 Okay, we're back at SiliconANGLE.tv's exclusive coverage of the H-page conference. This is the first ever H-page conference that's live in San Francisco. I'm John Furrier, the founder of SiliconANGLE.com and SiliconANGLE.tv. This is theCUBE, our flagship telecast. We go out to the events, extract the signal from the noise, share that with you. And my next guest is Michelle Bailey, a CUBE alumni, formerly of IDC. You've been on theCUBE before. That's right. You're now with a stealth startup called the VDP Finder. What does that stand for? Vertical data platform. Finder, some sort of search thing. So it's being discussed as the hottest startup here in H-page. So congratulations. Thank you, thanks. So it's about data. So the startups about data, social data. What's your take about this whole data marketplace right now and what's your assessment of the conference as an attendee here? Well, I think, first of all, the who's who is here in big data today, don't you think so? So I think while there's a lot of attention in the vendor community around big data, I think what you're seeing here is the core developers, the engineers, the programmers are all here today, really looking for what the next advancements are around Hadoop and H-Base specifically here. And I think a lot of focus around what I'm really excited about, which is what are you gonna do with all that data? So now that we've collected all this data and we understand how to store big data, what are you gonna do with it? What information are you gonna get out of it? And I think that's what we heard Mike Olsen say this morning pretty clearly is that there's not much point in having a lot of data if you don't do something with it. So in your new role at the Stealth Startup DVP Finder, you're the chief data scientist, what does that mean? What do you do? I mean, what are you gonna be doing? Yeah, so our application is built on H-Base and it's really specifically a tool around going out and finding people and very much about being able to extract very large quantities of data, analyze that data on the fly in real time, and to be able to produce information from that data that's relevant to organizations in today enterprises so that they can get a handle on what the social graph of their constituents look like, whether they're followers, customers, employees, whoever it may be. So this conference has traditionally been a batch environment or this marketplace had dupas been batch and not produced. Here the emphasis is on real time communications, real time analytics, real time business. So as the marketplace moves to this speed of business, as we've said on theCUBE before, real time is the fundamental thing. So that is the main theme here. What are you seeing as you look at the landscape of the participants and outside the show here, what's the status of the marketplace around real time analytics and Hadoop? Yeah, I mean, I think even for, I think a lot of the people here at this conference would agree that even with Hadoop, the way it is today, that the market's been about batch, right? It's been about being able to take data that's being stored and perhaps do reporting on it later, perhaps be able to come out and produce some graphics on it, but it hasn't really had a lot of emphasis around real time. And what you're seeing here today, and I think what a lot of people have come to this conference for is how do you take that next step? How do you go from taking something like Hadoop and HBase from being just a data repository to make it something where you can actually take that data in real time and put an application right on top of HBase? And it's incredibly challenging today. We have a development team that spends all of their time figuring out the nuances of trying to make all of that work together, because it's not a well, it's not a mature platform such as like what you would expect, the lamp stack, right? It's not like you have all of the elements, they work together, it's got good functionality, it's got well proven functionality, right? This is a very, very new environment where a lot of those elements that we take for granted today in traditional programming environments is built in, it's not built in today. So that's why I think you're seeing a lot of excitement here, lots of hiring going on here today, I think. I think what's really exciting is you're seeing that next generation of programmer. What are you excited about as you look at, because as a data scientist, you're looking at data models around figuring out using social data to get insight and find people. What are you excited about when you hear some of the talks here today about what's happening with HBase for customers and for developers at the same time? Well, I think for today it's very specific around the technical community and what I'm hearing from them is there's a lot of attention being paid to new developments that are going to shorten time to market for us. So as you think about a lot of the advances that they want to make, what it's going to mean is it's easier to build applications, it's easier to develop applications and it means you get to market a lot faster. And so that's what's incredibly exciting. I think that for all of the great functionality that you get out of a lot of these newer technologies and the ability to take a very, very large data set and very quickly glean information from that, the work that has to be done to get there is brute force a lot of the time. And so that's what's really exciting about this market. And I think that it's so necessary, I think that while a lot of the technology that you're seeing here today is complementary to existing technologies, traditional SQL databases, for example, for a while they'll sit alongside each other and they'll help each other out. I think you're starting to see the beginning of the next disruptive innovation. So you are talking with Kristoff Priscilla and he's got WB data, something similar to VDP Finder, so tell them about the things that you were working on the go-to market and the field trials. So in the field trials of VDP Finder, Kristoff had similar experiences where you want to get enough requirements, but you don't want to get too servicing to customers. What has been the reaction to the field trials with your big customers you're rolling out with? So we're actually, our customers are helping us to develop the product. So I think he's right. I think you want to be careful that your customers don't lead you down a path where you're helping with today's problem when you're sort of ignoring what everybody wants for tomorrow, right? So you've got to be very careful about that. But I think for the customers that we work with anyway, they're incredibly helpful about being able to tell us what would be interesting information to see, how it should be presented, what would be helpful for them and their team. So we actually try to take a lot of that feedback as part of our long-range plan. And Biddy's right, you want to be careful that you don't veer too far off course. What's been some of the reactions that customers have said to you when you show them the field trial product and what's the reaction? Well, everybody sort of has the same reaction. It's like, wow, you can do that? This is so great. And then the next question is, well, how can I use that and how can I get that into my company? And so that's where we are right now with a lot of our customers is helping them to bring a lot of that data in and really start to customize for them how they can use the information that we're able to glean out of the social information. Michelle Bailey, chief scientist at VDPfinder.com. Stealth startup, cutting-edge work at Data Science. New role for you, congratulations. Thank you, I'm excited. Not in the IDC analyst role, which was great. You were doing a lot of data center work with some big clients. It did for a long time. You had a good run at IDC. You've been in the queue. Great organization. I'm a huge fan of IDC. I think great organization. Love all the people over there. But what a nice switch to kind of go into a new, whole new direction. Yeah, I mean, it's actually, it's a pretty natural progression. At least it feels like for me, I have a background in statistics. I spend a lot of time looking at technology markets. Spent a lot of time really being able to identify when you see the next hardest thing coming along. And so it's actually, Data Science role is a really nice way to bring together a lot of what I've learned over the last 10 years and productize it and really be able to take it to customers and help out. Well congratulations. Thank you. New and different, exciting. Michelle Bailey with VDP finders, stealth startup, hotly discussed topic here. You guys getting a lot of good props here. Thank you. Congratulations. We'll be right back with our next guest after this short break.