 It's theCUBE covering HPE Big Data Conference 2016. Now, here are your hosts, Dave Vellante and Paul Gillan. Welcome back to the Boston Waterfront, everybody. This is theCUBE SiliconANGLE's special presentation of Hewlett Packard Enterprise's Big Data Conference. Hashtag is sees the data. Colin Mahoney is here. He's the Senior Vice President and General Manager of HPE's Big Data Division. Colin, great to see you again. Great to see you guys. Thank you for coming. So you are the man at this event. I see you've upgraded the outside speakers yet again, which is fantastic. Trying to do that every time. Vertica 8, big announcement this morning we were talking about, so how do you feel? Yeah, I feel great. You know, I actually, I said this this morning, but I think there's never been a better time to be involved with anything data. I think over the first couple of years, I mean, you recall from the beginning of the show, a lot of it was missionary work, trying to explain to people what Big Data was, how it could help. I think we're well into the zone of people understand how it can help. There's great examples, whether it's fighting cancer, making people healthier, fighting terrorism, finding better customers, serving your customers better. There's just countless examples of what people have been doing. And what I love about this conference is we bring together the customers and partners who have been doing this for a long time. It's just great hearing their stories. I was telling Paul, the first Big Data Conference I was doing the Phil Donahue and the MainTent and one of the audience members had a question and they were kind of antagonistic about when are we going to see Big Data applied to something besides ad tech? And we're seeing it. You gave a number of examples today. We are seeing it and as sometimes annoying as those ad tech algorithms have been in our lives as we're trying to surf the web and do other things, I think that they really paved the way for a lot of these other industries to not only have the right algorithms and be able to do those analytics but also have the right talent pool who can come in and apply data in a very practical way to the industry. So I think we've come a long way for sure but we've learned a lot as well. You talked this morning about just collecting data and how the early days of this conference it was focused on people who focused mainly on just getting a hold of that data and storing it and now you're talking about putting it to use. Where you find those breakthroughs are coming in customers' mindsets? What kind of, where are those aha moments that enlighten them to the potential of analytics? I think the aha moments that work the best for our customers are the ones where they don't try to boil the ocean, somebody just has a question or they have something that they want to test. They throw out a hypothetical and then they find the data and they see from maybe not even a huge set of data, maybe it's just a subset of data but they look historically at something and they're able to replay what happened and then very quickly prove that there is a positive return on understanding that data and I think when any organization starts with a project that might be small and they hit something like that it just opens up every other sort of curious questioning that you could imagine and then I think organizations themselves the culture starts shifting where people believe in it and they buy into it and then you don't go into a meeting without being armed with a lot of data but it oftentimes starts very small it's just somebody had a hypothesis, they go back they look at the data, they see something that happened and they realize, wow, that actually is hugely valuable to know so that we can do better this next time. So do you see this as more of a viral phenomenon where big data analytics awareness spreads from the bottom up? I think in many ways it does spread from the bottom up but I would also say that you have to buy into it as an organization from the top down. I think the leaders of the organizations that are best with data, what they do is they describe the importance of the data they describe the moonshot of the data they embrace data as a catalyst data as an accelerant, data as a disruptor and even when the data is not saying great things those leaders still embrace and want to hear about that data and so I do think there is a virality to it from the bottom up but I also think you have to get that support throughout the organization especially from the top to make it work. We're just a conference in Cambridge you mentioned in Cambridge is sort of the center of the Vertica universe. Conference in Cambridge last month about the Chief Data Officer Symposium and there's some 3,000 companies according to Forrest will now have Chief Data Officers. Are you talking increasingly to people with that title? We are, so I think in the early days where we saw Chief Data Officers was in companies like gaming companies and other more startup organizations. I think as data has become a critical asset and frankly it can be a liability from a compliance standpoint it's been really important for organizations to have a C-level executive who is the steward of data. The person whose only job is to make sure that on the one hand it's being protected, it's being used and compliant with government and other agencies and on the other hand that it's being monetized. So many banks that we talk to all the time say we've been collecting data forever we basically been forced to collect it for compliance reasons but now we actually think we can do a lot of great things with that information to give our customers a better experience or to reduce fees or to do this or do that. And oftentimes it's the Chief Data Officer that is in conjunction with IT and the business sitting in between and making that happen. You've been involved in big data before it was called big data. We're the better part of a decade in you had Phil Black, former Navy SEAL I guess once in Navy SEAL, always in Navy SEAL. In Navy SEAL. Up on stage today and he talked about ringing the bell 80% of the candidates ring the bell. They give up and no shame in giving up that. So not to be pejorative there. But the point is, the question I have for you is everybody talks about the data-driven organization. He gave this sort of great talk about being uncommon and having this uncommon grit and desire. So by definition, 80% of the organizations out there are not the best at being data-driven. Do Vertica customers, have you found a higher affinity for data-driven organizations? Because you were there early, you aligned with a lot of the analytics folks. Can you talk about that a little bit? And do you have any sort of evidence that some of the early Vertica adopters are leading that sort of big data-driven organization? Yeah, so first of all, I loved Phil's talk about grip because I think people are not born data scientists. Organizations are generally not born analytical. Even some of the new companies that are out there, some of our customers who seem like they're only about analytics, I don't think it happens overnight. I think it's something that you dedicate yourself to like anything else. You take risks and you persevere through it. And whether you're an individual trying to become a data scientist or you're an organization trying to compete on analytics and data, I think that is probably the most important trait. Of course, talk about technology and we love our technology, we love our solutions. But at the end of the day, it comes down to these companies and the people. And I think to your question about, have we had maybe a disposition towards some of the more analytical companies? I think in many ways, a lot of them found us in the early days because they were looking at such enormous challenges and just had to try something. But we also have a lot of customers that we've gone on this journey with that I wouldn't say started as analytic companies at all. And they have made amazing strides in progress in becoming analytic organizations. In fact, we had our customer advisory council yesterday. And one of the CIOs from this customer, large private company, said that most of his journey has been convincing the other people in his company that they need to be data-driven, that they need to monitor their delivery trucks. Even though everybody's saying, why are you going to do that? You're being big brother. He had to build up the argument and then build in the capability, that muscle memory to be analytical. And as Steve Speer talked about this morning as well, everyone can be a knowledge worker. And I think whether you're on a blue collar job, working on a line, you can be a great knowledge worker. And those organizations that build that into every part of the organization, take the feedback, monitor it, make the change and become better, they're the ones that are generating massively more profit than maybe a company that looks the same in the same market. What you talked to this morning about, Vertica's always kind of been in between the traditional enterprise data warehouse and this whole open system community that just exploded. And many, you said, we're sort of pushing you guys toward getting deeper into that community of picking up companies or maybe doing investing there and you resisted that. Why? Is it just a waiting game? You're like Bubba Gump Shrimp? Or was it something more fundamental than that? And what does that all say for the future of Vertica? Yeah, I think so. I think it was simple and maybe more fundamental. For us, we just wanted to focus on what we did well. And I think from the days that Vertica started and well into us being part of Hewlett Packard Enterprise, having that focus and knowing what you do well is critically important to any execution you can do in any business. Also though, the wonderful renaissance, whether it was open source or not open source technologies that were coming out in the analytic realm, there's just so many choices. And it's hard to know which ones are going to pan out, which ones aren't. It seems like acronyms pop up every week and we wanted to embrace it and we wanted to extend it and make sure that our products work with the ecosystem but we also wanted to make sure it was hardened and that it would fit into these deployments that we have. So I think it was really just about focus and staying power and coming into our own and then in a timing where the market is really now all about, okay, I've got the data, now what do I do with it? I'm really glad that we stayed focused on that because I think it's paying off. So how would you summarize what you do really well and what gives you confidence that some open source project isn't going to disintermediate what that is? Well, I'm always paranoid, as somebody who's an entrepreneur, I subscribe to the Andy Groves, only the paranoid survive. I'm always thinking that somebody is going to come out there and they're going to disrupt the market. So what we balance, I think really well, is being open-minded to these ideas that could be disruptive technologies, staying very close to the academic world, staying very close to the open source and other communities and embracing them when it makes sense, being flexible on our business model and driving that technology forward. I always do worry that there's something else out there but at the end of the day, what we do really well on the vertical side is we have a phenomenal analytic database engine and what we allow people to do is leverage this massive ecosystem that's been generated over the last three to four decades and tie directly into it but also it's a system that was designed for today and tomorrow's workloads. It's a much different architecture, completely built from the ground up and that has given us a lot of flexibility to plug into things like Hadoop with our storage APIs, to plug into various cloud platforms. We announced today support for Azure from Microsoft and I think that flexibility helps us as well. Even machine learning, you can plug in Spark. You can leverage a lot of these developments that are out there but what we do really well is our execution engine, our optimizer, being able to let people ask any questions that they want against the data and serving it out in a fast performance and efficient way. It turns out is pretty tough. You don't fit cleanly into the data warehousing box or into the big data Hadoop box. You're sort of somewhere in the middle. Is that a problem in market definition and getting prospects to understand what you really do? It's a great question. If you look at our market, on the one hand you have what I refer to as the high end dinosaurs that had proprietary data warehouse appliances and charge a lot of money. I won't name who because we all know who. On the other hand you have things like Hadoop at the low, low end of the market, kind of promising to be able to do a lot of different things and to your point, you're right. We're kind of right in the middle and sometimes it's easier to say, I'm the high end proprietary data warehouse or I'm the low, low end disruptor. I think it was a little bit more challenging than it is right now. I think right now everybody's saying, wow, between those two things, there was a massive market and opportunity. And if you can deliver economics like Hadoop, but performance and features like some of the traditional experience platforms that have been out there that these customers are used to, sign us up. And now that message is resonating a lot better. But you're right, we are a little bit of in between because we have some of the best things in a database and some of the best things in these new platforms. And it's not as easy as saying we're either or and sometimes that makes the messaging tougher. I always liken the run on the MPP databases that all of a sudden there was an acquisition spree like when left tackles go in the NFL draft and everybody starts picking them up. And for a while I was like, okay, that makes a lot of sense. Now you got this next big data wave coming, but you look at the big data wave and you're saying, wow, a lot of these companies are struggling. We've talked in the past about how it looks like the nose of the plane is up, but they're actually losing altitude. And when the funding dries up, it's going to get kind of ugly. And you're starting to see that today in some of the public companies and some of the private companies. Do you feel like there's going to be another renaissance in the space that Paul's describing in the middle space? I think that there are a lot of companies in the capital markets the way they've been, especially private companies, there's been a lot of innovation funded. And a lot of that innovation, people just believe that eventually they'll find the right business model and they'll make money and it'll all work. And in some cases that might be true, but I actually, maybe it's my East Coast roots, I don't know, I think building sustainable business models is really important. I think it's becoming much more challenging for a lot of companies in this climate to show what's on the come. You can't just survive on that alone. So I think everybody is being forced to some of the fundamentals that any good business has to show and that is leading to some challenges. But I do think there will always be innovation. There will always be opportunity for new entrants to come in and I do think the focus very much now is on this middle area of how do we actually monetize this data, how do we actually do things with this data? Do you agree? I mean, personally, I don't think it's unfair to say that the whole big data space was overfunded for the return that it provided, but that's not necessarily a bad thing, is it? It's not a bad thing and I look back to maybe some of the other bubbles that we've had. No one would argue today that this internet bubble was worth every bit that it was described. Eventually. It was understated at the time. It was probably understated, exactly. So the markets may do this, but I think the fundamental phenomenon that's happening with data and information, I think it's extremely undervalued right now. As to certain vendors playing in the market in short-term gains, execution, yeah, we can all argue about that. But I think fundamentally, the value of information and data is very real. Well, one of the things we said early on and when Hadoop started to explode was that was the practitioners that were going to make the big dollars, right? Not necessarily the next Microsoft coming out of this or the next Intel, and that was kind of the same with the internet. Where it was the people who applied the internet to transform business models. Amazon, obviously, is the poster child for that. Is that, I mean, do you see, I mean, I suppose you could say the same thing with ERP. If you could pick the companies who could apply ERP, you probably could have made a lot of money in the stock market back in the day. Are you seeing companies apply big data in a way that is really driving value that is consistent with what people expected on the vendor side, if you know what I'm saying. Yeah, there's no question. So I look at companies like Facebook and Uber and all these other companies that are, at the end of the day, they're data companies. And they are creating massive amounts of value. So I think that that same is true in many ways. The companies that are building on top of it can certainly capture value. But in there, if you're delivering value to them, as an arms dealer, if you will, I think there's plenty to be made there as well. Oh, absolutely. You may seem like a simple question, but it's one I've found everyone seems to have a different answer for. How do you define big data? It's a great question. And I get this, it's funny. I get it from, particularly, you know, since I'm with Hewlett Packard Enterprise, I get questions about all different types of technology, given our organization. But for me, it's not necessarily about the size or the scale. I think when I think of big data, I think about bringing data sets together that in the past never were combined. And they might be small data sets, they might be massive data sets. It could be a whole bunch of event detail data combining that with financial data or customer data. But the magic in it for me is when you start getting more and more people looking at that data and when it's combined and generating insights. To me, at the end of the day, that's what big data represents. And one other funny story is we never talked about big data. In the early days, even in the early days post our acquisition by Hewlett Packard, we never mentioned the words. And one day I was presenting to a South Korean company and I didn't have the words big data in any of my slides. And I don't speak Korean, so we had a translator in the room. And at the end of the presentation, we asked if there were any questions. And I couldn't understand the Korean, so probably every 10th and 11th word was big data. And I walked out of that and I had this epiphany and I basically said, you know what? It may be a confusing term, but it's certainly becoming a term that a lot of people are associating with projects. Some of these projects have been pent up over the last 30 years. People wanted to understand things. And finally, you're giving it a brand and a name. And I think in many ways Hadoop is very similar. It's become a brand and a battle cry. And so for me, I don't read too much into the big or the data, but it really represents this notion that there are so many things people are trying to do with information to better the world. And big data is giving them that battle cry and the ability to do it. And in some cases, companies now have big data budgets. So it represents, I think, a combination of mobility and the web and all these things coming together and the ability to analyze and move that much faster. So it's exciting. And Gartner did a fine job in defining the three Vs. That was good. It was well thought out, but it's a bit academic. Your definition that you just gave is a business outcome that was previously unattainable with traditional technologies that all of a sudden this confluence of whether it's mobile and social and data and came together to achieve something that you couldn't achieve before. And that's what you're seeing now, isn't it? Yeah, that's exactly what we're seeing. And it doesn't always, there are a lot of projects that involve data where the desired outcome or what people thought they were going to find was not true. But that is still a win. It's still a win to have learned that stuff. So we hear about a lot of projects where people analyze and do things and eradicate a cancer or make billions of dollars or do this and that. There's a lot of other cases where they actually went through the data and they found maybe bad things, negative things, but being able to understand that that's the case is also a huge win. And that's a part of big data that I think isn't always talked about, but it's a huge part of it. You're still winning. You're still gaining value from that insight. And for people to pick up a project after somebody does that work and then take it further, this collaborative aspect of data and data science is also becoming a huge thing now. And I think the ability for us to share that information and move projects forward through collaboration is also unmatched. Well, the strategy's been right on because you didn't just dive into this open source, we'll figure out how to make money later trend. So that was smart. You didn't say, okay, we'll just try to lock everybody in with a proprietary stack. You said, all right, we've got IP. We're going to keep it open, but we're going to add value because there's been a slow motion collapse in infrastructure pricing for a decade. You've been able to, seemingly anyway, withstand that by adding value in unique ways. I think it's all, I think anything we do, it's add value. You've got to add value, and if you add value, then there's usually people that are willing to reward you for that value. And I think we've stayed very focused on that. But we also look at the models. I mean, you mentioned this, being open, you don't have to be open source to be open to be frictionless in your sales model and to make it easy for people to try it out and see what the technology can do. And so, from the moment Andy Palmer, Mike Stonebreaker started the company, that openness, that culture was started and it continues. And I think Hewlett Packard Enterprise is very much the same culture. It's a culture of innovation and engineering collaboration. So as long as we keep doing those things, then we'll keep creating value. And I think the world in this space needs a lot of value creation right now. So give us the rundown on the event, had some great outside speakers this morning. What's going on? What's up for tonight, tomorrow? Yeah, so we got, it's action packed. It obviously kicked off this morning with the keynotes that we talked about. And then we go into breakout sessions. And these breakout sessions are either customer led or engineering team led. So they're very practical. They're very specific on the real issues that are going on. We've got a couple of events in the evening, of course, by different geographic locations. And part of what we set up is opportunity for our customers to be with each other. Partners and customers basically talking and collaborating. And tomorrow we're going to do it again. Robert Young-John's going to be here. He's going to talk about the Haven on Demand combinations announcement we made today. And we're going to continue with a lot of the breakout sessions and covering both idle Haven on Demand and Vertica. We'll end midday on Thursday. So the weather looks great outside. And it's a great time to be here in Boston on the waterfront. We're going to sustain droughts. So you're going to have a good day for a while here. One thing you talked about this morning is kind of unusual about this event is that it's engineering driven. The marketers can't drive any of the sessions. How do you sell that within the company? Company's been very supportive of it from the beginning. And again, I think this gets back to Hewlett-Packard Enterprise's engineering roots. It's just the straight talk on the technology. And we're very proud of that technology. We're very proud of people trying things. And we don't have to pound our chests and make up all these things about what we do. We just show people, this is what we do. And I think that fits great into the overall Hewlett-Packard Enterprise, whether it's our hardware, our services, or our software, that's what we do. And so we didn't really have to convince anyone. I think everyone welcomed the opportunity. And of course, our marketing plays a very important role in everything that we do. But I think what's great about our marketing teams in general is they're just sharing the capabilities that we have in the products. And so it's a great opportunity for our engineers to talk and to share what they've done and open up and take questions and collaborate. So I think everybody welcomes that opportunity. All right, well Colin, thanks for coming on theCUBE. Thank you guys, yeah, yeah, thank you. We'll see you around tonight and tomorrow. That sounds great, yeah, I'm looking forward to it. Thanks again. All right, keep it right there, everybody. Paul and I will be back with our next guest right after this short break. This is theCUBE.