 Live from Boston, Massachusetts, it's theCUBE at the HP Vertica Big Data Conference 2014. Brought to you by HP. With your hosts, John Furrier and Dave Vellante. Okay, welcome back. When we're here live in Boston, Massachusetts for HP, Big Data Conference is theCUBE, our flagship program. We go out to the events, instruct the students who know as I'm John Furrier, the founder of SiliconANGLE. I'm joined by co-host this segment, Chris Selland, who's the VP of Marketing and BizDev at HP. Chris, welcome to the co-host. Going back to the analyst days. Yeah, great to be here. Given how long we've worked together, no problem. I like sitting in this. Les Bonney, COO of CLIC is here. Great, I know about your company, one from one Rick Jackson who you hired, XBM where CMO, and also of the relationship news with Vertica. So, Chris, tell us, what's the partnership about? What's the big Vertica click story? Well, you know, I'll just say, CLIC is a very important, great partner of ours. We've been working together for a number of years. We use the product internally at HP. There was a demo done earlier in the conference, whole session actually, discussing how we use it for sales analytics and pipeline analytics. And there's a lot of new stuff that CLIC's got coming out and we're really excited about it. It's really going to deepen the relationship. It's going to really extend the level of integration. It's going to bring a lot of joint value or joint customers and we really expect our joint customer base to grow dramatically because of that. So, really excited to have Les here. So, first question, what's it like working with Rick Jackson? Well, he's a gentleman. It's very pleasant to work with him, but I think that would be confined to the bar if it wasn't just that. I think our collaboration with Hewlett Packard and HP Vertica particularly is a very rewarding one. So, I think it's happy socially, happy business. I want to get the Rick Jackson plug. I know he's going to watch it. So, I'm going to get in. Hi, Rick, how are you? So, talk about the business. What are you guys at? Talk about your product. I mean, self-service, data intelligence, all kind of converging. Tableau, set the table on visualization. Analytics and visualization are the killer apps right now on everyone's plate. So, what are you guys doing in that area? Well, we've been around for some time now, of course, and I think we definitely pioneered the market of business discovery, as we call it, Garner, call it data discovery and pioneered the democratization of business intelligence and to ensure that people, normal people, like perhaps you and I, John, can actually drive analytics in order that we can actually get meaningful results from perhaps very complex data environments. So, we've taken that approach to say, how can we simplify analytics so normal people can use it? You know, we were having a conversation earlier and it was a kind of a joke that we overheard and someone said, what's the statistician and a data scientist? And the answer is salary. Yeah, and then I got some flame mail on Twitter. That's irresponsible to say that. And then I just said, well, everyone's changing their title from statistician to data scientist. And then the flame came back in again saying, yeah, that's a pet pee because you can't just fake it as in certain different levels, as this project will fail. So, what's your take on all that? What it means is people want to be data scientists. And so, is it easy or hard? What's your take? I think this is being driven by some sort of global trends in business, really. I think organizations now, to be a modern relevant organization, need to empower their employees to become more comfortable with using data and getting insight into the data that's, as we know, being produced by leaps and bounds from a volume point of view. And to drive the sort of agility of modern enterprises, we believe data is at the core of this and empowering normal people to use their data in a much more constructive way. Yeah, we've had some pretty good interviews, Chris, here, with some of your keynote speakers. Obviously, the United States Postal Service really here. Being competitive, trying to compete with the Amazons, the UPS and the FedExes. And also, we had Tom Davenport, who's about to go on stage, talking about competitive strategy, process improvement. All that's changing. So, Chris, I mean, was it a method to your meds with these selections? Is it all convectoried with discovery? No, I mean, it's certainly, we have a lot of new breed businesses, but also a lot of businesses that have been around for a long time, very much including HP, as I mentioned earlier. We use CLIC internally. We're a 75-year-old company, right? So, the grounds of business, whether you're an old business or new business, are really changing. And also, globally, one of the things we really like about working with CLIC is the fact that we're a global organization. So, they've really got a footprint that a lot of technology obviously tends to be Silicon Valley-centric, US-centric, North America-centric, but this is truly going global and CLIC is a global company and a global partner. And obviously, HP is a global company as well. So, it's a great partnership that really extends worldwide. So, Les, I got to ask you the tough questions now. So, the real tough question is two things that drive validation in the market for your customers. Data sources and industries. How are you guys handling the plethora of sources, one, and two, across industries? Because there seems to be people just going in and saying, I'm going to own the vertical. Yeah. And have specialties. So, can you guys, what's your strategy there? Are you a multi-industry player and data sources? We're definitely data agnostic. We don't mind where data is from, what source it is. In fact, I think the major strength of our product set is our ability to aggregate and deal with multiple data sources and mash these together on the fly. So, this very powerfully supports the notion of being able to gain insight into your business or into your data because you can't really dictate where the data is or what source it may be in. And that's what we do very well. Sources could change overnight, really, right? Completely, or be invented overnight because I think in the, particularly as we're moving forward, I think people have the Excel spreadsheet in their bottom drawer and they'd love to be able to combine that with their Salesforce data and their SAP data. And that's what we do so well. So, Chris, I want you to take your HP hat off for a second, put your analyst hat on. How would you categorize these guys? And don't use the magic quadrant. Maybe make up some of the new metric or a wave or a quadrant or whatever you want. How would you, as an analyst, look at these guys and compare them to others? I would say, you know, an excellent product. As I said, we use it. A company that's got a global view, global footprint. They've got a tremendous partner network, which is another real differentiator as well. I mean, as you know, we have a very broad ecosystem. We try to work across the board with all of the major BI visualization platforms because it's all about customer success and we have to. But we really appreciate our partners and click is certainly a partner that, as I said, from a, you know, first of all, just excellent product and they've got some new products coming out which I'll let Les talk about more than myself over the next few months. I think something they've just released and, you know, great product, global footprint, great partner network, well established. They've been around for a while. So this is, you know, it's a really important partnership to us. Last one. Perhaps I'll add to that. I think there's one thing is that we do well we believe is that individuals and organizations, they go on a journey with analytics. It's not a point in time. They start often with very simple use cases. Then the human nature is one of inquisitive, wanting to ask the next question to push the boundaries of what they know. And we feel very strongly that a product has got to be easy to adopt and easy to use for often in the early stages of very simple use cases. But you also got to have a product that can hold the hand of the user or the business and take them to a journey for very complex analytics. And all these use cases exist at the same time in all organizations. It's like putting a child through college, right? Or school, you get them into kindergarten, you grow them up. Or indeed learning a language. You can start with having a very small vocabulary and point and grunt to buy a piece of bread. But if you want to write poetry or technical authorship, the language needs to grow with you. And we think a product like Click does that, allows the simple use case to be supported by people who are not IT literate. It's easy to get into. Absolutely. And you can grow with it. Very sticky, so fun to use, intuitive, gets you to value very quickly. And then you want to more, you want to express more and have a more robust, complex analytics conversation with your data, then the product can support you as well. So it takes you on the journey to a very high level of analytics confidence. We're here live in Boston. The people are rolling in for the keynote with Tom Davenport, who we just interviewed earlier. You can go to SiliconANGLE TV and see that. Unless I'm really intrigued. First of all, I think this is a smart strategy. This idea of getting someone in and growing in this growth market is embryonic still. We were talking with Chris earlier about that. But I got to ask for this data discovery. Business discovery is a really interesting concept. So I want you to give the audience a 101. Give us a data discovery 101. What is it? What does it mean and what is data discovery? Well, it's often, I think it's very difficult to describe in actual terms. But if I can draw some analogies between the individual and how individuals process information is very subject and very personalized. And different people view data in different ways. So the idea is to provide a capability, a technical capability, that gets away out of the way of the individual's capacity to hypothesize about certain data and the sent data often provides and say, where do I go next with my next question? We believe that actually when people look at data, they often may have the initial engagement with a question in mind. But the minute you look at the data, then they say, well, actually, I didn't expect Jill to be a poor salesperson. What's happening here? And then you go off. So that's the discovery. It's the serendipitous discovery of insight into your data. So let me just kind of parse this a little bit. So I'm going a little slow here in this late in the day. But so is it data discovery of the insight from the data or is it data discovery from what data is available to act on? No, it's insight into the data and the ability to mash data together from multiple sources, therefore expanding the horizon of insights. So Stonebreaker's new product goes out and does a sprawl and crawl and figures out what's out there to work with. This is not something like that. No, not like that. This is really the ability to be able for an individual to be able to say jump into their data and swim around in their data in any direction they like. They're not pre-constrained by somebody else's view of what questions are going to be asked. So it lets people wrangle data. Is that what I hear you saying, right? That must be an American term. Wrangle data. Is that like a cowboy thing? Well, and John, I think to your question before, the real difference between a statistician and a data scientist is what a data scientist should be also doing. A statistician can tell you sort of what the data says, but I think a data scientist is a good one. It should really be telling you what we should do and start to make suggestions and as Les was just alluding to, look forward. Take the meaning and make suggestions and it might not be make the final decision, but certainly make suggestions up forward direction. Thanks for coming on theCUBE. We got the keynote coming up. We got the great Tom Davenport up there. It's click with a Q-L-I-K. Check them out. Les, Chief Operating Officer. Thanks for joining us on theCUBE. My pleasure, John. Thank you very much. We are here live in Boston for the HP Big Data Conference. This is theCUBE. We'll be right back after this break and we'll have the keynote up shortly.