 The Cube at Big Data NYC 2014. Brought to you by headline sponsor, Juan Disco, with support from EMC, Mark Logic and TerraData. With hosts, Dave Vellante and Jeff Kelly. Hi everybody, we're back. Welcome to Big Data NYC. Big Data NYC is an event that SiliconANGLE Wikibon hold concurrent with Strata plus a dupe world. We've got a number of events going on here. We're running two days of live Cube here, Thursday and Friday interviewing folks. And then today at four o'clock, Jeff Kelly and I, and we got a great panel, we're going to give a capital markets presentation. Invited in a bunch of our Wall Street friends and some of our clients and other friends of the Cube. And we're going to really unpack what's going on in this business. And then we're going to spill out at six o'clock to the Cube party celebrating five years of the Cube at a dupe world. And one of our very first guests and most interesting guest at a dupe world was Abhi Mehta. Abhi's here, he's the CEO of Triseta, software company, solving problems in the financial services business. Abhi, always a pleasure. Great to see you. Congratulations on five years. You know what a brilliant five years it's been and it's good to be back. Always good to be back. Thank you very much. I mean, that was an unbelievable show. We were talking, you made some great predictions. You sort of educated us on the data factory. You had some great sound bites. Sampling is dead. A dupe is going to be big, big data is going to be bigger than Linux, right? And it looks like those things are really coming true. At the time, a lot of people didn't even know what a dupe was. And now everybody's doing a dupe. You had a chance probably to be, you were at the Strata and a dupe world earlier. What's the vibe like over there? The vibe is great. I was telling some of my friends that in the five years of when Strata wasn't called a dupe world or a dupe world wasn't called Strata, whichever you look at it, the conversations have changed dramatically. We met three potential customers today and they all want to go ahead and do deals. I think it is an interesting time in big data where I think there's a realization that we're in the middle of witnessing something that's going to be very dramatic, very Darwinian. And people don't know yet what it means for them, especially the big guys. They have no clue what's going to hit. It's going to hit them hard. They're going to fall even harder. But luckily in five years from now they all realize it. So the average age of Strata from five or a dupe world from five years ago, which was 30 years old. Today it's like 50 years old. So you're seeing the enterprise machines gear up to say, how hard will I be hit? What is going to be the impact? And what should I do with it? And you're seeing the customers finally embracing and saying, I buy it. This is a huge wave. The early adopters who were crazy enough to go surf it have proven it works. That I won't fall flat on my face. Let me dive in. And that dive in, that process has dramatically changed. Sales cycles, changed what people are looking at at the stack at. And where is the value in the stack? I think that remains the most interesting conversation. Where will the value lie? Where does enterprise software company make money? When databases, we said ETL is dead, databases, ETL, visualization, storage, when all of them are free, then how do you make money? How do you convince smart customers to be foolish enough to write a check for you? And it's an interesting question. And we keep at Trasada raising the bar and proving that there is money to be made when you do interesting things. Well, so let's talk about that a little bit. Yeah, the core of the infrastructure is really essentially free. Correct. And yeah, there's subscriptions on maintenance, but you've got to have massive volume. If you're going to give a perpetual license, you've got to have massive volume at the tail end. So that's a business model. Correct. The visualization piece is interesting. Visualization, analyzing log files, people like Tableau and Splunk make a lot of money doing that today. But then the big knock on them is, wow, it's really expensive. Open source is kind of the wild card there. And then the other big question we always ask is, where are all the applications? That's where you guys go. So applications are always a way to make money. So talk about what you guys are doing a little bit there. Yeah, absolutely. And I think I'll start with a very interesting story out of India, you know, how this one guy wanted to sell watermelons and he buys watermelons and he fills up a cart of them of 100 watermelons and he buys them at, let's say, a bucket of watermelons and he starts to go to the market. And he realizes in the market, he can only sell them for 80 cents and he says, that's okay, I'll make it up in volume. The reality is, when you buy something for X and you sell it for Y, when the Y is less than the X, you actually can't make it up at volume. I think the market is quickly realizing that. You can't make up stuff in volume. I think the pricing dynamic on enterprise software is completely changing. And I don't think any smart person has a clue where it goes to. And what I mean by that is this. The value has transitioned completely to the business user, AKA not in building reports and looking at visualization in the fact that if a chief marketing officer, if a chief risk officer, if a chief financial officer, cannot figure out how the raw data can help him make money, solve a regulatory problem, fix fraud, simple examples, they will not write a check for it. And you cannot make it up in volume. So if that becomes the dynamic and the underlying pieces of the traditional data analytics stack have been commoditized and demarcatized to sell the same coin, then you quickly start going into how do we deliver intelligence that can make money for the business user. And that's where everybody's headed to because the volume play in software is over. The play is now all around price. So then the P times V, every business P times V, if you can't figure out a way to deliver value and then translate that into an ROI for your business, you're not going to get any eyes and hence no arms. And I think that's where we believe, we have proven the vision we shared with you, right? It was a very interesting vision. We came to you five years ago, four years ago, three of you launched to sit on the cube, I remember the day, and we said fundamentally, the ability to find, mine, and monetize customer intelligence will be done in a way that has never been done before at scale, sampling is dead, the tools are free, and whoever can prove you can do that in the Hadoop stack is worth a lot of money, we'll make a lot of money. I'm happy to say in the five years of knowing you, to say that I was cash flow positive last year, we are profitable this year, we'll ask you to pay the government some taxes, give our dues, and that vision we shared with you, and we have a new term for it, we're calling the next generation of enterprise software customer intelligence management. That every single piece of customer intelligence management is done in Hadoop, and you're proving to the business user that the way to understand their customer behavior, mind the customer behavior, and build products and services customers actually want can be done at a scale, at a cost, that dramatically changes their own internal business models. Our average deal size is seven figures, it's multi-year, and we have today that vision in the market, so today we are announcing the fourth generation of our platform, which is building this new class of software, customer intelligence management in Hadoop. I think that's the core, that's the essence of it. Now, are you guys still self-funded? Have you done an outside business? We're still self-funded, no, we... I'm sorry, you're paying taxes. Yeah, some people tell me I'm doing it the hard way, I tell people, I guess I'm the only fool who is more interested in making money than raising money, but you know... Well, what about scale? I mean, the obvious question there is how do you scale that business? It's a great question. I think the reality is, so there are three things that we are gonna be announcing tomorrow, we'll give you a preview of on the scale part, which are very interesting. Number one, we do buy as big beneficiaries Trasada has been of the open source model, we also are big believers in contributing to it. So we're announcing, you know, Trasada Analytics Platform 4.0, and with it we're open sourcing our application development framework. It's called Scalding on Spark as my friends at Databricks correct me. And we've put together Scalding and Spark and as of now, as of I think a week ago, it's now completely open source and it's part of our SDK and anybody can go into our platform, build on our platform applications, as well as use it to build their own applications and big data in Hadoop. It's completely certified, works in Hadoop and open source. That's one, that's one big way, you know? So we want to incent people to build applications on top of what we do and what we call the core functions and then build their own applications in Hadoop and prove to the world that the value again is in the last mile of analytics, right? We've always spoken about how do you automate intelligence that humans like you and I can make better decisions at scale? That's number one. Number two, we're also announcing that we're gonna start giving stuff away for free because people in the world loves free, you know? You know what I know it. Powerful concept. Great concept. So we announced today our data transformation platform is free. So transformations in Hadoop at scale, you want to clean data, you want to go find the Loch Ness monster in the data lake, you can do it for free on Reseta Software. So I think that's another big model for us. On software that we built four years ago, that people are building companies now on, will give away. Good luck to those companies. I'm sure their investors will be very happy with the announcement and that's okay. So that's the second big thing on scaling. We're going to give some critical components of our software away for free. So people can figure out the lake. What we have from customers is, it's great, I'm building a data lake. What do I do with it? Lake is great, but without a boat to float on it, without going from A to B, without making any, without some concessions around the center, you can't make money on lakes. A lot of people are building data swamps. Yeah, exactly. In a tournament today. That's exactly right. So with giving away our data transformation for free, we're allowing people to figure out what is in the lake and if it is a Loch Ness monster, you should run. You're telling us out of time? We can't be out of time. Out of time? No way. So let me jump in. Jeff's got to get at least one question. Sometimes staples may ask a question. Let me jump in with a question. So it's great talking about the technology and bringing applications to the enterprise, but we were talking earlier in an earlier segment with Sree from Hex Data about the cultural changes that need to happen in the enterprise to take advantage of this because a lot of times what you're talking about is insights that are going to cut against the grain. You're going to find as a bank that your most profitable customer isn't your most profitable customer because now I have access to all the data. I'm running applications on customer intelligence and finding out, oh, I've been looking at this wrong way. That requires you to make some changes in your business processes the way you market, et cetera. A lot of change has to happen. And a lot of people have a lot invested in the way things have gone so far. What about that cultural component? How does that change happen to allow enterprises to really take advantage of all of the innovation that companies like Trusader are doing? That's a great question. I think there are two parts to the answer. One, we have to fundamentally understand how technology has evolved over the last 50 years. And the key part of technology or any industrialization that moves society forward is automation. And in order to enable the changing of business models without automating the last vestige which remains very manual in every single enterprise which is front office applications. Everything you mentioned, sales. Sales remains a manual task, right? That's why we call it customer intelligence management. CRM is dumb. CRM is helping you make meetings and reminding you when to meet. When you meet a customer and you can't tell the customer anything intelligent about the customer, it's like your wealth manager coming to you and saying, Dave, what do you think about the stock market? And you go, that's what I gotta do, what am I paying you for, right? So if CRM, and it was great, the last wave of technology automated dumb processes. This next wave of technology is automating intelligent processes that today humans could only do. So in our customer intelligence management suite, we have four key solutions built in. Identity intelligence, marketing intelligence, risk intelligence, and fraud intelligence. We have a large bank who bought our identity intelligence product for the corporate bank. They took away 400 resources that were doing the front office job manually by automating the task and repurposing them to be exactly what you were saying. If I now know that what I thought was my most profitable customer, simply because Jeff had every single product with me and I thought as a bank or as a retailer, as most were profitable, but then I realized that RBU only has one product that has 50,000 followers on Twitter who are very influential. Maybe RB is my most profitable customer. I gotta change how I mind the internet to get there. You can't get there before you automate it. So we are solving the last mile of technology that needs to be automated will be automated by this class of software we call customer intelligence management. We're going out of four areas. Once you do that, you start realizing that you have to retrain the people. But what we're finding is because technology has become so smart, because we can finally take technology and automate this last era of human led processes, the retrainings are simple. Because think of it, if you are a retailer, a clothing retailer and I could tell you that Dave loves wearing ties and cufflinks and shirts with pink stripes. Good on you my friend, you look great. Shove with pink stripes and you are a gap and you're walking in and it shows up on an iPad. Anybody can make money on that. What do you train the personnel? I'm telling you exactly what Dave likes at the moment when Dave walks into the store, which is a combination of and if we tell you that by the way Dave went on Twitter and tweeted something really cool about fishes, maybe you wanna sell him a fishing tie, right? If we can deliver that intelligence in the moment, you don't need rocket scientists to make money on that, right? The question is can we automate that last level of business process? I can tell you my friends, we can. That's what we're doing. Anyway, you brought this from your practitioner days and by the way, it's Sam and I think. Okay listen, we're gonna give you the microphone back at five o'clock at our Capital Markets event. Really appreciate you coming on, which we had a little bit more time. The planes are backing up, as often happens with the Cube as you know, Abhi. Thanks so much and we'll see you later on at Capital Markets event at four o'clock today at the New York Times Square Hilton. So thanks again. I just want to say one thing before I go, which is when you guys are doing this event on Wall Street, like five more years in the 10th anniversary, you have to wear dollar ties. I can tell you, green dollars. All right, keep it right there everybody. This is the Cube. We'll be right back with our next guest right after this. Thank you so much.