>> Announcer: Live from Munich, Germany; it's The Cube! Covering IBM Fast Track Your Data, brought to you by IBM. >> Welcome back to Munich, Germany everybody. This is The Cube, the leader in live tech coverage. We're covering Fast Track Your Data, IBM's signature moment here in Munich. Big themes around GDPR, data science, data science being a team sport. I'm Dave Vellante, I'm here with my co-host Jim Kobielus. Marc Altshuller is here, he's the general manager of IBM Business Analytics. Good to see you again Marc. >> Hey, always great to see you. Welcome, it's our first time together. >> Okay so we heard your key note, you were talking about the caveats of correlations, you were talking about rear view mirror analysis versus sort of looking forward, something that I've been sort of harping on for years. You know, I mean I remember the early days of "decision support" and the promises of 360 degree views of the customer, and predictive analytics, and I've always said it, "DSS really never lived up to that", y'know? "Will big data live up to that?" and we're kind of living that now, but what's your take on where we're at in this whole databean? >> I mean look, different customers are at different ends of the spectrum, but people are really getting value. They're becoming these data driven businesses. I like what Rob Thomas talked about on stage, right. Visiting companies a few years ago where they'd say "I'm not a technology company.". Now, how can you possibly say you're not a technology company, regardless of the industry. Your competitors will beat you if they are using data and you're not. >> Yeah, and everybody talks about digital transformation. And you hear that a lot at conferences, you guys haven't been pounding that theme, other than, y'know below the surface. And to us, digital means data, right? And if you're going to transform digitally, then it's all about the data, you mentioned data driven. What are you seeing, I mean most organizations in our view aren't "data driven" they're sort of reactive. Their CEO's maybe want to be data driven, maybe they're aboard conversations as to how to get there, but they're mostly focused on "Alright, how do we keep "the lights on, how do we meet our revenue targets, "how do we grow a little bit, and then whatever money "we have leftover we'll try to, y'know transform." What are you seeing? Is that changing? >> I would say, look I can give you an example right from my own space, the software space. For years we would have product managers, offering managers, maybe interviewing clients, on gut feel deciding what features to put at what priority within the next release. Now we have all these products instrumented behind the scenes with data, so we can literally see the friction points, the exit points, how frequently they come back, how long they're sessions are, we can even see them effectively graduating within the system where they continue to learn, and where they had shorter sessions, they're now going the longer sessions. That's really, really powerful for us in terms of trying to maximize our outcome from a software perspective. So that's where we kind of like, drink our own champagne. >> I got to ask you, so in around 2003, 2004 HBR had an article, front page y'know cover article of how "gut feel beats data and analytics", now this is 2003, 2004, software development as you know it's a lot of art involved, so my question is how are you doing? Is the data informing you in ways that are nonintuitive? And is it driving y'know, business outcomes for IBM? >> It is, look you see, I'll see like GM's of sports teams talking about maybe pushing back a little bit on the data. It's not all data driven, there's a little bit of gut, like is the guy going to, is he a checker in hockey or whatever that happens to be, and I would say, when you actually look at what's going on within baseball, and you look at the data, when you watch baseball growing up, the commentator might say something along the lines of "the pitcher has their stuff" right? "Does the pitcher have their stuff or not?". Now they literally know, the release point based on elevation, IOT within the state of the release point, the spin velocity of the ball, where they mathematically know "does the pitcher have their stuff?", are they hitting their locations? So all that stuff has all become data driven, and if you don't want to embrace it, you get beat, right? I mean even in baseball, I remember talking to one of these Moneyball type guys where I said like "Doesn't weather impact baseball?" And they're like "Yeah, we've looked at that, it absolutely impacts it." 'Cause you always hear of football and remember the old Peyton Manning thing? Don't play Peyton Manning in cold weather, don't bet on Peyton Manning in cold weather. So "I'm like isn't the same in baseball?", And he's like, absolutely it's the same in baseball, players preform different based on the climate. Do any mangers change their lineup based on that? Never. >> Speaking of HBR, I mean in the last few years there was also an article or two by Michael Shrage about the whole notion of real world experimentation and e-commerce, driven by data, y'know in line, to an operational process, like tuning the design iteratively of say, a shopping cart within your e-commerce environment, based on the stats on what work and what does not work. So, in many ways I mean AB testing, real world experimentation thrives on data science. Do you see AB testing becoming a standard business practice everywhere, or only in particular industries like you know, like the Wal-marts of the world? >> Yeah, look so, AB testing, multi-variant testing, they're pervasive, pretty much anyone who has a website ought to be doing this if they're not doing it already. Maybe some startups aren't quite into it. They prioritized in different spots, but mainstream fortune 500 companies are doing this, the tools have made it really easy. I would say, maybe the Achilles heel or the next frontier is, that is effectively saying, kind of creating one pattern of user, putting everyone in a single bucket, right? "Does this button perform better "when it's orange or when it's green? "Oh, it performs better orange." Really, does it perform well for every segmentation orange better than green or is it just a certain segmentation? So that next kind of frontier is going to be, how do we segment it, know a little bit more about you when you're coming in so that AB testing starts to build these kind of sub-profiles, sub-segmentation. >> Micro-segmentation, and of course, the end extreme of that dynamic is one-to-one personalization of experiences and engagements based on knowing 360 degrees about you and what makes you tick as well, so yeah. >> Altshuller: And add onto that context, right? You have your business, let's even keep it really simple, right, you've got your business life, you've got your social life, and your profile of what you're looking for when you're shopping your social life or something is very different than when you're shopping your business life. We have to personalize it to the idea where, I don't want to say schizophrenic but you do have multiple personalities from an online perspective, right? From a digital perspective it all depends in the moment, what is it that you're actually doing, right? And what are you, who are you acting for? >> Marc, I want to ask you, you're homies, your peeps are the business people. >> Yes. >> That's where you spend your time. I'm interested in the relationship between those business people and the data science teams. They're all, we all hear about how data science and unicorns are hard to find, difficult to get the skills, citizen data science is sort of a nirvana. But, how are you seeing businesses bring the domain expertise of the business and blending that with data science? >> So, they do it, I have some cautionary tales that I've experienced in terms of how they're doing it. They feel like, let's just assign the subject matter expert, they'll work with the data scientist, they'll give them context as they're doing their project, but unfortunately what I've seen time and time again, is that subject matter expert right out of the gate brings a tremendous amount of bias based on the types of analysis they've done in the past. >> Vellante: That's not how we do it here. >> Yeah, exactly, like "did you test this?". "Oh yeah, there's no correlation there, we've tried it." Well, just because there's no correlation, as I talked about onstage, doesn't mean it's not part of the pattern in terms of, like you don't want someone in there right off the bat dismissing things. So I always coach, when the business user subject matter experts become involved early, they have to be tremendously open-minded and not all of them can be. I like bringing them in later, because that data scientist, they are unbiased, like they see this data set, it doesn't mean anything to them, they're just numerically telling you what the data set says. Now the business user can then add some context, maybe they grabbed a field that really is an irrelevant field and they can give them that context afterwards. But we just don't want them shutting down, kind of roots, too early in the process. >> You know, we've been talking for a couple of years now within our community about this digital matrix, this digital fabric that's emerged and you're seeing these horizontal layers of technology, whether it's cloud or, you know, security, you all OAuth in with LinkedIn, Facebook, and Twitter. There's a data fabric that's emerging and you're seeing all these new business models, whether it's Uber or Airbnb or WAZE, et cetera, and then you see this blockbuster announcement last week, Amazon buying Whole Foods. And it's just fascinating to us and it's all about the data that a company like an Amazon can be a content company, could be a retail company, now it's becoming a grocer, you see Apple getting into financial services. So, you're seeing industries being able to traverse or companies being able traverse industries and it's all because of the data, so these conversations absolutely are going on in boardrooms. It's all about the digital transformation, the digital disruption, so how do you see, you know, your clients trying to take advantage of that or defend against that? >> Yeah look, I mean, you have to be proactive. You have to be willing to disrupt yourself in all these tech industries, it's just moving too quickly. I read a similar story, I think yesterday, around potentially Blockchain disrupting ridesharing programs, right? Why do you need the intermediary if you have this open ledger and these secure transactions you can do back and forth with this ecosystem. So there's another interesting disruption. Now do the ridesharing guys proactively get into that and promote it, or do they almost in slow motion, get replaced by that at some point. So yeah I think it's a come-on on all of us, like you don't remain a market lead, every market leader gets destructive at some point, the key is, do you disrupt yourself and you remain the market leader, or do you let someone else disrupt you. And if you get disrupted, how quickly can you recover. >> Well you know, you talked to banking executives and they're all talking Blockchain. Blockchain is the future, Bitcoin was designed to disintermediate the bank, so they're many, many banks are embracing it and so it comes back to the data. So my question I have, the discussion I'd like to have is how organizations are valuing data. You can't put data as a value on, y'know an asset on your balance sheet. The accounting industry standards don't exist. They probably won't for decades. So how are companies, y'know crocking data value, is it limiting their ability to move toward a data driven economy, is it a limiting factor that they don't have a good way to value their data, and understand how to monetize it. >> So I have heard of cases where companies have but data on their balance sheet, it's not mainstream at this point, but I mean you've seen it sometimes, and even some bankruptcy proceedings, their industry that's being in a bankruptcy protection where they say "Hey, but this data asset "is really where the value is." >> Vellante: And it's certainly implicit in valuations. >> Correct, I mean you see bios all the time based on the actual data sets, so yeah that data set, they definitely treasure it, and they realize that a lot of their answers are within that data set. And they also I think, understand that they're is a lot of peeling the onion that goes on when you're starting to work through that data, right? You have your initial thoughts, then you correct something based on what the data told you to do, and then the new data comes in based on what your new experience is, and then all of a sudden you have, you see what your next friction point is. You continue to knock down these things, so it is also very iterative working with that data asset. But yeah, these companies are seeing it's very value when they collect the data, but the other thing is the signal of what's driving your business may not be in your data, more and more often it may be in market data that's out there. So you think about social media data, you think about weather data and being able to go and grab that information. I remember watching the show Millions, where they talk about the hedge fund guys running satellites over like Wal-mart parking lots to try to predict the redux for the quarter, right? Like, you're collecting all this data but it's out there. >> Or maybe the value is not so much in the data itself, but in what it enables you to develop as a derivative asset, meaning a statistical predictive model or machine learning model that shows the patterns that you can then drive into, recommendation engines, and your target marketing y'know applications. So you see any clients valuate, doing their valuation of data on those derivative assets? >> Altshuller: Yeah. >> In lieu of... >> In these new business models I see within corporations that have been around for decades, it's actual data offers that they make to maybe their ecosystem, their channel. "Here's data we have, here's how you interpret it, "we'll continue to collect it, we'll continue to curate it, "we'll make it available." And this is really what's driving your business. So yeah, data assets become something that, companies are figuring out how to monetize their data assets. >> Of course those derived assets will decay if those models of, for example machine learning models are not trained with fresh, y'know data from the sources. >> And if we're not testing for new variable too, right? Like if the variable was never in the model, you still have to have this discovery process, that's always going on the see what new variables might be out there, what new data set, right. Like if a new IOT sensor in the baseball stadium becomes available, maybe that one I talked about with elevation of the pitcher, like until you have that you can't use it, but once you have it you have to figure out how to use it. >> Alright lets bring it back to your business, what can I buy from you, what do sell, what are your products? >> Yeah so after being in business analytics is Cognos analytics, Watson analytics, Watts analytics for social media, and planning analytics. Cognos is the "what", what's going on in my business. Watts analytics is the "why", planning analytics is "what do we think is going to happen?". We're starting to do more and more smarter, what do we think's going to happen based on these predictive models instead of just guessing what's going to happen. And then social media really gets into this idea of trying to find the signal, the sentiment. Not just around your own brand, it could be a competitor recall, and what now the intent is of that customer, are they going to now start buying other products, or are they going to stick with the recall company. >> Vellante: Okay so the starting point of your business having Cognos, one of the largest acquisitions ever in IBM's history, and of course it was all about CFO's and reporting and Sarbanes-Oxley was a huge boom to that business, but as I was saying before it, it never really got us to that predictive era. So you're layering those predictive pieces on top. >> That's what you saw on stage. >> Yes, that's right, what, so we saw on stage, and then are you selling to the same constituencies? Or how is constituency that you sell to changing? >> Yeah, no it's actually the same. Well Cognos BI, historically was selling to IT, and Cognos Analytics is selling to the business. But if we take that leap forward then we're now in the market, we have been for a few years now at Cognos Analytics. Yeah, that capability we showed onstage where we talked about not only what's going on, why it's going on, what will happen next, and what we ought to do about it. We're selling that capability for them, the business user, the dashboard becomes like a piece of glass to them. And that glass is able to call services that they don't have to be proficient in, they just want to be able to use them. It calls the weather service, it calls the optimization service, it calls the machine learning data sign service, and it actually gives them information that's forward looking and highly accurate, so they love it, 'cause it's cool they haven't had anything like that before. >> Vellante: Alright Marc Altshuller, thanks very much for coming back on The Cube, it's great to see you. >> Thank you. >> "You can't measure heart" as we say in boston, but you better start measuring. Alright keep right there everybody, Jim and I will right back after this short break. This is The Cube, we're live from Fast Track Your Data in Munich. We'll be right back. (upbeat jingle) (thoughtful music)

>> 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)