 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. Hi buddy, we're back to wrap up. RapidMiner Wisdom 16, we're here in New York City. Day long event, actually started yesterday, last night, the place was crawling with data scientists, Jeff. Good event, a lot of good thought leading conversations. Clearly, this industry, what do we want to call it? Big data, predictive analytics, machine learning, Hadoop, et cetera, evolving beyond the tire kicking phase. We're well beyond that. I think we were beyond that last year. Really into the, okay, how do I get more value out of this? We know this is an imperative. RapidMiner, as a company, fresh injection, I think it was 16 million? 16, yeah. New cash from Nokia Ventures. So that's key, I think that's really important because funding is drying up. B rounds are getting very difficult. You talk to you guys out in the West Coast. Furrier has his nose to the ground on this stuff and it's very clearly the sentiment is changing. People are nervous, obviously you see that in the stock market, so having a fresh injection of cash, obviously is important because now you can focus on the business. New leadership in Peter Lee, Ingo, visionary leader. So good story, a big install base. I think obviously the challenge of course that RapidMiner and companies like that with that open core face is how do you translate that and convert that into paid and add enough value to do that. So version seven is clearly designed to help that, expand the platform and expand the monetization model. I mean that's the big question is when the dust settles, who's gonna be standing, making money with a viable business model, right? You got, these guys are more, I would say, Cloudera-like than they are Hortonworks-like and that Hortonworks is selling just the subscription to the maintenance. Cloudera has an open core and then sells value modules on top of that. That's really precisely what RapidMiner's doing. The former model, the Hortonworks model is predicated on massive volume. They're in that for the long game. The Cloudera model, you're seeing their private company, they obviously have a lot more cash from their private investments can stay a little bit longer. You're seeing companies, as we've talked about many years in theCUBE, staying private longer, not having to go public. It's tough being a public company, right? Especially now, Hortonworks just had to do another raise, they beat revenue but they had to do another raise. Had to raise a hundred million. We saw that coming, George Gilbert predicted that and so it was pretty obvious. But they're in the game, now they have fresh cash. It's going to be really interesting, Jeff, when the dust settles to see who's standing and these guys got a good, strong community, a core to build from. What's your take? I couldn't help but think of Michael Dell over the last couple of weeks as the stock markets had a rough entry into 2016 and him smiling like the cat with the canary going private and not being exposed to this kind of whipsaw and massive changes and evaluation. I think what's interesting is this pursuit of the citizen data scientist. How do you get the data science out of the hallowed halls of the big brain PhDs? How do you get it down to the analysts? How do you really drive, as I think, Paul talked about, building a culture of analytics and that's where I'm really curious to know where are the entry points? Who are the people? What is the function? What is the application where you can start to move kind of mid to your decision makers into data driven decision making or they can really start to pull that in. The marketing example was interesting because you start to hear a lot of that in marketing. We don't cover a lot of pure marketing events but marketing is one of these scientists that's really moving now because they have the data to more of a data driven execution as opposed to the classic marketing, 50% of my marketing budget is wasted. The only problem is I don't know which 50% it is. So I think that pursuit of the data driven citizen data scientist is really important and I think that's really going to define it because that's the only way you're going to really grow your TAM, that's the way you're going to really bring a lot more people into this portfolio, a lot more people using the applications. And then the other thing that will be interesting to see how it sorts out is the ecosystems and the continuing shifting sands in the ecosystems. Clearly you have to have an ecosystem whether you jump on one like at AWS or pick your favorite big vendor that pulls along the wave or you can create your own. I don't think RapidMiner is big enough to really create their own, maybe they can around the open source piece of it, but ecosystems are so important in plugging into a system of other applications, providers. It's interesting, Peter said they have no delivery capability, right now they depend 100% on their partners. So that's goodness in that it's a clear value proposition for somebody like PWC. And if they're here, obviously they wouldn't be here if they didn't see the opportunity to build a really big practice. But to me that's it, Dave. How do we get out of the data scientist? How do we get out of kind of the unfulfilled promise of traditional BI, which I think people love to still put that in a bucket and get more data driven decision making broader into the company? So we've been on this theme for a while now of systems of intelligence expanding on Jeffrey Moore's concept and in our world it's really when we talk to our practitioners it's all about bringing analytic and transaction systems together. Leveraging the systems of record and evolving those into systems of intelligence. Not creating separate bespoke systems of insight. Really bringing those two worlds together. And speaking to George Gilbert, some of the things that he's going to be focused on is helping practitioners look, think about machine learning in the context of focusing on both business outcomes but at the same time improving their analytics capabilities. And they're complementary but you don't have the resources to do all of them. So how, based on your business requirements, can you sort those out? How do you prioritize projects? Should you look at the business impact and how do you assess the technical capabilities and the risks associated with that? Still a lot of customization in predictive analytics and machine learning. How can we move toward and leverage more commercial off the shelf software, clearly rapid miners trying to create that platform but still a lot of customization going on. How do you protect your IP if you're bringing in outside consultants? Talked about PWC here, world-class consultant. How do you deal with that? Could you deal with that contractually? I mean, you have to be very careful with when you bring in professional services for that customization. How do you protect your IP? You know the people skills we heard from David Weissman today, the organizational models centralized versus distributed, hybrid with a lean towards centralized. George talking about helping customers sort of sort through that with frameworks that allow us to sort of better understand what the right fit is for your particular situation. So that's where a lot of the research is going to be focused this coming year. Obviously theCUBE plays a big part of that. We're expanding our networks. theCUBE alumni network is a huge part of that. We did 77 shows last year. We're kicking off this year, obviously in our big data wheelhouse. We've got I think six big data events planned this year. We've got Spark Summit East. We'll probably do Spark Summit West. We've got the big data SV and NYC that we do every year. That's four. Then we've got Hadoop Summit and San Jose. We're doing Hadoop Summit in Dublin, which I think is in April. So that's at least six big data shows independent of the other ones where we go. It's where it's a vendor show, like a Splunk for example, or a Tableau. Hopefully those guys will have us back. So lots of focus on big data. Obviously infrastructure. Our tradition, Stu's doing the Gillette Stadium today, the V-Tug, the core, VMware, audience. Obviously Oracle is infrastructure and apps. The service now shows. And of course cloud, big, big cloud emphasis. We do reinvent every year as well as other cloud shows. We've got IBM Interconnect coming up, which is a big cloud show. So talk a little bit about so the year plans for theCUBE in 2016. So we're going to continue to do what we've done and we're going to try to do more. So as you said, we're going to continue our real big presence in big data. We're going to really continue to go in cloud. I think we're going to pick up a couple of new AWS shows. We're talking about potentially in Chicago. I don't know, Dave, we've ever done a show in Chicago with theCUBE. AWS Summit is going to be there. They have their regional shows. We've done San Francisco for a number of years. AWS Summit, I think we might do New York this year. And then the other opportunity is that we're looking at is a continued global reach. We just came back from HPE Discover. In London, not too long ago, you mentioned Dublin. There's some other opportunities we're working on that we have not closed yet. Keep an eye on SiliconANGLE.tv. And then we'll continue our application focus as well. We're starting to see this, I see big data is kind of this infrastructure layer thing. And I think eventually we're not going to talk about big data is big data. It's an enabler. And now we start to move to the applications. And the ones that are coming up next that we haven't really covered per se specifically is Internet of Things. And Internet of Things is defined by what's going on automotive, what's going on in wearables, what's going on in sports. There's a lot of now applications that have been enabled by computing power on tap, cloud, this really crazy connectivity that we're seeing now on the wireless space and the applications that are going in over the top of that. The other thing, we were at the Ford launch of their innovation center in Palo Alto. I'll actually be talking to Ford shortly and we've talked to Jim. What's fascinating is the whole transformation in the automotive industry around thinking about the whole experience, not about car ownership. A car ownership is a piece of your transportation experience that's probably multimodal. You may take a car sometimes, you may take a bike sometimes, you may take the bus sometimes, you may take the train sometimes. The fact that somebody like Ford recognizes the importance of changing the way that they look at transportation is pretty significant. We talked to GE in the context of the Predix cloud for Internet of Things, which is going to be coming down the pike. Bill Rue and Tim, I think he's got almost 1,000 people in San Ramon, which is not an easy place to hire. In the Bay Area, we're really talking to them about even within the jet engine group, they're thinking about the entire experience from the time you leave your house to the time you arrive at your destination. Oh, by the way, there's an airplane in the middle there that's using a GE engine to get you there. But the whole Predix cloud and what General Electric is doing, I think we're really going to see this huge push on IoT. The other thing that I think is really fun, similar is the virtual reality and seeing more and more applications. We did an interview with Spacetime Insight where they're using a combination of virtual reality with like a heads up display and Internet of Things in power grids and power stations so you can look at a piece of equipment and it tells you it's running hot, it's running cold, it needs maintenance, this one's fine, hey, pay attention over here. So I think that's another area that we're going to see. It's, again, it's a combination of these other technologies coming together and it's big data and it's cloud, but now I think we'll see a lot more in the application layer and really ways people are transforming their business and I'm really excited about some of those opportunities. I'm glad you brought that up about IoT. So George Gilbert's been doing a lot of work there along with David Floyer talking to Ingo off camera and basically there's the piece of, the humans are the last mile narrative and that's certainly true for much of the predictive analytics but a lot of it is going to be machines talking to machines and predictive analytics fits in there as well. When's the window mill going to break? I don't want to have to do a truck roll every time to figure out when that windmill is going to break. Ferry and I talked a lot about this at HPE Discover and basically of course you want to instrument the things but the things better be connected if you're going to instrument them because otherwise the data's going to be out there in an island and then of course what data actually comes back. So a lot of really interesting issues that we're working on and helping our communities understand. So we're a wrap, thanks very much for kicking off the year with me here. Thanks for coming down to New York once again. It's a pleasure, it's a pleasure coming out and the gents, Greg, Patrick Leonard, nice job, Bert back on the crowd chat land and all the team with Kristen Nicole and the writers and thank you guys for watching thanks for a rapid minor for having us here. So that's a wrap, this is theCUBE. Let's see, what's next for us? What is? Spark Summit. Spark Summit East. Spark Summit East, we're back in Manhattan. Mid-Feb, right? Mid-Feb back in Manhattan so look for that and as always thanks for watching everybody and we'll see you next time. Bye for now.