 Live from San Jose, it's theCUBE, presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. 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 be welcome back to theCUBE, an alumni, Satya Sangani, the co-founder and CEO of Elation. Welcome back. Thank you, it's wonderful to be here again. So you guys finished up your fiscal year end of December 2017. We're in the first quarter of 2018. You guys had some really strong results, really strong momentum. Tell us what's going on at Elation. How are you pulling this momentum through 2018? Well, I think we have had an enterprise-focused business historically because we solved 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 12X 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 Elation does again just to refresh where you are on the product side? You mentioned some new accounts, some new use cases. What's the update? Take a minute to talk about the update. Absolutely, so for, you know, you certainly know, John, but Elation's a data catalog. And a data catalog, essentially, you can think of it as Yelp or Amazon for data and information inside 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 in TIDA Fortune 500s where you have hundreds of thousands of databases, you have a situation where people just have too much signal, right? Or too much noise, not enough signal, right? And so what we do is we provide this Yelp for that information. You can come to Elation 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 love, 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, you know, where's it stored, you got to manage it. And then people are actually using data so they're realizing how hard it is. How much data do we have? So it's kind of like a trough of disillusionment, if you will. Now they got to get their hands on it. They got to put it to work. And they know that. So it's now becoming really hard in their mind. This is business people. 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, you don't have the right people, you really, it's hard to get it going. How do you guys solve that problem? How do you talk to customers? So we talk a lot about data literacy, right? I mean, 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 web logs 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, right? It's a way to go away. It's 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, right? 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 with. Now take me through how you guys play out in an engagement with a customer. It's 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, use cases? Where is it kind of coming together for you guys? Yeah, so we literally, we have a mantra, right? I mean, 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, 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 First Street in San Jose. If I'm going to go do that, I'm going to do that search, I'm going to find the thing that I'm looking for and then I'm going to figure 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, right? 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. What is that conversation like to really, as you say on your website, the first data catalog design for collaboration. How do you help these organizations as large like a Coca-Cola, understand where all the data are and enable the human resources to extract value. You said 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, right? 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, right? 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, right? 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 to these cheat 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. And this is interesting. A lot of people, you mentioned social networks, use data to optimize for something. In case of Facebook, 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. That's right. So you're saying. We're measuring how you can use data. And that's interesting because, I mean, I hear a lot of stories like, we bought a tool, we never used it. Where 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 you're 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? I mean, how are you guys treating that gamification in this way? It's interesting. I mean, what happens? I mean, do I become like, you know. 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 said, we've got page rank. 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 use 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 could 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 could be really useful for me if I have no clue as to what I'm looking at. And so how do you demystify it through the social connections? So the problem that you saw, 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, cataloging. And then as you get people using it, this is where you take the data literacy and go into operationalizing data. So I mean, this just seems to be the challenge. So if I'm a customer and I have a problem, the profile of your target customer or your customers are, people who need to expand and operationalize data, is that, how would you, how would you talk about it? Yeah, so it's really interesting. I mean, we talk about, one of our customers called us this sort of, the social network for nerds inside of an enterprise, right? And I think for me, that's a compliment, right? But what I took from that, and when I explained the business of elation, 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 IVs and then went to the rest of the top 20 schools and then ultimately to mom and dad and grandma and grandpa, right? 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 in 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. You know, everyone used to talk about who did it was this big thing and then there was a creation of data lakes and swampiness and all these things that sort of becoming more complex in an organization. And you know, 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 as an enterprise needs to be competitive and move really, really quickly. I guess what's the kind of the process? Yeah, how do you start and what do people do? Yeah. So it's interesting, I mean, 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, you know, 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, right? And those customers do get started and they start with hundreds of users. In some case, they start, you know, with, you know, thousands of users day one and they just go big bang, right? And interestingly enough, we can get those customers enabled in matters of weeks or months to go do that, right? 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, you know, we can accommodate either model or either approach. From our perspective, you just have to have the resources and the investment corresponding to what you're trying to do, right? If you're going to say, look, we're going to have, you know, $2 of budget, right? 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 behind it, you can do a lot of good. So you can really facilitate the whole bigger, go bigger, go home approach, as well as the let's start small, think fast approach. That's right. And we always actually ironically recommend the ladder. The 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 one metric I track. So year over year for our business, we basically grow in accounts by net-a-churn by 55%, right? Year over year. And that's actually up from the prior year. And so from my perspective. What does that mean? So what that means is the same customer gave us 55% more on the dollar than they did the prior year, right? 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 inside of my company. Every person knows everything there is to know. That's like never been said, right? 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 seems to be getting easier. But the tsunami of new data coming in, IoT data, whatever, and new regulatory issues 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? Because it sounds like as people get networked together, the natural outcome of that is obviously more usage as you track, but is there a community vibe that you're seeing? Is there an internal collaboration where they sit and have meetups, lunches? I mean, there's a social aspect of a human aspect. It's phenomenal. No, it's amazing. I mean, 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 suddenly, like the 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. Postalation, 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 knew that they had this report and they had this intelligence, right? So by just discovering people and who they are, you pick up on what people can know. So people are 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. 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 the mechanism why I wish they satisfied 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 Satya, the tagline, first data catalog designed for collaboration. Is there a customer that comes to mind to you as really one that articulates that point exactly, that really where Elation has come in and really kicked open the door in terms of facilitating collaboration? Oh, absolutely. I mean, I was literally this morning talking to one of our customers, Munich re-insurance, largest re-insurance customer or company in the world, right? 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, right? And all 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, you know, skiing, right? You know, are you kind of blue diamond or black diamond or you know, that's right. And they do ski in pairs. And what they end up ultimately doing is they say, 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. Yeah, I mean, process improvement at a whole another 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 elation? What are some of the things that you found that would tell a potential customer that they should be talking to you guys? But I think that they've got to throw out the old playbook, right? 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 DNA on this playbook was all about providing the right answer. Forget about that. Just allow people to ask the right questions, right? 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 poor career to go choose. Because data is all about sort of freedom and innovation and understanding and we're trying to push that along. Well, Satya, thanks so much for stopping by and sharing how you guys are helping organizations, enterprises unlock data curiosity. We appreciate your time. Appreciate the time too. Thank you. And thanks, John. Thank you. Thanks for co-hosting with me. For John Farrier, I'm Lisa Martin. You are 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.