. You know as is Blue Prism's picture and even Pega. Although Blue Prism automation anywhere UiPtah and power automate all have net scores on this chart as you can see the tables in the upper right over 40%, Pega does not. But you can see Pega as a pure play RPA vendor it's a little bit of sort of apples and oranges there but they do sell RPA and ETR captures in their taxonomy so why not include them. Also note that UiPath has as I said before more mentions in the survey than power automate which is actually quite interesting given the ubiquity of Microsoft. Now, one other notable note is the bright red that's defections and only UiPath is showing zero defections Everybody else has at least little of the slims on defections. Okay, so take that as you will but its another data point, the one that is powerful nit only for UiPath but really for the entire sector. Now the last ETR data point that we want to share is the famous two dimensional view. Like the sector chart we showed earlier, this graphic shows the net score on the vertical axis that's against spending velocity and market share or pervasiveness on the horizontal axis. So as we said earlier, UiPath actually has a greater presence in the survey than the ever present Microsoft. Remember, this is the July survey. We don't have full results from the September-October survey yet and we can't release them until ETR is out of its quiet period but I expect the entire sector, like everything is going to be slightly down because as reported last week tech spending is moderated slightly in the second half of this year. But we don't expect the picture to change dramatically UiPath and power automate we think are going to lead in market presence and those two plus automation anywhere is going to show the strength in spending momentum as will most of the sector. We'll see who comes in above the 40% line. Okay, what to watch at Forward IV. So in summary I'll be looking for a few things. One, UiPath has hinted toward a big platform announcement that will deepen its capabilities to beyond being an RPA point tool into much more of an enterprise automation platform, rewriting a lot of the code Linux, cloud, better automation of the UI, you are going to hear all kind of new product announcements that are coming so I'll be listening for those details. I want to hear more from customers that further confirm what I've been hearing from them over the last couple of years and get more data especially on their ROI, on their land and expand, I want to understand that dynamic and that true enterprise automation. It's going to be good to get an update face to face and test some of our assumptions here and see where the gaps are and where UiPath can improve. Third, I want to talk to ecosystem players to see where they are in participating in the value chain here. What kind of partner has UiPath become since its IPO, are they investing more in the ecosystem, how do partners fit into that flywheel. Fourth, I want to hear from UiPath management Daniel Dines and other UiPath leaders, their exiting toddler wheel and coming into an adolescence phase or early adulthood. And what does that progression look like, how does it feel, what's the vibe at the show. And finally I'm very excited to participate in a live in-person event to see what's working, to see how hybrid events are evolving, we got to good glimpse at Mobile congress and this week in DC at public sector summit. As you know theCube is doing hybrid events for years and we intend to continue to lead in this regard and bring you the best real time information as possible. Okay, that's it for today. Remember these episodes are all available as podcasts wherever you listen, all you do is search breaking analysis podcast. We publish each week on Wikibound.com and Siliconangle.com and you can always connect on twitter @dvellante or email me at David.vellante@siliconangle.com Appreciate the comments on LinkedIn and don't forget to check out ETR.plus for all the survey data. This is Dave Vellante for theCube insights powered by ETR. Be well and will see you next time. (upbeat music)

>> Narrator: Live from the Bill Graham Auditorium in San Francisco, it's The Cube. Covering Pure Storage Accelerate 2018. Brought to you by Pure Storage. (techno music) >> Back to The Cube, we are live at Pure Storage Accelerate 2018. We are in San Francisco at the Bill Graham Civic Auditorium. This is a really cool building built in 1915, loads of history with artists. I'm with Dave Vellante. I'm wearing prints today in honor of the venue and we're excited to be joined by longtime Pure Storage customer Mercedes AMG Petronas Motorsport head of IT Matt Harris. Matt, it's great to see you again. >> Hey, good up, good morning I should say. >> I think it is still morning somewhere. (laughter) >> So, Matt, you know, for folks who aren't that familiar with Formula One one of the things, you know I'm a fan. It's such a data intense sport. You've got to set up a data center 21 times a year, across the globe, with dramatically different weather conditions, humidity, etc. Give our viewers an idea of your role as head of IT and what it is that your team needs to enable the drivers to do? >> Okay, so in general terms, we're but like any other normal business around the world. Yeah we have huge amounts of data created depending on what your company is doing. Ours comes from two cars going around the track. That is the lifeblood of our of our work, our day work, and all that data is always analyzed to work out how we can improve the car. But what we really have is an infrastructure the same as many other companies. We have some slight differences as you say. We go to 21 countries. In those countries we turn around and we have 36 hours roughly to put everything together in a different world, different place and then everybody turns up and uses it as though it's a branch office. A hundred people roughly sat there working in the normal environment. We use it for five days and then we take it apart in six hours, put it in two boxes, take it to another country, and we do the same thing again. We do that 21 times. Sometimes back-to-back, sometimes with a week in between. Week in between is quite easy. Back to back sometimes we go from Canada maybe all the way across the world from Monaco within the space of a week so if we've got the flights in the way and everything else and we also end up having to an engineer a car, run a car around the track, and hopefully win races. >> So, you basically got a data kit that you take around with you. >> Yeah. >> And then what did you do before you had this capability? Was it just gut feel? Was it finger in the wind? >> Um, so. For about 15 years, we've been running what everybody's classes and Internet of Things we've been doing for about 15-20 years the car. It's got around these days around 300 sensors on it. Without those sensors realistically we'll be running the car blind and we probably couldn't even start the car let alone actually run it these days or improve things. We turn around and we're always ingesting data from the cars real-time. That real-time data actually we transfer to the garage. That's no problem at all but we also bring it back to the factory because we're limited on the number of people that are allowed to travel with the team. So, we're physically only allowed to take 60 people. Rules tell us we can only take 60 people to work on the car. Now of those, around about 15 are probably looking at data. We're generating around about half a terabyte per race weekend these days and 15 people, it's not enough eyes realistically to turn around and look at all that data all the time. So we take it back to the UK and in the UK, again, we have anywhere between another 30 and maybe 800 staff will be looking at that data to help analyze particularly on a Friday. Friday is about running the car and learning. We discussed a few minutes ago, what's the weather like? What are the tires like? What's the track like? Has there been any change in track? Has it been resurfaced? What's going on with the car compared to what we think is its optimum? And on a Friday's iterative change and learning about tire degradation, tire life, tire wear, the weather conditions, how they're going to interact with the car, all based on data. The interesting thing for me has always been that we have all this data but the two drivers in the car are the biggest sensor for us. They turn around and tell us how they felt. When they were going round corners, Was it good, bad, indifferent? But as soon as they tell us something, we always go to data. We've taken their interpretation of how their body felt, we turn around and then look at the data to prove what they've told us. So, an interesting anecdote very quickly. last year in Singapore, Valtteri was going across the bridge and he said he could feel that the throttle felt like it was cutting and we couldn't see in data and we were looking and looking and eventually he said, "No, it absolutely happens every time I cross the bridge." and they found a 20 millisecond gap in throttle application basically because there was a magnetic field that the bridge was creating so a sensor was actually cutting the throttle. he could feel it. we could fit that eventually see in data, shielded the sensor, everybody's happy. so you go from the human being could feel a 20th, a 20 millisecond gap in throttle application for us finding in data, engineering a solution, and changing things. >> So, the human's still a critical part of? (crosstalk) >> So, where does Pure Storage fit into this whole thing? and give us the before and after on that. >> So, three years ago we started working with Pure because I have two different solutions. one in the track and one in the factory. one in the track realistically I have some constraints around space, power, heat. that most people would love to take the racks as we were talking about we take around the world, they would love to leave in a nice air-conditioned computer room and just leave it there all year. we move it around but that rack of information we have to spend $298 per kilo to transport IT equipment around, well any equipment, around the world. So, we've got tons of equipment that we take around the world. it's thousands and thousands of pounds of freight cost. So, we went from forty U of old-school spinning disk, lots of complexity in cabling, administration, down to 2-3 U and 20 arrays. Now, they're more heat tolerant. I have two power cables in each and two network cables so complexity is gone. it just works. It's heat tolerant. it doesn't create a lot of heat so I haven't got the added issue of that. it's not using a huge amount of power so my UPS solution has to be smaller. so everything just got smaller, cheaper. really simply at the track, we improve the performance for everybody. from an IT point of view, we got very, very simple. incredibly easy to look after and manage but it's very reliable and performant at the same time. we then went to the factory where I've got 800 people looking at data. the problem is when a car goes round and we offload it, there's one single file. we haven't got this distributed amount of data that everybody. so you got one file that everybody's trying to open, old-school discs, you've now got contention for that one file that everybody's opening. So, people would come back from the track and go, "Why is it so slow to open information in the factory compared to at the track?" Trying to explain to them contention of data in those days was a little bit difficult but now we have 800 people that don't need to care and why that matters for us is decision making. So, if you think about qualifying, those that don't understand Formula One, we have three sessions of qualifying and the car goes out roughly two times in each qualifying session with around about a couple of minute gap in between the times the car goes out. that couple of minutes is about changing the car to be optimal for the next run. if it takes you minutes and minutes to offload data, open the data, review the information that the driver told you, and make a change, you can't go back out a second time. So, everything is about optimal performance for those engineers to optimize the performance of the car. what we are able to do now is to turn around and make sure that we're making correct decisions because rather than data taking two or three minutes to open, it's in seconds instead. So, you can look at the data, make an informed decision, change the car, hopefully improve every time the car goes out. >> One of the things, Matt, that Charlie Giancarlo, the CEO of Pure Storage, said this morning during the keynote was that less than half a percent of data in the world is analyzed. talk to us about what Pure Storage is able to facilitate for your team to be able to analyze that data. how much of that data are you able to analyze? and talk to us about the speed criticality. >> Yeah, okay, so, and quite a lot of the work over the previous probably 10 or 15 years has been very human centric. So, it's what data I know I need to go and look at to understand to be able to compute, to turn around and maybe infer information from to be able to make a better decision. So, strategy is probably one of the best places these days where the data that we're learning all the time. we have data about ourselves but we also have data about the other teams. those teams have the same data about us as well, your GPS data, timing data, so we know what's going on so we can infer information on a competitor as well as ourselves. tire degradation, tire wear, tire life, all things that you can infer that mean that you were mentioning earlier on about a pit stop. if a safety car comes out should you pick, shouldn't you pick. those decisions are now based on accurate data about whether we think competitor will pit, whether we think the competitors tires will last, can we overtake that competitor? because actually the track does or doesn't allow overtaking. So, lots of decisions made real-time based on exactly what's happening now but inferred from previous races and we're always learning all the time. everything is about the previous races. information we're learning every time. >> and how much of that heavy lifting of that data is machines versus humans. Are the machines increasingly, I don't want to say making the decisions, but helping? >> Yes, so, we're not in a position at the moment where the machines are making decisions. they're helping us to be informed, to visualize. Yeah, we work with the likes of TIBCO as well as Pure and other partners or sponsors that we have where they turn around and actually they help us to visualize that data. the problem we've got at the moment is we're still looking at all the data. where we really want to get to is looking at exceptions. So, actually the norm, don't show us that data. we don't need to know, don't need to care. >> Want the outliers. >> we want the outliers that. our problem though is that our car changes every time it goes out. So, an outlier could be because we've made a change. So, now you've got to still have some human that's helping at moto. we're trying to understand how we can use machine learning techniques. in certain places we can so image recognition and another bits and piece like that we can actually start to take advantage of but decisions necessarily around configuration and the next change to the car at the moment it's still indicators given to us by simulation and then a human at the end of the day is making the decision. >> and the data that you talked about that is on your competitors, is that a shared data source or is that but it is. >> Yeah. >> everybody shares the same data. >> every car has a transponder on it. basically it's GPS with longitude, latitude, and all sorts but incredibly accurate. if you consider the cars are doing 200 mile-an-hour, we have an accuracy of around about it's less than 10 centimeters accuracy at 200 miles per hour. Now, if you think of your GPS on your phone, you struggle to know whether you're on the right street sometimes. >> but your differentiation there is your your speed at which you can analyze the data, your algorithms, your skill sets you're telling. and then obviously we're here at Pure there's a component of that speed which is Pure. aren't you worried that your competitors are going to get your secrets or is everybody in the track use Pure Storage? >> everybody is turning around and using their own methodologies, their main, their own software. the thing for us at the moment is to make sure that we keep the really secret things ourselves, our IP sensitive, keep those to ourselves. So, what we do with our storage people know about and other teams are copying and seeing the advantages of Pure as well as some of the other tools and partners we partner with. the benefit of us though is that we have a partnership with Pure not just a purchasing so we work, we've known about some of the products. So, flash blade we knew about a long time before it was released. Yeah, we work with the team on what's coming. we know some of the advances in the technology before it's live and that's critical for us because we can get a stick, a march on everybody else even if we're six months ahead of somebody else on a technology or a way of doing something, six months is a long time in F1. >> Yeah. >> sorry Dave, I was going to say, Pure calls this the unfair advantage. (laughter) and you are, Mercedes has last fall won the fourth consecutive Constructors Championship. Coincidence, I don't know, but talk to us about this symbiotic relationship. are you also able to help influence the design of the technologies at Pure? >> Yeah, so, and I wouldn't say that we help design necessarily but they'll take into consideration our requirements and our wishes. like a number of other people that will be here, you've heard other people talking on stage and we'll always be talking about what we would like to be doing, what we could be doing if we had, I don't know, some new technology whether it's s3 connectivity to the flash blade, s whether it's NFS, whether it's SIF, whatever that would be, the containerization of them, the storage front end, whatever that would be we're always talking about how we can work with the Pure Storage to improve what we're doing. so that ideally I take out the way of the business. my ideal is that IT's not seen, it's not heard, and it just works. obviously in IT that's not always the case but. >> I want to unpack something you said earlier. you said it was I believe two or three years ago, three years ago that you brought in Pure and you had substantial performance improvement. I talk to a lot of customers and what they'll typically do in that situation is they'll compare what they saw in 2015 with what they replaced which was probably a five or eight year old array. true in your case or not? if it is true, which I suspect it is, it had to be something else that led you to Pure because you could have bought the incumbents all flash array and got you know much better performance. What, first of all true or not? and what was it that led you to Pure to switch from the incumbent which is not trivial? >> So quickly and was it five or eight year old hardware? in some places yes, some places no. So, it wasn't, we took a decision to take a step back and look at storage from a different standpoint because we just kept adding more discs to try and get around an issue, you know, and we've got a fairly strange data model to compute. we don't need much compute, we need lots of storage. so some of the models that were talked about on stage where I need, you know, Matt Baer was talking about the fact of I want some more storage, you need to buy some more compute and that was just so annoying for us. so there was different reasons but the end goal, you're quite right, performance. Yeah, we could have got it probably from anywhere and being brutally honest lots of other technologies could give the performance 'cause we don't give that level of performance maybe if your a service now or a big financial institution, we've got data, it's important. we've got critical time scales to open and save data, okay critical to us as far as erasing, but what was important for me was simplicity. Absolutely, now we got other benefits. the Evergreen model was brilliant for us but simplicity was critical. we had a storage guy that was spending his life managing storage. nobody manages storage now. they turn around and they go into Vmware. they want a new VMware server, they just spin it up, and the disk is associated. we don't have to think about it. you don't have that storage specialist any longer. Yeah, we started working with other partners, you know, Rubric for instance, integration with them, the Pure arrays as well, again enabling us to get out the way and not having to worry about backup. traditionally or we'd headed a guy that was always changing tape. I saw on the slide several time today about tape archive, I'm going I never want to see a tape archive. I just don't care about it any longer. I just want to be able to turn around and give the business, the SLAs they want on the their data and then not care about it. Also, can I then still turn around and mine that data in those archive or backup, not back up bin, the archive location? So, there's huge differences but simple is the best thing for me. we could have a small IT team that we have to look after a huge amount of kit and if it's complex it's just I can't employ the right people. >> Simplicity, performance, portability, you mentioned integration. you've got a big partner ecosystem here that. >> Yeah. >> So, having the ability to integrate seamlessly with Rubric, TIBCO, Satirize Key. >> and yeah for us, the partners are extension of the team. my team in particular because I can't turn around and just keep adding staff. we have to look after the day-to-day and keep the lights on but I can't just keep adding staff to look after a new technology. it needs to look after itself so the simplicity is absolutely. performance was a sort of a no-brainer. evergreen was a brilliant one for us because just not having to do those forklift upgrades. I think in the three years, we've gone from M450s to M70s, we've gone from M20s to M50s, M50R2s. we've done all of these. I've been stood on stage before in a day when we've been doing an upgrade during the time I've been stood on stage. You know and so people talk about the forklift upgrade, I don't have to worry about it, it doesn't happen. >> totally non-disruptive. >> Yeah, yeah. >> you do change out the controllers right? >> Yeah, so we change out controllers. we've done all sorts, we've gone from capacity upgrade so complete shells of discs and completely different on from I can't remember the exact size from two terabyte to three terabyte drives, new controllers to give us the new functionality with the nvme and all during the day. we don't do it out of hours. there's a lot of the business a scared stiff when we turn around the wisp and they go oh no no no but we're running the winds on low. we're doing this CFD, we go doesn't matter zero downtime no matter zero no planned. obviously no one play it's planned? >> Yes, it's planned downtime but the user doesn't see it they no performance no downtime no nothing that's Nevada for RIT. Yeah, well it means I don't have to keep asking people to do long shifts through the night to do a simple upgrade what should be a simple your weekends are nice back hopefully we end up with we end up racing those unfortunately okay but that's the fun stuff yeah for those who aren't that familiar was Formula One I encourage you to check it out it's one of the coolest strategic sports that is really fueled by technology it's amazing without technology honestly the cars wouldn't be anywhere near their what they are today and IT systems go we underpin everything that the company does nobody really wants to say that I t's the lifeblood of the company they don't but we need to be able to deliver and actually let the business actually take on new technologies new techniques and get out the way so we've got a huge amount of work a lot of what Charlie said on stage earlier on I've been having conversations with the guys here about autonomous data centers immutable infrastructure it's critical for us to go out the way and allow business to if they want some new VMs new storage it just happens not not need a person to be in the way make it sound so simple well you one of your primary sensors Lewis Hamilton is currently in in the number one position battery talked to us in third Monaco coming up this weekend introduction of a new hyper soft tire some pretty exciting stuff yeah so the hope of soft tires going to be interesting first race with it before the Monaco track yeah so and they originally designed it for Monaco I believe it will go to another race as well in the short term but we didn't even run it in winter testing earlier in the year so the first time we ran it was actually Barcelona test last week I've actually heard nothing about it so I don't know whether it's good bad or indifferent I don't know what's going to happen but it's going to be an interesting week because it's a very different track to where we've been to so far traditionally some of the other teams are quite strong there so the this weekend's going to be an interesting one to see where we end up Monica is always exciting grace Matt thanks so much for stopping by the cube and sharing with us what you're doing and how you're enabling technology to drive the Sportage no comatose again I'm Lisa Martin with Dave Volante live at pure storage accelerate 2018 we were at the Bill Graham Civic I'm Prince for the day stick around Dave and I will be right back with our next guest

>> Narrator: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in San Mateo, California right at the crossroads. The building's called The Crossroads but it's right at the crossroads of 92 and 101. It's a really interesting intersection over the years as you watch these buildings that are on the corner continue to change names. I always think of the Seibel, his first building came up on this corner and we're here to see a good friend of SnapLogic and their brand new building. Gaurav Dhillon, Chairman and CEO, great to see you. >> Pleasure to be here. >> So how long you been in this space? >> Gosh, it's been about a year. >> Okay. >> Although it feels longer. It's a high-growth company so these are dog years. (laughs) >> That's right. and usually, you outgrow it before you all have moved in. >> The years are short but the days are long. >> And it's right next Rakuten, I have to mention it. We all see it on the Warriors' jerseys So now we know who they are and where they are exactly. >> No they're a good outfit. We had an interesting time putting a sign up and then the people who made their sign told us all kinds of back stories. >> Oh, good, good Alright. So give us an update on SnapLogic. You guys are in a great space at a really, really good time. >> You know, things been on a roll. As you know, the mission we set out to... engage with was to bring together applications and data in the enterprise. We have some of the largest customers in high technology. Folks like Qualcomm, Workday. Some of the largest customers in pharmaceuticals. Folks like Astrazeneca, Bristol-Meyers Squibb. In retail, Denny's, Wendy's, etc. And these folks are basically bringing in new cloud applications and moving data into the cloud. And it's really fun to wire that all up for them. And there's more of it every day and now that we have this very strong install-base of customers, we're able to get more customers faster. >> Right. >> In good time. >> It's a great time and the data is moving into the cloud, and the public cloud guys are really making bigger plays into the enterprise, Microsoft and, Amazon and Google. And of course, there's IBM and lots of other clouds. But integration's always been such a pain and I finally figured out what the snap in SnapLogic means after interviewing you >> (laughs) a couple of times, right. But this whole idea of, non-developer development and you're taking that into integration which is a really interesting concept, enabled by cloud, where you can now think of snapping things together, versus coding, coding, coding. >> Yeah Cloud and A.I, right We feel that this problem has grown because of the change in the platform. The compute platform's gone to the cloud. Data's going to the cloud. There was bunch of news the other day about more and more companies moving the analytics into the cloud. And as that's happening, we feel that this approach and the question we ask ourselves when we started this company, we got into building the born in the cloud platform was, what would Apple do if they were to build an integration product? And the answer was, they would make it like the iPhone, which is easy to use, but very powerful at the same time. And if you can do that, you can bring in a massive population of users who wouldn't have been able to do things like video chat. My mom was not able to do video chat, and believe me, we tried this and every other thing possible 'till facetime came along. And now she can talk to my daughter and she can do it without help, any assistance from teenage grandchildren on that side, Right? >> Right, Right >> So what we've done with SnapLogic, is by bringing in a beautiful, powerful, sleek interface, with a lot of capability in how it connects, snaps together apps and data, we've brought in a whole genre of people who need data in the enterprise so they can serve themselves data. So if your title has analyst in it, you don't have to be programmer analyst. You could be any analyst. >> Right >> You could be a compensation analyst, a commissions analyst, a finance analyst, an HR analyst. All those people can self-serve information, knock down silos, and integrate things themselves. >> It's so interesting because we talk a lot about innovation and digital transformation, and in doing thousands of these interviews, I think the answer to innovation is actually pretty simple. You give more people access to the data. You give them more access to the tools to work with the data and then you give them the power to actually do something once they figure something out. And you guys are really right in the middle of that. So before, it was kind of >> (laughs) Yeah >> democratization of the data, democratization of the tools to work with the data, but in the API economy, you got to be able to stitch this stuff together because it's not just one application, it's not just one data source. >> Correct >> You're bringing from lots and lots of different things and that's really what you guys are taking advantage of this cloud infrastructure which has everything available, so it's there to connect, >> (laughs) Versus, silo in company one and silo in company two. So are you seeing it though, in terms of, of people enabling, kind of citizen integrators if you will, versus citizen developers. >> Yeah. Heck Yeah. So I'll give you an example. One of our large customers... Adobe Systems, right here in San Jose has been amazingly successful flagship account for us. About 800 people at Adobe come to www.snaplogic.com, every week to self-serve data. We replaced legacy products like TIBCO, informatica web methods about four years ago. They first became a customer in 2014 and usage of those products was limited to Java programmers and Sequel programmers, and that was less than 50 people. And imagine that you have about 800 people doing self-service getting information do their jobs. Now, Adobe is unique in that, it's moved the cloud in a fantastic way, or it was unique in 2014. Now everybody is emulating them and the great success that they've had. With the cloud economic model, with the cloud ID model. This is working in spades. We have customers who've come on board in Q4. We're just rounding out Q1 and in less than 60, 90 days, every time I look, 50, 100, 200 people, from each large company, whether it's a cosmetics company, pharmaceuticals company, retailer, food merchandise, are coming in and using data. >> Right >> And it's proliferating, because the more successful they are, the better they are able to do in their jobs, tell their friends about it sort-of-thing, or next cubicle over, somebody wants to use that too. It's so interesting. Adobe is such a great example, cause they did transform their business. Used to be a really expensive license. You would try to find your one friend that worked there around Christmas >> (laughs) Cause you think they got two licenses a year they can buy for a grand. Like, I need an extra one I can get from you. But they moved to a subscription model. They made a big bet. >> Yes. Yes >> And they bet on the cloud, so now if you're a subscriber, which I am, I can work on my home machine, my work machine, go to machine, machine. So, it's a really great transformation story. The other piece of it though, is just this cloud application space. There's so many cloud applications that we all work with every day whether it's Basecamp, Salesforce, Hootsuite. There's a proliferation of these things and so they're there. They've got data. So the integration opportunity is unlike anything that was ever there before. Cause there isn't just one cloud. There isn't just one cloud app. There's a lot of them. >> Yes. >> How do I bring those together to be more productive? >> So here's a stat. The average enterprise has most cloud services or SAS applications, in marketing. On the average, they have 91 marketing applications or SAS applications. >> 91. That's the average. >> 96% of them are not connected together. >> Right. >> Okay. That's just one example. Now you go to HR, stock administration. You go into sales, CRM, and all the ancillary systems around CRM. And there is this sort of massive, to us, opportunity of knocking down these silos and making things work together. You mention the API economy and whilst that's true that all these SAS applications of APIs. The problem is, most companies don't have programmers to hook up those API's. >> Right. To connect them. >> Yes, in Silicon Valley we do and maybe in Manhattan they do, but in everywhere else in the world, the self-service model, the model of being able to do it to something that is simple, yet powerful. Enterprise great >> Right. Right >> and simple, beautiful is absolutely the winning formula in our perspective. So the answer is to let these 100 applications bloom, but to keep them well behaved and orchestrated, in kind of a federated model, where security, having one view of the world, etc., is managed by SnapLogic and then various people and departments can bring in a blessed, SAS applications and then snap them in and the input and the way they connect, is done through snaps. And we've found that to be a real winning model for our customers. >> So you don't have to have like 18 screens open all with different browsers and different apps. >> Swivel chair integration is gone. Swivel chair integration is gone. >> Step above sneakernet but still not-- >> Step above but still not. And again, it may make sense in very, very specific super high-speed, like Wall Street, high frequency trading and hedge funds, but it's a minuscule minority of the overall problems that there needs to be solved. >> Right. So, it's just a huge opportunity, you just are cleaning up behind the momentum in the SAS applications, the momentum of the cloud. >> Cloud data. Cloud apps. Cloud data. And in general, if a customer's not going to the cloud, they're probably not the best for us. >> Right. >> Right. Our customers' almost always going towards the cloud, have lots of data and applications on premise. And in that hybrid spot, we have the capability to straddle that kind of architecture in a way that nobody else does. Because we have a born in the cloud platform that was designed to work in the real world, which is hybrid. >> So another interesting thing, a lot of talk about big data over the years. Now it's just kind of there. But AI and machine learning. Artificial intelligence which should be automated intelligence and machine learning. There's kind of the generic, find an old, dead guy and give it a name. But we're really seeing the values that's starting to bubble up in applications. It's not, AI generically, >> Correct. >> It's how are you enabling a more efficient application, a more efficient workflow, a more efficient, get your job done, using AI. And you guys are starting to incorporate that in your integration framework. >> Yes. Yes. So we took the approach, 'doctor heal thyself.' And we're going to help our customers do better job of having AI be a game changer for them. How do we apply that to ourselves? We heard one our CIOs, CI of AstraZeneca, Dave Smoley, was handing out the Amazon Alexa Echo boxes one Christmas. About three years ago and I'm like, my gosh that's right. That was what Walt Mossberg said in his farewell column. IT is going to be everywhere and invisible at the same time. Right. >> Right. >> It'll be in the walls, so to speak. So we applied AI, starting about two years ago, actually now three, because we shipped Iris a year ago. The artificial intelligence capability inside SnapLogic has been shipping for over 12 months. Fantastic usage. But we applied to ourselves the challenge about three years ago, to use AI based on our born in the cloud platform. On the metadata that we have about people are doing. And in the sense, apply Google Autocomplete into enterprise connectivity problems. And it's been amazing. The AI as you start to snap things together, as you put one or two snaps, and you start to look for the third, it starts to get 98.7% accurate, in predicting how to connect SAS applications together. >> Right. Right. >> It's not quite autonomous integration yet but you can see where we're going with it. So it's starting to do so much value add that most of our customers, leave it on. Even the seasoned professionals who are proficient and running a center of excellence using SnapLogic, even those people choose to have sort-of this AI, on all the time helping them. And that engagement comes from the value that they're getting, as they do these things, they make less mistakes. All the choices are readily at hand and that's happening. So that's one piece of it >> Right. >> Sorry. Let me... >> It's Okay. Keep going. >> Illustrate one other thing. Napoleon famously said, "An army marches on its stomach" AI marches on data. So, what we found is the more data we've had and more customers that we've had, we move about a trillion documents for our customers worldwide, in the past 30 days. That is up from 10 million documents in 30 days, two years ago. >> Right. Right >> That more customers and more usage. In other words, they're succeeding. What we've found as we've enriched our AI with data, it's gotten better and better. And now, we're getting involved with customers' projects where they need to support data scientists, data engineering work for machine learning and that self-service intricate model is letting someone who was trying to solve a problem of, When is my Uber going to show up? So to speak. In industry X >> Right. Right. >> These kinds of hard AI problems that are predictive. That are forward changing in a sense. Those kind of problems are being solved by richer data and many of them, the projects that we're now involved in, are moving data into the cloud for data lake to then support AI machine learning efforts for our customers. >> So you jumped a little bit, I want to talk on your first point. >> Okay. Sorry >> That's okay. Which is that you're in the very fortunate position because you have all that data flow. You have the trillion documents that are changing hands every month. >> Born in the cloud platform. >> So you've got it, right? >> Got it. >> You've got the data. >> It's a virtual cycle. It's a virtual cycle. Some people call it data capitalism. I quibble with that. We're not sort-of, mining and selling people's personal data to anybody. >> Right. Right. >> But this is where, our enterprise customers' are so pleased to work with us because if we can increase productivity. If we can take the time to solution, the time to integration, forward by 10 times, we can improve the speed that by SAS application and it gets into production 10 times faster. That is such a good trade for them and for everyone else. >> Right. Right. >> And it feeds on itself. It's a virtual cycle. >> You know in the Marketo to the Salesforce integration, it's nothing. You need from company A to company B. >> I bet you somebody in this building is doing it on a different floor right now. >> Exactly. >> (laughs) >> So I think that's such an interesting thing. In the other piece that I like is how again, I like your kind of Apple analogy, is the snap packs, right. Because we live in a world, with even though there 91 on-averages, there's a number of really dominant SAS application that most people use, you can really build a group of snaps. Is snap the right noun? >> That's the right word. >> Of snaps. In a snap pack around the specific applications, then to have your AI powered by these trillion transactions that you have going through the machines, really puts you in a unique position right now. >> It does, you know. And we're very fortunate to have the kind of customer support we've had and, sort of... Customer advisory board. Big usages of our products. In which we've added so much value to our customers, that they've started collaborating with us in a sense. And are passing to us wonderful ideas about how to apply this including AI. >> Right. >> And we're not done yet. We have a vision in the future towards an autonomous integration. You should be able to say "SnapLogic, Iris, "connect my company." And it should. >> Right. Right. >> It knows what the SAS apps are by looking at your firewall, and if you're people are doing things, building pipelines, connecting your on-premise legacy applications kind of knows what they are. That day when you should be able to, in a sense, have a bot of some type powered by all this technology in a thoughtful manner. It's not that far. It's closer at hand than people might realize. >> Which is crazy science fiction compared to-- I mean, integration was always the nightmare right back in the day. >> It is. >> Integration, integration. >> But on the other hand, it is starting to have contours that are well defined. To your point, there are certain snaps that are used more. There are certain problems that are solved quite often, the quote-to-cash problem is as old as enterprise software. You do a quote in the CRM system. Your cash is in a financial system. How does that work together? These sort of problems, in a sense, are what McKinsey and others are starting to call robotic process automations. >> Right. >> In the industrial age, people... Stopped, with the industrial age, any handcrafted widget. Nuts, and bolts, and fasteners started being made on machines. You could stamp them out. You could have power driven beams, etc., etc. To make things in industrial manner. And our feeling is, some of the knowledge tasks that feel like widget manufactures. You're doing them over and over again. Or robotic, so to speak, should be automated. And integration I think, is ripe as one of those things and using the value of integration, our customers can automate a bunch of other repeatable tasks like quote-to-cash. >> Right. Right. It's interesting just when you say autonomous, I can't help but think of autonomous vehicles right, which are all the rage and also in the news. And people will say "well I like to drive "or of course we all like to drive "on Sunday down at the beach" >> Sure. Yeah. >> But we don't like to sit in traffic on the way to work. That's not driving, that's sitting in traffic on the way to work. Getting down the 101 to your exit and off again is really not that complicated, in terms of what you're trying to accomplish. >> Indeed. Indeed. >> Sets itself up. >> And there are times you don't want to. I mean one of the most pleasant headlines, most of the news is just full of bad stuff right. So and so and such and such. But one of the very pleasing headlines I saw the other day in a newspaper was, You know what's down a lot? Not bay area housing prices. >> (laughs) >> But you know what's down a lot? DUI arrests, have plummeted. Because of the benefits of Lyft and Uber. More and more people are saying, "You know, I don't have to call a black cab. "I don't need to spend a couple hundred bucks to get home. "I'm just getting a Lyft or an Uber." So the benefits of some of these are starting to appear as in plummeting DUIs. >> Right. Right >> Plummeting fatalities. From people driving while inebriated. Plunging into another car or sidewalk. >> Right. Right. >> So Yes. >> Amara's Law. He never gets enough credit. >> (laughs) >> I say it in every interview right. We overestimate in the short term and we underestimate in the long term the effects of these technologies cause we get involved-- The Gartner store. It's the hype cycle. >> Yeah, Yeah >> But I really I think Amara nailed it and over time, really significant changes start to take place. >> Indeed and we're seeing them now. >> Alright well Gaurav, great to get an update from you and a beautiful facility here. Thanks for having us on. >> Thank you, thank you. A pleasure to be here. Great to see you as well. >> Alright He's Gaurav, I'm Jeff. And you're watching theCUBE from SnapLogic's headquarters Thanks for watching. (techno music)

(upbeat techno music) >> Announcer: From Boston, Massachusetts, it's the Cube, covering Cloud Foundry Summit 2018, brought to you by the Cloud Foundry Foundation. >> Welcome back to the Cube, I'm Stu Miniman, and this is Cloud Foundry Summit 2018, in Boston. Talking a lot about digital transformation, and love when we get to talk to the users here, at the show. One of the great stories told on the keynote stage this morning was from T-Mobile. So, while Rob wasn't on the stage, he's involved in the activity. This is Rob Hansen, Director of Operations at T-Mobile. Thank you for joining me. >> Yeah. Absolutely. Thank you for having me. >> So, Rob, we were talking before and the twitters, there's lots of stuff that goes on, but everybody, there was a great story talking about 17 hundred developers and only 10 operators, underneath, making those work. So, maybe before we get into it, tell us a little bit about your role, your background, what you do at T-mobile. >> Sure, my role is, I lead a team on the operations side. So, we operate the software and when we look over the last 10 years or so, that software's been predominately large monoliths. Look at, use TIBCO as an example. We've been a heavy user of TIBCO BW for many many years and my environment supporting TIBCO BW accounts for about 2,000 physical servers across multiple data centers, and that carries a high operational cost. We're doing all our changes in the middle of the night. Things break, seeming randomly at times, causing customer impact, just a lot of paint and patching. One of my favorite topics is patching. >> (laughs) Oh, boy. Tuesday's your favorite day of the week, right? It's taco Tuesday and patch Tuesday. >> Oh, my god. Yes. Exactly Every quarter I get the list of servers, here's the list of servers that needs to be patched, and it's just a nightmare, right. >> So, Rob, can we talk a little bit about the developer and operator interaction at your company? I interviewed Solomon Hykes last year at DockerCon, and he said, "Believe it or not, "I created Docker mostly for the operators." That's his background in there, >> Oh, yeah. >> But everybody, they're, "This show, "it's developers, developers, developers." So, what's that dynamic inside T-Mobile? >> Historically, before we got into kind of the cloud-native space, it was really an us versus them, right. There's the mentality of, oh, it's an ops problem now. There's a great meme out there. It's one of my favorites, the little girl standing in front of the burning house, and it says, "Worked in dev, it's an ops problem now." (Stu laughs) So, as we've gone through this cloud-native journey, and we've moved into using like Pivotal, within our environment, we've seen that community within our organization come together, and really start working closer and closer together. Right now, we're going through a migration into the TIBCO Container Edition project, or application, and as we've been doing that, we literally have our ops base folks and the development base folks sitting in a room together, day and night, just working together. Historically, developers have a point of view, operators have a different point of view. It's really brought them together into a singular point of view and ownership of the software, and just providing business capabilities. >> Rob, could you give us a little bit of picture, kind of your application portfolio, how much have you been kind of moving onto the platforms, how much do you build new on the platform, those kind of things? >> Yeah, absolutely. So, I mentioned earlier, legacy, we were about 2,000 physical servers. Right now, I'm trying to remember the actual application count, but I've taken, or we've taken one of our historical job applications, moved it completely into PCF, running a complete spring boot now. We're doing that with our TIBCO environment. We have a number of other applications that we've spun up, running in spring and whatnot. What we've been able to do is just explode the amount of stuff we're deploying, and just new functionality. We're able to develop it much faster. So, when we look at the developers, more people are coming on board, because you can just get the code out there so much faster, and really in smaller increments. So, a lot of times, when we've developed things and we've delivered functionality for the business, because you're dealing with large monoliths, you have to change, you know, one of the applications I mentioned, you've got 200 services, about 600 operations, bundled into the same ball of code. Now, we've separated that out into a bunch of microservices, so now, we can just implement this one thing, with very little to no impact to the business. I think one of the big fundamental shifts that we've seen, we have historically done the large Saturday night deployments, right. You show up Saturday night at 7:00 p.m. and you hope you get to go home Sunday. We've really shifted that model, so in Q1, in my space, we did 86 and a half percent of our changes in production, during the day, right in the middle of the business day. >> Stu: Is it scary? >> It was at first, in all honesty, because my biggest fear is having to explain things to leadership, you know why did it go wrong, the root cause, and all that kind of stuff. But because we're able to move so fast now, we're able to get the code out there. We're able to see, okay, is this working? Roll it back really quickly, leveraging blue-green. Scale is another thing. Every year, iPhone. iPhone is a scary time I think, for pretty much any wireless operator. And historically, we've had to go out and buy more physical servers. So, you're buying these servers, you're building em. It takes months to build em, stand em up, and you're doing that for a two-day event, a year. You end up carrying the costs of that hardware. Well, this last iPhone in September, you know the iPhone 8 and the iPhone X, because we were predominately running in our cloud-native environment, and our cloud foundry environment, spun up the containers. >> Does that live in a public cloud? >> That lives in a private cloud, On-Prem. >> Stu: Okay. So we just spun up the containers, got through the event, spun em down. >> Okay, you had enough infrastructure capacity, you just didn't need it to be kind of-- >> Yeah. Well, and we're able to target the specific services, right. In our TIBCO landscape, we operate, in the old BW environment we operated about 200 years, comes out to about 14 hundred services. So, when you're scaling up, you're having to do it, more or less, for everything, but running in the Pivotal environment, we're able to just target, okay this, you know, like a get customer info. It's like a basic call when you log into MyT-Mo. You're able to just take that, double it, triple it, whatever you need to do. Maybe this other call over here, you know, we don't have to touch that. So you're just being way more efficient with your resources. >> So, Rob, if you can do these updates all the time, do you still love patching as much as you used to? >> The patching is the devil. I actually, the 10 to 15 people that Chuck was talking about on stage today, those are the guys that actually operate the physical hardware, you know, the Diego cells and whatnot. I meet with them on a weekly basis, and we kind of go through the state of things, and planning, and all that kind of stuff. Almost every time, I end that meeting with, "I just don't want to patch anything, anymore." So, the more we get onto this environment, the easier it is for me As we're trying to do this dev/ops transformation at T-Mobile, we're getting there, and we're doing it. You know, one of the things we ask ourselves is, should a dev/ops team have to care about patching? Why is a developer going to say, "Oh, my OS is a version behind, "I need to take care of that." That's not useful to the business, right? That takes away time that that developer can be creating new things and adding value. >> Yeah, absolutely. I mean, if you think about, you know in a public cloud environment, I don't think about that, you know, what version of ah-jur-ware you're running isn't something that people ask. Private cloud, if it's going to live up to what we want it to, it should have a similar type of dynamic. >> Exactly, and our platform team is amazing. I mean, they take care of that stuff for us. I'm a heavy user. So I think Chuck talked about this a little. He didn't really talk about the volume, but we started on our Pivotal journey a couple years ago. I think first started dabbling 2015, but we really didn't start converting our large monolithic middleware until the beginning of 2017. So, right now, we are doing 250 million transactions a day, on our Pivotal platform, just with two, or, I'm sorry, three of my platforms running in there. >> Last thing I want to ask you, Rob. What key learnings have you had, going through this transformation? What do you say to your peers, that they could do better or look out for or plan, to help them? >> I think the main learning that we've had is just how important it is to partner together, with the hardware people, the developers, and the operations people. Coming together, it's a cultural shift in many respects. Like they say in dev/ops, a lot of people talk about it, they don't realize how hard it is to do, but hardware has to be a part of that. Coming together, luckily, we had a couple stumblings, in the beginning, but we were quickly able to huddle together between kind of these three core groups and really work together and overcome those challenges. I think the second thing that's really important is just to be open and honest with each other. Everybody makes mistakes. I think a lot of times, there's cases of, oh this is platform problem, oh it's a software problem. You know, there's a little finger-pointing here and there, from time to time, but getting through that, being open, honest, communicative with each other, it just makes the world so much easier and better for us. >> Well, Rob, my entire IT career, you know we've wanted everybody to hold hands (Rob laughs) and get in the circle together, bust through those silos, so, you know, making progress though. Thank you so much for sharing the story of T-Mobile. Watch more coverage here from the Cloud Foundry Summit, here in Boston, Massachusetts. I'm Stu Miniman. You're watching the Cube.

>> Hi I'm Peter Burris. Welcome to another Wikibon Action Item. (upbeat music) We're once again broadcasting from theCube's beautiful Palo Alto, California studio. I'm joined here in the studio by George Gilbert and David Floyer. And then remotely, we have Jim Kobielus, David Vellante, Neil Raden and Ralph Finos. Hi guys. >> Hey. >> Hi >> How you all doing? >> This is a great, great group of people to talk about the topic we're going to talk about, guys. We're going to talk about the notion of de-risking digital business. Now, the reason why this becomes interesting is, the Wikibon perspective for quite some time has been that the difference between business and digital business is the role that data assets play in a digital business. Now, if you think about what that means. Every business institutionalizes its work around what it regards as its most important assets. A bottling company, for example, organizes around the bottling plant. A financial services company organizes around the regulatory impacts or limitations on how they share information and what is regarded as fair use of data and other resources, and assets. The same thing exists in a digital business. There's a difference between, say, Sears and Walmart. Walmart mades use of data differently than Sears. And that specific assets that are employed and had a significant impact on how the retail business was structured. Along comes Amazon, which is even deeper in the use of data as a basis for how it conducts its business and Amazon is institutionalizing work in quite different ways and has been incredibly successful. We could go on and on and on with a number of different examples of this, and we'll get into that. But what it means ultimately is that the tie between data and what is regarded as valuable in the business is becoming increasingly clear, even if it's not perfect. And so traditional approaches to de-risking data, through backup and restore, now needs to be re-thought so that it's not just de-risking the data, it's de-risking the data assets. And, since those data assets are so central to the business operations of many of these digital businesses, what it means to de-risk the whole business. So, David Vellante, give us a starting point. How should folks think about this different approach to envisioning business? And digital business, and the notion of risk? >> Okay thanks Peter, I mean I agree with a lot of what you just said and I want to pick up on that. I see the future of digital business as really built around data sort of agreeing with you, building on what you just said. Really where organizations are putting data at the core and increasingly I believe that organizations that have traditionally relied on human expertise as the primary differentiator, will be disrupted by companies where data is the fundamental value driver and I think there are some examples of that and I'm sure we'll talk about it. And in this new world humans have expertise that leverage the organization's data model and create value from that data with augmented machine intelligence. I'm not crazy about the term artificial intelligence. And you hear a lot about data-driven companies and I think such companies are going to have a technology foundation that is increasingly described as autonomous, aware, anticipatory, and importantly in the context of today's discussion, self-healing. So able to withstand failures and recover very quickly. So de-risking a digital business is going to require new ways of thinking about data protection and security and privacy. Specifically as it relates to data protection, I think it's going to be a fundamental component of the so-called data-driven company's technology fabric. This can be designed into applications, into data stores, into file systems, into middleware, and into infrastructure, as code. And many technology companies are going to try to attack this problem from a lot of different angles. Trying to infuse machine intelligence into the hardware, software and automated processes. And the premise is that meaty companies will architect their technology foundations, not as a set of remote cloud services that they're calling, but rather as a ubiquitous set of functional capabilities that largely mimic a range of human activities. Including storing, backing up, and virtually instantaneous recovery from failure. >> So let me build on that. So what you're kind of saying if I can summarize, and we'll get into whether or not it's human expertise or some other approach or notion of business. But you're saying that increasingly patterns in the data are going to have absolute consequential impacts on how a business ultimately behaves. We got that right? >> Yeah absolutely. And how you construct that data model, and provide access to the data model, is going to be a fundamental determinant of success. >> Neil Raden, does that mean that people are no longer important? >> Well no, no I wouldn't say that at all. I'm talking with the head of a medical school a couple of weeks ago, and he said something that really resonated. He said that there're as many doctors who graduated at the bottom of their class as the top of their class. And I think that's true of organizations too. You know what, 20 years ago I had the privilege of interviewing Peter Drucker for an hour and he foresaw this, 20 years ago, he said that people who run companies have traditionally had IT departments that provided operational data but they needed to start to figure out how to get value from that data and not only get value from that data but get value from data outside the company, not just internal data. So he kind of saw this big data thing happening 20 years ago. Unfortunately, he had a prejudice for senior executives. You know, he never really thought about any other people in an organization except the highest people. And I think what we're talking about here is really the whole organization. I think that, I have some concerns about the ability of organizations to really implement this without a lot of fumbles. I mean it's fine to talk about the five digital giants but there's a lot of companies out there that, you know the bar isn't really that high for them to stay in business. And they just seem to get along. And I think if we're going to de-risk we really need to help companies understand the whole process of transformation, not just the technology. >> Well, take us through it. What is this process of transformation? That includes the role of technology but is bigger than the role of technology. >> Well, it's like anything else, right. There has to be communication, there has to be some element of control, there has to be a lot of flexibility and most importantly I think there has to be acceptability by the people who are going to be affected by it, that is the right thing to do. And I would say you start with assumptions, I call it assumption analysis, in other words let's all get together and figure out what our assumptions are, and see if we can't line em up. Typically IT is not good at this. So I think it's going to require the help of a lot of practitioners who can guide them. >> So Dave Vellante, reconcile one point that you made I want to come back to this notion of how we're moving from businesses built on expertise and people to businesses built on expertise resident as patterns in the data, or data models. Why is it that the most valuable companies in the world seem to be the ones that have the most real hardcore data scientists. Isn't that expertise and people? >> Yeah it is, and I think it's worth pointing out. Look, the stock market is volatile, but right now the top-five companies: Apple, Amazon, Google, Facebook and Microsoft, in terms of market cap, account for about $3.5 trillion and there's a big distance between them, and they've clearly surpassed the big banks and the oil companies. Now again, that could change, but I believe that it's because they are data-driven. So called data-driven. Does that mean they don't need humans? No, but human expertise surrounds the data as opposed to most companies, human expertise is at the center and the data lives in silos and I think it's very hard to protect data, and leverage data, that lives in silos. >> Yes, so here's where I'll take exception to that, Dave. And I want to get everybody to build on top of this just very quickly. I think that human expertise has surrounded, in other businesses, the buildings. Or, the bottling plant. Or, the wealth management. Or, the platoon. So I think that the organization of assets has always been the determining factor of how a business behaves and we institutionalized work, in other words where we put people, based on the business' understanding of assets. Do you disagree with that? Is that, are we wrong in that regard? I think data scientists are an example of reinstitutionalizing work around a very core asset in this case, data. >> Yeah, you're saying that the most valuable asset is shifting from some of those physical assets, the bottling plant et cetera, to data. >> Yeah we are, we are. Absolutely. Alright, David Foyer. >> Neil: I'd like to come in. >> Panelist: I agree with that too. >> Okay, go ahead Neil. >> I'd like to give an example from the news. Cigna's acquisition of Express Scripts for $67 billion. Who the hell is Cigna, right? Connecticut General is just a sleepy life insurance company and INA was a second-tier property and casualty company. They merged a long time ago, they got into health insurance and suddenly, who's Express Scripts? I mean that's a company that nobody ever even heard of. They're a pharmacy benefit manager, what is that? They're an information management company, period. That's all they do. >> David Foyer, what does this mean from a technology standpoint? >> So I wanted to to emphasize one thing that evolution has always taught us. That you have to be able to come from where you are. You have to be able to evolve from where you are and take the assets that you have. And the assets that people have are their current systems of records, other things like that. They must be able to evolve into the future to better utilize what those systems are. And the other thing I would like to say-- >> Let me give you an example just to interrupt you, because this is a very important point. One of the primary reasons why the telecommunications companies, whom so many people believed, analysts believed, had this fundamental advantage, because so much information's flowing through them is when you're writing assets off for 30 years, that kind of locks you into an operational mode, doesn't it? >> Exactly. And the other thing I want to emphasize is that the most important thing is sources of data not the data itself. So for example, real-time data is very very important. So what is your source of your real-time data? If you've given that away to Google or your IOT vendor you have made a fundamental strategic mistake. So understanding the sources of data, making sure that you have access to that data, is going to enable you to be able to build the sort of processes and data digitalization. >> So let's turn that concept into kind of a Geoffrey Moore kind of strategy bromide. At the end of the day you look at your value proposition and then what activities are central to that value proposition and what data is thrown off by those activities and what data's required by those activities. >> Right, both internal-- >> We got that right? >> Yeah. Both internal and external data. What are those sources that you require? Yes, that's exactly right. And then you need to put together a plan which takes you from where you are, as the sources of data and then focuses on how you can use that data to either improve revenue or to reduce costs, or a combination of those two things, as a series of specific exercises. And in particular, using that data to automate in real-time as much as possible. That to me is the fundamental requirement to actually be able to do this and make money from it. If you look at every example, it's all real-time. It's real-time bidding at Google, it's real-time allocation of resources by Uber. That is where people need to focus on. So it's those steps, practical steps, that organizations need to take that I think we should be giving a lot of focus on. >> You mention Uber. David Vellante, we're just not talking about the, once again, talking about the Uberization of things, are we? Or is that what we mean here? So, what we'll do is we'll turn the conversation very quickly over to you George. And there are existing today a number of different domains where we're starting to see a new emphasis on how we start pricing some of this risk. Because when we think about de-risking as it relates to data give us an example of one. >> Well we were talking earlier, in financial services risk itself is priced just the way time is priced in terms of what premium you'll pay in terms of interest rates. But there's also something that's softer that's come into much more widely-held consciousness recently which is reputational risk. Which is different from operational risk. Reputational risk is about, are you a trusted steward for data? Some of that could be personal information and a use case that's very prominent now with the European GDPR regulation is, you know, if I ask you as a consumer or an individual to erase my data, can you say with extreme confidence that you have? That's just one example. >> Well I'll give you a specific number on that. We've mentioned it here on Action Item before. I had a conversation with a Chief Privacy Officer a few months ago who told me that they had priced out what the fines to Equifax would have been had the problem occurred after GDPR fines were enacted. It was $160 billion, was the estimate. There's not a lot of companies on the planet that could deal with $160 billion liability. Like that. >> Okay, so we have a price now that might have been kind of, sort of mushy before. And the notion of trust hasn't really changed over time what's changed is the technical implementations that support it. And in the old world with systems of record we basically collected from our operational applications as much data as we could put it in the data warehouse and it's data marked satellites. And we try to govern it within that perimeter. But now we know that data basically originates and goes just about anywhere. There's no well-defined perimeter. It's much more porous, far more distributed. You might think of it as a distributed data fabric and the only way you can be a trusted steward of that is if you now, across the silos, without trying to centralize all the data that's in silos or across them, you can enforce, who's allowed to access it, what they're allowed to do, audit who's done what to what type of data, when and where? And then there's a variety of approaches. Just to pick two, one is where it's discovery-oriented to figure out what's going on with the data estate. Using machine learning this is, Alation is an example. And then there's another example, which is where you try and get everyone to plug into what's essentially a new system catalog. That's not in a in a deviant mesh but that acts like the fabric for your data fabric, deviant mesh. >> That's an example of another, one of the properties of looking at coming at this. But when we think, Dave Vellante coming back to you for a second. When we think about the conversation there's been a lot of presumption or a lot of bromide. Analysts like to talk about, don't get Uberized. We're not just talking about getting Uberized. We're talking about something a little bit different aren't we? >> Well yeah, absolutely. I think Uber's going to get Uberized, personally. But I think there's a lot of evidence, I mentioned the big five, but if you look at Spotify, Waze, AirbnB, yes Uber, yes Twitter, Netflix, Bitcoin is an example, 23andme. These are all examples of companies that, I'll go back to what I said before, are putting data at the core and building humans expertise around that core to leverage that expertise. And I think it's easy to sit back, for some companies to sit back and say, "Well I'm going to wait and see what happens." But to me anyway, there's a big gap between kind of the haves and the have-nots. And I think that, that gap is around applying machine intelligence to data and applying cloud economics. Zero marginal economics and API economy. An always-on sort of mentality, et cetera et cetera. And that's what the economy, in my view anyway, is going to look like in the future. >> So let me put out a challenge, Jim I'm going to come to you in a second, very quickly on some of the things that start looking like data assets. But today, when we talk about data protection we're talking about simply a whole bunch of applications and a whole bunch of devices. Just spinning that data off, so we have it at a third site. And then we can, and it takes to someone in real-time, and then if there's a catastrophe or we have, you know, large or small, being able to restore it often in hours or days. So we're talking about an improvement on RPO and RTO but when we talk about data assets, and I'm going to come to you in a second with that David Floyer, but when we talk about data assets, we're talking about, not only the data, the bits. We're talking about the relationships and the organization, and the metadata, as being a key element of that. So David, I'm sorry Jim Kobielus, just really quickly, thirty seconds. Models, what do they look like? What are the new nature of some of these assets look like? >> Well the new nature of these assets are the machine learning models that are driving so many business processes right now. And so really the core assets there are the data obviously from which they are developed, and also from which they are trained. But also very much the knowledge of the data scientists and engineers who build and tune this stuff. And so really, what you need to do is, you need to protect that knowledge and grow that knowledge base of data science professionals in your organization, in a way that builds on it. And hopefully you keep the smartest people in house. And they can encode more of their knowledge in automated programs to manage the entire pipeline of development. >> We're not talking about files. We're not even talking about databases, are we David Floyer? We're talking about something different. Algorithms and models are today's technology's really really set up to do a good job of protecting the full organization of those data assets. >> I would say that they're not even being thought about yet. And going back on what Jim was saying, Those data scientists are the only people who understand that in the same way as in the year 2000, the COBOL programmers were the only people who understood what was going on inside those applications. And we as an industry have to allow organizations to be able to protect the assets inside their applications and use AI if you like to actually understand what is in those applications and how are they working? And I think that's an incredibly important de-risking is ensuring that you're not dependent on a few experts who could leave at any moment, in the same way as COBOL programmers could have left. >> But it's not just the data, and it's not just the metadata, it really is the data structure. >> It is the model. Just the whole way that this has been put together and the reason why. And the ability to continue to upgrade that and change that over time. So those assets are incredibly important but at the moment there is no way that you can, there isn't technology available for you to actually protect those assets. >> So if I combine what you just said with what Neil Raden was talking about, David Vallante's put forward a good vision of what's required. Neil Raden's made the observation that this is going to be much more than technology. There's a lot of change, not change management at a low level inside the IT, but business change and the technology companies also have to step up and be able to support this. We're seeing this, we're seeing a number of different vendor types start to enter into this space. Certainly storage guys, Dylon Sears talking about doing a better job of data protection we're seeing middleware companies, TIBCO and DISCO, talk about doing this differently. We're seeing file systems, Scality, WekaIO talk about doing this differently. Backup and restore companies, Veeam, Veritas. I mean, everybody's looking at this and they're all coming at it. Just really quickly David, where's the inside track at this point? >> For me it is so much whitespace as to be unbelievable. >> So nobody has an inside track yet. >> Nobody has an inside track. Just to start with a few things. It's clear that you should keep data where it is. The cost of moving data around an organization from inside to out, is crazy. >> So companies that keep data in place, or technologies to keep data in place, are going to have an advantage. >> Much, much, much greater advantage. Sure, there must be backups somewhere. But you need to keep the working copies of data where they are because it's the real-time access, usually that's important. So if it originates in the cloud, keep it in the cloud. If it originates in a data-provider, on another cloud, that's where you should keep it. If it originates on your premise, keep it where it originated. >> Unless you need to combine it. But that's a new origination point. >> Then you're taking subsets of that data and then combining that up for itself. So that would be my first point. So organizations are going to need to put together what George was talking about, this metadata of all the data, how it interconnects, how it's being used. The flow of data through the organization, it's amazing to me that when you go to an IT shop they cannot define for you how the data flows through that data center or that organization. That's the requirement that you have to have and AI is going to be part of that solution, of looking at all of the applications and the data and telling you where it's going and how it's working together. >> So the second thing would be companies that are able to build or conceive of networks as data. Will also have an advantage. And I think I'd add a third one. Companies that demonstrate perennial observations, a real understanding of the unbelievable change that's required you can't just say, oh Facebook wants this therefore everybody's going to want it. There's going to be a lot of push marketing that goes on at the technology side. Alright so let's get to some Action Items. David Vellante, I'll start with you. Action Item. >> Well the future's going to be one where systems see, they talk, they sense, they recognize, they control, they optimize. It may be tempting to say, you know what I'm going to wait, I'm going to sit back and wait to figure out how I'm going to close that machine intelligence gap. I think that's a mistake. I think you have to start now, and you have to start with your data model. >> George Gilbert, Action Item. >> I think you have to keep in mind the guardrails related to governance, and trust, when you're building applications on the new data fabric. And you can take the approach of a platform-oriented one where you're plugging into an API, like Apache Atlas, that Hortonworks is driving, or a discovery-oriented one as David was talking about which would be something like Alation, using machine learning. But if, let's say the use case starts out as an IOT, edge analytics and cloud inferencing, that data science pipeline itself has to now be part of this fabric. Including the output of the design time. Meaning the models themselves, so they can be managed. >> Excellent. Jim Kobielus, you've been pretty quiet but I know you've got a lot to offer. Action Item, Jim. >> I'll be very brief. What you need to do is protect your data science knowledge base. That's the way to de-risk this entire process. And that involves more than just a data catalog. You need a data science expertise registry within your distributed value chain. And you need to manage that as a very human asset that needs to grow. That is your number one asset going forward. >> Ralph Finos, you've also been pretty quiet. Action Item, Ralph. >> Yeah, I think you've got to be careful about what you're trying to get done. Whether it's, it depends on your industry, whether it's finance or whether it's the entertainment business, there are different requirements about data in those different environments. And you need to be cautious about that and you need leadership on the executive business side of things. The last thing in the world you want to do is depend on data scientists to figure this stuff out. >> And I'll give you the second to last answer or Action Item. Neil Raden, Action Item. >> I think there's been a lot of progress lately in creating tools for data scientists to be more efficient and they need to be, because the big digital giants are draining them from other companies. So that's very encouraging. But in general I think becoming a data-driven, a digital transformation company for most companies, is a big job and I think they need to it in piece parts because if they try to do it all at once they're going to be in trouble. >> Alright, so that's great conversation guys. Oh, David Floyer, Action Item. David's looking at me saying, ah what about me? David Floyer, Action Item. >> (laughing) So my Action Item comes from an Irish proverb. Which if you ask for directions they will always answer you, "I wouldn't start from here." So the Action Item that I have is, if somebody is coming in saying you have to re-do all of your applications and re-write them from scratch, and start in a completely different direction, that is going to be a 20-year job and you're not going to ever get it done. So you have to start from what you have. The digital assets that you have, and you have to focus on improving those with additional applications, additional data using that as the foundation for how you build that business with a clear long-term view. And if you look at some of the examples that were given early, particularly in the insurance industries, that's what they did. >> Thank you very much guys. So, let's do an overall Action Item. We've been talking today about the challenges of de-risking digital business which ties directly to the overall understanding of the role of data assets play in businesses and the technology's ability to move from just protecting data, restoring data, to actually restoring the relationships in the data, the structures of the data and very importantly the models that are resident in the data. This is going to be a significant journey. There's clear evidence that this is driving a new valuation within the business. Folks talk about data as the new oil. We don't necessarily see things that way because data, quite frankly, is a very very different kind of asset. The cost could be shared because it doesn't suffer the same limits on scarcity. So as a consequence, what has to happen is, you have to start with where you are. What is your current value proposition? And what data do you have in support of that value proposition? And then whiteboard it, clean slate it and say, what data would we like to have in support of the activities that we perform? Figure out what those gaps are. Find ways to get access to that data through piecemeal, piece-part investments. That provide a roadmap of priorities looking forward. Out of that will come a better understanding of the fundamental data assets that are being created. New models of how you engage customers. New models of how operations works in the shop floor. New models of how financial services are being employed and utilized. And use that as a basis for then starting to put forward plans for bringing technologies in, that are capable of not just supporting the data and protecting the data but protecting the overall organization of data in the form of these models, in the form of these relationships, so that the business can, as it creates these, as it throws off these new assets, treat them as the special resource that the business requires. Once that is in place, we'll start seeing businesses more successfully reorganize, reinstitutionalize the work around data, and it won't just be the big technology companies who have, who people call digital native, that are well down this path. I want to thank George Gilbert, David Floyer here in the studio with me. David Vellante, Ralph Finos, Neil Raden and Jim Kobelius on the phone. Thanks very much guys. Great conversation. And that's been another Wikibon Action Item. (upbeat music)

>> Announcer: Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017. Brought to by Hortonworks. >> Welcome back to theCUBE, we are live, on day two of the DataWorks Summit. I'm Lisa Martin. #DWS17, join the conversation. We've had a great day and a half. We have learned from a ton of great influencers and leaders about really what's going on with big data, data science, how things are changing. My cohost is George Gilbert. We're joined by my old buddy, the COO of Hortonworks, Rajnish Verma. Raj, it's great to have you on theCUBE. >> It's great to be here, Lisa. Great to see you as well, it's been a while. >> It has, so yesterday on the customer panel, the Raj I know had great conversation with customers from, Duke Energy was one. You also had Black Knight on the financial services side. >> Rajnish: And HSC. >> Yes, on the insurance side, and one of the things that, a couple things that really caught my attention, one was when Duke said, kind of, where they were using data and moving to Hadoop, but they are now a digital company. They're now a technology company that sells electricity and products, which I thought was fantastic. Another thing that I found really interesting about that was they all talked about the need to leverage big data, and glean insights and monetize that, really requires this cultural shift. So I know you love customer interactions. Talk to us about what you're seeing. Those are three great industry examples. What are you seeing? Where are customers on this sort of maturity model where big data and Hadoop are concerned? >> Sure, happy to. So one thing that I enjoy the most about my job is meeting customers and talking to them about the art of the possible. And some of the stuff that they're doing, and, which was only science fiction, really, about two or three years ago. And they're a couple of questions that you've just asked me as to where they are on their journey, what are they trying to accomplish, et cetera. I remember about, five, seven, 10 years ago where Marc Andreessen said "Software is eating the world." And to be honest with you, now, it's now more like every company is a data company. I wouldn't say data is eating the world, but without effective monetization of your data assets, you can't be a force to reckon with as a company. So that is a common theme that we are seeing irrespective of industry, irrespective of customer, irrespective of really the size of the customer. The only thing that sort of varies is the amount and complexity of data, from one company to the other. Now, when, I'm new to Hortonworks as you know. It's really my fifth month here. And one of the things that I've seen and, Lisa, as you know, are coming from TIBCO. So we've been dealing with data. I have been involved with data for over a decade and a half now, right. So the difference was, 15 years ago, we were dealing with really structured data and we actually connected the structured data and gleaned insights into structured data. Now, today, a seminal challenge that every CIO or chief data officer is trying to solve is how do you get actionable insights into semi-structured and unstructured data. Now, so, getting insights into that data first requires ability to aggregate data, right. Once you've aggregated data, you also need a platform to make sense of data in real-time, that is being streamed at you. Now once you do those two things, then you put yourself in a position to analyze that data. So in that journey, as you asked, where our customers are. Some are defining their data aggregation strategy. The others, having defined data aggregation, they're talking about streaming analytics as a platform, and then the others are talking about data science and machine learning and deep learning, as a journey. Now, you saw the customer panel yesterday. But the one point I'd like to make is, it's not only the Duke Energies and the Black Knights of the world, or the HSC, who I believe are big, large firms that are using data. Even a company like, an old agricultural company, or I shouldn't say old but steeped in heritage is probably the right word. 96, 97 year old agricultural company that's in the animal feed business. Animal feed. Multi-billion dollar animal feed business. They use data to monetize their business model. What they say is, they've been feeding animals for the last 70 years. Sp now they go to a farmer and they have enough data about how to feed animals, that they can actually tell the farmer, that this hog that you have right now, which is 17 pounds, I can guarantee you that I will have him or her on a nutrition that, by four months, it'll be 35 pounds. How much are you willing to pay? So even in the animal feed business, data is being used to drive not only insights, but monetization models. >> Wow. >> So. >> That's outstanding. >> Thank you. >> So in getting to that level of sophistication, it's not like every firm sort of has the skills and technology in place to do that. What are some of the steps that you find that they typically have to go through to get to that level of maturity? Like, where do they make mistakes? Where do they find the skills to manage on-prem infrastructure, if it is on-premmed? What about, if they're trying to do a hybrid cloud setup. How complex is that? >> I think that's where the power of the community comes through at multiple levels. So we're committed to the open-source movement. We're committed to the community-based development of data. Now, this community-based business model does a few things. Firstly, it keeps the innovation at the leading edge, bleeding edge, number one. But as you heard the panel talk about yesterday, one of the biggest benefits that our customers see of using open source, is, sure economics is good, but that's not the leading reason. Keeping up with innovation, very high up there. Avoiding when to lock in, again very, very high up there. But one of the biggest reasons that CIOs gave me for choosing open source as a business model is more to do with the fact that they can attract good talent, and without open source, you can't actually attract talent. And I can relate to that because I have a sophomore at home. And it just happened to me that she's 15 now but she's been using open source since she was 11. The iPhone and, she downloads an application for free. She uses it, and if she stretches the limit of that, then she orders something more in a paid model. So the community helps people do a few things. Be able to fail fast if they need to. The second is, it lowers the barriers of entry, right. Because it's really free. You can have the same model. The third is, you can rely on the community for support and methodologies and best practices and lessons learned from implementations. The fourth is, it's a great hiring ground in terms of bringing people in and attracting Millennial talent, young talent, and sought-after talent. So that's really probably the answer that I would have for that. >> When you talk about the business model, the open-source business model and the attraction on the customer side, that sounded like there's this analogy with sort of the agro-business customer in the sense that there are offering data along with their traditional product. If your traditional product is open-source data management, what a room started telling us this morning was the machine learning that goes along with operating not only your own sort of internal workloads but customers, and being to offer prescriptive advice on operations, essentially IT operations. Is that the core, will that become the core of sort of value-add through data for an open-source business model like yours? >> I don't want to be speculative but I'll probably answer it another way. I think our vision, which was set by our founder Rob Bearden, and he took you guys through that yesterday, was way back when, we did say that our mission in life is to manage the world's data. So that mission hasn't changed. And the second was, we would do it as a open-source community or as a big contributing part of that community. And that has really not changed. Now, we feel that machine learning and data science and deep learning are areas that we're very, very excited about, our customers are very, very excited about. Now, the one thing that we did cover yesterday and I think earlier today as well, I'm a computer science engineer. And when I was in college, way back when, 25 years ago, I was interested in AI and ML. And it has existed for 50 years. The reason why it hasn't been available to the common man, so as to speak, is because of two reasons. One is, it did not have a source of data that it could sit on top of, that makes machine learning and AI effective. Or at least not a commercially-viable option to do so. Now, there is one. The second is, the compute power required to run some of the large algorithms that really give you insights into machine learning and AI. So we've become the platform on which customers can take advantage of excellent machine learning and AI tools to get insights. Now, that is two independent sort of categories. One is the open source community providing the platform. And then what tools the customer has used to apply data science and machine learning, so. >> So, all right. I'm thinking something that is slightly different and maybe the nuance is making it tough to articulate. But it's how can Hortonworks take the data platform and data science tools that you use to help understand how to operate important works, whether it's on a customer prem, or in the cloud. In other words, how can you use machine learning to make it a sort of a more effective and automated manage service? >> Yeah, and I think that's, the nuance's not lost in me. I think what I'm trying to sort of categorize is, for that to happen, you require two things. One is data aggregator across on-prem and cloud. Because when you have data which is multi-tenancy, you have a lot of issues with data security, data governance, all the rest of it. Now, that is what we plan to manage for the world, so as to speak. Now, on top of that, customers who require to have data science or deep learning to be used, we provide that platform. Now, whether that is used as a service by the customer, which we would be happy to provide, or it is used inhouse, on-prem, on various cloud models, that's more a customer decision. We don't want to force that decision. However, from the art of the possible perspective, yes it's possible. >> I love the mission to manage the world's data. >> Thank you. >> That's a lofty goal, but yesterday's announcements with IBM were pretty, pretty transformative. In your opinion as chief operating officer, how do you see this extension of this technology and strategic partnership helping Hortonworks on the next level of managing the world's data? >> Absolutely, it's game-changing for us. We're very, very excited. Our colleagues are very, very excited about the opportunity to partner. It's also a big validation of the fact that we now have a pretty large open-source community that contributes to this cause. So we're very excited about that. The opportunity is in actually our partnering with a leader in data science, machine learning, and AI, a company that has steeped in heritage, is known for game-changing, next technology moves. And the fact that we're powering it from a data perspective is something that we're very, very excited and pleased about. And the opportunities are limitless. >> I love that, and I know you are a game-changer, in your fifth month. We thank you so much, Raj, for joining us. It was great to see you. Continued success, >> Thank you. >> at managing the world's data and being that game-changer, yourself, and for Hortonworks as well. >> Thank you Lisa, good to see you. >> You've been watching theCUBE. Again, we're live, day two of the DataWorks Summit, #DWS17. For my cohost, George Gilbert, I'm Lisa Martin. Stick around guys, we'll be right back with more great content. (jingle)

>> Narrator: Live from Munich, Germany it's the CUBE, covering Dataworks Summit Europe 2017. Brought to you by Hortonworks. >> Okay, welcome back everyone here at day two coverage of the CUBE here in Munich, Germany for Dataworks 2017. I'm John Furrier, my co-host Dave Vellante. Two days of wall to wall coverage SiliconANGLE Media's the CUBE. Our next guest is Raj Verma, the president and COO of Hortonworks. First time on the CUBE, new to Hortonworks. Welcome to the CUBE. >> Thank you very much, John, appreciate it. >> Looking good with a three piece suit we were commenting when you were on stage. >> Raj: Thank you. >> Great scene here in Europe, again different show vis-a-vis North America, in San Jose. You got the show coming up there, it's the big show. Here, it's a little bit different. A lot of IOT in Germany. You got a lot of car manufacturers, but industrial nation here, smart city initiatives, a lot of big data. >> Uh-huh. >> What's your thoughts? >> Yeah no, firstly thanks for having me here. It's a pleasure and good chit chatting right before the show as well. We are very, very excited about the entire data space. Europe is leading many initiatives about how to use data as a sustainable, competitive differentiator. I just moderated a panel and you guys heard me talk to a retail bank, a retailer. And really, Centrica, which was nothing but British Gas, which is rather an organization steeped in history so as to speak and that institution is now, calls itself a technology company. And, it's a technology company or an IOT company based on them using data as the currency for innovation. So now, British Gas, or Centrica calls itself a data company, when would you have ever thought that? I was at dinner with a very large automotive manufacturers and the kind of stuff they are doing with data right from the driving habits, driver safety, real time insurance premium calculation, the autonomous drive. It's just fascinating no matter what industry you talk about. It's just very, very interesting. And, we are very glad to be here. International business is a big priority for me. >> We've been following Hortonworks since it's inception when it spun out of Yahoo years ago. I think we've been to every Hadoop World going back, except for the first one. We watched the transition. It's interesting, it's always been a learning environment at these shows. And certainly the customer testimonials speaks to the ecosystem, but I have to ask you, you're new to Hortonworks. You have interesting technology background. Why did you join Hortonworks? Because you certainly see the movies before and the cycles of innovation, but now we're living in a pretty epic, machine learning, data AI is on the horizon. What were the reasons why you joined Hortonworks? >> Yeah sure, I've had a really good run in technology, fortunately was associated with two great companies, Parametric Technology and TIBCO Software. I was 16 years at TIBCO, so I've been dealing with data for 16 years. But, over the course of the last couple of years whenever I spoke to a C level executive, or a CIO they were talking to us about the fact that structured data, which is really what we did for 16 years, was not good enough for innovation. Innovation and insights into unstructured data was the seminal challenge of most of the executives that I was talking to, senior level executives. And, when you're talking about unstructured data and making sense of it there isn't a better technology than the one that we are dealing with right now, undoubtedly. So, that was one. Dealing with data because data is really the currency of our times. Every company is a data company. Second was, I've been involved with proprietary software for 23 years. And, if there is a business model that's ready for disruption it's the proprietary software business model because I'm absolutely convinced that open source is what I call a green business model. It's good for planet Earth so as to speak. It's a community based, it's based on innovation and it puts the customer and the technology provider on the same page. The customer success drives the vendor success. Yeah, so the open source community, data-- >> It's sustainables, pun intended, in the sense that it's had a continuing run. And, it's interesting Tier One software is all open source now. >> 100%, and by the way not only that if you see large companies like IBM and Microsoft they have finally woken up to the fact that if they need to attract talent and if they want to be known as talk leaders they have to have some very meaningful open source initiatives. Microsoft loves Linux, when did we ever think that was going to happen, right? And, by the way-- >> I think Steve Bauman once said it was the cancer of the industry. Now, they're behind it. But, this is the Linux foundation has also grown. We saw a project this past week. Intel donated a big project to the Linux now it's taking over, so more projects. >> Raj: Yes. >> There's more action happening than ever before. >> You know absolutely, John. Five years ago when I would go an meet a CIO and I would ask them about open source and they would wink, they say "Of course, "we do open source. But, it's less than 5%, right? Now, when I talk to a CIO they first ask their teams to go evaluated open source as the first choice. And, if they can't they come kicking and screaming towards propriety software. Most organizations, and some organizations with a lot of historical gravity so as to speak have a 50/50 even split between proprietary and open source. And, that's happened in the last three years. And, I can make a bold statement, and I know it'll be true, but in the next three years most organizations the ratio of proprietary to open source would be 20 proprietary 80 open source. >> So, obviously you've made that bet on open source, joining Hortonworks, but open is a spectrum. And, on one end of the spectrum you have Hortonworks which is, as I see it, the purest. Now, even Larry Ellison, when he gets onstage at Oracle Open World will talk about how open Oracle is, I guess that's the other end of the spectrum. So, my question is won't the Microsofts and the Oracles and the IBM, they're like recovering alcoholics and they'll accommodate their platforms through open source, embracing open source. We'll see if AWS is the same, we know it's unidirectional there. How do you see that-- >> Well, not necessarily. >> Industry dynamic, we'll talk about that later. How do you see that industry dynamic shaking out? >> No, absolutely, I think I remember way back in I think the mid to late 90s I still loved that quote by Scott McNeely, who is a friend, Dell, not Dell, Digital came out with a marketing campaign saying open VMS. And, Scott said, "How can someone lie "so much with one word?" (laughs) So, it's the fact that Oracle calling itself open, well I'll just leave it at, it's a good joke. I think the definition of open source, to me, is when you acquire a software you have three real costs. One is the cost of initial procuring that software and the hardware and all the rest of it. The second is implementation and maintenance. However, most people miss the third dimension of cost when acquiring software, which is the cost to exit the technology. Our software and open source has very low exit barriers to our technology. If you don't like our technology, switch it off. You own the software anyways. Switch off our services and the barrier of exits are very, very low. Having worked in proprietary software, as I said, for 23 years I very often had conversations with my customers where I would say, "Look, you really "don't have a choice, because if you want to exit "our technology it's going to probably cost you "ten times more than what you've spent till date." So, it a lock in architecture and then you milk that customer through maintenance, correct? >> Switching costs really are the metric-- >> Raj: Switching costs, exactly. >> You gave the example of Blockbuster Camera, and the rental, the late charge fees. Okay, that's an example of lock in. So, as we look at the company you're most compared with, now that's it's going public, Cloudera, in a way I see more similarities than differences. I mean, you guys are sort of both birds of a feather. But, you are going for what I call the long game with a volume subscription model. And, Cloudera has chosen to build proprietary components on top. So, you have to make big bets on open. You have to support those open technologies. How do you see that affecting the long term distance model? >> Yeah, I think we are committed to open source. There's absolutely no doubt about it. I do feel that we are connected data platform, which is data at rest and data in motion across on prem and cloud is the business model the going to win. We clearly have momentum on our side. You've seen the same filings that I have seen. You're talking about a company that had a three year head start on us, and a billion dollars of funding, all right, at very high valuations. And yet, they're only one year ahead in terms of revenue. And, they have burnt probably three times more cash than we have. So clearly, and it's not my opinion, if you look at the numbers purely, the numbers actually give us the credibility that our business model and what we are doing is more efficient and is working better. One of the arguments that I often hear from analysts and press is how are your margins on open source? According to the filings, again, their margins are 82% on proprietary software, my margins on open source are 84%. So, from a health of the business perspective we are better. Now, the other is they've claimed to have been making a pivot to more machine learning and deep learning and all the rest of it. And, they actually'd like us to believe that their competition is going to be Amazon, IBM, and Google. Now, with a billion dollars of funding with the Intel ecosystem behind them they could effectively compete again Hortonworks. What do you think are their chances of competing against Google, Amazon, and IBM? I just leave that for you guys to decide, to be honest with you. And, we feel very good that they have virtually vacated the space and we've got the momentum. >> On the numbers, what jumps out at you on filing since obviously, I sure, everyone at Hortonworks was digging through the S1 because for the first time now Cloudera exposes some of the numbers. I noticed some striking things different, obviously, besides their multiple on revenue valuation. Pretty obvious it's going to be a haircut coming after the public offering. But, on the sales side, which is your wheelhouse there's a value proposition that you guys at Hortonworks, we've been watching, the cadence of getting new clients, servicing clients. With product evolution is challenging enough, but also expensive. It's not you guys, but it's getting better as Sean Connolly pointed out yesterday, you guys are looking at some profitability targets on the Ee-ba-dep coming up in Q four. Publicly stated on the earnings call. How's that different from Cloudera? Are they burning more cash because of their sales motions or sales costs, or is it the product mix? What's you thoughts on the filings around Cloudera versus the Hortonworks? >> Well, look I just feel that, I can talk more about my business than theirs. Clearly, you've seen the same filings that I have and you've see the same cash burn rates that we have seen. And, we clearly are ore efficient, although we can still get better. But, because of being public for a little more than two years now we've had a thousand watt bulb being shown at us and we have been forced to be more efficient because we were in the limelight. >> John: You're open. >> In the open, right? So, people knew what our figures are, what our efficiency ratios were. So, we've been working diligently at improving them and we've gotten better, and there's still scope for improvement. However, being private did not have the same scrutiny on Cloudera. And, some would say that they were actually spending money like drunken sailors if you really read their S1 filing. So, they will come under a lot of scrutiny as well. I'm sure they'll get more efficient. But right now, clearly, you've seen the same numbers that I have, their numbers don't talk about efficiency either in the R and D side or the sales and marketing side. So, yeah we feel very good about where we are in that space. >> And, open source is this two edged sword. Like, take Yarn for example, at least from my perspective Hortonworks really led the charge to Yarn and then well before Doctor and Kubernetes ascendancy and then all of a sudden that happens and of course you've got to embrace those open source trends. So, you have the unique challenge of having to support sort of all the open source platforms. And, so that's why I call it the long game. In order for you guys to thrive you've got to both put resources into those multiple projects and you've got to get the volume of your subscription model, which you pointed out the marginal economics are just as good as most, if not any software business. So, how do you manage that resource allocation? Yes, so I think a lot of that is the fact that we've got plenty of contributors and committers to the open source community. We are seen as the angel child in open source because we are just pure, kosher open source. We just don't have a single line of proprietary code. So, we are committed to that community. We have over the last six or seven years developed models of our software development which helps us manage the collective bargaining power, so as to speak, of the community to allocate resources and prioritize the allocation of resources. It continues to be a challenge given the breadth of the open source community and what we have to handle, but fortunately I'm blessed that we've got a very, very capable engineering organization that keeps us very efficient and on the cutting edge. >> We're here with Raj Verma, With the new president and COO of Hortonworks, Chief Operating Officer. I've got to ask you because it's interesting. You're coming in with a fresh set of eyes, coming in as you mentioned, from TIBCO, interesting, which was very successful in the generation of it's time and history of TIBCO where it came from and what it did was pretty fantastic. I mean, everyone knows connecting data together was very hard in the enterprise world. TIBCO has some challenges today, as you're seeing, with being disrupted by open source, but I got to ask you. As a perspective, new executive you got, looking at the battlefield, an opportunity with open source there's some significant things happening and what are you excited about because Hortonworks has actually done some interesting things. Some, I would say, the world spun in their direction, their relationship with Microsoft, for instance, and their growth in cloud has been fantastic. I mean, Microsoft stock price when they first started working with Hortonworks I think was like 26, and obviously with Scott Di-na-tell-a on board Azure, more open source, on Open Compute to Kubernetes and Micro Services, Azure doing very, very well. You also have a partnership with Amazon Web Services so you already are living in this cloud era, okay? And so, you have a cloud dynamic going on. Are you excited by that? You bring some partnership expertise in from TIBCO. How do you look at partners? Because, you guys don't really compete with anybody, but you're partners with everybody. So, you're kind of like Switzerland, but you're also doing a lot of partnerships. What are you excited about vis-a-vis the cloud and some of the other partnerships that are happening. >> Yeah, absolutely, I think having a robust partner ecosystem is probably my number one priority, maybe number two after being profitable in a short span of time, which is, again, publicly stated. Now, our partnership with Microsoft is very, very special to us. Being available in Azure we are seeing some fantastic growth rates coming in from Azure. We are also seeing remarkable amount of traction from the market to be able to go and test out our platform with very, very low barriers of entry and, of course, almost zero barriers of exit. So, from a partnership platform cloud providers like Amazon, Microsoft, are very, very important to us. We are also getting a lot of interest from carriers in Europe, for example. Some of the biggest carriers want to offer business services around big data and almost 100%, actually not almost, 100% of the carriers that we have spoken to thus far want to partner with us and offer our platform as a cloud service. So, cloud for us is a big initiative. It gives us the entire capability to reach audiences that we might not be able to reach ringing one door bell at a time. So, it's, as I said, we've got a very robust, integrated cloud strategy. Our customers find that very, very interesting. And, building that with a very robust partner channel, high priority for us. Second, is using our platform as a development platform for application on big data is, again, a priority. And that's, again, building a partner ecosystem. The third is relationships with global SIs, Extensia, Deloitte, KPMG. The Indian SIs of In-flu-ces, and Rip-ro, and HCL and the rest. We have some work to do. We've done some good work there, but there's some work to be done there. And, not only that I think some of the initiatives that we are launching in terms of training as a service, free certification, they are all things which are aimed at reaching out to the partners and building, as I said, a robust partner ecosystem. >> There's a lot of talk a conferences like this about, especially in Hadoop, about complexity, complexity of the ecosystem, new projects, and the difficulties of understanding that. But, in reality it seems as though today anyway the technology's pretty well understood. We talked about Millennials off camera coming out today with social savvy and tooling and understanding gaming and things like that. Technology, getting it to work seems to not be the challenge anymore. It's really understanding how to apply it, how to value data, we heard in your panel today. The business process, which used to be very well known, it's counting, it's payroll, simple. Now, it's kind of ever changing daily. What do you make of that? How do you think that will effect the future of work? Yeah, I think there's some very interesting questions that you've asked in that the first, of course, is what does it take to have a very successful big data, or Hadoop project. And, I think we always talk about the fact that if you have a very robust business case backing a Hadoop project that is the number one key ingredient to delivering a Hadoop project. Otherwise, you can tend to boil the ocean, all right, or try and eat an elephant in one bite as I like to say. So, that's one and I think you're right. It's not the technology, it's not the complexity, it's not the availability of the resources. It is a leadership issue in organizations where the leader demands certain outcomes, business outcomes from the Hadoop project team and we've seen whenever that happens the projects seem to be very, very successful. Now, the second part of the question about future of work, which is a very, very interesting topic and a topic which is very, very close to my heart. There are going to be more people than jobs in the next 20, 25 years. I think that any job that can be automated will be automated, or has been automated, right? So, this is going to have a societal impact on how we live. I've been lucky enough that I joined this industry 25 years ago and I've never had to change or switch industries. But, I can assure you that our kids, and we were talking about kids off camera as well, our kids will have to probably learn a new skill every five years. So, how does that impact education? We, in our generation, were testing champions. We were educated to score well on tests. But, the new form of education, which you and I were talking about, again in California where we live, and where my daughter goes to high school and in her school the number one, the number one priority is to instill a sense of learning and joy of learning in students because that is what is going to contribute to a robust future. >> That's a good point, I want to just interject here because I think that the trend we're seeing in the higher Ed side too also point to the impact of data science, to curriculum and learning. It's not just putting catalogs online. There's now kind of an iterative kind of non-linear discovery to proficiency. But, there's also the emotional quotient aspect. You mentioned the love of learning. The immersion of tech and digital is creating an interdisciplinary requirement. So, all the folks say that, what the statistic's like half the jobs that are going to be available haven't even been figured out yet. There's a value creation around interdisciplinary skill sets and emotional quotient. >> Absolutely. >> Social, emotional because of the human social community connectedness. This is also a big data challenge opportunity. >> Oh, 100% and I think one of the things that we believe is in the future, jobs that require a greater amount of empathy are least susceptible to automation. So, things like caring for old age people in the world, and nursing, and teaching, and artists, and all the rest will be professions which will be highly paid and numerous. I also believe that the entire big data challenge about how you use data to impact communities is going to come into play. And also, I think John, you and I were again talking about it, the entire concept of corporations is only 200 years old, really, 200, 300 years old. Before that, our forefathers were individual contributors who contributed a certain part in a community, barbers, tailors, farmers, what have you. We are going to go back to the future where all of us will go back to being individual contributors. And, I think, and again I'm bringing it back to open source, open source is the start of that community which will allow the community to go back to its roots of being individual contributors rather than being part of a organization or a corporation to be successful and to contribute. >> Yeah, the Coase's Penguin has been a very famous seminal piece of work. Obviously, Ronald Coase who's wrote the book The Nature of the Firm is interesting, but that's been a kind of historical document. You look at blockchain for instance. Blockchain actually has the opportunity to disrupt what the Nature of the Firm is about because of smart contracts, supply chain, and what not. And, we have this debate on the CUBE all the time, there's some naysayers, Tim Conner's a VC and I were talking on our Friday show, Silicon Valley Friday show. He's actually a naysayer on blockchain. I'm actually pro blockchain because I think there's some skeptics that say blockchain is really hard to because it requires an ecosystem. However, we're living in an ecosystem, a world of community. So, I think The Nature of the Firm will be disrupted by people organizing in a new way vis-a-vis blockchain 'cause that's an open source paradigm. >> Yeah, no I concur. So, I'm a believer in that entire concept. I 100%-- >> I want to come back to something you talked about, about individual contributors and the relationship in link to open source and collaboration. I personally, I think we have to have a frank conversation about, I mean machines have always replaced humans, but for the first time in our history it's replacing cognitive functions. To your point about empathy, what are the things that humans can do that machines can't? And, they become fewer and fewer every year. And, a lot of these conferences people don't like to talk about that, but it's a reality that we have to talk about. And, your point is right on, we're going back to individual contribution, open source collaboration. The other point is data, is it going to be at the center of that innovation because it seems like value creation and maybe job creation, in the future, is going to be a result of the combinatorial effects of data, open source, collaboration, other. It's not going to because of Moore's Law, all right. >> 100%, and I think one of the aspects that we didn't touch upon is the new societal model that automation is going to create would need data driven governance. So, a data driven government is going to be a necessity because, remember, in those times, and I think in 25, 30 years countries will have to explore the impact of negative taxation, right? Because of all the automation that actually happens around citizen security, about citizen welfare, about cost of healthcare, cost of providing healthcare. All of that is going to be fueled by data, right? So, it's just, as the Chinese proverb says, "May you live in interesting times." We definitely are living in very interesting times. >> And, the public policy implications are, your friend and one of my business heroes, Scott McNeally says, "There's no privacy in "the internet, get over it." We interviewed John Tapscott last week he said "That's unacceptable, "we have to solve that problem." So, it brings up a lot of public policy issues. >> Well, the social economic impact, right now there's a trend we're seeing where the younger generation, we're talking about the post 9/11 generation that's entering the workforce, they have a social conscience, right? So, there's an emphasis you're seeing on social good. AI for social good is one of the hottest trends out there. But, the changing landscape around data is interesting. So, the word democratization has been used whether you're looking at the early days of blogging and podcasting which we were involved in and research to now in media this notion of data and transparency and open source is probably at a tipping point, an all time high in terms of value creation. So, I want to hear your thoughts on this because as someone who's been in the proprietary world the mode of operation was get something proprietary, lock it dowm, build a fence and a wall, protect it with folks with machine guns and fight for the competitive advantage, right? Now, the competitive advantage is open. Okay, so you're looking at pure open source model with Hortonworks. It changes how companies are competing. What is the competitive advantage of Hortonworks? Actually, to be more open. >> 100%. >> How do you manage that? >> No absolutely, I just think the proprietary nature of software, like software has disrupted a lot of businesses, all right? And, it's not a resistance to disruption itself. I mean, there has never been a business model in the history of time where you charge a lot of money to build a software, or sell a software that you built and then whatever are the defects in that software you get paid more money to fix them, all right? That's the entire perpetual and maintenance model. That model is going to get disrupted. Now, there are hundreds of billions of dollars involved in it so people are going to come kicking and screaming to the open source world, but they will have to come to the open source world. Our advantage that we're seeing is innovation now in a closed loop environment, no matter what size of a company you are, cannot keep up with the changing landscape around you from a data perspective. So, without the collective innovation of the community I don't really think a technology can stay at par with the changes around them. >> This is what I say about, this is what I think is such an important point that you're getting at because we were started SiliconANGLE actually in the Cloudera office, so we have a lot of friends that work there. We have a great admiration for them, but one of the things that Cloudera has done through their execution is they have been very profit oriented, go public at all costs kind of thing that they're doing now. You've seen that happen. Is the competitive advantage that you're pointing out is something we're seeing that similar that Andy Jasseys doing at AWS, which is it's not so much to build something proprietary per se, it's just to ship something faster. So, if you look at Amazon's competitive advantage is that they just continue to ship product faster and faster and faster than companies can build themselves. And also, the scale that they're getting with these economies is increasing the quality. So, open source has also hit the naysayers on security, right? Everyone said, "Oh, open source is not secure." As it turns out, it's more secure. Amazon at scale is actually becoming more secure. So, you're starting to see the new competitive advantage be ship more, be more open as the way to do business. What do you think the impact will be to traditional companies whether it's a startup competing or an existing bank? This is a paradigm shift, what's the impact going to be for a CIO or CEO of a big company? How do they incorporate that competitive advantage? Yeah, I think the proprietary software world is not going to go away tomorrow, John, you know that. There so much of installed software and there's a saying from where I come from that "Even a dead elephant is worth a million dollars," right? So, even that business model even though it is sort of dying it'll still be a good investment for the next ten years because of the locked in business model where customers cannot get out. Now, from a perspective of openness and what that brings as a competitive differentiators to our customer just the very base at which, as I've said I've lived in a proprietary world, you would be lucky if you were getting the next version of our software every 18 months, you'd be lucky. In the open source community you get a few versions in 18 months. So, the cadence at which releases come out have just completely disrupted the proprietary model. It is just the collective, as I said, innovative or innovation ability of the community has allowed us to release, to increase the release cadence to a few months now, all right? And, if our engineering team had it's way it'll further be cut short, right? So, the ability of customers, and what does that allow the customer to do? Ten years ago if you looked for a capability from your proprietary vendor they would say you have to wait 18 months. So, what do you do, you build it yourself, all right? So, that is what the spaghetti architecture was all about. In the new open source model you ask the community and if enough people in the community think that that's important the community builds it for you and gives it to you. >> And, the good news is the business model of open source is working. So, you got you guys have been public, you got Cloudera going public, you have MuleSoft out there, a lot of companies out there now that are public companies are open source companies, a phenomenal change over. But, the other thing that's interesting is that the hiring factor for the large enterprise to the point of, your point about so proprietary not updating, it's the same is true for the enterprise. So, just hiring candidates out of open source is now increased, the talent pool for a large enterprise. >> 100%, 100%. >> Well, I wonder if I could challenge this love fest for a minute. (laughs) So, there's another saying, I didn't grow up there, but a dying snake can still bite you. So, I bring that up because there is this hybrid model that's emerging because these elephants eventually they figure it out. And so, an example would be, we talked about Cloudera and so forth, but the better example, I think, is IBM. What IBM has done to embrace open source with investing years ago a billion dollars into Linux, what it's doing with Spark, essentially trying to elbow its way in and say, "Okay, "now we're going to co-opt the ecosystem. "And then, build our proprietary pieces on top of it." That, to me, that's a viable business model, is it not? >> Yes, I'm sure it is and to John's point with the Mule going IPO and with Cloudera having successfully built a $250 million, $261 million business is testimony, yeah, it's a testimony to the fact that companies can be built. Now, can they be more efficient, sure they can be more efficient. However, my entire comment on this is why are you doing open source? What is your intent of doing open source, to be seen as open, or to be truly open? Because, in our philosophy if you a add a slim layer of proprietariness, why are you doing that? And, as a businessman I'll tell you why you increase the stickiness factor by locking in your customer, right? So, let's not, again, we're having a frank conversation, proprietary code equals customer lock in, period. >> Agreed. And, as a business model-- >> I'm not sure I agree with that. >> As a business model. >> Please. (laughs) We'll come back to that. >> So, it's a customer lock in. Now, as a business model it is, if you were to go with the business models of the past, yes I believe most of the analysts will say it a stickier, better business model, but then we would like to prove them wrong. And, that's our mission as open source purely. >> I would caution though, Amazon's the mother of all lock in's. You kind of bristled at that before. >> They're not, I mean they use a lot of open source. I mean, did they open source it? Getting back to the lock in, the lock in is a function of stickiness, right? So, stickiness can be open source. Now, you could argue that Horonworks through they're relationship with partnering is a lock in spec with their stickiness of being open. Right, so I come back down to the proprietary-- >> Dave: My search engine I like Google. >> I mean Google's certainly got-- >> It's got to be locked in 'cause I like it? >> Well, there's a lot of do you care with proprietary technology that Google's built. >> Switching costs, as we talked about before. >> But, you're not paying for Si-tch >> If the value exceeds the price of the lock in then it's an opportunity. So, Palma Richie's talking about the hardened top, the hardened top. Do you care what's in an Intel processor? Well, Intel is a proprietary platform that provides processing power, but it enables a lot of other value. So, I think the stickiness factor of say IBM is interesting and they've done a lot open source stuff to defend them on Linux, for example they do a (mumbles) blockchain. But, they're priming the pump for their own business, that's clear for their lock In. >> Raj wasn't saying there's not value there. He's saying it's lock in, and it is. >> Well, some customers will pay for convenience. >> Your point is if the value exceeds the lock in risk than it's worth it. >> Yeah, that's my point, yeah. >> 1005, 100%. >> And, that's where the opportunity is. So, you can use open source to get to a value projectory. That's the barriers to entry, we seen 'em on the entrepreneurship side, right? It's easier to start a company now than ever before. Why? Because of open source and cloud, right? So, does that mean that every startup's going to be super successful and beat IBM? No, not really. >> Do you thinK there will be a red hat of big data and will you be it? >> We hope so. (laughs) If I had my that's definitely. That's really why I am here. >> Just an example, right? >> And, the one thing that excites us about this this year is as my former boss used to say you could be as good as you think you are or the best in the world but if you're in the landline business right now you're not going to have a very bright future. However, the business that we are in we pull from the market that we get, and you're seeing here, right? And, these are days that we have very often where customer pool is remarkable. I mean, this industry is growing at, depending on which analyst you're talking to somewhere between 50 to 80% ear on ear. All right, every customer is a prospect for us. There isn't a single conversation that we have with any organization almost of any size where they don't think that they can use their data better, or they can enhance and improve their data strategy. So, if that is in place and I am confident about our execution, very, very happy with the technology platform, the support that we get from out customers. So, all things seem to be lining up. >> Raj, thanks so much for coming on, we appreciate your time. We went a little bit over, I think, the allotted time, but wanted to get your insight as the new President and Chief Operating Officer for Hortonworks. Congratulations on the new role, and looking forward to seeing the results. Since you're a public company we'll be actually able to see the scoreboard. >> Raj: Yes. >> Congratulations, and thanks for coming on the CUBE. There's more coverage here live at Dataworks 2017. I John Furrier, stay with us more great interviews, day two coverage. We'll be right back. (jaunty music)

>> Announcer: Live from San Jose, California, it's theCUBE, covering Big Data Silicon Valley 2017. (light techno music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Big Data SV, wrapping up with two days of wall-to-wall coverage of Big Data SV which is associated with Strata Comp, which is part of Big Data Week, which always becomes the epicenter of the big data world for a week here in San Jose. We're at the historic Pagoda Lounge, and we're excited to have our next two guests, talking a little bit different twist on big data that maybe you hadn't thought of. We've got Ravi Dharnikota, he is the Chief Enterprise Architect at SnapLogic, welcome. - Hello. >> Jeff: And he has brought along a customer, Katharine Matsumoto, she is a Data Scientist at eero, welcome. >> Thank you, thanks for having us. >> Jeff: Absolutely, so we had SnapLogic on a little earlier with Garavs, but tell us a little bit about eero. I've never heard of eero before, for folks that aren't familiar with the company. >> Yeah, so eero is a start-up based in San Francisco. We are sort of driven to increase home connectivity, both the performance and the ease of use, as wifi becomes totally a part of everyday life. We do that. We've created the world's first mesh wifi system. >> Okay. >> So that means you have, for an average home, three different individual units, and you plug one in to replace your router, and then the other three get plugged in throughout the home just to power, and they're able to spread coverage, reliability, speed, throughout your homes. No more buffering, dead zones, in that way back bedroom. >> Jeff: And it's a consumer product-- >> Yes. >> So you got all the fun and challenges of manufacturing, you've got the fun challenges of distribution, consumer marketing, so a lot of challenges for a start-up. But you guys are doing great. Why SnapLogic? >> Yeah, so in addition to the challenges with the hardware, we also are a really strong software. So, everything is either set up via the app. We are not just the backbone to your home's connectivity, but also part of it, so we're sending a lot of information back from our devices to be able to learn and improve the wifi that we're delivering based on the data we get back. So that's a lot of data, a lot of different teams working on different pieces. So when we were looking at launch, how do we integrate all of that information together to make it accessible to business users across different teams, and also how do we handle the scale. I made a checklist (laughs), and SnapLogic was really the only one that seemed to be able to deliver on both of those promises with a look to the future of like, I don't know what my next Sass product is, I don't know what our next API point we're going to need to hit is, sort of the flexibility of that as well as the fact that we have analysts were able to pick it up, engineers were able to pick it up, and I could still manage all the software written by, or the pipelines written by each of those different groups without having to read whatever version of code they're writing. >> Right, so Ravi, we heard you guys are like doubling your customer base every year, and lots of big names, Adobe we talked about earlier today. But I don't know that most people would think of SnapLogic really, as a solution to a start-up mesh network company. >> Yeah, absolutely, so that's a great point though, let me just start off with saying that in this new world, we don't discriminate-- (guest and host laugh) we integrate and we don't discriminate. In this new world that I speak about is social media, you know-- >> Jeff: Do you bus? (all laugh) >> So I will get to that. (all laugh) So, social, mobile, analytics, and cloud. And in this world, people have this thing which we fondly call integrators' dilemma. You want to integrate apps, you go to a different tool set. You integrate data, you start thinking about different tool sets. So we want to dispel that and really provide a unified platform for both apps and data. So remember, when we are seeing all the apps move into the cloud and being provided as services, but the data systems are also moving to the cloud. You got your data warehouses, databases, your BI systems, analytical tools, all are being provided to you as services. So, in this world data is data. If it's apps, it's probably schema mapping. If it's data systems, it's transformations moving from one end to the other. So, we're here to solve both those challenges in this new world with a unified platform. And it also helps that our lineage and the brain trust that brings us here, we did this a couple of decades ago and we're here to reinvent that space. >> Well, we expect you to bring Clayton Christensen on next time you come to visit, because he needs a new book, and I think that's a good one. (all laugh) But I think it was a really interesting part of the story though too, is you have such a dynamic product. Right, if you looked at your boxes, I've got the website pulled up, you wouldn't necessarily think of the dynamic nature that you're constantly tweaking and taking the data from the boxes to change the service that you're delivering. It's not just this thing that you made to a spec that you shipped out the door. >> Yeah, and that's really where the auto connected, we did 20 from our updates last year. We had problems with customers would have the same box for three years, and the technology change, the chips change, but their wifi service is the same, and we're constantly innovating and being able to push those out, but if you're going to do that many updates, you need a lot of feedback on the updates because things break when you update sometimes, and we've been able to build systems that catch that that are able to identify changes that say, not one person could be able to do by looking at their own things or just with support. We have leading indicators across all sorts of different stability and performance and different devices, so if Xbox changes their protocols, we can identify that really quickly. And that's sort of the goal of having all the data in one place across customer support and manufacturing. We can easily pinpoint where in the many different complicated factors you can find the problem. >> Have issues. - Yeah. >> So, I've actually got questions for both of you. Ravi, starting with you, it sounds like you're trying to tackle a challenge that in today's tools would have included Kafka at the data integration level, and there it's very much a hub and spoke approach. And I guess it's also, you would think of the application level integration more like the TIBCO and other EAI vendors in a previous generation-- - [Ravi] Yeah. >> Which I don't think was hub and spoke, it was more point to point, and I'm curious how you resolve that, in other words, how you'd tackle both together in a unified architecture? >> Yeah, that's an excellent question. In fact, one of the integrators' dilemma that I spoke about you've got the problem set where you've got the high-latency, high-volume, where you go to ETL tools. And then the low-latency, low-volume, you immediately go to the TIBCOs of the world and that's ESB, EAI sort of tool sets that you look to solve. So what we've done is we've thought about it hard. At one level we've just said, why can integration not be offered as a service? So that's step number one where the design experience is through the cloud, and then execution can just happen anywhere, behind your firewall or in the cloud, or in a big data system, so it caters to all of that. But then also, the data set itself is changing. You're seeing a lot of the document data model that are being offered by the Sass services. So the old ETL companies that were built before all of this social, mobile sort of stuff came around, it was all row and column oriented. So how do you deal with the more document oriented JSON sort of stuff? And we built that for, the platform to be able to handle that kind of data. Streaming is an interesting and important question. Pretty much everyone I spoke to last year were, streaming was a big-- let's do streaming, I want everything in real-time. But batch also has it's place. So you've got to have a system that does batch as well as real-time, or as near real-time as needed. So we solve for all of those problems. >> Okay, so Katharine, coming to you, each customer has a different, well, every consumer has a different, essentially, a stall base. To bring all the telemetry back to make sense out of what's working and what's not working, or how their environment is changing. How do you make sense out of all that, considering that it's not B to B, it's B to C so, I don't know how many customers you have, but it must be in the tens or hundreds. >> I'm sure I'm not allowed to say (laughs). >> No. But it's the distinctness of each customer that I gather makes the support challenge for you. >> Yeah, and part of that's exposing as much information to the different sources, and starting to automate the ways in which we do it. There's certainly a lot, we are very early on as a company. We've hit our year mark for public availability the end of last month so-- >> Jeff: Congratulations. >> Thank you, it's been a long year. But with that we learn more, constantly, and different people come to different views as different new questions come up. The special-snowflake aspect of each customer, there's a balance between how much actually is special and how much you can find patterns. And that's really where you get into much more interesting things on the statistics and machine learning side is how do you identify those patterns that you may not even know you're looking for. We are still beginning to understand our customers from a qualitative standpoint. It actually came up this week where I was doing an analysis and I was like, this population looks kind of weird, and with two clicks was able to send out a list over to our CX team. They had access to all the same systems because all of our data is connected and they could pull up the tickets based on, because through SnapLogic, we're joining all the data together. We use Looker as our BI tool, they were just able to start going into all the tickets and doing a deep dive, and that's being presented later this week as to like, hey, what is this population doing? >> So, for you to do this, that must mean you have at least some data that's common to every customer. For you to be able to use something like Looker, I imagine. If every customer was a distinct snowflake, it would be very hard to find patterns across them. >> Well I mean, look at how many people have iPhones, have MacBooks, you know, we are looking at a lot of aggregate-level data in terms of how things are behaving, and always the challenge of any data science project is creating those feature extractions, and so that's where the process we're going through as the analytics team is to start extracting those things and adding them to our central data source. That's one of the areas also where having very integrated analytics and ETL has been helpful as we're just feeding that information back in to everyone. So once we figure out, oh hey, this is how you differentiate small businesses from homes, because we do see a couple of small businesses using our product, that goes back into the data and now everyone's consuming it. Each of those common features, it's a slow process to create them, but it's also increases the value every time you add one to the central group. >> One last question-- >> It's an interesting way to think of the wifi service and the connected devices an integration challenge, as opposed to just an appliance that kind of works like an old POTS line, which it isn't, clearly at all. (all laugh) With 20 firmware updates a year (laughs). >> Yeah, there's another interesting point, that we were just having the discussion offline, it's that it's a start-up. They obviously don't have the resources or the app, but have a large IT department to set up these systems. So, as Katharine mentioned, one person team initially when they started, and to be able to integrate, who knows which system is going to be next. Maybe they experiment with one cloud service, it perhaps scales to their liking or not, and then they quickly change and go to another one. You cannot change the integration underneath that. You got to be able to adjust to that. So that flexibility, and the other thing is, what they've done with having their business become self-sufficient is another very fascinating thing. It's like, give them the power. Why should IT or that small team become the bottom line? Don't come to me, I'll just empower you with the right tool set and the patterns and then from there, you change and put in your business logic and be productive immediately. >> Let me drill down on that, 'cause my understanding, at least in the old world was that DTL was kind of brittle, and if you're constantly ... Part of actually, the genesis of Hadoop, certainly at Yahoo was, we're going to bring all the data we might ever possibly need into the repository so we don't have to keep re-writing the pipeline. And it sounds like you have the capability to evolve the pipeline rather quickly as you want to bring more data into this sort of central resource. Am I getting that about right? >> Yeah, it's a little bit of both. We do have a central, I think, down data's the fancy term for that, so we're bringing everything into S3, jumping it into those raw JSONs, you know, whatever nested format it comes into, so whatever makes it so that extraction is easy. Then there's also, as part of ETL, there's that last mile which is a lot of business logic, and that's where you run into teams starting to diverge very quickly if you don't have a way for them to give feedback into the process. We've really focused on empowering business users to be self-service, in terms of answering their own questions, and that's freed up our in list to add more value back into the greater group as well as answer harder questions, that both beget more questions, but also feeds back insights into that data source because they have access to their piece of that last business logic. By changing the way that one JSON field maps or combining two, they've suddenly created an entirely new variable that's accessible to everyone. So it's sort of last-leg business logic versus the full transport layer. We have a whole platform that's designed to transport everything and be much more robust to changes. >> Alright, so let me make sure I understand this, it sounds like the less-trained or more self-sufficient, they go after the central repository and then the more highly-trained and scarcer resource, they are responsible for owning one or more of the feeds and that they enrich that or make that more flexible and general-purpose so that those who are more self-sufficient can get at it in the center. >> Yeah, and also you're able to make use of the business. So we have sort of a hybrid model with our analysts that are really closely embedded into the teams, and so they have all that context that you need that if you're relying on, say, a central IT team, that you have to go back and forth of like, why are you doing this, what does this mean? They're able to do all that in logic. And then the goal of our platform team is really to focus on building technologies that complement what we have with SnapLogic or others that are accustomed to our data systems that enable that same sort of level of self-service for creating specific definitions, or are able to do it intelligently based on agreed upon patterns of extraction. >> George: Okay. >> Heavy science. Alright, well unfortunately we are out of time. I really appreciate the story, I love the site, I'll have to check out the boxes, because I know I have a bunch of dead spots in my house. (all laugh) But Ravi, I want to give you the last word, really about how is it working with a small start-up doing some cool, innovative stuff, but it's not your Adobes, it's not a lot of the huge enterprise clients that you have. What have you taken, why does that add value to SnapLogic to work with kind of a cool, fun, small start-up? >> Yeah, so the enterprise is always a retrofit job. You have to sort of go back to the SAPs and the Oracle databases and make sure that we are able to connect the legacy with a new cloud application. Whereas with a start-up, it's all new stuff. But their volumes are constantly changing, they probably have spikes, they have burst volumes, they're thinking about this differently, enabling everyone else, quickly changing and adopting newer technologies. So we have to be able to adjust to that agility along with them. So we're very excited as sort of partnering with them and going along with them on this journey. And as they start looking at other things, the machine learning and the AI and the IRT space, we're very excited to have that partnership and learn from them and evolve our platform as well. >> Clearly. You're smiling ear-to-ear, Katharine's excited, you're solving problems. So thanks again for taking a few minutes and good luck with your talk tomorrow. Alright, I'm Jeff Frick, he's George Gilbert, you're watching theCUBE from Big Data SV. We'll be back after this short break. Thanks for watching. (light techno music)

>> 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.

>> Narrator: You are Cube Alumni. Live from Silicon Valley, it's the Cube. Covering Google Cloud Next 17. >> Welcome back to the Cube's coverage of Google Next 2017. Having a lot of conversations as to how enterprises are really grappling with cloud. You know, move from on premises to public cloud, multi-cloud, hybrid-cloud, all those pieces in between. Happy to welcome to the program a first time guest, Paul Scott-Murphy who's the vice president of product management at WANdisco, thanks so much for joining us. >> Yeah, thanks very much, it's great to be here and join your program. >> Alright, so you know, Paul, I think a lot of our audience probably is familiar with WANdisco, we've had many of your executives on, really dug into your environment for the last few years, usually see you guys a lot of not only the big data shows, we've got Strata coming up next week, last time I did an interview with you guys was at AWS re:Invent. So you know, WAN, replication, data, all those things put together, you've got a big bucket of big data in cloud. Tell us a little bit about kind of your background, your role at the company. >> Okay. So I've been at WANdisco now for about two and a half years. I previously worked for TIBCO Software for a decade. Working out of Asia-Pacific, held the CTO role there for APJ. And joined WANdisco two and half years ago, just as we were entering into the big data market with our replication capabilities. I now run product management for the company and work out of our headquarters here in the Bay area. >> Stu Miniman: Great. And connect with us you know, what you guys are doing at Google, what's the conversations you're having with customers that are attending. >> Yeah, so Google is definitely one of the key strategic partners for WANdisco, obviously particularly in the Cloud space for us. We're hosting a booth fair for the conference and using that as an opportunity to speak to other vendors and the customers that we have attending the Google conference. Particularly around what we're doing for replication between on premises and cloud environments, and how we support Google Cloud. Dataproc, and Google Cloud Storage as well. >> Can you help unpack for us a little bit, where are your customers, give us a tip of the customers, you know they're saying hey, I want to start using this cloud stuff, how are they figuring out what applications stay on premises, what goes to the public cloud, and that data piece is a challenging thing, moving data is not easy, there's a whole data gravity piece that fits into it, maybe you can help walk us through some of the scenarios. >> Yeah, as we're progressing the technology, we're certainly finding a broader and broader range of customers getting interested in what they can do around data replication. The sorts of organizations that we deal with primarily are those who are looking to leverage both on premises and cloud infrastructure. All those who are moving from a situation where they've been toying with these environments and moving into production-ready scenarios where the demands or enterprise level SLAs or availability, or the needs around disaster recovery, backup and migration use cases become a lot more dominant for them. The organizations that we work with typically they are larger organizations, we deal a lot with retail, with financial services, telecommunications, with research institutions as well. All of whom have larger needs around taking advantage of cloud infrastructure. Of course they all share the same challenge of the availability of their data, where it's sourced from, isn't always necessarily in the cloud, taking advantage of cloud infrastructure then requires them to think about how they make their information available both to their on premises systems and to the cloud environment where they can run perhaps larger analytic workloads against it, or use the cloud services that they would otherwise not have access to. >> One of the challenges we've seen is when we've got kind of that hybrid or multi-cloud environment, you know, manages my data, kind of the holes, you know, orchestrating pieces and getting my arms around how I take care of it and leverage it can be challenging. Is that something you guys help with or are there other partners that get involved, how are customers helping to sort out and mature these environments? >> Yeah it's a big question of course, you've touched on the management of data as a whole and what they means, and how organizations handle that. WANdisco's role in supporting organizations with those challenges is in ensuring that when they need to take advantage of more than one environment or when they need their data to be available in more than one place. They can do that seamlessly and easily. What we aport to do and what we encourage our customers to do with our technology is rather than keeping one copy of data on premises and using it solely there, or copying your data to another location in order that you can act upon it there, we treat those environments as the same and say well, have the best of both worlds. Have your data available in each location, let your applications use it at the local speed and do that without regard to the need for retaining a workflow by which you exchange data between environments. WANdisco's technology can take care of all of that, and to do so it has to do some very smart things under the covers, around consistency and making it work across wide-area networks. Makes it particularly suited to cloud environments where we can leverage those underlying capabilities in conjunction with the scale of the cloud which is a native home for data at scale. >> Can you give us some, you know, where do you see customers kind of in this maturation, Dan Green made a statement that today 5% of the data is in the public cloud, so what are some of those barriers that are stopping people from getting more data in the Cloud, is it something that we will just see a massive adoption of data in the cloud, or what's your guys viewpoint as to where data's going to live, how that movement is happening. >> Yeah, I think longer term the economic advantages of using cloud environments are undeniable. The cost advantages of hosting information in the cloud and the benefits that come from the scalability of those environments is certainly far surpassing the capabilities that organizations can invest in themselves through their own data centers. So that natural migration of data to the cloud is a common theme that we see across all sorts of organizations. But as many people say, data has gravity, and if the majority of your application information resides today in your own environments or in environments outside of the cloud, whether that's internet connected devices, or in points of ingest that reside outside of cloud environments, there's a natural tendency for data to remain in place where they're either ingested or created. What you need to do to better take advantage of cloud environments then is the ability to easily access that data from cloud infrastructure. So the sorts of organizations that are looking to that are those with either burgeoning problems around consuming data at multiple points. They might operate environments that span multiple contents. They might have jurisdictional restrictions around where their data can reside but need to control its flow between separate environments as well. So WANdisco can certainly help with all of those problems, the underlying replication technology that we bring to bear is very well suited to it. But we are a part of the overall solution. We're not the full answer to everything. We certainly deal very well with replication and we believe we cover that very well. >> I'm curious when you talk about kind of the dispersion of data and where it's being created, of course edge-use cases for things like IOT, are quite a hot topic at that point. Is that something you guys are touching on yet, gets involved in discussions, you know, where does that sit? >> Yeah, definitely. The interesting thing about WANdisco's approach to data replication is that we base it on this foundation of consistency. And using a mathematically proven approach to distributed consensus to guarantee that changes made in one environment are represented in others equally, regardless of where those changes occur. Now when you apply that to batch based data storage or streaming environments, or other forms of ingest is relatively irrelevant as long as you have that same underlying capability to guarantee consistency regardless of where changes occur. If you're talking about high IT environments where you naturally have infrastructure sitting outside of the cloud, and this is the type of infrastructure that needs to reside out of the cloud, right, your edge points where data are captured, where your consuming information are generating it from devices perhaps from an automotive vehicle or from an embedded device, some sort of sensor array, whatever that happens to be, these are the types of environments where it means you're generating data outside of the cloud. So if you're looking to use that inside of the cloud itself, you need some way of moving data around, and you need to do that with some degree of consistency between those environments to make sure you're not just challenged with extra copies of information. >> The other really interesting topic around data that's being discussed at the Google Cloud event is artificial intelligence, machine learning, I'm curious, are your customers involved in that, where do you see that kind of on the radar today? >> Yeah, it's obviously an absolutely critical part of where the IT industry in general is going, and the type of solution that's fed off data. These systems are better as your data set grows. The more information you have, the better they work, and the more capable they become. It's certainly an aspect of how well machine learning technique and artificial intelligence approaches have been adopted in the industry, and the rapid rate of change in that side of IT is driving a lot of the demand for increasing access to data sets. We see some of our customers using that for really interesting things. You might've seen some of the recent news around our involvement in a research project led through the University of Sheffield, looking to use data sets captured from a variety of research institutions and medical environments to solve the problem of identifying and responding to dementia. And it's a great outcome from that type of environment. Through which machine learning techniques are being applied across data sets. What you find though is that because there's a large set of institutions sharing access to data, no single data set is sufficient to support those outcomes, regardless of what intelligence you can place against the machine learning models that you build up. So by enabling the ability to bring those data sets together, have them available in a single location, being the cloud, where larger models can be assessed against the data sets means much better outcomes for those types of environments. >> Okay. Paul, in your role of product management, we've been through some of the hot buzz terms out there, how do you help the company identify those trends, focused on the ones that are important to your customers and the kind of feedback loops that you get from them. >> I guess a lot of work in the end is how we do it but we need to listen to customers directly of course, understand what they're looking to do with their information systems. What they're aiming for. Their goals at a business level, what type of value that they want to get out of their data, and how they're approaching that. That's really critical. We also need to look to the industry in general. We're obviously in a very rapidly changing environment where technologies, the organizations that build IT systems, are increasingly adopting new approaches and building systems that simply weren't available days ago. You look at the announcements from Google of late around their video intelligence APIs as a service, their image APIs as well, all new capabilities that organizations today now have access to. So bringing those things together, understanding where the general IT trends are, how that applies to our customers, and what WANdisco can do with the unique value that we bring is really key to the product management role. >> Alright, and Paul, you've been at the show, curious, any cool things you saw, interesting customer conversations that may want to give our audience a flavor of what's going on, why 10 thousand people are excited to be at the event. >> Yeah well it is a very exciting event, just the scale of these types of events run by Google and similar organizations is something in itself to behold. We're really excited to be a part of that. The things that are really interesting for me out of the show tend to be where we see customers or opportunities coming to us, identifying challenges that they can't address without the type of technology that we bring to bear. Those tend to be areas where either they're looking to do migration from on premises systems into the cloud which is obviously very strong interest for Google themselves, they need to bring customers in to take better advantage of the services that they have. WANdisco can play a strong role in that. We're seeing a lot of interesting things around the edge too, so all of the ways in which data can be used are always exciting and interesting to see. The combination of technologies like artificial intelligence, like virtual reality, the type of work that WANdisco does also, is certainly going to bring forward I think a new wave of applications and systems that we just hadn't considered even a few years ago. >> Yeah. Lots of really interesting things. There's personal assistants at home and personal assistants that are listening. Okay Google, subscribe to SiliconANGLE on Youtube. We'll be back with lots more coverage here from the Cube, talking about Google Next 2017. 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>> Announcer: Live from Silicon Valley, it's theCUBE, covering Mobile World Congress 2017. Brought to you by Intel. >> Okay, welcome back, everyone. We're live here in Palo Alto, California for SiliconANGLE Media's theCUBE special two-day coverage of Mobile World Congress in Barcelona, Spain. As people starting to get ready to take that nap to go out all night in Barcelona after they've had their tapas and wine we're here in California breaking it all down. Two days of coverage, this is end of day two in Spain. We're in the middle of it here, and breaking down the analysis, covering all the news, commentary, identifying the trends and talking to the folks here in the Bay Area that can add value to the conversation, and our next guest is Bob Stefanski, who's the managing director of eLab, located in Palo Alto, a venture capitalist making investments and really a key player bridging Silicon Valley with Michigan Motor City here bringing the two worlds together as the autonomous vehicles and the automotive industry's under massive disruption and change, and the car companies know about it and they're not afraid of it. Ford's here, GM's here, they're all here, and now we have Bob Stefanski here in theCUBE. Bob, good to see ya, thanks for coming in. >> John, thanks for having me on. It's good to be here. >> I love this story, and I think this is not really well documented, but this is the beginning of what's been happening for a while, kind of as an outpost to Michigan and Motor City, you have some satellite offices in Palo Alto or Silicon Valley. They're close to Stanford, close to Cal, close to a lot of the research, but now it's a change where you're starting to see Ford, GM, all the car companies, BMW, big venture fund as well, all here in Silicon Valley because the software defined blank is everything, so software-defined radios in 5G, big story at Mobile World Congress, software-defined networks, the world is software-driven, so they're here. You're bridging the investments, trying to identify the key trends. >> Bob: You bet. >> To help identify this new game-changing technology that's going to bring a whole new world together, and certainly Intel and others are changing the networks, creating an end-to-end architecture digitally to bring autonomous vehicles, media entertainment, smart cities, the smart home, and we're seeing Alexa, Google's got their device, and you're seeing smart cities. What's the big bridge being built around? I mean, obviously, the cars themselves are changing. What is this bridge between Silicon Valley and Michigan Motor City? Obviously, that's a big part of Uber and whatnot. >> Absolutely, John, you know, I grew up in Michigan, I grew up in the days before there was a single chip, I think, in cars. I worked for General Motors when I was a summer intern in the early '80s in the engineering group there. There was a very distinct automotive culture. I then fast forward 20 years, and I'm in Silicon Valley. I've spent the majority of my career here in Silicon Valley doing Silicon Valley things, so software, enterprise software was where I spent most of my career with TIBCO software. We are now bridging these two things. We're bridging, the automotive industry is, I think we all know, anyone who's paying attention, the car now has a lot of chips in it, and it's about to have a lot more, the car is becoming a data center on wheels. It's becoming another mobile device, a very big mobile device, and the really neat thing is with, we're the only venture fund with offices and partners located in both places. We have fairly deep networks and connections into the whole Michigan ecosystem back there in automotive, and of course, we're out here in Silicon Valley as well. It's been fascinating to see after spending, after having that early childhood experience, young adult experience as I was growing up in the auto industry, and really kind of the heyday of the auto industry, maybe the beginning of the decline in the '70s and early '80s, and then having sort of spent the career working on the latest, greatest, newest technologies as they've come along out here in Silicon Valley. This is a fascinating time to see these two now finally merging together with autonomous vehicles. >> One of the things that we're seeing in Intel, obviously the bellwether, and they always have the long game going and make the big bets, and autonomous vehicles and virtual reality is that showcase, but what I find interesting and I want to get your thoughts on and reaction to is that I shared on my Facebook feed a post by autoblog.com that says, "Race for autonomous cars is over in Silicon Valley." And they were kind of pointing to the obvious things that people are seeing today, which is myopic and narrow in my opinion, but obviously Apple kind of tapped out of building a car, and I think a lot of people thought, "Oh, Apple should build a car. "They built a watch, why not build a car?" Obviously, they forgot about Teslas here, so I'm not sure what they're thinking, but I think they missed the point that it's bigger than the actual car. Could you share some color commentary around the mindset of Detroit? Because we're seeing that certainly Ford's not lookin' the other way, they have their finger on the pulse. Others do as well. What is the general mindset for the folks in both ecosystems and how are they working together right now? >> Sure, that's a great question, John. And you said it right at the outset, look, all the autos are here, and they're here in our backyard in Palo Alto. They've really sort of migrated here over the last five, seven years probably. GM is here, Ford is here in a big way, BMW's here, Mercedes' here. So they all obviously recognize that the car's becoming all about technology, and they need to be, if they're going to be a key part of that in the future, they need to be out here, and they need to be understanding that, on the other hand, making cars is hard. Making cars is not a simple thing, and this is where 70% of auto research in the U.S. is still happening in Michigan in the Detroit area. Michigan has a very high density of automotive engineers, and integration engineers and integrating IT with the autos and so forth. There's a lot of talent there, there's a lot of experience there. I think, you know, frankly probably the biggest and most interesting thing in this bridge is going to be to watch the cultures either integrate or not, and there's a lot of talk about who wins and the autos can't move fast enough, and that may be the case, but we'll find out. I'm not so sure. They know how to compete and there's a lot of smart people. >> There's no way that Detroit's going away. >> Bob: Not at all. >> My view is they're very solid, and I think they got good self-awareness, and I think if you look at the signals, I would say that I'm pretty confident it's just a matter of how they get reconfigured in this new value-creation model around 5G and whatnot. But I want to get your thoughts on another point, which is if you look at what the iPhone did, that created a new class of app developer and that, I would call them, on one hand artisan developers, people who are composing much more design-centric, obviously, and then, you still had the hardcore developers, and that was lower in the stack, but also other harder problems. But when you talk about automotive, there are some serious technology challenges that require, I won't say old-school engineering, but really hardcore engineering. You're talking about wireless, which is a physics issue, you have all kinds of policy challenges, but really hardcore engineering and software development. I'm not discounting what the app guys are doing, but certainly there will be plenty of apps like all that more the finishing touches in, say, cars for instance. What are some of those technologies because that's really where you need to see the classic double-E, computer science, physics gurus, the real PhD kind of guys. What's your thoughts and what trends do you see in that hardcore area? >> Absolutely, you know, I mean, look, we all know that cars are no longer about just axles and engines, and those hard things. But I think when we make this transition to highly automated, to fully autonomous vehicles, the technologies that are driving that, the fundamental technologies and the really hard stuff are around sensors, right. We're constantly developing newer, faster, better, further range, more precise sensors, so we're talking about Lidar, we're talking about of course, Mobileye and what's happening with the camera and vision processing. We're talking about even radar, a 1940s technology that actually is changing very fast. There's a lot of interesting things happening. >> AI's an old technology coming back now and getting rebooted with cloud computing and whatnot. >> Yeah, absolutely, and then, connecting all that to the cloud, right. I think the hardest, and I think we talked about this before, probably still the single hardest piece and the point of this fear on this is artificial intelligence at the end of the day. It's the same stuff that's driving virtual reality, it's the same stuff that's driving a lot of different things right now, but it's also true in self-driving cars. These things, when you make a car, first of all, it's got to be safe. It has got to be safe. The Department of Transportation, the government regulatory interest is in safety. To make a car safe, they have to be tested, tested, tested, tested, what's that about? Well, when autonomous takes over, it's no long John Furrier driving that car, it's the AI driving the car, right? How do you make it AI smart? >> The crash test dummy's inside AI. >> Right, this is fundamental deep learning. This is fundamental deep learning that the guys at Google know as much as anybody in the world and Facebook and all, you know, that we all know about the arms race in artificial intelligence, but that's at the core of what's happening in self-driving vehicles, and most of that talent, the talent is spread out, it's all over the world, but there's a lot of it out here. And they know they need to have those engineers here. >> What's interesting about your background, you mentioned when we started this segment, you have an enterprise software background in Silicon Valley and you've been very successful, it's interesting, we were talking yesterday and we kind of validated this morning on our opening segment around Mobile World Congress, it's a two-show game right now. It's kind of a bipolar show. You got devices, the new phones, the glam and the sizzle, Samsung and so on, so forth, LG. >> Bob: Can't wait. >> And then you got the TelCo show, which is, TelCo's trying to figure things out, but what's interesting is what we noticed is that there's really a trend between enterprise computing concepts, network data center with consumer clash, so there's a direct collision course between the TelCos which serve as consumers, but the infrastructure challenges are all enterprise. >> Bob: Right, right. >> And the number one thing that's key there is integration and ecosystems. So, you kind of have the right background for this, so we want to get your thoughts on ecosystem integration concepts where a lot of boats in the harbor, so rising tide will float all boats, we see that as a trend, but also integrating. You mentioned the testing, so it's not one company's going to do all this. >> It's not one company that's going to do all this, and in fact, it's going to one of the more complex integrations we've ever undertaken because we're going to have to have those automotive engineers, we're going to have to have those, the software developers, we're going to have to have the AI guys, we're going to have to have the sensor guys, and it's all going to the cloud ultimately. And don't forget GPS, you got GPS. You got a lot. >> Connectivity challenges. Mobility. >> Connectivity challenges, and of course, 5G when 5G comes down the line is going to be a critical part of this as well. You're also going to have smart cities, you're going to have infrastructure embedded in the environment, and in particular, the highly dense areas is where it'll happen first. It's not going to, rural America and so forth, they're going to be probably driving their cars without the embedded sensor for a while, but there are a lot of different components to integrate. >> We had a CTO on earlier before, Val Bercovici, he was talking about the cloud native architecture really plays well in this market because it's not so much about the one car, it's about the one cars in relations to thousands of other cars that are self-driving. It's a multi-touch data equation. Alright, Bob, final question I want to get to you is what are you investing in? What are some of the things that you're looking at? Can you share? I know some of the stuff is pretty stealthy on your end, 'cause it's pretty high end, but can you share any, show a little leg on investments you've made? >> You bet, you bet. Yeah, John, we're, some of the, probably the coolest stuff I can't talk about right now, you're right. Hint hint, it's in some of the things I've already talked about. We're certainly in artificial intelligence. We have a portfolio company in that. We're looking at others. In better sensors, some of the sensor areas I talked about, we are in the process of looking at companies. We have investments in the connected space, not autonomous, but connected space, which is also going to be a very big and important part of this. Company called Aperia right up here that is, at the end of the day, they're tire inflation, but it's all about data. They do automated tire inflation, connected, they'll be connecting every fleet in America. And so we're-- >> It's those boring little efficiency areas that really yield a lot of cash. We just talked about a guest about waste optimize, waste disposal industries. >> Absolutely. >> Little things that are luring billion dollar innovations. >> Little things, very big problems, right, and it's where you can marry things like tire inflation on commercial fleets with data, with lots of data that we never had before. And then apply artificial intelligence to that to learn what's happening and map an entire fleet or multiple fleets nationwide, worldwide, collect all that data and start to correlate and understand what. Those are the problems that are, where a lot of value can be added actually with these technologies. >> It's super interesting, and I think you got a great opportunity, congratulations. Great to see the bridge between Silicon Valley and Michigan Motor City, and I think that's anecdotally means automotive, but there's probably other bridges your connecting, too. Bob, thanks for coming in and sharing. Final question for you while we got you, got a little bit more time. What premises would you, are you betting on? I mean, everyone has a premise, and you mentioned before you came on-camera that one of your premises is that automotive won't miss mobility. What other premises are you investing, what thesises are you building around? >> Well, look, for the, are you talking about autonomous vehicles or much--? >> For the bridge fund and how you're looking at the future of autonomous driving in the connected ecosystem, what are the premise, what's on the premise? >> The premise there is that we're in for what I think is going to be the biggest change in the biggest thing to happen in transportation ever, but it's not just transportation, so we're looking at areas that are not autonomous per se, but that are going to be fundamentally impacted, so services. We're talking about things like insurance, we're talking about all the shared services that are going to come out of this. Medicine is going to probably change, and there's some interesting plays there. And so all of this sort of periphery that is going to be disrupted, we're trying to look five years, 10 years ahead and look at how life is going to change, people's individual experiences are going to change, and how new services, in particular shared services, are going to be enabled by autonomy. >> Bob Stefanski here inside theCUBE, breaking down his commentary and direction of his investments bridging Silicon Valley with Michigan Motor City, or really looking at the autonomous future of vehicles and transportation. This is theCUBE, I'm John Furrier. We'll be back with more coverage and analysis of Mobile World Congress 2017 after this short break. (upbeat electronic music)