 Live here in Silicon Valley, the heart of Silicon Valley, San Jose Convention Center. This is Hadoop 2013, this is siliconangle.com and wikibond.org's exclusive coverage of Hadoop World 2013. This is theCUBE, this is our flagship program. When we go out to the events and extract the signal from the noise, I'm John Furrier, the founder of Silicon Angle. I'm George, my co-host. Hi everybody, I'm Dave Vellante, wikibond.org. Sanjay Mathur is here. Sanjay is the co-founder and CEO of Silicon Valley Data Science, a company that we introduced to you last week at the GE Industrial Internet when we had a dumb bill on. Sanjay, welcome to theCUBE. Thank you David, thanks John. We love theCUBE because it allows us to sit down and talk, kind of tech, like it's a sports talk show or a video show, like Sports Center on ESPN or CNN, whatever media metaphor, and go deep and have those conversations about what's going on in the landscape. So first of all, love the company you guys put together. Thank you. One, love this fact it's got Silicon Valley in it because it's a Silicon Angle. But Data Science right now is exploding. There's a lot of navigation discovery challenges around companies looking for use cases, best practices, how to hire, tooling. It's all early innings in here and it's all early in the game, so great market. So first introduce your company to the audience out there about the firm and about the team you're putting together and your mission. Sure. Again, thanks for having me here. Our name of our company is Silicon Valley Data Science and what we're doing is bringing a team of engineers, data scientists and platform experts together to solve difficult problems for data-driven companies and whether that is identifying critical insights, whether it's creating new capabilities, whether it's frankly assisting with new product development when you're really looking to innovate, that's what we're trying to attack. My team and I have a deep background in both data science and engineering. We've pulled some of the best experts out of Accenture, out of Walmart, a number of different locations to really bring a team of experts that can work well together to solve difficult problems. So you mentioned data-driven companies. My question is, are there enough data-driven companies or do you guys have to somewhat evangelize to get people data-driven? Do you go in and ask people, hey, are you a data-driven company? Oh yes, I am and they really not and you run away. How does that all work? How does that dynamic work? Yeah, I mean, we got friends on the back today. The interesting question is everyone focuses on whether it's the three V's or as it's been extended four or five V's. Whether you look at it as a big data explosion or frankly what most companies have been doing, a throwing away data explosion, everyone's data-driven. Everyone could be using their data for better analytics and better decision making. I've been doing data for 20 years and it's not surprising to me the transformation we're going through because it's been building for a long time. So some companies may not realize they're data-driven but it's really a question of how do you want to drive your business forward and if you don't use data today, you're going to be at a disadvantage. Yeah, Dave and I always talk about this. We've mentioned on theCUBE now going on three years, we believe and we've said this is the first time in the history of business, in the history of enterprising that you can actually instrument the business end to end completely. Not just supply chain, not just everything with mobile computers, you have full instrumentation and ultimately surveillance as we learned from the NSA. You know, it's slippery slope, there's positive and negatives. So how do you talk to a business owner because business value is a big conversation here and of course that's always the high ground. Business value tries prices, tries economics, tries wealth creation, solutions, et cetera. But you got to walk in and say hello, Mr. Customer, whether it's business line manager, CIO or CEO, look at the way you've been doing business has been great but now there's a better way because of the data and your value chains that you have and the value activities are going to be reconstructed and either be omnidirectional, different or just changed. How do you have that coverage? You kind of slowly go in there and ingratiate yourself in there, do you hit them over the head with a hammer? What do you do? Well, I think fundamentally it's two things. One, you got to have familiarity with today's tools and technologies and techniques and know how to apply them. And if you don't know how to apply them then you can't actually have a legitimate conversation with your customer. The second side of it is understanding the domain the customer's operating in. Even if you don't know their products and services specifically, you understand what's going on. Your previous guest was talking about healthcare. It's a very difficult market to sell into but we all know that they have massive big data problems. So if you're going to go talk to a health insurer who reprises their insurance once a year, what's the big data conversation you're having with them? You're having a giant optimization discussion with them on all the data that they have and what you said, you've instrumented everything. How do I react to that? So our approach is to go in and have that discussion about- Domain expertise is critical. Domain expertise is critical but it's also technology domain expertise, right? If you're entering in with the CIO or the CTO, I understand your technology problems and I understand them moving forward. So it's one domino, it's the other. Now it's an education problem because wherever you had the intersection between healthcare and an example, domain expertise in healthcare, the industry, and now the technologies, you have the siloed IT department with HIPAA regulations. I mean, that's just one example of many. Yeah, and that's one of the reasons we brought Ed Dumble onto our team is he's fantastic at education. We realize that for us to be successful with our customers, we're going to have to educate them about that path to being a data-driven organization but our focus is delivering the capability to them. You engage with us, our focus is we go solve the problem and show you how to be data-driven and then teach you how to do it. Which is an important part. So huge skills gap, you guys are helping solve that problem and then you're teaching the customers how to fish, so to speak. And then sort of moving on, Sam Palmasano said no matter what business you're in you're going to get commoditized. Oh, so I know there's tons, Sanjay, this is sort of a crazy, silly question in a way because there's so much opportunity now but where do you see that all going? I mean, essentially you're going to be teaching these companies to be data-driven, transferring your knowledge. You've been in the services business for a long time so you know that trend that I'm talking about. Where do you see this going? Where you are today? What's your vision in terms of the types of services that you're going to be providing in the future? Sure, that's a great question. So we are going to teach our customers how to do what they're doing better, especially if we knew it and they didn't, which is part of why you would engage with someone like us. But over the long term, what we need to do is continue to stay ahead of them on technology. Our investment in our people will be around understanding technologies, how they're evolving and how we continue to apply them back to our customers. Our customers will come back to us and say, all right, you helped us out, this was great. What's the next thing that you can do to move us forward? The other side of that is really thinking about how you use data. And as much as we think that companies are data-driven, in fact, the question you asked me at the beginning, most aren't. And back to my original point, we got to help them become data-driven and then there's a journey that they're going to go through to make that happen. It will take time. It's not going to happen in one year or two years. I think for a company that hasn't fundamentally used this data, they're on a 10-year journey to become data-driven. Consulting firms like your former employer, the big guys, they tend to have very deep industry expertise. They've always had their hands in technology, a lot of great technology expertise, but generally speaking, they haven't productized that they've used bodies to deliver. How is that changing? And how is Silicon Valley Data Science going to capitalize on that? You obviously don't have the global presence and you've got some, certainly, some industry expertise, but not nearly as deep as some of those large guys. So how is that business changing and how are you guys going to capitalize on that? You know, the point I tease out of that is how do you deliver the skills to the marketplace? And I think the bigger you get, the more you have to take people and make them a little bit more interchangeable so that you can attack your customers all over the world and maintain that relationship that you have. We're actually taking the opposite view, which is, hey, we're small, we know that. Let's get teams of experts that work very well together and keep them together. Because when you get engineers and data scientists to work well, that's kind of an aha moment. That takes some doing and if you talk to the experts out in the field, if you look at what LinkedIn has done, you frankly look at what Google has done, they've gotten these teams that worked terrifically well together and they go solve multiple problems. If you're inside a product company or one of the clients that we're pursuing, at some point you get bored of solving that problem over and over again. Our attraction and what people are coming to us for is, let me work really well in a great team and go from problem to problem and solve them from domain to domain. I think the large SIs, they could probably do the same thing, but once you've gone public, then you've got a different overlord that's driving you. And they're still going to tend to more brute force it than what sounds like what you guys are doing. Talk a little bit about the typical engagement with you. So who's, you get a call or somebody calls you in? Who are you talking to? What are they asking you? Where are they at in the whole maturity model? Yeah, we've been approached by all kinds of people. So I'll break them into two broad categories. There is people from an architecture or infrastructure level that are over one with data and they've probably made a choice and said, okay, Hadoop's a little bit cheaper, I should look at it. Maybe they've done some experimentation. Maybe they've done a little bit of a pilot, but they're not sure how to make that work. So that's the CIO or CTO led conversation. We've got a data problem as technology infrastructure. The flip side is the, let's call it the chief marketing officer or COO or CFO led discussion on we know we need to use our data better. We've been throwing out data. We got to do better marketing. Our competitors doing X, Y, Z, how do we make it happen? So we enter from one of those two domains. It's a fundamental technology problem or you've got a business imperative that you're trying to solve and we try to match the two then because one doesn't get solved without the other. Yeah, and so are you seeing an excitement amongst those two constituencies to work together? Is there a culture clash, a combination of the two? All of the above, right? And I think some of the customers we've talked to are looking for someone to come in that can bridge the gap between both sides. So if you can communicate with both sides, provide skills on both sides and actually go solve the problem, they're much more excited to engage with you. Talk about your team. Talk about the Silicon Valley Data Science team. Obviously, no one can really just jump in and be a leader here. It's very difficult. You mentioned Accenture. You're talking about the old kind of, I call them the big six accounting firms that ended up running and deploying a lot of the old school client server days which actually transformed a lot of the workflows that we know and we see an automation now. But now that's, we're completely rebooting that model. So there's delivery, there's expertise. How are you structuring your team and talk about the team? The team itself, we're just getting going. We launched in April. We're getting close to 10 people at this point. We're balancing between data scientists and engineers. And as I said, we're purling people out that have really deep expertise in the field building these solutions. Whether solving large scale data problems, whether driving real time analytics to make operational decisions. We're getting people from those backgrounds and having expertise in delivering those things. Are you looking just Silicon Valley obviously hiring is tough, obviously in this area to find a data scientist is challenging. But like obviously other geographies global, you guys looking at remote workforces. We had, you know, most of us come from a heritage of dealing with collaborative teams. So absolutely, I, you know. Skype. Skype. They go to me. You guys, Silicon, Silicon Angle, you're bringing news about what's relevant to Silicon Valley. For us, having Silicon Valley in the name was about bringing the style of work, agile product delivery, iterative progression towards a goal and bringing in the tools and techniques and data management expertise from the Valley out to companies everywhere else. And it's in the innovation edge too. Silicon Valley has that cutting edge. So that's obviously well known. Bringing that to kind of Main Street America. Yeah. Absolutely. I mean, I'll call out the guys at Hortonworks just cause I think they're a great example on this front. By putting a distribution on Windows, they're going and attacking a very large part of the market that has not moved off Windows. So be it. They made a great choice with Microsoft. Likewise, when it comes to Hadoop, I think many organizations have not yet even scratched at it. Well, Sanjay, thanks for coming on theCUBE. We really appreciate, I want to thank Ed Dunbill for teeing us up together to come on. We really love, we'd love to have you on again now that you're in the Bay Area. I'm in Palo Alto, Dason, Boston area. So love to continue the conversation. We are looking for signal in this area. This is an area that has a high demand certainly from a use case and demand in the marketplace. So we're looking forward to future conversations. Silicon Valley Data Science. This is the new consultancy, the new tooling that will be coming on. Data-driven businesses is the future. It's here and will continue to grow very, very early. Congratulations on your business model. Really like where it's going. This is theCUBE. We are data-driven and we'll be right back with a couple more interviews. We have two more segments and then wrap up for Dave and I. This is theCUBE. We'll be right back. Silicon Angle and Wikibon after the short break.