 Okay, we're back live at Strata Conference. We're here at siliconangle.com, siliconangle.tv. It's actually not a cube, that's the name, but we'll soon have a cube, Dave, and actually be a real cube. We're like, where's the cube? I was looking for a cube. So we're here live on the ground eight hours a day, bringing you knowledge and content from Strata with analysis. Tons of live streaming going on the web. O'Reilly has their own conference being streamed on the keynotes and the sessions. They even have their own little news desk and how-to clinic. So O'Reilly's got a ton of video. Go check out their site, but also stay on siliconangle.tv because we're bringing you the independent analysis and the commentary and opinion. I'm John Furrier, the founder of siliconangle.com and I'm with my co-host. I'm Dave Vellante of wikibond.org and we're here with Rob Metcalf, who's the president and COO of Digital Reasoning. Rob, welcome to the cube. Hey, it's great to be back, good to see you guys. So we got a spotlight, really, what I would call putting content into context, which is sort of what you guys do. So we're going to drill into that topic a little bit and talk about where you guys have come from, which is sort of an interesting background. Yeah, so at Hadoop World, we had a great conversation talking about the semantic and some of the cool things going on with big data. And one of them's obviously using the data. You hear the use cases are all about watching trends, looking at patterns, seeing all kinds of things. So this is a semantic web problem and you guys were really the company that had the most, what I would call, IQ points in my mind around this problem. I also understand that you guys have a customer that you really can't talk about and US government uses you guys, is that true? Yeah, we've been working, serving in the defense and intel sector for a number of years now, yeah. I can only imagine the intel that they're using big data for and you guys have a cloud project we signed up for, what is that about? Yeah, so we basically took our technology products called Synthesis and it's a large scale engine for analyzing unstructured data, turning it into a usable form, facts and relationships. And we took that and we basically did the tooling so we could scale that out and people could access it in a hosted model. And we call that Synthesis Cloud. So talk about the business that you guys have then we'll drill down into some of the more trends. Obviously you guys are in Tennessee and you have people kind of around north and west coast. What's the business like for you guys now? Obviously you have a big government relationship with around their intel stuff. But commercially, what's going on with the business and where you guys at? How are you responding to all this growth in the marketplace? We had Mike Olson on there doing extremely well with Cloudera and just in general the massive growth has actually surprised me. I knew it was going to be big but not this fast. Are you guys seeing the same thing and how are you guys managing that growth? Yeah, absolutely. So a couple things since we were together in November we announced a new board member that came on, John Brennan of Silver Lake Sumerio. It was part of a little bit of fundraising that we did and announced in early December. So part of it is getting the right set of partners and capital to grow the business. We're looking to use the same sort of technology, our core synthesis engine. So I was talking about with Synthesis Cloud so enabling a number of customers to come on and basically build applications on our platform. And we're using the same technology with a pretty interesting set of customers in a couple different verticals. Things that we're particularly interested on, very much sort of themes with this conference are where our sort of pockets of really, really valuable unstructured data that aren't being used. We're doing a lot of stuff in the financial sector. Can't talk too much about that, some early pilots, but generally kind of core use cases around, how do you bridge this sort of massive open information with more proprietary information inside the firewall? What are the tools that sort of bridge that semantic gap and how do you link together facts and relationships that may be relevant for a number of different ends? For the folks that don't understand digital reasoning's value proposition because big data is new even for advanced enterprises, what are you guys offering them in terms of the core value? Not so much the product, but like why would somebody be working with digital reasoning else so we know why the government would be surveillance? But in general, from a commercial standpoint, why would someone engage digital reasoning? What's the big data angle that you have there? Yeah, it's a great question. So you've got these set of industries and I think it does cut across a number of different industries that are data intensive, that are trying to make data-driven decisions and they're increasingly aware that there's really large pockets of important information on structured data, it could be in emails, it could be in reports, it could be out in blogs and open web that they're really not making enough use out of. And so if I'm in a financial services company that could mean trying to understand at a deeper and broader level what's going on in a particular region of the world that might affect commodity prices, it may be something that's more particular to a company that's about ready to be bought or sold and then there are a number of other use cases that you can imagine as well. But it's ultimately about, I think, very much in line with the theme of this conference, sort of putting data to work and what we offer in our product synthesis is the ability to take unstructured data from different domains and be able to put it into useful form very easily. So by that I mean taking in multiple languages or information that may be medical or information that may be and one of the things we're featuring here and I know Tim will talk about it a little later on this afternoon with you guys is what we're doing with patent data. So a whole bunch of different types of data sources and how do you use software to bring the information value out of that without turning that into a massive services exercise? So it's a lot of tech space stuff, right? Yeah, yeah, our core strength is on the unstructured side although we do increasingly do sort of fusion of unstructured and unstructured. So how does it work with synthesis? You bring in all this largely tech space but other information as well and then what happens? What happens once it gets into synthesis and then what's the outcome? Yeah, that's a great question. So a really sort of basic level we're taking in and we're doing kind of core processing of the tech. So we're doing things like you and I do when we read sentences. What's the subject and what's the predicate? What's the object? How do you encode that using a computer and then how do you store it? And then we're doing sort of analytics on top of that to figure out, well, I talked about Rob Metcalfe, he's a person and who's he similar to? Who's he connected to? Who did he speak with? What types of things has he done in particular locations or over particular periods of time? And then I'm enabling analysis to happen on top of that. So it's really about taking and doing a lot of things that we do when we read information using machine learning techniques to do that and massively scale it out over Hadoop and other NoSQL technologies. Are you guys targeting developers and commercial companies or both? Yeah, both. From a business perspective, obviously, we're working mostly within customers, companies. But we're actively hiring smart folks who wanna, and data scientists, they can help us out on this, but also, I think there's particularly interesting use cases that someone might be able to leverage our own synthesis cloud for and do a new type of analysis. So how different are you and why is what you do so different than other techniques and approaches? Yeah, we're different because of the depth of the analysis that we're doing on unstructured data, the way we're doing it. That is, it's driven by machine learning techniques and it's highly statistical. What that means in pretty simple terms is that in terms is that we can take a new domain and we can put a relatively, a person with basically good English skills and we can teach the computer to read that type of text that can learn to identify certain types of entities, certain types of facts. So the ability to, it's kind of, and really the ability to do that all at scale. So it's a combination of the scale of what we're doing. A lot of that is leveraging the Hadoop and other technologies, as well as the depth of the analysis and the way we're going about it and the ability to go through multiple domains and languages with relatively limited services or customization. Okay, so you started in the government business in places that you can't talk about and financial services, more stuff that you can't talk about, makes marketing hard sometimes. But now I'm basically going into the broader commercial applications and what are you seeing in terms of adoption and how people are intending to use it or actually using it? Yeah, well it's relatively early in the process, to be honest. We have a number of folks that are using our synthesis cloud product and working through a number of pilots in, as I mentioned, in financial services and in some other areas and we're going to keep working at that. I think it's, we're seeing an incredible amount of demand. I think a lot of the stuff that this conference and others are emphasizing is that there's sort of worlds of data out there that are relatively unused and there's really an incredible need and Mike talked about this this morning for tools that sit on top of the infrastructure that ultimately help customers get the better answers. That's really the name of the game for whichever domain we happen to be in. How does your engagement typically work? I'm trying to, because people want to know how to engage with big data solutions. So when people come to you, what are you seeing as the top driver around engaging with digital reasoning? And what does it look like? I mean, is it like you come in, you sit with the company, do you do consulting, you do develop with them, you just give them the product. Walk us through that. Yeah, someone comes in and they say, I've got a need for a new type of analysis I want to do. I'm looking at a new domain. I want you to look at this type of news article or I want you to look at a medical domain or want you to analyze, as I said, patents. So we'll take that, we'll talk to them, we'll understand what the need is and we'll run it through our engine and we'll say here's what the system shows. Here's a set of relationships and facts that you couldn't have gotten otherwise and we did it in a very short period of time and you can access it because it's all hosted. So the classic big data kind of conversation line is you got to know what questions to ask, right? So are the clients getting, your customer's getting smarter now with that? Are there tools that they use or do you guys still have to hold their hand and kind of walk through it slowly? How automated and how, the simplicity side of it's a big focus, Mike was talking about that as well, I agree. What is, where are we at with that? Yeah, I think it's certain domains, it's really easy. They're trying to figure out and build a profile of a particular company or in a particular person or organization and the system takes in data and shows you a map of the things that are connected to it or generates a list of similar related entities. That's pretty straightforward and folks are pretty comfortable with that, it's pretty, it's out of the box. But I think what's happening in this space is the scale the data expands and as the sort of the appetite for taking on more complicated things, we're also having conversations where someone says, well actually what I'd really like to do is take your outputs, entities and relationships and I want to do more advanced statistics on that and we would work with them and enable that capability and they may want to do it in a new domain or they may want to do it in a new language and we'd work with them on that as well. So it kind of spans from folks who sort of out of the box say yeah I want to do this type of thing to folks who are looking for a more complicated extended solution and we're comfortable working with them. So what's the big picture aspiration in terms of how you guys want to change things? How are you going to change the world? Yeah, I mean I think we're about making information far more useful to many more folks and I think the scale of the data and we want to do that across verticals, we want to do that in missions that matter for all of us every day and we want to do that in domains that are not government and I think we're really trying to make a difference is we're very, very confident that there's a large number of data sources that are untapped, people are making decisions that could be improved sometimes dramatically and at far lower cost and we want to provide a software solution that makes them more effective in doing that and we want to do that across a number of verticals and we want to do it with a great team of folks and we're one of the great things about being here is we're actively talking to a ton of folks and we're hiring so find us if we're not finding you. Who are you guys looking to hire? Give us your roadmap for this year, what's on your plan for the company? Yeah, we've got pretty ambitious plans in terms of finding folks that have deep expertise in machine learning, we need Java guys, we need folks who are familiar with all of the sort of big data technologies, we need folks who can connect with specific customer needs, so subject matter experts in some of the fields that I've mentioned and then there's always need for folks that can do business development itself so we're looking across the spectrum. People got to move to Tennessee or? No, we've got customers all over. Tennessee's a great place. We love it. You got warm weather? I love that. That's the eight income tax? Close to Nashville. Just far enough away? Just close enough, right? No road rage. Palo Alto, it's just far enough away from San Francisco but close enough, right? Well, thanks for coming on theCUBE, you guys are great. We want to personally thank you guys for the tremendous support. We really enjoy you guys on theCUBE at Hadoop World. We love your team, we love your approach. You're bringing some reality and some smarts to the semantic web. The machine learning is how we heard the guy from O'Reilly saying that the thing that he's really pumped about is the machine learning, applied machine learning and so all those cool things you guys are doing is really going to really do well. So congratulations and thanks for your support. We love you guys. Thanks for having me. Appreciate it.