 from San Jose. It's theCUBE, presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. We're back at Big Data SV. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante. Praveen Konkaria is here. He's the CEO of a company called Ipidus. Company's been around. The Big Data space, before Hadoop even. Praveen, thanks for coming back in theCUBE. Good to see you. So, as I said in the open, you've seen a lot. You kind of really get it to the Big Data space in 2007. Seen it blow through the Hadoop sort of batch world into the real-time world. Seen the data management, headwinds. From your perspective, what kind of problems are you solving today in the Big Data world? So I can go into the details of what we're doing, but at a high level, we are helping companies converge to a singular enterprise-wide data model. Because I think that is a crisis in the Fortune 500 today. And there'll be haves and have nots. What do you mean a crisis? I routinely run into companies who do not have their data models 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're flying partially blind. I have a theory on this, but I want to hear yours. Why is that such a big problem? So the most efficient business in the world is a one-man business. Because everything's flowing in the same brain. The moment you hire your first employee, you start having communication breakdowns. 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 C 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. Okay, well, so let's bring this into the world of digital, 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. 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. Is that an accurate description? And how can you help close that gap? So they have data in silos. And even in 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. So there's a lot of sluggishness overall. 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 are 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 a 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 to go in and develop their own applications. So shadow IT is being promoted in this environment. It's not being looked up, 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's 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 some of the challenges as well as cultural? There are some industries that might say, or some executives, they say, well, you know, my industry healthcare is an example. It 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 and 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 market. It's true. You see what happens in grocery. You see what happens. We haven't really seen, as they 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, 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 make it accessible. You got to make it of high quality. You've got to get the right skill set. So, there's a lot to it, more than just the technology. Maybe you could talk about that. So, the way I would like to preach, if I'm allowed to. Please, it's the cue. No, no, I mean, I don't mean here, but if any CEO is 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 onto 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 is 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 have made my flight way more comfortable. Well, and 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, identifying new data sources, create new products for the customers. Have you seen, what kind of impacts have you seen within your customers? I mean, you gave the example before of that sort of single data model, the single version of the truth. What business impacts have you been able to affect for your customers? So, 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 clickstream, right, to product usage data, to support data, to every marketing email open, 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 reduce the inconvenience. It used to be embarrassing for me to give out a card and get a decline 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? There seem to be entering a new era, we call it digital, there's a cognitive era, AI, machine intelligence, what do you see that's exciting and real? So number one, I like to divide the space into two parts. The whole space of data analytics. There's a 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 start of that he's cooking and took him just a month to pick it up and start applying it. 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 day right now. You know, I mean, this is all, this was a dream 10 years ago. Somebody had said, you know, for an old corporation to stitch all its data, what are you talking about? You know, it's not going to happen. But now this is possible and it's feasible. It's not going to, you know, require make a massive hole in their budgets. But don't you think it's also table stakes to compete in the next 10 years? It is stable stakes. It's actually kind of late from my perspective. If I had to go and 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 are 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. Is there a clear mandate? 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 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? Absolutely. All right, Praveen, we have to leave it right there. Thanks very much for coming back on theCUBE. Thank you, Dave. Thank you for the opportunity. You're very welcome. All right, keep it right there, everybody. This is theCUBE. We're live from the Forger in San Jose, Big Data SV. We'll be right back.