>> Announcer: Live from Munich, Germany, it's theCUBE. Covering IBM: Fast Track Your Data. Brought to you by IBM. >> Welcome, everybody, to Munich, Germany. This is Fast Track Your Data brought to you by IBM, and this is theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise. My name is Dave Vellante, and I'm here with my co-host Jim Kobielus. Rob Thomas is here, he's the General Manager of IBM Analytics, and longtime CUBE guest, good to see you again, Rob. >> Hey, great to see you. Thanks for being here. >> Dave: You're welcome, thanks for having us. So we're talking about, we missed each other last week at the Hortonworks DataWorks Summit, but you came on theCUBE, you guys had the big announcement there. You're sort of getting out, doing a Hadoop distribution, right? TheCUBE gave up our Hadoop distributions several years ago so. It's good that you joined us. But, um, that's tongue-in-cheek. Talk about what's going on with Hortonworks. You guys are now going to be partnering with them essentially to replace BigInsights, you're going to continue to service those customers. But there's more than that. What's that announcement all about? >> We're really excited about that announcement, that relationship, just to kind of recap for those that didn't see it last week. We are making a huge partnership with Hortonworks, where we're bringing data science and machine learning to the Hadoop community. So IBM will be adopting HDP as our distribution, and that's what we will drive into the market from a Hadoop perspective. Hortonworks is adopting IBM Data Science Experience and IBM machine learning to be a core part of their Hadoop platform. And I'd say this is a recognition. One is, companies should do what they do best. We think we're great at data science and machine learning. Hortonworks is the best at Hadoop. Combine those two things, it'll be great for clients. And, we also talked about extending that to things like Big SQL, where they're partnering with us on Big SQL, around modernizing data environments. And then third, which relates a little bit to what we're here in Munich talking about, is governance, where we're partnering closely with them around unified governance, Apache Atlas, advancing Atlas in the enterprise. And so, it's a lot of dimensions to the relationship, but I can tell you since I was on theCUBE a week ago with Rob Bearden, client response has been amazing. Rob and I have done a number of client visits together, and clients see the value of unlocking insights in their Hadoop data, and they love this, which is great. >> Now, I mean, the Hadoop distro, I mean early on you got into that business, just, you had to do it. You had to be relevant, you want to be part of the community, and a number of folks did that. But it's really sort of best left to a few guys who want to do that, and Apache open source is really, I think, the way to go there. Let's talk about Munich. You guys chose this venue. There's a lot of talk about GDPR, you've got some announcements around unified government, but why Munich? >> So, there's something interesting that I see happening in the market. So first of all, you look at the last five years. There's only 10 companies in the world that have outperformed the S&P 500, in each of those five years. And we started digging into who those companies are and what they do. They are all applying data science and machine learning at scale to drive their business. And so, something's happening in the market. That's what leaders are doing. And I look at what's happening in Europe, and I say, I don't see the European market being that aggressive yet around data science, machine learning, how you apply data for competitive advantage, so we wanted to come do this in Munich. And it's a bit of a wake-up call, almost, to say hey, this is what's happening. We want to encourage clients across Europe to think about how do they start to do something now. >> Yeah, of course, GDPR is also a hook. The European Union and you guys have made some talk about that, you've got some keynotes today, and some breakout sessions that are discussing that, but talk about the two announcements that you guys made. There's one on DB2, there's another one around unified governance, what do those mean for clients? >> Yeah, sure, so first of all on GDPR, it's interesting to me, it's kind of the inverse of Y2K, which is there's very little hype, but there's huge ramifications. And Y2K was kind of the opposite. So look, it's coming, May 2018, clients have to be GDPR-compliant. And there's a misconception in the market that that only impacts companies in Europe. It actually impacts any company that does any type of business in Europe. So, it impacts everybody. So we are announcing a platform for unified governance that makes sure clients are GDPR-compliant. We've integrated software technology across analytics, IBM security, some of the assets from the Promontory acquisition that IBM did last year, and we are delivering the only platform for unified governance. And that's what clients need to be GDPR-compliant. The second piece is data has to become a lot simpler. As you think about my comment, who's leading the market today? Data's hard, and so we're trying to make data dramatically simpler. And so for example, with DB2, what we're announcing is you can download and get started using DB2 in 15 minutes or less, and anybody can do it. Even you can do it, Dave, which is amazing. >> Dave: (laughs) >> For the first time ever, you can-- >> We'll test that, Rob. >> Let's go test that. I would love to see you do it, because I guarantee you can. Even my son can do it. I had my son do it this weekend before I came here, because I wanted to see how simple it was. So that announcement is really about bringing, or introducing a new era of simplicity to data and analytics. We call it Download And Go. We started with SPSS, we did that back in March. Now we're bringing Download And Go to DB2, and to our governance catalog. So the idea is make data really simple for enterprises. >> You had a community edition previous to this, correct? There was-- >> Rob: We did, but it wasn't this easy. >> Wasn't this simple, okay. >> Not anybody could do it, and I want to make it so anybody can do it. >> Is simplicity, the rate of simplicity, the only differentiator of the latest edition, or I believe you have Kubernetes support now with this new addition, can you describe what that involves? >> Yeah, sure, so there's two main things that are new functionally-wise, Jim, to your point. So one is, look, we're big supporters of Kubernetes. And as we are helping clients build out private clouds, the best answer for that in our mind is Kubernetes, and so when we released Data Science Experience for Private Cloud earlier this quarter, that was on Kubernetes, extending that now to other parts of the portfolio. The other thing we're doing with DB2 is we're extending JSON support for DB2. So think of it as, you're working in a relational environment, now just through SQL you can integrate with non-relational environments, JSON, documents, any type of no-SQL environment. So we're finally bringing to fruition this idea of a data fabric, which is I can access all my data from a single interface, and that's pretty powerful for clients. >> Yeah, more cloud data development. Rob, I wonder if you can, we can go back to the machine learning, one of the core focuses of this particular event and the announcements you're making. Back in the fall, IBM made an announcement of Watson machine learning, for IBM Cloud, and World of Watson. In February, you made an announcement of IBM machine learning for the z platform. What are the machine learning announcements at this particular event, and can you sort of connect the dots in terms of where you're going, in terms of what sort of innovations are you driving into your machine learning portfolio going forward? >> I have a fundamental belief that machine learning is best when it's brought to the data. So, we started with, like you said, Watson machine learning on IBM Cloud, and then we said well, what's the next big corpus of data in the world? That's an easy answer, it's the mainframe, that's where all the world's transactional data sits, so we did that. Last week with the Hortonworks announcement, we said we're bringing machine learning to Hadoop, so we've kind of covered all the landscape of where data is. Now, the next step is about how do we bring a community into this? And the way that you do that is we don't dictate a language, we don't dictate a framework. So if you want to work with IBM on machine learning, or in Data Science Experience, you choose your language. Python, great. Scala or Java, you pick whatever language you want. You pick whatever machine learning framework you want, we're not trying to dictate that because there's different preferences in the market, so what we're really talking about here this week in Munich is this idea of an open platform for data science and machine learning. And we think that is going to bring a lot of people to the table. >> And with open, one thing, with open platform in mind, one thing to me that is conspicuously missing from the announcement today, correct me if I'm wrong, is any indication that you're bringing support for the deep learning frameworks like TensorFlow into this overall machine learning environment. Am I wrong? I know you have Power AI. Is there a piece of Power AI in these announcements today? >> So, stay tuned on that. We are, it takes some time to do that right, and we are doing that. But we want to optimize so that you can do machine learning with GPU acceleration on Power AI, so stay tuned on that one. But we are supporting multiple frameworks, so if you want to use TensorFlow, that's great. If you want to use Caffe, that's great. If you want to use Theano, that's great. That is our approach here. We're going to allow you to decide what's the best framework for you. >> So as you look forward, maybe it's a question for you, Jim, but Rob I'd love you to chime in. What does that mean for businesses? I mean, is it just more automation, more capabilities as you evolve that timeline, without divulging any sort of secrets? What do you think, Jim? Or do you want me to ask-- >> What do I think, what do I think you're doing? >> No, you ask about deep learning, like, okay, that's, I don't see that, Rob says okay, stay tuned. What does it mean for a business, that, if like-- >> Yeah. >> If I'm planning my roadmap, what does that mean for me in terms of how I should think about the capabilities going forward? >> Yeah, well what it means for a business, first of all, is what they're going, they're using deep learning for, is doing things like video analytics, and speech analytics and more of the challenges involving convolution of neural networks to do pattern recognition on complex data objects for things like connected cars, and so forth. Those are the kind of things that can be done with deep learning. >> Okay. And so, Rob, you're talking about here in Europe how the uptick in some of the data orientation has been a little bit slower, so I presume from your standpoint you don't want to over-rotate, to some of these things. But what do you think, I mean, it sounds like there is difference between certainly Europe and those top 10 companies in the S&P, outperforming the S&P 500. What's the barrier, is it just an understanding of how to take advantage of data, is it cultural, what's your sense of this? >> So, to some extent, data science is easy, data culture is really hard. And so I do think that culture's a big piece of it. And the reason we're kind of starting with a focus on machine learning, simplistic view, machine learning is a general-purpose framework. And so it invites a lot of experimentation, a lot of engagement, we're trying to make it easier for people to on-board. As you get to things like deep learning as Jim's describing, that's where the market's going, there's no question. Those tend to be very domain-specific, vertical-type use cases and to some extent, what I see clients struggle with, they say well, I don't know what my use case is. So we're saying, look, okay, start with the basics. A general purpose framework, do some tests, do some iteration, do some experiments, and once you find out what's hunting and what's working, then you can go to a deep learning type of approach. And so I think you'll see an evolution towards that over time, it's not either-or. It's more of a question of sequencing. >> One of the things we've talked to you about on theCUBE in the past, you and others, is that IBM obviously is a big services business. This big data is complicated, but great for services, but one of the challenges that IBM and other companies have had is how do you take that service expertise, codify it to software and scale it at large volumes and make it adoptable? I thought the Watson data platform announcement last fall, I think at the time you called it Data Works, and then so the name evolved, was really a strong attempt to do that, to package a lot of expertise that you guys had developed over the years, maybe even some different software modules, but bring them together in a scalable software package. So is that the right interpretation, how's that going, what's the uptake been like? >> So, it's going incredibly well. What's interesting to me is what everybody remembers from that announcement is the Watson Data Platform, which is a decomposable framework for doing these types of use cases on the IBM cloud. But there was another piece of that announcement that is just as critical, which is we introduced something called the Data First method. And that is the recipe book to say to a client, so given where you are, how do you get to this future on the cloud? And that's the part that people, clients, struggle with, is how do I get from step to step? So with Data First, we said, well look. There's different approaches to this. You can start with governance, you can start with data science, you can start with data management, you can start with visualization, there's different entry points. You figure out the right one for you, and then we help clients through that. And we've made Data First method available to all of our business partners so they can go do that. We work closely with our own consulting business on that, GBS. But that to me is actually the thing from that event that has had, I'd say, the biggest impact on the market, is just helping clients map out an approach, a methodology, to getting on this journey. >> So that was a catalyst, so this is not a sequential process, you can start, you can enter, like you said, wherever you want, and then pick up the other pieces from majority model standpoint? Exactly, because everybody is at a different place in their own life cycle, and so we want to make that flexible. >> I have a question about the clients, the customers' use of Watson Data Platform in a DevOps context. So, are more of your customers looking to use Watson Data Platform to automate more of the stages of the machine learning development and the training and deployment pipeline, and do you see, IBM, do you see yourself taking the platform and evolving it into a more full-fledged automated data science release pipelining tool? Or am I misunderstanding that? >> Rob: No, I think that-- >> Your strategy. >> Rob: You got it right, I would just, I would expand a little bit. So, one is it's a very flexible way to manage data. When you look at the Watson Data Platform, we've got relational stores, we've got column stores, we've got in-memory stores, we've got the whole suite of open-source databases under the composed-IO umbrella, we've got cloud in. So we've delivered a very flexible data layer. Now, in terms of how you apply data science, we say, again, choose your model, choose your language, choose your framework, that's up to you, and we allow clients, many clients start by building models on their private cloud, then we say you can deploy those into the Watson Data Platform, so therefore then they're running on the data that you have as part of that data fabric. So, we're continuing to deliver a very fluid data layer which then you can apply data science, apply machine learning there, and there's a lot of data moving into the Watson Data Platform because clients see that flexibility. >> All right, Rob, we're out of time, but I want to kind of set up the day. We're doing CUBE interviews all morning here, and then we cut over to the main tent. You can get all of this on IBMgo.com, you'll see the schedule. Rob, you've got, you're kicking off a session. We've got Hilary Mason, we've got a breakout session on GDPR, maybe set up the main tent for us. >> Yeah, main tent's going to be exciting. We're going to debunk a lot of misconceptions about data and about what's happening. Marc Altshuller has got a great segment on what he calls the death of correlations, so we've got some pretty engaging stuff. Hilary's got a great piece that she was talking to me about this morning. It's going to be interesting. We think it's going to provoke some thought and ultimately provoke action, and that's the intent of this week. >> Excellent, well Rob, thanks again for coming to theCUBE. It's always a pleasure to see you. >> Rob: Thanks, guys, great to see you. >> You're welcome; all right, keep it right there, buddy, We'll be back with our next guest. This is theCUBE, we're live from Munich, Fast Track Your Data, right back. (upbeat electronic music)

>> Announcer: Live, from San Jose, in the heart of Silicon Valley, it's The Cube, covering DataWorks Summit 2017. Brought to you by Hortonworks. >> Welcome back to The Cube. We are live at DataWorks Summit 2017. I'm Lisa Martin with my cohost, George Gilbert. We've just come from this energetic, laser light show infused keynote, and we're very excited to be joined by one of the keynotes today, the CTO of Hortonworks, Scott Gnau. Scott, welcome back to The Cube. >> Great to be here, thanks for having me. >> Great to have you back here. One of the things that you talked about in your keynote today was collaboration. You talked about the modern data architecture and one of the things that I thought was really interesting is that now where Horton Works is, you are empowering cross-functional teams, operations managers, business analysts, data scientists, really helping enterprises drive the next generation of value creation. Tell us a little bit about that. >> Right, great. Thanks for noticing, by the way. I think the next, the important thing, kind of as a natural evolution for us as a company and as a community is, and I've seen this time and again in the tech industry, we've kind of moved from really cool breakthrough tech, more into a solutions base. So I think this whole notion is really about how we're making that natural transition. And when you think about all the cool technology and all the breakthrough algorithms and all that, that's really great, but how do we then take that and turn it to value really quickly and in a repeatable fashion. So, the notion that I launched today is really making these three personas really successful. If you can focus, combining all of the technology, usability and even some services around it, to make each of those folks more successful in their job. So I've broken it down really into three categories. We know the traditional business analyst, right? They've Sequel and they've been doing predictive modeling of structured data for a very long time, and there's a lot of value generated from that. Making the business analyst successful Hadoop inspired world is extremely valuable. And why is that? Well, it's because Hadoop actually now brings a lot more breadth of data and frankly a lot more depth of data than they've ever had access to before. But being able to communicate with that business analyst in a language they understand, Sequel, being able to make all those tools work seamlessly, is the next extension of success for the business analyst. We spent a lot of time this morning talking about data scientists, the next great frontier where you bring together lots and lots and lots and lots of data, for instance, Skin and Math and Heavy Compute, with the data scientists and really enable them to go build out that next generation of high definition kind of analytics, all right, and we're all, certainly I am, captured by the notion of self-driving cars, and you think about a self-driving car, and the success of that is purely based on the successful data science. In those cameras and those machines being able to infer images more accurately than a human being, and then make decisions about what those images mean. That's all data science, and it's all about raw processing power and lots and lots and lots of data to make those models train and more accurate than what would otherwise happen. So enabling the data scientist to be successful, obviously, that's a use case. You know, certainly voice activated, voice response kinds of systems, for better customer service; better fraud detection, you know, the cost of a false positive is a hundred times the cost of missing a fraudulent behavior, right? That's because you've irritated a really good customer. So being able to really train those models in high definition is extremely valuable. So bringing together the data, but the tool set so that data scientists can actually act as a team and collaborate and spend less of their time finding the data, and more of their time providing the models. And I said this morning, last but not least, the operations manager. This is really, really, really important. And a lot of times, especially geeks like myself, are just, ah, operations guys are just a pain in the neck. Really, really, really important. We've got data that we've never thought of. Making sure that it's secured properly, making sure that we're managing within the regulations of privacy requirements, making sure that we're governing it and making sure how that data is used, alongside our corporate mission is really important. So creating that tool set so that the operations manager can be confident in turning these massive files of data to the business analyst and to the data scientist and be confident that the company's mission, the regulation that they're working within in those jurisdictions are all in compliance. And so that's what we're building on, and that stack, of course, is built on open source Apache Atlas and open source Apache Ranger and it really makes for an enterprise grade experience. >> And a couple things to follow on to that, we've heard of this notion for years, that there is a shortage of data scientists, and now, it's such a core strategic enabler of business transformation. Is this collaboration, this team support that was talked about earlier, is this helping to spread data science across these personas to enable more of the to be data scientists? >> Yeah, I think there are two aspects to it, right? One is certainly really great data scientists are hard to find; they're scarce. They're unique creatures. And so, to the extent that we're able to combine the tool set to make the data scientists that we have more productive, and I think the numbers are astronomical, right? You could argue that, with the wrong tool set, a data scientist might spend 80% or 90% of his or her time just finding the data and only 10% working on the problem. If we can flip that around and make it 10% finding the data and 90%, that's like, in order of magnitude, more breadth of data science coverage that we get from the same pool of data scientists, so I think that from an efficiency perspective, that's really huge. The second thing, though, is that by looking at these personas and the tools that we're rolling out, can we start to package up things that the data scientists are learning and move those models into the business analysts desktop. So, now, not only is there more breadth and depth of data, but frankly, there's more depth and breadth of models that can be run, but inferred with traditional business process, which means, turning that into better decision making, turning that into better value for the business, just kind of happens automatically. So, you're leveraging the value of data scientists. >> Let me follow that up, Scott. So, if the, right now the biggest time sync for the data scientist or the data engineer is data cleansing and transformation. Where do the cloud vendors fit in in terms of having trained some very broad horizontal models in terms of vision, natural language understanding, text to speech, so where they have accumulated a lot of data assets, and then they created models that were trained and could be customized. Do you see a role for, not just mixed gen UI related models coming from the cloud vendors, but for other vendors who have data assets to provide more fully baked models so that you don't have to start from scratch? >> Absolutely. So, one of the things that I talked about also this morning is this notion, and I said it this morning, kind of opens where open community, open source, and open ecosystem, I think it's now open to the third power, right, and it's talking about open models and algorithms. And I think all of those same things are really creating a tremendous opportunity, the likes of which we've not seen before, and I think it's really driving the velocity in the market, right, so there's no, because we're collaborating in the open, things just get done faster and more efficiently, whether it be in the core open source stuff or whether it be in the open ecosystem, being able to pull tools in. Of course, the announcement earlier today, with IBMs Data Science Experience software as a framework for the data scientists to work as a team, but that thing in and of itself is also very open. You can plug in Python, you can plug in open source models and libraries, some of which were developed in the cloud and published externally. So, it's all about continued availability of open collaboration that is the hallmark of this wave of technology. >> Okay, so we have this issue of how much can we improve the productivity with better tools or with some amount of data. But then, the part that everyone's also point out, besides the cloud experience, is also the ability to operationalize the models and get them into production either in Bespoke apps or packaged apps. How's that going to sort of play out over time? >> Well, I think two things you'll see. One, certainly in the near term, again, with our collaboration with IBM and the Data Science Experience. One of the key things there is not only, not just making the data scientists be able to be more collaborative, but also the ease of which they can publish their models out into the wild. And so, kind of closing that loop to action is really important. I think, longer term, what you're going to see, and I gave a hint of this a little bit in my keynote this morning, is, I believe in five years, we'll be talking about scalability, but scalability won't be the way we think of it today, right? Oh, I have this many petabytes under management, or, petabytes. That's upkeep. But truly, scalability is going to be how many connected devices do you have interacting, and how many analytics can you actually push from model perspective, actually out to the center or out to the device to run locally. Why is that important? Think about it as a consumer with a mobile device. The time of interaction, your attention span, do you get an offer in the right time, and is that offer relevant. It can't be rules based, it has to be models based. There's no time for the electrons to move from your device across a power grid, run an analytic and have it come back. It's going to happen locally. So scalability, I believe, is going to be determined in terms of the CPU cycles and the total interconnected IOT network that you're working in. What does that mean from your original question? That means applications have to be portable, models have to be portable so that they can execute out to the edge where it's required. And so that's, obviously, part of the key technology that we're working with in Portworks Data Flow and the combination of Apache Nifi and Apache Caca and Storm to really combine that, "How do I manage, not only data in motion, but ultimately, how do I move applications and analytics to the data and not be required to move the data to the analytics?" >> So, question for you. You talked about real time offers, for example. We talk a lot about predicted analytics, advanced analytics, data wrangling. What are your thoughts on preemptive analytics? >> Well, I think that, while that sounds a little bit spooky, because we're kind of mind reading, I think those things can start to exist. Certainly because we now have access to all of the data and we have very sophisticated data science models that allow us to understand and predict behavior, yeah, the timing of real time analytics or real time offer delivery, could actually, from our human being perception, arrive before I thought about it. And isn't that really cool in a way. I'm thinking about, I need to go do X,Y,Z. Here's a relevant offer, boom. So it's no longer, I clicked here, I clicker here, I clicked here, and in five seconds I get a relevant offer, but before I even though to click, I got a relevant offer. And again, to the extent that it's relevant, it's not spooky. >> Right. >> If it's irrelevant, then you deal with all of the other downstream impact. So that, again, points to more and more and more data and more and more and more accurate and sophisticated models to make sure that that relevance exists. >> Exactly. Well, Scott Gnau, CTO of Hortonworks, thank you so much for stopping by The Cube once again. We appreciate your conversation and insights. And for George Gilbert, I am Lisa Martin. You're watching The Cube live, from day one of the DataWorks Summit in the heart of Silicon Valley. Stick around, though, we'll be right back.