>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to theCUBE, I'm Lisa Martin with John Furrier. We are covering our second day of our event Big Data SV. We've had some great conversations, John, yesterday, today as well. Really looking at Big Data, digital transformation, Big Data, plus data science, lots of opportunity. We're excited to welcome back to theCUBE an alumni, Satyen Sangani, the co-founder and CEO of Alation. Welcome back! >> Thank you, it's wonderful to be here again. >> So you guys finish up your fiscal year end of December 2017, where in the first quarter of 2018. You guys had some really strong results, really strong momentum. >> Yeah. >> Tell us what's going on at Alation, how are you pulling this momentum through 2018. >> Well, I think we have had an enterprise focused business historically, because we solve a very complicated problem for very big enterprises, and so, in the last quarter we added customers like American Express, PepsiCo, Roche. And with huge expansions from our existing customers, some of whom, over the course of a year, I think went 12 X from an initial base. And so, we found some just incredible momentum in Q4 and for us that was a phenomenal cap to a great year. >> What about the platform you guys are doing? Can you just take a minute to explain what Alation does again just to refresh where you are on the product side? You mentioned some new accounts, some new use cases. >> Yeah. >> What's the update? Take a minute, talk about the update. >> Absolutely, so, you certainly know, John, but Alation's a data catalog and a data catalog essentially, you can think of it as Yelp or Amazon for data and information side of the enterprise. So if you think about how many different databases there are, how many different reports there are, how many different BI tools there are, how many different APIs there are, how many different algorithms there are, it's pretty dizzying for the average analyst. It's pretty dizzying for the average CIO. It's pretty dizzying for the average chief data officer. And particularly, inside of Fortune 500s where you have hundreds of thousands of databases. You have a situation where people just have too much signal or too much noise, not enough signal. And so what we do is we provide this Yelp for that information. You can come to Alation as a catalog. You can do a search on revenue 2017. You'll get all of the reports, all of the dashboards, all of the tables, all of the people that you might need to be able to find. And that gives you a single place of reference, so you can understand what you've got and what can answer your questions. >> What's interesting is, first of all, I love data. We're data driven, we're geeks on data. But when I start talking to folks that are outside the geek community or nerd community, you say data and they go, "Oh," because they cringe and they say, "Facebook." They see that data issues there. GDPR, data nightmare, where's the store, you got to manage it. And then, people are actually using data, so they're realizing how hard (laughs) it is. >> Yeah >> How much data do we have? So it's kind of like a tropic disillusionment, if you will. Now they got to get their hands on it. They've got to put it to work. >> Yeah. >> And they know that So, it's now becoming really hard (laughs) in their mind. This is business people. >> Yeah. >> They have data everywhere. How do you guys talk to that customer? Because, if you don't have quality data, if you don't have data you can trust, if you don't have the right people, it's hard to get it going. >> Yeah. >> How do you guys solve that problem and how do you talk to customers? >> So we talk a lot about data literacy. There is a lot of data in this world and that data is just emblematic of all of the stuff that's going on in this world. There's lots of systems, there's lots of complexity and the data, basically, just is about that complexity. Whether it's weblogs, or sensors, or the like. And so, you can either run away from that data, and say, "Look, I'm going to not, "I'm going to bury my head in the sand. "I'm going to be a business. "I'm just going to forget about that data stuff." And that's certainly a way to go. >> John: Yeah. >> It's a way to go away. >> Not a good outlook. >> I was going to say, is that a way of going out of business? >> Or, you can basically train, it's a human resources problem fundamentally. You've got to train your people to understand how to use data, to become data literate. And that's what our software is all about. That's what we're all about as a company. And so, we have a pretty high bar for what we think we do as a business and we're this far into that. Which is, we think we're training people to use data better. How do you learn to think scientifically? How do you go use data to make better decisions? How do you build a data driven culture? Those are the sorts of problems that I'm excited to work on. >> Alright, now take me through how you guys play out in an engagement with the customer. So okay, that's cool, you guys can come in, we're getting data literate, we understand we need to use data. Where are you guys winning? Where are you guys seeing some visibility, both in terms of the traction of the usage of the product, the use cases? Where is it kind of coming together for you guys? >> Yeah, so we literally, we have a mantra. I think any early stage company basically wins because they can focus on doing a couple of things really well. And for us, we basically do three things. We allow people to find data. We allow people to understand the data that they find. And we allow them to trust the data that they see. And so if I have a question, the first place I start is, typically, Google. I'll go there and I'll try to find whatever it is that I'm looking for. Maybe I'm looking for a Mediterranean restaurant on 1st Street in San Jose. If I'm going to go do that, I'm going to do that search and I'm going to find the thing that I'm looking for, and then I'm going to figure out, out of the possible options, which one do I want to go to. And then I'll figure out whether or not the one that has seven ratings is the one that I trust more than the one that has two. Well, data is no different. You're going to have to find the data sets. And inside of companies, there could be 20 different reports and there could be 20 different people who have information, and so you're going to trust those people through having context and understanding. >> So, trust, people, collaboration. You mentioned some big brands that you guys added towards the end of calendar 2017. How do you facilitate these conversations with maybe the chief data officer. As we know, in large enterprises, there's still a lot of ownership over data silos. >> Satyen: Yep. >> What is that conversation like, as you say on your website, "The first data catalog designed for collaboration"? How do you help these organizations as large as Coca-Cola understand where all the data are and enable the human resources to extract values, and find it, understand it, and trust it? >> Yeah, so we have a very simple hypothesis, which is, look, people fundamentally have questions. They're fundamentally curious. So, what you need to do as a chief data officer, as a chief information officer, is really figure out how to unlock that curiosity. Start with the most popular data sets. Start with the most popular systems. Start with the business people who have the most curiosity and the most demand for information. And oh, by the way, we can measure that. Which is the magical thing that we do. So we can come in and say, "Look, "we look at the logs inside of your systems to know "which people are using which data sets, "which sources are most popular, which areas are hot." Just like a social network might do. And so, just like you can say, "Okay, these are the trending restaurants." We can say, "These are the trending data sets." And that curiosity allows people to know, what data should I document first? What data should I make available first? What data do I improve the data quality over first? What data do I govern first? And so, in a world where you've got tons of signal, tons of systems, it's totally dizzying to figure out where you should start. But what we do is, we go these chief data officers and say, "Look, we can give you a tool and a catalyst so "that you know where to go, "what questions to answer, who to serve first." And you can use that to expand to other groups in the company. >> And this is interesting, a lot of people you mentioned social networks, use data to optimize for something, and in the case of Facebook, they they use my data to target ads for me. You're using data to actually say, "This is how people are using the data." So you're using data for data. (laughs) >> That's right. >> So you're saying-- >> Satyen: We're measuring how you can use data. >> And that's interesting because, I hear a lot of stories like, we bought a tool, we never used it. >> Yep. >> Or people didn't like the UI, just kind of falls on the side. You're looking at it and saying, "Let's get it out there and let's see who's using the data." And then, are you doubling down? What happens? Do I get a little star, do I get a reputation point, am I being flagged to HR as a power user? How are you guys treating that gamification in this way? It's interesting, I mean, what happens? Do I become like-- >> Yeah, so it's funny because, when you think about search, how do you figure out that something's good? So what Google did is, they came along and they've said, "We've got PageRank." What we're going to do is we're going to say, "The pages that are the best pages are the ones "that people link to most often." Well, we can do the same thing for data. The data sources that are the most useful ones are the people that are used most often. Now on top of that, you can say, "We're going to have experts put ratings," which we do. And you can say people can contribute knowledge and reviews of how this data set can be used. And people can contribute queries and reports on top of those data sets. And all of that gives you this really rich graph, this rich social graph, so that now when I look at something it doesn't look like Greek. It looks like, "Oh, well I know Lisa used this data set, "and then John used it "and so at least it must answer some questions "that are really intelligent about the media business "or about the software business. "And so that can be really useful for me "if I have no clue as to what I'm looking at." >> So the problem that you-- >> It's on how you demystify it through the social connections. >> So the problem that you solve, if what I hear you correctly, is that you make it easy to get the data. So there's some ease of use piece of it, >> Yep. >> cataloging. And then as you get people using it, this is where you take the data literacy and go into operationalizing data. >> Satyen: That's right. >> So this seems to be the challenge. So, if I'm a customer and I have a problem, the profile of your target customer or who your customers are, people who need to expand and operationalize data, how would you talk about it? >> Yeah, so it's really interesting. We talk about, one of our customers called us, sort of, the social network for nerds inside of an enterprise. And I think for me that's a compliment. (John laughing) But what I took from that, and when I explained the business of Alation, we start with those individuals who are data literate. The data scientists, the data engineers, the data stewards, the chief data officer. But those people have the knowledge and the context to then explain data to other people inside of that same institution. So in the same way that Facebook started with Harvard, and then went to the rest of the Ivies, and then went to the rest of the top 20 schools, and then ultimately to mom, and dad, and grandma, and grandpa. We're doing the exact same thing with data. We start with the folks that are data literate, we expand from there to a broader audience of people that don't necessarily have data in their titles, but have curiosity and questions. >> I like that on the curiosity side. You spent some time up at Strata Data. I'm curious, what are some of the things you're hearing from customers, maybe partners? Everyone used to talk about Hadoop, it was this big thing. And then there was a creation of data lakes, and swampiness, and all these things that are sort of becoming more complex in an organization. And with the rise of myriad data sources, the velocity, the volume, how do you help an enterprise understand and be able to catalog data from so many different sources? Is it that same principle that you just talked about in terms of, let's start with the lowest hanging fruit, start making the impact there and then grow it as we can? Or is an enterprise needs to be competitive and move really, really quickly? I guess, what's the process? >> How do you start? >> Right. >> What do people do? >> Yes! >> So it's interesting, what we find is multiple ways of starting with multiple different types of customers. And so, we have some customers that say, "Look, we've got a big, we've got Teradata, "and we've got some Hadoop, "and we've got some stuff on Amazon, "and we want to connect it all." And those customers do get started, and they start with hundreds of users, in some case, they start with thousands of users day one, and they just go Big Bang. And interestingly enough, we can get those customers enabled in matters of weeks or months to go do that. We have other customers that say, "Look, we're going to start with a team of 10 people "and we're going to see how it grows from there." And, we can accommodate either model or either approach. From our prospective, you just have to have the resources and the investment corresponding to what you're trying to do. If you're going to say, "Look, we're going to have, two dollars of budget, and we're not going to have the human resources, and the stewardship resources behind it." It's going to be hard to do the Big Bang. But if you're going to put the appropriate resources up behind it, you can do a lot of good. >> So, you can really facilitate the whole go big or go home approach, as as well as the let's start small think fast approach. >> That's right, and we always, actually ironically, recommend the latter. >> Let's start small, think fast, yeah. >> Because everybody's got a bigger appetite than they do the ability to execute. And what's great about the tool, and what I tell our customers and our employees all day long is, there's only metric I track. So year over year, for our business, we basically grow in accounts by net of churn by 55%. Year over year, and that's actually up from the prior year. And so from my perspective-- >> And what does that mean? >> So what that means is, the same customer gave us 55 cents more on the dollar than they did the prior year. Now that's best in class for most software businesses that I've heard. But what matters to me is not so much that growth rate in and of itself. What it means to me is this, that nobody's come along and says, "I've mastered my data. "I understand all of the information side of my company. "Every person knows everything there is to know." That's never been said. So if we're solving a problem where customers are saying, "Look, we get, and we can find, and understand, "and trust data, and we can do that better last year "than we did this year, and we can do it even more "with more people," we're going to be successful. >> What I like about what you're doing is, you're bringing an element of operationalizing data for literacy and for usage. But you're really bringing this notion of a humanizing element to it. Where you see it in security, you see it in emerging ecosystems. Where there's a community of data people who know how hard it is and was, and it seems to be getting easier. But the tsunami of new data coming in, IOT data, whatever, and new regulators like GDPR. These are all more surface area problems. But there's a community coming together. How have you guys seen your product create community? Have you seen any data on that, 'cause it sounds like, as people get networked together, the natural outcome of that is possibly usage you attract. But is there a community vibe that you're seeing? Is there an internal collaboration where they sit, they're having meet ups, they're having lunches. There's a social aspect in a human aspect. >> No, it's humanal, no, it's amazing. So in really subtle but really, really powerful ways. So one thing that we do for every single data source or every single report that we document, we just put who are the top users of this particular thing. So really subtly, day one, you're like, "I want to go find a report. "I don't even know "where to go inside of this really mysterious system". Postulation, you're able to say, "Well, I don't know where to go, but at least I can go call up John or Lisa," and say, "Hey, what is it that we know about this particular thing?" And I didn't have to know them. I just had to know that they had this report and they had this intelligence. So by just discovering people in who they are, you pick up on what people can know. >> So people of the new Google results, so you mentioned Google PageRank, which is web pages and relevance. You're taking a much more people approach to relevance. >> Satyen: That's right. >> To the data itself. >> That's right, and that builds community in very, very clear ways, because people have curiosity. Other people are in the mechanism why in which they satisfy that curiosity. And so that community builds automatically. >> They pay it forward, they know who to ask help for. >> That's right. >> Interesting. >> That's right. >> Last question, Satyen. The tag line, first data catalog designed for collaboration, is there a customer that comes to mind to you as really one that articulates that point exactly? Where Alation has come in and really kicked open the door, in terms of facilitating collaboration. >> Oh, absolutely. I was literally, this morning talking to one of our customers, Munich Reinsurance, largest reinsurance customer or company in the world. Their chief data officer said, "Look, three years ago, "we started with 10 people working on data. "Today, we've got hundreds. "Our aspiration is to get to thousands." We have three things that we do. One is, we actually discover insights. It's actually the smallest part of what we do. The second thing that we do is, we enable people to use data. And the third thing that we do is, drive a data driven culture. And for us, it's all about scaling knowledge, to centers in China, to centers in North America, to centers in Australia. And they've been doing that at scale. And they go to each of their people and they say, "Are you a data black belt, are you a data novice?" It's kind of like skiing. Are you blue diamond or a black diamond. >> Always ski in pairs (laughs) >> That's right. >> And they do ski in pairs. And what they end up ultimately doing is saying, "Look, we're going to train all of our workforce to become better, so that in three, 10 years, we're recognized as one of the most innovative insurance companies in the world." Three years ago, that was not the case. >> Process improvement at a whole other level. My final question for you is, for the folks watching or the folks that are going to watch this video, that could be a potential customer of yours, what are they feeling? If I'm the customer, what smoke signals am I seeing that say, I need to call Alation? What are some of the things that you've found that would tell a potential customer that they should be talkin' to you guys? >> Look, I think that they've got to throw out the old playbook. And this was a point that was made by some folks at a conference that I was at earlier this week. But they basically were saying, "Look, the DLNA's PlayBook was all about providing the right answer." Forget about that. Just allow people to ask the right questions. And if you let people's curiosity guide them, people are industrious, and ambitious, and innovative enough to go figure out what they need to go do. But if you see this as a world of control, where I'm going to just figure out what people should know and tell them what they're going to go know. that's going to be a pretty, a poor career to go choose because data's all about, sort of, freedom and innovation and understanding. And we're trying to push that along. >> Satyen, thanks so much for stopping by >> Thank you. >> and sharing how you guys are helping organizations, enterprises unlock data curiosity. We appreciate your time. >> I appreciate the time too. >> Thank you. >> And thanks John! >> And thank you. >> Thanks for co-hosting with me. For John Furrier, I'm Lisa Martin, you're watching theCUBE live from our second day of coverage of our event Big Data SV. Stick around, we'll be right back with our next guest after a short break. 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>> Speaker: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its eco-system partners. (soothing electronic music) >> Good morning everybody, and welcome to Big Data SV. My name is Dave Vellante, and this is our 10th big data event, we started in New York City, we've done five now, and this'll be our fifth in Silicon Valley, we've done five in New York City. And we started SiliconANGLE and Wikibon started covering the Big Data space in 2010, we did our first Hadoop World, which was actually the second Hadoop World in New York City. In 2011, we put out the industry's first big data report, and it caught the industry by fire, it was the hot topic. The concept of Hadoop was profound in that the idea was to take five megabytes of code and bring it to a petabyte of data, metaphorically if you will. Because moving data around was so problematic, and that concept really took hold. We asked questions at the time. Who will be the Red Hat of big data? Is this going to be a winner-take-all market? Will this trend, this big data trend, solve the problems that decision support, and business intelligence couldn't solve? We're going to talk about that today, and throughout the week. We've just released Wikibon's big data market study, and big data market shares, and key findings, I'm here with Peter Burris, who heads up the Wikibon research organization, and George Gilbert, who leads our big data research, gentleman, welcome to theCUBE. >> Hi Dave. >> Good to see you guys. >> Good to be here. >> So, we have this open source marketplace, it's been plagued by complexity, competition, the cloud really changed things. Peter, you've been studying this for a while, you just dropped that awesome report on Wikibon.com, what did you find? What were the key trends that you saw in that report? Lay it out for us. >> Well the most important trend is that users are starting drive what happens in the big data universe. For many years, it was the individuals that were primarily responsible for creating a lot of these open source tools, and in the process of creating these open source tools, they solved each other's problems, as opposed to solving user problems. Users then found themselves, or in a process found themselves, building out clusters, deploying Hadoop, really focusing a lot on the infrastructure, which had its pluses and minuses. But what we see happening in the marketplace today really is an emphasis on bifurcation, in the big data space, where we're seeing a continuing focus on the infrastructure elements, and we'll spend a fair amount of time talking about what that means from a hardware database and related technology standpoint, and then, a much more focused, based on user and enterprise experience, of how to turn this into applications that actually have a consequential impact on the business on machine learning, AI, how the pipelines work, how the personnel work, integrating business change and the way business thinks about the role that data's going to play, and that bifurcation is going to carry forward over the next few years, as we gain more experience, and the entire industry is going to go through a process of restructuring itself to serve both sides of those needs. >> Great, so George, I want to ask you, so this is not a winner-take-all market, there is no Red Hat of big data, it certainly is not Cloudera, you know, Hortonworks kind of threw a wrench maybe into some of those plans, and tryin' to play the long game with the pure open source play. The return on investment of big data oftentimes turned out to be a reduction in the denominator, a reduction of investment, if you will. Lowering spending relative to traditional data warehouses. I ask you, you've been following this business for a long time, did the big data promise fail to live up to expectations? >> (laughs) There are multiple layers to that question, and to the answer. I would say that let's offload some data warehousing, processing, was the application that IT could attack to justify their experimentation with big data technologies, which remain notoriously complicated to provision and to manage on PRIM. But as Peter was saying, to get sort of more value out of this investment, we're sort of now bumping up against the complexity of all the data science pipelines, whereas before we were bumping up the complexity of administering these Hadoop clusters, so no we've got the data there, it's kind of hard to manage, but now we have to sort of learn how to apply that using much more sophisticated techniques. It's interesting that you say denominator shrinks, because the cost of operation as you move to the cloud, there are many more options, and they're managed much better, so that cost comes down as people have more cloud options. The last point I would make is I do think packaged applications, whether they're from the big guys, or a lot of vertically focused, or even semi-custom apps from folks like IBM, or Accenture, those are going to be what drives mainstream deployment, to reach hundreds of millions of users of this technology. >> So I would just observe that, in my view, this whole big data trend wasn't a failure, we observed early on that the folks that were going to make the most money in big data were the practitioners, not the vendors. So we made a correct call there. In many respects I look at this as, you know when you paint, you got to prep. I feel like that last eight years has been the preparatory phases, you know, scraping, and getting things ready, getting your house in order, and now Peter, we're setting up for the digital business era, and the digital business era is about data, it's about applying machine intelligence, it's certainly taking advantage of cloud economics. Do you buy that premise? That we're now in a position to actually, many companies anyway, or some companies, to affect digital transformation? >> Well, the whole concept of digital transformation starts with the idea of data, and our observation, here at theCUBE and Wikibon, ultimately, is that the difference between a business and a digital business is, a digital business uses data as an asset, and that has an enormous implications, on operations, how you engage customers, how you institutionalize work, what your relationships are with technology companies, et cetera. But that core concept of using your data differently, and creating value, is absolutely essential, to this notion of big data and all the various things that we're talking about, because big data is the process by which you create business value out of data, that's ultimately what we're trying to do with all this stuff. So, to George's point, if we think about where we've been, and where we're going, in many respects, fundamentally, we're just kind of following almost a normal adoption process. So if we go back 10 years, to Yahoo, Google, and some of the tech companies that initiated a lot of this motion, they had very specific types of problems that they wanted to solve, they had enormous volumes of data that they wanted to use to solve their problem, and they created technology to do so. Where we kind of get hung up is in the diffusion out of those relatively, certainly very challenging, and very rich set of problems, that Facebook, and Yahoo, and everybody else had, as they try to diffuse that technology into other industries, we got caught up in the bumps. We had more failures, and we didn't get the returns we wanted. So, now what's happening is a lot of that domain expertise is coming back in, we're startin' to say, "Now we know "how to solve the problem, we have an approach "to how we're going to solve the problem," and the technology's being snapped into place to solve problems, as opposed to technology being snapped into place, or solve business problems, as opposed to technology being snapped into place to solve the technology problems of big data. >> So we're here talking to Peter Burris and George Gilbert, two analysts at Wikibon, we're here at the Forager, in San Jose, it's at 420 1st Street, and theCUBE has a week long, 1/2 a week long anyway, set of activities going on, we've got an event going on this evening, I think it starts at six o'clock, so come by, we got a breakfast briefing tomorrow, where the Wikibon analysts are laying out their recent market studies, we just dropped two market studies on Wikibon, one is the overall market size, and the other goes into market shares. I want to touch on those briefly. We're lookin' at about a 35 billion dollar market, growing to 100 billion over the next 10 years. As we observed early on, open source software had an effect where, most businesses, most industries start off, software's a big component of it, because of open source, the software revenues were muted in this business, but they're really starting to pick up now, it was a heavily services-oriented business, and still is, about 40%, right? And then software comprises about 30%, and hardware about 29%. You guys see that changing over time, correct? >> Well yeah, and in many respects, again, this is following almost a natural evolution, that's made more interesting by the fact that these are very complex problems, and new types of business problems, but, certainly George has done a lot of research on this, ultimately, what every company that operates in this space should be thinking about is, how is the industry, in aggregate, going to get to 100, to 200 million users in the next decade. Where a user is not someone who's playing with the data, or looking at Tableau, but a user is fundamentally someone who's using an application, or making a decision that's informed by data, that's made possible by these tools. And that's not something that's going to happen at a very, very low, hardware, cluster, database, level. It's going to happen elsewhere, and one of the big trends we see is, that there's going to be a lot of new packaged applications entering into the marketplace, that consume these tools, and make them viable for business to actually use. >> Well George, in 2012, Mike Olson declared it the year of the big data applications, that never happened. The action in software has been around database and software infrastructure, but what do you see in terms of the evolution of that software business? >> Well, continuing on the theme of the bifurcation, it was interesting to hear Peter talk about how the infrastructure that the big tech companies, and internet companies developed as a byproduct of building their own services, that stuff didn't work for mainstream, it didn't even work for most of the sophisticated enterprises, on the infrastructure side, what we're doing now is, we're seeing a convergence, where we're putting those pieces together in a way where they fit easily together enough so admins, mere admin, mortal admins and developers can work with them-- >> With cloud being the ultimate convergence. >> Yes, yes. And I would also say then it's the applications will really take it mainstream. Because even when we fit the platform stuff together, it's not going to be enough to go mainstream. >> Okay, and we got to wrap, but I just wanted to touch on some of the market share stuff that you guys just produced, and we'll be presenting this data tomorrow morning, Thursday morning here at the Forager, it's 420 1st Street, in San Jose. Not surprisingly, IBM came out as the leader, because of the large services component, they got about 8% of that-- >> Well, they play in all parts. >> They play in all, but services they dominate. So IBM, Splunk, actually, who never used the term big data during their ascendancy, they didn't tie into that meme, but they are a big data company-- >> And an example of a packaged application company leading a-- >> Both-- >> Absolutely. >> Both, the platform and app. >> And apps, right. Dell, Oracle, and now if you look at this, that's the overall, if you look at the software top 10, Splunk comes out on top, then Oracle, then IBM, and we'll be getting into that tomorrow morning at the breakfast, Peter Burris, George Gilbert, thanks so much for setting this up, that's for watching, we've got wall-to-wall coverage here, this is day one, Big Data SV. From San Jose, you're watching theCUBE. We'll be right back. (soothing electronic music)