>> Narrator: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media, and its ecosystem partners. (electronica flourish) >> We're back at Big Data SV. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante. Praveen Kankariya is here. He's the CEO of a company called Impetus. Company's been around the Big Data space before Hadoop, even. Praveen, thanks for back in theCUBE, good to see you. >> Thank you, Dave. >> So, as I said in the open, you've seen a lot. You kind of really got into the Big Data space in 2007, seen it blow through the Hadoop, you know, sort of batch world into the real time world, seen the data management headwinds. From your perspective, you know, what kind of problems are you solving today in the Big Data world? >> So I can go into the details of what we are doing, but at a high level, we are helping companies converge to a singular, enterprise-wide data model. 'Cause I think that is a crisis in the Fortune 500 today, and there'll be have and have-nots. >> Dave: What do you mean a crisis? >> I routinely run into companies who do not have their data model stitched. So they know the same customer, they know me by five different handles, and they don't have it figured out, that I'm the same guy. So, that I think is a major problem. So I think the C-suite is, they would not like to hear this, but they are flying partially blind. >> I have a theory on this, but I want to hear yours-- >> Sure. >> Why is that such a big problem? >> So, the most efficient business in the world is a one-man business, because everything is flowing in the same brain. The moment you hire your first employee, you start having communication breakdowns. And now these companies have hundreds and thousands of employees. Hundreds of thousands of employees. There's a lot of breakdown. There are airlines that, when I'm upgraded to first class, are offering me an economy-plus seat when I go to check in. That's ... they're turning me off, and they're losing an opportunity to, real opportunity to upsell something else to me. So. >> Okay, well, so let's bring this into the world of digital transformation. Everybody talks about those buzzwords, so let's try to put some sort of meat on that bone. If you look at the top five companies by market cap, Amazon, Apple, Facebook, Google. I'm missing somebody. Anyway, they're big. 500 billion, 700 billion dollars. They're all sort of what we would call data-driven. What does that mean? Data is at the core of their enterprise. A lot of the companies you're talking about, human expertise is the core of their enterprise, and they've got data that's sort of in silos, surrounding it. >> Praveen: Yes, yes. >> Is that an accurate description? >> That's-- And how can you help close that gap? >> So they have data in silos, and even that data in silos is not being used at velocity, with velocity. That data is, you know, it's taking much longer for them to even clean up that data, get access to that data, derive insights from that data. >> Dave: Right. >> So there's a lot of sluggishness, overall. >> Dave: So how do you help? >> How do we help? Great question. We help in many different ways. So we actually, so my company provides solutions. So we have some, a few products of our own, and then we work with all kinds of product companies. But we're about solving a problem, so when the customers we engage with, we actually solve a problem, so that there's a business outcome before we walk out. That's the big difference. We're not here to just sell the next sexy platform, or this or that, you know. We're not just here to excite the developers. >> So, maybe you could give me some of your favorite examples of where you've helped some of your clients. >> So there's one fairly large company, it's a household name around the world. And we have helped them create a single source of truth using a Big Data infrastructure. This has about six and a half thousand feeds of data coming in, continuously. Some continuously, some every few minutes, every few hours, whatnot. But then all their data is stitched together, and it's got guardrails, there's full governance. So, and now this platform is available to every business unit, to run their own applications. There's a set of APIs who go in and develop their own applications. So shadow idea is being promoted in this environment. It's not being looked down upon. >> So it's not sitting in one box, presumably, it's distributed throughout the organization? >> It is distributed. And you know, there're are some, you know, as long as you stay within the governance structure, you can derive, you know, somebody wants a graph database, they can derive a graph database from this massive, fully-connected data set, which is an enterprise-wide data set. >> Don't you see as some of the challenges, as well as cultural, there are some industries that might say, or some executives that say, "Well, you know my industry, "healthcare is an example, really hasn't been disrupted. "We're maybe insulated from that." I feel as though that's somewhat risky thinking, and it's easy to maybe sit back say, "Well, I'm going to wait, see what happens." What are your thoughts on that? >> Look at the data. The week Jeff Bezos announced that he is tying up with JPMC and Warren Buffet, some of the largest healthcare companies, and I'm talking of Fortune 10 companies, they lost about 20% of their market cap that week. So, you don't have to listen to me. Listen to the markets. >> Well, that's true. We see what happens in grocery, see what happens in... We haven't really seen, as I say, the disruption in healthcare, financial services, but it's all data, and that changes the equation. So why, let's see, not why. How when, if you get to this, so it sounds like step one is to get that sort of single data model across the organization, but there's other steps. You got to figure out how to monetize the data, not necessarily by selling it, but how data contributes to the monetization of the company. You got to it accessible, you got to make it of high quality, you've got to get the right skill sets. So there's a lot to it, and more than just the technology. Maybe you could talk about that. >> So the way, I would like to preach, if I'm allowed to-- >> Dave: Please, it's theCUBE... (laughs) >> No, no, I mean, I don't mean here, but if any CEO was listening to me, what I would like to tell them is, just create a vision of your ultimate connected data model. And then start looking at how do you converge out of that vision. It may not happen in one day, one week, one year. It's going to take time, and you know, every business is in flight, so they have to operate continuously, but they have to keep gravitating. And the biggest casualty is going to be their customer relationship if they don't do this. Because most companies don't know their customers fully. I mean, that little example of the airline which was showing me, flashing an ad for economy seats, premium economy seats when I'm already in first class, they don't know me. Some part of that company doesn't know me. So they're not able to service me well. Here now they lost an opportunity to monetize, but I think from another perspective, they lost an opportunity to really offer me something which would've made my flight way more comfortable. >> Well. >> So. >> Then you wonder if that's the dynamic that you encountered, what's the speed to market, the agility of that organization? They're hampered by their ability to, whether it's roll out new apps, identify new data sources, create new products for the customers. Have you seen, what kind of impacts have you seen within your customers? You gave the example before, of that sort of single data model, the single version of the truth. What business impacts have been able to affect for your customers? >> So, there, I mean I can go on giving you anecdotes from my observations, my front row observations into these companies. >> Yeah, it'd be good to have some kind of proof points, right? Our audience would love to hear that. >> So, you know there's a company not too far from here. They've stitched every click stream, right to product usage data. To support data, to every marketing email opened. And they can tell who's buying, what happened, what is their support experience, who's upgrading, who's upgrading faster because they had a positive support experience, or not. So everything is tied. Any direction you want to look into your customer space, you can go and get visibility from every perspective you can think of. That's customer 360. We worked with a credit card company where they had a massive rules engine, which had been developed over generations to report fraud, to catch fraud, while a transaction's being processed. We actually, once they got all their data together, we could apply a massive machine learning engine. And we started learning from customers' own behavior, so we completely discarded the rules engine, and now we have a learning system which is flagging fraudulent transactions. So they managed to cut down their false positives tremendously, and in turn reduced inconvenience. It used to be embarrassing for me to give out a card and get it declined in front of a customer. >> So, as I said at the top, you've seen sort of the evolution of this whole Big Data meme before it was called Big Data. What are the things that may be exciting you? We seem to be entering a new era we call digital. There's a cognitive era, AI, machine intelligence. What do you see that's exciting, and real? >> So number one, so I like to divide this space into two parts, the whole space of data analytics. There's the data plumbing, which we call data management, and whatnot. I have to plumb all my data together. Only then I can feed this data into my AI models. Now I can do in my silos today, but for me to do at a global level for my entire corporation, I need it all stitched together. And then, of course, these models are very real. My son, my 22-year old son is using TensorFlow for some little startup that he's cooking. And it took him just a month to pick it up and start applying it. So why can't our large companies do so? And in turn, bring down the cost of services, cost of products, the velocity of delivering those things to us, and make life better. >> So, the barriers to technology deployment are getting lower. >> And this is all feasible, Dave, right now. >> Yeah. >> You know, I mean, this is all, this is a dream 10 years ago. If somebody had said, you know, for an old corporation to stitch all its data, "What're you talking about? "It's not going to happen." But now, this is possible, and it's feasible. It's not going to require, make a massive hole in their budgets. >> But don't you think it's also table stakes to compete in over, the next 10 years? >> It is, there is table stakes. It's actually kind of late, from my perspective. If I had to go invest in the market, I mean, I would invest in companies who have their data act together. >> Yeah, yeah. So, what's the, how do you tell, when a company has its data act together? When you walk into a prospect, how do you know, what do you see, what're the characteristics of somebody who has that act together? >> It's hard for me to give you a few characteristics, but you know, you can tell what is the mandate they're operating under, if there are clear mandates. Because, for most companies, this is lost because of turf battle. This whole battle is lost due to turf issues. And the moment you see senior executives working together, with a massive willingness to bring everything together. You know, they'll have different turfs, and they're willing to contribute data, and bring it together. That's a phenomenally positive sign, because once that happens, then every large company has the wherewithal to go hire 50 data scientists, or work with all kinds of companies, including mine, to get data science help. >> Yeah, it comes back to the culture, doesn't it? >> Yes, absolutely. >> All right, Praveen, we have to leave it right there. Thanks very much for coming back in theCUBE. >> Thank you Dave, thank you. Thank you for the opportunity. >> You're very welcome. All right, keep it right there, everybody. This is theCUBE. We're live from the Forager in San Jose, Big Data SV. We'll be right back. (electronica flourish)