 Live from New York, it's the Cube. Covered, Big Data NYC 2015. Brought to you by Hortonworks, IBM, EMC, and Pivotal. Now your host, Dave Vellante and George Gilbert. Welcome back to the Big Apple, everybody. This is the Cube, where we go out to the events. We extract the signal from the noise. We're here at Strada and Hadoop World. This is our sixth year doing Hadoop World. This is our event, within the event, Big Data NYC. Amit Balia is here. He's the Chief Product Officer at Informatica. Welcome back to the Cube. Good to see you. Thank you. Good to be here in New York City. I wasn't at Informatica World last year, although I happened to be in Vegas doing a different show. But I did actually remember you now from the interviews. It was a great show. You guys had a lot of great customer stories. What's your story this week in Big Data? Well, it's an area that we've been investing in a lot the last couple of years. And so we're actually taking it a step further. So we just announced, by the way, a million dollar contest for our customers. That's what happens when you go private. You can do a lot of stuff now. You know, we're excited about Big Data. Of course, we've been the pioneers in terms of helping Big Data ecosystems get a lot more operationalized with our customers, right? Because, you know, helping Data move there for them to be able to scale it, operationalize it, do the stuff they really want to do. So we're actually coming on the core tails of a big launch for us in Big Data. And November we'll announce a brand new product. We basically have spent the last 12, 18 months, you know, kind of taking the current products to a whole new level. And we are giving our customers, as I said, a million dollars of products or services to get them what we call Big Data ready. Truly get the value out of Big Data. You know, I want to ask you, I mean, your perspective on this. So when disruptive technologies come out and it's conventional wisdom that the new stuff is going to replace the old stuff and the guys who have been around for a while are in all in big trouble. And, you know, that was probably true back in the mini computer days, you know, when the guys like Digital and Wang and all my colleagues on the East Coast just sort of ignored the microprocessor trend. It's not the same today. The larger companies, the established companies, they read the tea leaves better. They're able to, they've architecting products, since it's really a product question, that can adapt. So I wonder if you could take us back to when the early Doop days, and you heard it all, oh, Informatica screwed, and the same thing with Oracle and Teradata and everything else. Yes, you have challenges, you have headwinds, but you've evolved. You've evolved the product architecture, you listen to customers, and you're actually succeeding in, for a number of reasons. But just from a product standpoint, how challenging was that? How did you adapt? I don't know if you could take us back. Yeah, I think it's a little down the history lane, right? So one of the things that we realized early on is that, see, one thing that we do is we help our customers go through any kind of technology change, without really having to re-archinate everything at their end. We take that complexity within our product. So when Hadoop started in its early days, the use cases were still getting formalized, people could take the existing product and somehow jam stuff through it to solve the early use cases, because the early use cases were getting formed. But we know all the customers were trying to use Hadoop, or the large customers that we worked with them to understand what really are they trying to do. And that led us actually to step back and say, you know what, let's not try to retrofit something that exists in our product, because it's kind of like, do you want to remodel or build a new house? And we actually said, no, remodeling won't work. What we have works very well in the traditional workloads, data warehousing. We went back to the drawing board and started building something from scratch. And that was a big fork in the road for us, because typically large companies, legacy companies, try to continue what they have and hope everything conforms to it. And we took down the path that no, go build something brand new, which leverages the best of the past, what we had in our products, but build something from Hadoop. And from there we started with what we call a big data addition. And today we're taking it a step further in November, the launch I talked about, where we're going to unveil a big data management framework, a product in that, that not only covers classic integration, but taking it a step further because big data is a wall to talk about governance, security, because those are the big issues in big data. When I talk to customers and I've spent my last three days here talking to at least a dozen customers, they're done with, okay, I want to move data in, but I'm trying to solve something where security of that data is important, governance of that data is important, so I can solve a financial services use case or a healthcare use case. So we went ground up totally new and a completely foot end to end, you know, platform which then allows customers to continue down their innovation journey without having to worry about piecemeal products. It was a start-up play, essentially. It's basically incubated, actually, like a start-up. Incubated a small team and gave them almost like VC dollars to go at it. So you still have what all the upstarts wish they had, which was a big customer base. And you can take your customers step by step into the future. And when you talk about security and governance, it's essentially helping Hadoop become the new trusted platform that a data warehouse had, you know, took many years to become. What do you see them trying to do once Hadoop is that new trusted platform? Not replacing the data warehouse complimenting it. What's that journey look like after that point? Yeah, and I think you basically articulated it very well. So I'll give you some examples of customers and what they're trying to do. Take Western Union. It's a great customer of ours. And Western Union is not taking the classic data where I was saying it says I'm going to rip and replace and move to Hadoop. What they're trying to do with Hadoop is they do money transfers, right? But just imagine the amount of fraud detection we have to do with that. The scale of data that's coming in to do fraud detection. Wrong money from wrong person to wrong hands. And that's the kind of problem they're solving that is not a data warehousing problem. That's a data at scale problem. So that is a problem they're solving. And they went with Cloud Era and us and we, by the way, we were already there in the traditional data warehousing environment. And they had trust in us. And they said, look, you can help us bridge this gap. So that's the kind of problem they're solving there. Take another healthcare company. They're trying to solve the same thing in a way where they have a lot of customer data, client data. They also have clinical data coming from the doctor's devices. So much volume of data that is they want to put it in their Hadoop ecosystem to do analytics to make sure can they make their hospitals more operationally efficient. Can they make sure they can cut the doctor's moves from a patient to another, make it much more efficient. That kind of stuff is what I call data at scale problems. But productivity, fraud, you know, all kinds of improvement that is not necessarily data warehousing. That's where customers are going. And that's where they need the help from us because we understand those data problems and now we can do them at scale for them. Yeah, so customers, sort of enterprise customers' attitudes, they don't change just because it's a new technology. You mentioned governance, you mentioned security. The problems that you were talking about are the problems that they always have wanted to solve. The difference now is data. I wonder if you could talk about that change because it used to be data. Oh, I got so much data. It's a problem. I got to manage it. That's still the case, but the opportunity has overwhelmed that. I wonder if you could talk about that dynamic. So take a great example of how the world has changed. Talk about security. Security is a solved problem on the infrastructure side. Network security, endpoint security. We all know those products. I used to work at a security company before, know that very well. But as you just talked about, with the growth of data, especially in the world of Hadoop, now the problem is, geez, I have a lot of data and I'm collecting more of it, which means sensitive data is growing. And I want more and more people to access that for them to be able to do something worthwhile from it so the organization can benefit. But that creates a huge security risk at the data level. And that is a problem that none of the traditional security products can solve. So we basically went back to the drawing board and said, look, that's a problem that we need to solve for our customers for big data to be operational. So we as a data company brought a data security product to market, secured source, launched it at RSA, won the best new product award. And again, that's where customers are like, hey, if I have all my data in Hadoop, I better understand my exposure, my risk who's touching it. That's not a network security problem. That's not a mobile security problem. That's a data security problem. So another way of looking in the world of big data security changes, governance changes, right? So those are the kind of things that we are solving in a new way. One thing that we hear from chief data officers, what might be their biggest pain point is with the proliferation of platforms, there's proliferation of the data, like the copies itself. And so they, you know, again a part of making data trusted. If there's so many more copies, how can Informatica help sort of keep everything in sync so that they know it's still righteous? So that is one of the use cases of secured source. So what we do is that, look, you have a lot of data that's coming into, let's say in this case, I want all data to go into a data lake, whatever you want. You want to use reservoir, whatever people want. Now from there, I want people to access that to make test copies, dev copies, or even accessing it. So the tailored source allows you to have a single pane of glass to understand what kind of data is in the lake, who is accessing that data from the lake if that's the moniker you want to use. So it's kind of like taking data as an atomic level and adding context around it. So if somebody like me makes 10 test copies, that product will tell you right away. And then it'll also tell you the risk exposure you have. And if you allow them to do it, it also allow you to put any control like, hey, you know what, just mask it for me. I'm a tester, I want to take it just. So that's what, that's the problem with Informatica not being a security company, realized when we heard from customers and said, you know what, for this to get operation at scale, we need to bring data security to our customers and that's what we did. I want to ask you about life as a private company. We've had Michael Dell in the Cube a number of times and it was really interesting. Last time we were at Dell World to talk about life as a private company and he would talk, he of course sees this CEO chairman and talks about the changes. We were also talking about incubating, you know, start up. Is life as a private company, how is it different? Do you measure things differently? Do you spend your time differently? Is the culture somewhat changed? I wonder if you could talk about that a little bit. I'd give the example like this. Silicon Valley folks will understand, right? We were a billion dollar publicly traded company and I call us now, we're a billion dollar startup. And unlike the unicorns which are billion dollar by market cap, we are billion dollar by revenue. So we are some kind of a mega unicorn if you may call it. A deca-corn. Whatever. We should probably coin a new word. No. So life for us is we went private for the sake of growth. I mean, the Permira and CPBIB that took us private invested half of the value of the company as their own investment. One of the largest private equity investments ever in the world attack. And Salesforce and Microsoft also joined in as strategic investors. It's a pure growth story. We are looking to solve three things. We believe we stand at the cusp of amazing opportunity in the world of data, right? We've grown significantly. A, we can let our customers choose whatever business model they want to choose. Perpetual license, subscription. We can go through that change away from the wall strip, right? Makes it easy. Giving customer the choice. Second is, so much innovation in front of us. We can actually invest as you talked about, right? Picking the areas we want to invest for the long term and make those bets without having to worry about short term results. Sometimes, you know, it's kind of, we can be, we're not a Google. We're not an Apple, but we can take those risks because we believe the opportunity there is very big. And the third one is there is a huge opportunity for our employees in this, right? Because the value properties we can accelerate are obviously growth in terms of multiplying our equity. And, you know, we can all come out of this, you know, a much bigger company, a far more valuable company, and we're excited about it. Yeah, I mean, it's very important. I mean, a company in a transition, you know what to do. You know, you got to play some bets. And, but a lot of times Wall Street, you know this, George, won't let you make those bets. There's so much noise and then, you know, shareholder advocates so-called come in. And so, but the beauty of being a private company is you can essentially write your own narrative and focus on the things that you want to focus on. When you try to do that as a public company, a lot of times, nobody listens because there's an overwhelming amount of noise. Focus on the quarter. Yeah, and it's a focus on the quarter. I mean, although I tell the reps, they still have to close the quarter. But, you know, we don't have to worry about closing the quarter in an artificial way. As I said, let the customers take a choice of what they want to do. We have to bring great technologies to bear, solve customers' use cases, and the customers are good. And they have shown confidence in us. Well, do you, I mean, I don't know how much time you spent with Wall Street. I'm sure your executive spent a lot of time with Wall Street, but has there been a positive ripple effect in terms of just time spent on the business versus talking to investors? Well, I mean, look, I'm a product guy. I love building great products, solving customer use cases. For me, it allows me to kind of do a lot of good stuff, but you're right. I mean, it allows us to kind of take the long-term view that necessarily getting caught up in the short term. Because, you know, the long-term, if you're placing the right bets, has a much bigger multiplier effect. Yeah. Just in terms of products and placing long-term bets, and you talked about security, one of the things that we see is this convergence between getting the operational data and performing the analytics on it. The old model was, you know, we went through this pipeline and it was kind of batch-oriented. And now it's, the value is in squeezing that amount of time to as short as possible. Absolutely. What's, what are your thoughts on that? What are your directions? No, real-time. I mean, so think about this way. When we think of two big bets, big data and cloud. Those are the big areas where we, so we have four markets we think where we are investing. Big data, cloud, security, and master data management. In all of those markets, especially big data and cloud, it's the real-time, right? I want to bring in, if I, if I'm a customer, I want to understand where you are. What are you trying to do today? Can I put you an offer in front of you right now? And that's where, so we've always had real-time capabilities in our traditional product. And in fact, we are scaling those capabilities in the big data or cloud products because that's where the use case is a lot more real-time, whether it's giving you the next best offer or fraud, right? And think about, you want to make decisions real-time. So, without doubt, that's an area where we see a ton of investment and a ton of use cases, and that's where we are absolutely investing in the context of both big data and cloud. And obviously, security is applied because you, security is also, when you think of the world of data, when we solve real-time for those use cases, our security product definitely, derivatively benefits from that, right? Because it runs off the same platform and benefits from real-time understanding of the data. I mean, we're out of time, but I want to give you a chance for the last word, thinking from Informatica's perspective on sort of the bumper sticker of this year's Duke World. What's your takeaway? What's the bumper sticker say? Well, I mean, we've always, we've had the history and we continue on the history of being the company that is all things data for our customers. Whether it's big data, whether it's data in the cloud, whether it's still the data that sits in on-prem and legacy systems, whether it's data for security, whether it's data for mastering. We want to be that one place you go to be the data platform or the data fabric and allow our customers to solve their business problems while not worrying about underlying database technology, is application to take the best of breed, but what matters is the data. That's the livelihood of a company and that's what the problem is. All things data, I love it. Thanks very much for coming back to theCUBE. Well, thank you. Pleasure. All right, keep it right there, everybody. We'll be back with our next guest right after this word. This is theCUBE. We're live from the Big Apple, NYC, right back.