 Okay, we're back with no quick break there. I'm John Furrier, the founder of SiliconANGLE.com, and I'm here with my co-host as always. I'm Dave Vellante of Wikibon.org, and we have a spotlight, an ongoing spotlight on putting content into context. We're here with Tim Estes, CEO of Digital Reasoning. Tim, welcome back to theCUBE. It's great to see you guys. Yeah, we met last fall at Hadoop World, and you guys talked about how you were doing a lot of, you know, your main focus was in places that you couldn't talk about in the government. We tried to get that out of there. It's a great line, isn't it? I was just saying, to Rob, it makes it tough for marketing, but it's intriguing, and you talked about the time how you feel as though this technology has wide appeal in commercial enterprises, and so what's changed since last fall? Well, I did think both of you all, because you all made a great push about venture guys getting in, and since last we talked, we closed around. We're growing, we're making some big investments now, making this pivot to commercial, as Rob was talking about, and so, but thanks, people took your advice, and we got a great group on board with Silver Lake, so. You know, Dave and I didn't get a lick off the cone there, where you get a little percentage, what's the 8% of the round? Always negotiating, always negotiating. You guys deserve it, and like I said, I was really impressed, Dave and I, both were talking after Hadoop World, that, you know, a lot of the Alpha Geeks here at O'Reilly Conference in Hadoop World, and I'll say, you know, I was a year and a half at Cloudera, you can just, when you see good stuff, you're like, wow, that's really relevant. You guys were really doing that, really bridging that theory of semantic web into practice, which is really the killer app for social data, and all data is to make use of it, and make use of it fast. Some of us, we call it like the holy grail, so I got to be a little careful though. I think one of the things in this space that, you have to always, especially come to this conference, you get humility out of it, right? Because there's some brilliant people here, lots of them, and I think we're trying to do something very interesting, and it has to be done, and so a lot of what I'm talking about this afternoon here at O'Reilly is what has to be done, but you got to be very honest about where you're at in the process. There's a lot of trust you're hoping people put in you, and part of that's being honest, first and foremost. So what kind of advancements are you seeing in technology that you're able to take advantage of? You've seen tons of technology here, but what are the things that are interesting to you? Well, I think really there's been, even since the last time we talked, there was some talk about at Hadoop World, but there's a major release of MapReduce coming out, MapReduce 2.0 inside Hadoop coming out, and one of the big hindrances in Hadoop has been it's fundamentally a batch processing system. So we're looking very, very earnestly at what will happen with Hadoop in the next year as other frameworks that help deal with real-time issues, deal with stability issues are addressed by people like Cloud Air and others. So I think for us, I mean, we are an analytics company making sense of information, but we ride heavily on the shoulders, you know, people like Cloud Air and others that have put out a great platform as, you know, Mike got to show this morning about an application we've been able to put out in the Intel space where we could clip some stuff we could share and give to him and such, but I'm, you know, I think it's got to be a team effort to solve a problem this hard. So how close are you guys with Cloud Air? Share with the world out there, your relationship with Cloud Air, do you guys have a partnership with Cloud Air? Are you using just Hadoop? What are you guys? Yeah, no, I think we have an announced partnership there. We have run on various types of Hadoop distributions. I think it's public knowledge. We've run on the Apache, the Cloud Air, we've run on data stacks and on MapR's version and IBM's, that's not terribly public knowledge, but we are close to Cloud Air, I think, part of that's Mike's a friend of mine. We've had a chance to go after some really interesting missions together on the federal side. One thing I guess I'd like to say is I think there are great people here in terms of technologies that they need to kind of put the investment time in. This is me kind of a little bit challenging them to help the guys on the government side solve these problems. We need more product vendors willing to fight and stick it out through the bureaucracy, through the confusion because that's what's going to actually create very effective call savings, change capability improvements for that area and that actually protects all of us. So I look forward, I really respect Mike because he's one of those Silicon Valley companies that has made the investment into the government space. They have weighted it out. It's a long cycle. It's not a quick cycle. The VCs don't always go for that. And I think- You got to pay your dues. I mean- Yeah, I think it's gutsy. It's a year of just slogging it out before you see anything, minimum. Oh yeah, I mean, 12 years in, yeah. I mean, so we're a 12 year old emerging company that has a lot of federal stuff going on, but yeah, I think that we need people like that committed in going after it. So I'll talk about them more and some other partners. I mean, Datastacks knew those guys since before they found the company. So they did some amazing stuff there. We're heavy users of Cassandra early on, still are, I think it has unique capabilities. So I don't want to be, if you will, like too much on one side, I want to give respect to where it's due. Yeah, it was different approaches to different solutions. We had Datastacks on earlier in East. Those guys have tech chops, so they're solid. It's not uncommon that you'll see, you know, companies start in the commercial sector and then eventually after the VCs, you know, are satisfied to go into the government. Yeah. Unless they don't have VCs like us, Dave. Yeah, that's right. Then we can do whatever we want. That's right, see? For those that used to be VC free, I'm no longer in the club, unfortunately. Yeah, well, good luck with that. Yeah, good luck with that. Yeah. You better have them far away from the kitchen. And the reverse is true like you guys. I mean, it happens, it's less common. I mean, for instance, CleverSafe's is another startup. They've done a lot of stuff in the intelligence communities. What are the, how do those disciplines apply to the commercial world, you know, without giving away stuff that you can't talk about? But talk about those use cases and how they apply to the broader commercial sector. Well, I think the biggest use case, and I hear it more and more, I'm spending more time in New York now. We're looking at talking to people in the banking sector. I think the absolute biggest use case that isn't well-addressed right now, and it's going to come from someone who understands secure data and noisy data together, is where you can take the data in the open world and make sense of it and make data behind the firewall make sense, if you will, in the same model. And that cries out for software because they don't want to hand the data to the cloud, which a lot of these banks are not going to do that for a lot of good reasons. Then you got to have software that runs on both sides. So you see that with what we're doing in our strategy, we have, since this is cloud, where we're trying to take very interesting public data sets like the US Patent Office, process that, connect every technology and every single inventor of 11 million patents since 1976, being able to weave that together into one common knowledge representation purely through algorithms without a lot of hand jamming and nice demoing of the thing. And do that on a public source, but most of our use cases are behind a firewall and frankly on networks, people can't even read things without clearances. So the ability to handle secure sensitive but noisy information behind the firewall and noisy information and diverse information at scale outside the firewall. Those two things are a very, very big gap, which I haven't seen anyone fill yet effectively and we're going to try to fill that pretty effectively. And there's a big theme at this event around machine learning, I presume, that you guys are talking about. We have a little data project that we'd like to brag about, but never show anyone because it's like a top secret project. Yeah, we know all about those. The Twitter data that we're using. So we have the tool that we built using Hadoop and HBase, where we take all the Twitter data and we look at what's going on with our audience that we're serving. The number one trending topic in the vertical that we cover is mobile data and mobile security. Right now, of the clustering around the groups of people who care about our content, that's the trending item right now. So I think people are trying to make sense. We had Nokia on earlier, which is also using Cloudera and they're trying to figure all this out. So why is mobile so hot? Is it because of, is it security threat? Is it because that's the best data acquisition side of the new consumer data or both? What's your angle on that? It's ironic because it's, I think the reason mobile is so big is that we have an internet right now that is, for the most part, uncurated except in very limited ways. Meaning you have to consume the information to know what's important. But you don't have much time to consume that information. You have a limited amount of time, therefore you either look at less information or you read it at really shallow levels. Now, what happens is we get addicted to having that connectivity, so we always have it available and that's through our mobile device. So I think mobile is trending up primarily because that's how we access the information, but it's actually an effect of filling the need to be connected to information all the time because we don't have good filtering. So I don't know, you start up with machine learning. I think machine learning is critical to filtering so we can recover our time. So big data will actually reduce your ability to use your mobile device? It already is. I mean, I think that's sort of the problem is that you're going to use it more and get less out of it because you have no more attention and until technology gives you better return on attention, you won't break through that. So mobile is how it should be. I mean, I think three or four years from now we're going to have a big TV screen that's a computer when Apple ships something right, like at the TV, we're going to have a phone and we're going to have a tablet and there won't be most of the laptops. I love them, but they're probably going to go away. I mean, five years out where most people don't have that. And so if you think about it. And also mobile, mobile and Dave was talking about earlier about what real time means is, you know, you don't miss your plane and you don't lose a customer. So you don't have to be exactly real time, but mobile actually is about real time. You're on the go and you want, if you want to find out, you know, the nearest garage sale, you're in your car. Go take the section or not. Yeah, I mean, you want to do that too, dinner or whatever you're going, mobile is the real time format, right? So it's the most available to show that value. Did you agree with that? I do agree with that. I think it also creates some real troubles because the inability of the software we have now to summarize the information on those devices means we have to distract ourselves more and more. If you do that when you're driving, you could die, right? We have all these laws about this now. And so we are unfortunately working around the limitations of technology and how much we have to obsessively use devices versus use them for the ends we want. And so that's something I think that analytics and big data together versus just big data on its own is pretty key to. If it knows more about you in the cloud or even, you know, whatever's on your device running, then it should know how to prioritize things and know when to interrupt your attention and when not to. So Tim, let's drill down on a couple things on the spotlight right now. One mindset and two kind of practical use cases that are coming out as first steps. The people who are getting into big data and we're excited because big data is now being talked about in the New York Times, Wall Street Journal, although the Wall Street Journal got the story a little bit wrong, we corrected them with a post and linked to them. So they kind of got the big picture, that's cool, you know. People are asking questions, so it's legitimately now on everyone's to-do list for at least for the next decade is to do big data. A lot of people don't have the mindset, they don't have that culture. Share with us the culture that someone, mindset someone has to have to attack a big data, make big data part of their world. And then two, some of the use cases and things that we were just mentioning around mobile, one of the top things that are going to give them benefits because people like to think, okay, I'm going to spend the time thinking about it. What benefits am I going to get? What's in it for me? Where will I see the improvement? So first talk about the mindset. What is the mindset someone has to have, whether the CEO, CIO or guy in the trenches? And then use cases. I think what's happening, the world is changing very rapidly where the availability of information at scale, our ability to communicate so quickly and easily, is creating an implied responsibility to have awareness over that information. Okay, so as a result of having an implied responsibility, we're having to re-engineer everything to hold the data and we've been doing that the last year or two with Hadoop, I mean mainstreaming, right? It's all about how do you hold the data and how do you process it? Now the real fun stuff is what do we process it with to get value out of it? So transition to use cases is how do we give people sufficient awareness so they feel that new responsibility which is I'm connected to the world, therefore I have to know something about it. I mean I'm not on social media anymore, I got off in some things last year, Facebook just was too much for me in terms of the eruption of an attention and the trust in privacy. It's a time suck and I don't trust the privacy issues, honestly, that's just me. So given that. Uh-oh, and you know what's going on with the government. Yeah, well I just, no, I actually trust the government a lot more than I do Facebook just to be blunt. I mean, I think that. Whoa, that says it all. Oh my God. Like they're regulated, the people that do that on the government side, they have laws and regulations they have to abide by. Where is the regulations and what they do, when they go public what can they with our data? We should be asking that question right now. Okay, okay, side tangent. But what- Are you on Google Plus, speaking of trust? Nope, definitely not. So, but I say this because I really believe we're just starting to wrestle with the use cases which is what are the applications that take the data that's stored that we're now figuring out how to handle infrastructure-wise and make sense of it so we get value because we didn't put down 1,000 Odub clusters moving the industry because it was fun. I mean, yeah, it was kind of fun but we did it with the investments of CIOs because we wanted to learn something that would affect a decision that would save money or make money. Or for some of us on the creative side, it was to show something to be possible that was never possible before. And I think we're just starting to get into the fun stuff which is now that infrastructure is possible thanks to some of the infrastructure people we talked about earlier. Now we're in the area of where's all the value coming from. So, it's almost like we have motherboards. Who's going to build the CPU to make sense of the data? Who's going to build the memory, the fast access side? Who's going to build the graphics card of it? We are in a virtualized building of the personal big data abstraction right now. And the companies will do it first and then people will bring it back in their lives. Okay, we're running close on time here but I want to make sure I just get in a couple of questions on product and how are you going to spend that VC money you have? We heard from Rob earlier that you're obviously hiring people. But on the product side, you have a cloud now. We signed up for it. I got in there. I don't know what we're doing with it yet. I haven't played with it yet. It's going to get dangerous. What are you guys going to invest in on the tech and the product side? What's your key focus areas? I think that our major concern is to be the best quality and automated analytics understanding on unstructured data in the world. Because if you cannot trust it, you won't use it. And many people have promised and few have finally delivered. So I think that our primary investment is going to be on, it's only having the highest quality and going after use cases of value. So I mentioned dealing with large scale external data. So date on the web, we start with the patents. We'll have other data sets throughout the year. We'll show and announce and maybe very, very large sets. And then we're going to make it really, really easy for people to build applications on that cloud or build them on the system as it's deployed inside the firewalls of banks and government and elsewhere. So we think making it a solid software development capability for other people to build off of is a major use of those funds. And people kind of overuse the word platform. But I do think part of it is, how do you make a platform that people can show value from? And we'll probably show some of those in terms of applications. But I think we were trying to build an ecology around, that's an ecosystem that other people will show value from it. And we're going to take a lot of hard things people have not seen done before on that kind of data and make it really easy and then show what's possible when that data is available to you. So. Okay, again, congratulations on your funding round. We're glad that we could be part of it in some indirect way. I guess, you know, a lot to take credit for but you guys really did that on your own. Congratulations, great product. We'll certainly be talking to you guys. We love you. And want to personally thank you for the support of being a sponsor on theCUBE with helping us bring this knowledge out to folks. So we really appreciate that giving back to also helping us enable us to do the content and great stuff. Thank you very much. Thank you, John. All right, Tim, we'll talk later. We'll be keeping in touch with these guys, obviously. We love them. Great supporters of us and vice versa. We'll be right back. We're getting a little bit behind schedule, Dave, but it's worth it. And support our sponsors. Watch the ads.