 Live from New York, extracting the signal from the noise. It's theCUBE, covering RapidMiner Wisdom 2016, brought to you by RapidMiner. Now, your hosts, Dave Vellante and Jeff Brick. Welcome back to New York City, everybody. This is theCUBE. We go out to the events. We extract the signal from the noise. We're here at RapidMiner Wisdom 2016, hanging out with the data scientists. Big announcements today. RapidMiner version seven is out talking about citizen data scientists. We're here with Peter Lee. We're going to talk football, kickboxing, and a bunch of other stuff. Peter, welcome to theCUBE. Great to see you. Great to see you, Dave. So you got the little kickboxing. Yeah, you know, not getting better at golf. Obviously, I need a little bit of room for improvement in kickboxing. Great to spar with. You can make a lot of contact. So it's your big customer event. You got to be excited. Yeah, huge announcement today. Recent funding event. Fantastic, fantastic stuff. Give us the update. I can't say enough about Nokia. Nokia is just a tremendous investor. Did a ton of diligence. We're really pleased. They're global. We're moving so rapidly, not just here in North America, but roughly half our business is coming out of Europe. And Asia is a massive expansion opportunity. And Nokia is in all three places. We're delighted with them and our existing investor group. So the strategy of the company early on is to have an open core, a lot of users, and now you're in the process of sort of converting them into your paid model. Talk about that opportunity a little bit. Sure. I mean, we call ourselves by far and away the number one open source predictive analytics platform. And I'd say there's really three stages in which we engage with our fans. So first of all, data scientists, citizen data scientists, advanced business analysts. These guys can easily come in and start with very rapid prototyping. And then after you go through a prototyping phase, because especially in this era of big data, you don't know a priori what is going to be the best model or models that turn out to be the best predictors in a particular data set. Once you rapidly prototype, you can move to the next phase, which is you can really take, go beyond sample data size and really do validation. And that's typically where you're going to find tremendous new insights, gain better confidence. And then ultimately, and I think one of the reasons why Nokia invested is we have a very unique platform where you can move from the data science part, prototyping, validation to really the business value part, which is operationalization, where you take those predictive insights and then you embed them downstream into human or automated intervention. And so that's pretty cool. So Nokia, it's a strategic investment. I mean, they've always had a big, big data play. No, Nokia Growth Partners is a financial investor. Now we're pleased, Nokia has also become a tremendous customer of ours, and we have much to announce in that regard. But Nokia Growth Partners, a financial investor, they're backed by Nokia, and that's how we got connected. Well, time is good. I mean, you know this world and things are, B rounds are getting tougher, and VCs are nervous out in your neck of the woods. So the timing's good. So you must feel good about that. Yeah, absolutely. We were really pleased. We were able to demonstrate some pretty significant growth. We're doubling, tripling on multiple dimensions and we're able to really step up. And I think now that we've really gone through a couple hundred clients, we're well past the validation stage, and it's really time to step on the gas. So we're making investments both on the product roadmap, and I think we talked a little bit about that. We made some announcements today. Our fans are super excited about that, our customers here, as well as go to market. So you'll see a much more aggressive pace of investment as we work with our customers on high value use cases. Well, I'd say the timing's great because this business, the big data, Hadoop business, everybody went crazy. Tons of funding rolled in. And you have a lot of companies doing this that looks like they're growing, but there might be losing altitude and there's a lot of people concerned when the funding dries up. You feel like you're in pretty good position, obviously. Yeah, we feel like we're extremely well positioned. I think looking beyond the cash, I think one of the key elements as a disruptor in this market is that you can get started in an open source model at a price point that is very hard to beat. And you can continue to really grow and develop and invest in your analytics at an ROI, which is simply world class. And I think that's really what's propelled us to the number one spot. And so now we're building a much more aggressive community today, certainly a great example of that. I think you guys can see the momentum and the activity and the energy here, but we're also investing heavily in our marketplace. So you'll see that our partners, our SI partners and our other partners are really able to come into our marketplace and really engage with our community. The data analysts, the data scientists, the business analysts, citizen data scientists, and really have significant innovation. So we're extending our platform at a rate that's far beyond our core R&D and really embracing all the solutions in the field that you see with our partners out in various use cases. So that core community, that core data science community is very valuable, useful. I mean, people would kill themselves and try to get the data scientists to be loyal to their product. So that's great. Now, what about expanding beyond that? Is it time now or is there going to be a big effort to do that beyond that? Well, I'd say that if you think about the business of predictive analytics, the data scientists, that's really where the modeling is taking place. But we really engage with the full portfolio of enterprise personas. It really depends on the use case. For example, we've just heard from one of our partners, Pricewaterhouse here in a financial services context and really talking about using joint solutions, rapid miner, married to Pricewaterhouse expertise and transaction monitoring. In that use case, you're really engaged with not just data scientists who are modeling potentially fraudulent behavior, but really, and I think more substantively in an enterprise context, you're really dealing with the chief compliance officer, the chief risk officer, who's really looking to get in front of significant risk and cost inefficiencies in their business processes. And so that's a huge opportunity for us to really expand the vision and the embrace. So yes, we are moving well beyond just the data scientists and really heavily into line of business-based, use case-based discussions. And I think that's true in financial services vertical, consumer products and retail, and then asset-intensive businesses, midstream and oil and gas manufacturing. There's a number of really interesting use cases now. And I think people who have gone up the data visualization curve are now so comfortable with analytics, they now really want to move to the next step, which is really predictive analytics. And so we're in that. We've been tracking this now for many, many years, well over five or six years before people knew what Hadoop was and early on there was some big winners, particularly in financial services markets, some leading edge, typical for financial services, particularly coming out of the downturn. And then you had a lot of money pouring into experimentation. And a lot of the decisions were based on technical feasibility. Well, we can do that, but the ROI wasn't there. I'm sensing that's starting to change. There's more of an emphasis on starting with that business outcome and they're learning how to deal with the technical aspects. Is that fair? It's a great comment. I would say we are well beyond the stage of proof of concept type engagements across the board in the world of big data. I think it's a well understood paradigm. I think the challenge is really what I call a proof of value engagement. And so it's really about the end result, which is if you can operationalize your predictive insights, there's significant ROI and then let's engage and really kind of quantify how we can create a self-financing project that is one of three, that's gonna drive one of three outcomes, transformational, know your customers, so revenue-based ROI, customer and marketing analytics, radically reduced costs. So think about operational use cases or reduced risk. You know what's really seeing around the corner, not just what's directly in front of you. And cost reduction in terms of business process. Absolutely. As opposed to what the ROI in the early days was kind of reduction of investments, lowering the denominator of what it cost to build a data warehouse or whatever it was. And now you're talking about deeply embedding into the business process. Yeah, well we just had another customer really talk pretty eloquently, Siemens, just really talked about fantastic use cases around business process transformation. In audit. In audit. We had Lars, right? Yeah, Boris. Boris, yeah, Boris. Yeah, Boris on this morning. Oh, he was unbelievable. The number of questions that he got and you talk about that is what a great comment because really, to your point, business process, it's not about forming an ideal business process. In fact, one of the use cases he pointed out is in a procurement in Latin American country in Brazil, 12% according to his stats of the actual purchases that went through that country, followed the standard business process. So using RapidMiner, they were able to really isolate the 88% of the business process. So they have an ideal business process. 88% of the order flow doesn't follow it. And then you really begin getting into the predictive analytics of how do we begin altering which one are the ones to prioritize and then what do you begin doing to really move the needle around picking off the high value orders that really should follow those processes. So I think that's a whole rich new area of innovation that predictive analytics is really, I think it's been around for a long time. The legacy approaches, I think, have really been statistically oriented. Sample size, let's take the, I like to jokingly summarize it, let me take the smallest sample and draw the biggest inference from it. Let me do the least work possible. In the RapidMiner context, the more data, the better. Kill sampling, sampling's dead. Give me everything, the richer the data, the more likely we're going to have richer context and be able to make better predictive decisions. So that's something that I think is really a sea change in the way modern cutting edge data science platforms are really attacking this problem. Besides the fact that you can't, insight without action has no value. So it does no good to have a woohoo moment and you find the best predictive insight for particular customer segmentation analysis or basket affinity in a retail context and two guys in a trick duck know about that in your organization. You really now need to coordinate that insight both in the marketing area as well as the supply chain area, for example. And so I think it's that closing the loop that has significant business impact and that's what we're seeing and that's what's propelling our business. So there's a lot of action in this space. Yeah. You joined RapidMiner last summer. I did. What was special? May. May, excuse me. What did you see? What was it that really you decided to, all right, I'm gonna put a bet on this horse. Couple things, Jeff. So first of all, it's important to know I'm a Die Hard New England Patriots fan. So moving to Boston, I mean, there's nothing bad about that. No downside there as the Patriots repeat. So that was a big draw for me. But seriously, RapidMiner, I would say stepping back. I've had some experience in analytics-themed businesses. I was pleased to spend four and a half years with Tipco Software running a number of analytics-themed go-to-market product groups. Before that, I was the co-founder of another New York-based software company. I just stepped back and say really when you take a macro view of the opportunity, analytics has been around for a very long time. Now we're finally in an era and it picks up a little bit on what Dave talked about, particularly in the era of big data, we are now in an era where we can actually, nothing is impossible, let me put it this way, so you can actually take advantage of really taking all of the data in any form, at any scale, and you can answer any question. And when you think about how powerful that is and what RapidMiner can really do to propel transformation in your business, it's a no-brainer. I think for me, the opportunity to partner with Ingo and his team was just too good to pass up. He's a brilliant guy. He built the world's most recognized open-source predictive analytics platform and so we're really doing two things. We're both industrializing a product to have significant ubiquitous uptake across enterprise use cases and then we're really substantially increasing the velocity of how we go to market and we're doing that in a way that is completely disruptive, open-source innovation married to enterprise industrialization. It's too good to pass up. So what should we be looking for in the next 12, 18 months? What are your sort of objectives for the organization? What should observers like us be paying attention to? I think that what you'll see is we've been well-known to a pretty unique audience. I think we're gonna be aggressively establishing our ecosystem of partners in the big data space and I think you see some of them here today. Hortonworks, Tableau, PWC, many others will be joining us and we're aggressively investing in those relationships. I think it's fair to say that no one partner in the big data stack has all the goods. So I think it's very important for our joint customers that we're able to easily and seamlessly fit into the other significant investments they're making in data storage, in data visualization and I think predictive analytics fits neatly into a full stack concept. So I'd say that's one area that I'd absolutely call out. I think a second area is as I mentioned before our marketplace, we as an open source platform have tremendous R&D and innovation from our customers engaging with us across hundreds of clients and dozens of use cases but even more importantly, we don't have any delivery or consulting services capabilities. We rely on our external partners, our systems integrators and other partners to work with our clients hand in hand. So we're now investing in a marketplace where all of those partners can really reach out and get repeatability around their field solutions built with our product. I think that's a tremendous capability. We're already at something in the neighborhood of 15,000 monthly downloads today from a handful, 50 contributors in our marketplace. You'll see that number begin to move dramatically and you'll see the contributors into that marketplace expand pretty significantly. So I'd say that's a second area that you'll see in the next 12 months. Innovate, tap the TAM, you go deeper to the TAM and then leverage the ecosystem. Absolutely. Peter Lee, thanks very much for coming to theCUBE. Go Pat's, you're making a prediction? I think you just did, right? Well, it won't be pretty for you if you're Denver fan. So two or three interceptions, single digits on their side. I see a repeat of the Peyton Manning Indianapolis Colts game when we won it 20 to three, something like that. In the Super Bowl, it'll be Carolina. It'll be, again, it'll be another repeat. It's going to be tough sledding if you're a Carolina Panthers fan. It's going to be a lot of Superman action from our side of the fence. Love the confidence. I hope you're right. Peter, Lee, great to see that. Great count on it. That's a predictive. I love it. You better make it. Keep right there, everybody, we'll be back with our next guest, with our wrap, actually. This is theCUBE, we're live from Rapid Minor Wisdom 16, right back.