 Live from Munich, Germany, it's theCUBE. Covering IBM, Fast Track Your Data, brought to you by IBM. We're back, this is Dave Vellante with Jim Kobielus, and this is theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise. We are here covering special presentation of IBM's Fast Track Your Data, and we're in Munich, Germany. It's been a day long session. We started this morning with a panel discussion with five senior level data scientists that Jim and I hosted. Then we did CUBE interviews in the morning. We cut away to the main tent. Kate Silverton did a very choreographed scripted, but very well done, main keynote set of presentations. IBM made a couple of announcements today, and then we finished up the CUBE interviews. Jim and I are here to rap. We're actually running on ibmgo.com. We're running live Hillary Mason, talking about what she's doing in data science. And also, we got a session on GDPR. You got to log in to see those sessions. So go ahead to ibmgo.com and you'll find those, hit the schedule and go to the Hillary Mason and GDPR channels and check that out. But we're going to wrap now. Jim, two main announcements today. I hesitate to call them big announcements. I mean, they were, you know, just kind of, I think the word you used last night was perfunctory. You know, I mean, they're okay. I'll explain that one. But they're not game-changing. So what did you mean? Well, first of all, when you look at most, the IBM is not calling this a signature event. It's essentially a signature event. They do these every June or so. You know, in the past several years, the signature events have had like a one track theme, whether it be IBM announcing their investing deeply in Spark or IBM announcing that they're focusing on, investing on R as the core language for data science development. This year at this event in Munich, it's really a three track event in terms of the broad themes. And none, I mean, they're all important tracks, but none of them is like game-changing. Perhaps IBM doesn't intend them to be. It seems like one of which is obviously Europe. We're holding this in Munich and a couple of things of importance to European customers. First and foremost, GDPR, the deadline next year, in terms of compliance is approaching. So sound, the alarm as it were, and IBM has rolled out compliance or governance tools, download and go for the information catalog, governance catalog and so forth. Now announcing the consortium with Hortonworks to build governance on top of Apache Atlas. But also IBM announcing that they've opened up a DSX center in England and a machine learning hub here in Germany to help their European clients in those countries, especially to get deeper down into data science and machine learning in terms of developing those applications. That's important for the audience, the regional audience here. The second track, which is also important and I alluded to it, it's governance. In all of its manifestations, you need a master catalog of all the assets for building and maintaining and controlling your data applications and your data science applications. The catalog, the consortium of the various offerings that IBM has announced and discussed in great detail that they've brought in customers and partners like Northern Trust to talk about the importance of governance, not just as a compliance mandate, but also as a potential strategy for monetizing your data. That's important. Number three is what I call cloud native data applications and how the state of the art in developing data applications is moving towards containerized and orchestrated environments that involve things like Docker and Kubernetes. The IBM DB2 developer community edition has been on the market for a few years. The latest version they announced today includes Kubernetes support, support for JSON, so it's geared towards the new generation of cloud native apps. What I'm getting at is there are three, those three core themes are Europe, governance and cloud native data application development. Each of them is individually important, but none of them is a game changer. And one last thing, data science and machine learning is one of the overarching envelope themes of this event. They've had Hilary Mason, a lot of discussion there. My sense, I was a little bit disappointed that there wasn't any significant new announcements related to IBM evolving their machine learning portfolio into deep learning for artificial intelligence in an environment where their direct competitors like Microsoft and Google and Amazon are making a huge push in AI in terms of their investments. There's a bit of a discussion and Rob Thomas got to it this morning about DSX working with Power AI of the IBM platform. We'd like to hear more going forward about IBM investments in these areas. So I thought it was an interesting bunch of announcements. I'll backtrack on perfunctory. I'll just say it was good that they had this for a lot of reasons, but like I said, none of these individual announcements is really changing the game. In fact, like I said, I think I'm waiting for the fall to see where IBM goes in terms of doing something that's actually differentiating and innovative. Well, I think that the event itself is great. We've got a bunch of partners here, a bunch of customers, I mean, it's active. IBM knows how to throw a party. They always have. And the sessions were really individually awesome. I mean, in terms of what you learn. The content is very good. I would agree. The two announcements that they made was sort of, you know, DB2, sort of what I call community edition, simpler, easy to download. Even Dave can download DB2. I really don't want to download DB2, but I could. And play with it, I guess. You know, I'm not a database guy, but those of you out there that are, go check it out. And the other one was this sort of unified data governance. They tried to tie it in. I think they actually did a really good job of tying it into GDPR. We're going to hear over the next 11 months just a ton of GDPR readiness, fear, uncertainty, and doubt from the vendor community. Kind of like we heard with Y2K. Yeah. We'll see what kind of impact GDPR has. I mean, it looks like it's the real deal, Jim. I mean, it looks like, you know, there's 4% of turnover, penalty. The penalties are much more onerous than any other sort of, you know, regulation that we've seen in the past where you could just sort of fluff it up. Say, ah, just pay the fine. I think you're going to see a lot of, well, pay the lawyers to delay this thing and battle it. And one of our people on theCUBE that we interviewed said it exactly right. GDPR is like the inverse of Y2K. Where Y2K, everybody was freaking out. It was actually nothing when it came down to it. Where nobody on the street is really buzzing, I mean, the average person is not buzzing about GDPR, but it's hugely important. And like you said, I mean, some serious penalties may be in the works for companies that are not complying. Companies not just in Europe, but all around the world who do business with European customers. Right, okay. So now, bring it back to sort of machine learning, deep learning, you basically said to Rob Thomas, I see machine learning here. I don't see a lot of the deep learning stuff quite yet. He said, stay tuned. You know, you were talking about TensorFlow and things like that. Yeah, they supported that, you know, Rob said, yeah, so Rob indicated that IBM very much, like with Power, AI and DSX provides an open framework or toolkit for plugging in your, you, the developers preferred machine learning or deep learning toolkit of an open source nature. And there's a growing range of open source, deep learning toolkits beyond, you know, TensorFlow, including Fiano and MXNet and so forth. That IBM is supporting within the overall DSX framework, but also within the Power AI framework. In other words, they've got those capabilities. They're sort of burying that message under a bushel basket, at least in terms of this event. Also, one of the things that I said this to Manish Goyal is that Watson Data Platform, which they launched last fall, very important product, very important platform for collaboration among data science professionals in terms of the machine learning development pipeline. I wish there was more about Watson Data Platform here about where they're taking it and what the customers are doing with it. Like I said a couple of times, I see Watson Data Platform as very much a DevOps tool for the new generation of developers that are building machine learning models directly into their applications. I'd like to see IBM going forward, turn Watson Data Platform into a true DevOps platform in terms of continuous integration of machine learning and deep learning and other statistical models, continuous training, continuous deployment, iteration. I believe that's where they're going or probably will be going. I'd like to see more, I'm expecting more along those lines going forward. And that's what I just described about DevOps for Data Science, is a big theme that we're focusing on at Wikibon in terms of where the industry's going. Yeah, and I want to come back to that and get an update on what you're doing and your team and talk about the research. Before we do that, I mean, one of the things we talked about on theCUBE in the early days of Hadoop is that the guys are going to make the money in this big data business of the practitioners. They're not going to see these multi, 100 billion dollar valuations come out of the Dupe world. And so far, that prediction has held up well. It's the Airbnb's and the Uber's and the Spotify's and the Facebook's and the Googles, the practitioners who are applying big data that are crushing it and making all the money. And you see Amazon now buying Whole Foods, that in our view is a data play. But who's winning here in either the vendor or the practitioner community? Who's winning are the startups with a hot new idea that's changing, that's disrupting some industry or set of industries with machine learning, deep learning, big data, et cetera. So if you're, for example, everybody's with beta breadth waiting for self-driving vehicles and that ecosystem as it develops, somebody's going to clean up one or more companies, companies we probably never heard of. Leveraging everything we're describing here today, DevOps for data science and containerized, distributed applications that involve deep learning for image analysis and sensor analytics and so forth, putting it all together in some new fabric that changes the way we live on this planet. But as you said, the platforms themselves, whether they be Hadoop or Spark or TensorFlow, they're open source. And the fact is, by this very nature, open source based solutions in terms of the profit margins on selling those, inexorably migrate to zero. So you're not going to make any money as a tool vendor or a platform vendor. You got to make money, if you're going to make money, you make money, for example, providing an ecosystem within which innovation can happen. Okay, let's get a few minutes left. Let's talk about the research that you're working on. What's exciting you these days? And what have you got going? So I think a lot of people know I've been around the analyst space for a long, long time. I've joined the Silicon Angle Wikibon team just recently. I used to work for a very large solution provider. And what I do here for Wikibon is I focus on data science as the core of next generation application development. When I say next generation application development, it's the development of AI, deep learning, machine learning, and the deployment of those data-driven statistical assets into all manner of applications. And you look at the hot stuff like chat bots, for example, transforming the experience in e-commerce, on mobile devices, Siri, and Alexa, and so forth. Hugely important. So what we're doing is we're focusing on AI and everything. We're focusing on containerization and building of microservices, AI microservices, and the ecosystem of the pipelines and the tools that allow you to do that. DevOps for data science, distributed training, federated training of statistical models, so forth. We are also very much focusing on the whole distributed containerized ecosystem, Docker, Kubernetes, and so forth. Where that's going in terms of changing the state of the art in terms of application development. We're focusing on the API economy. All of those things that you need to wrap around the payload of AI to make it, to deliver it into every fabric. You're focused on that intersection between AI and the related topics in the developer community. Who is winning in that developer community? Obviously Amazon's winning. You've got Microsoft doing a good job there. Google, Apple, who else? I mean, how's IBM doing, for example? Maybe name some names. Who impresses you in the developer community? But specifically, let's start with IBM. How is IBM doing in that space? IBM's doing really well. I mean, in terms of, IBM has been for quite a while been very good about engaging with the new generation of developers using Spark and R and Hadoop and so forth. To build applications rapidly and deploy them rapidly into all manner of applications. So IBM has very much reached out to in the last several years the millennials for whom all of these new tools have been their core repertoire from the very start. And I think in many ways, like today, like developer edition, the DB2 developer community edition is very much geared to that market. Saying to the cloud native application developer, take a second look at DB2. There's a lot in DB2 that you might bring into your next application development initiative alongside your Spark toolkit and so forth. So IBM has startup envy. They're a big, old company, been around for more than 100 years. And they're trying to very much bootstrap and restart their brand in this new context for the 21st century. I think they're making a good effort at doing it. In terms of community engagement, they have a really good community engagement program all around the world in terms of hackathons and developer days, meetups here and there. And they get lots of turnout and very loyal customers. And IBM's got the broadest portfolios. So you're still bleeding a little bit of blue. So I got to squeeze it out of you now. So let me ask you, let me push a little bit on what you're saying. So DB2 is the emphasis here, trying to make position DB2 as appealing for developers, but why not some of the other acquisitions that they've made? I mean, you don't hear that much about Cloudant and DashDB and things of that nature. You would think that that would be more appealing to some of the developer communities than DB2. Or am I mistaken? Is it IBM sort of going after the core, trying to evolve that core constituency? No, they've done a lot of strategic acquisitions like Cloudant and like they've acquired the graph databases and brought them into their platform. IBM has every type of database or distributed file system that you might need for web or social or internet of things and so forth. All of the development challenges, IBM's got a really high quality fit to purpose, best of breed platform, underlying data platform for it. They've got huge amounts of developers energized all around the world and working on those platforms. DB2, in the last several years, they've taken all of their legacy, that's the wrong word, all their existing mature platforms like DB2 and brought them into the IBM Cloud. I think the legacy's the right word. Yeah, these things have been around for 30 years. And they're not going away because they're field proven and they're customers have implemented them everywhere and they're evolving. If you look at how IBM has evolved DB2 in the last several years into, for example, they responded to the challenge from SAP HANA when brought blue acceleration technology and memory technology into DB2 to make it scrimmingly fast and so forth. IBM has done a really good job of turning around these product groups and the product architectures, making them cloud first and then reaching out to new generation of cloud application developers. Like I said, today, things like DB2, Developer Community Edition is just the next chapter in this ongoing saga of IBM turning itself around. So like I said, each of the individual announcements today is like, okay, that's interesting. I'm glad to see IBM showing progress. None of them is individually disruptive. I think the last week though, I think the one important works was disruptive in the sense that IBM recognized that big insights didn't really have a lot of traction in the Hadoop spaces, not as much as they would have wished. Hortonworks very much does and IBM has cast its slot with Hort with HDP. But Hortonworks recognizes they haven't achieved any attraction with data scientists. Therefore, DSX makes sense as part of the Hortonworks portfolio. Likewise, a big SQL makes perfect sense as the SQL front end to HDP. So I think the teaming of IBM and Hortonworks is propitious of further things that they'll be doing in the future, not just governance, but really putting together a broader cloud portfolio for the next generation of data scientists doing work in the cloud. Do you think Hortonworks is a legitimate acquisition target for IBM? Of course they are. Why would IBM educate us? Why would IBM want to acquire Hortonworks? What does that give IBM? Open source mojo, obviously. Yeah, mojo. What else? A strong loyal team with the Hadoop market with developers. And also our... So the developer angle would supercharge the developer angle and maybe make it more relevant outside of some of those legacy systems. Yeah, but also remember that Hortonworks came from Yahoo, the team that developed much of what became Hadoop. They've got an excellent team, a strategic team. So in many ways, you can look at Hortonworks as one part Aquahire, if they ever do that. And one part really substantial and growing solution portfolio that in many ways is complementary to IBM. I mean, Hortonworks is really deep on the governance of Hadoop. IBM has gone there, but I think Hortonworks is even deeper in terms of their laser focus on government. So ecosystem expansion and it actually really wouldn't be that expensive of an acquisition. I mean, it's in the north of, maybe a billion dollars might get it done. So would you pay a billion dollars for Hortonworks? Not out of my own pocket. No, I mean, if you're IBM, you think that would deliver that kind of value. I mean, you know how IBM thinks about acquisitions. They're good at acquisitions. They look at the IRR, they have their formula, they blue wash the companies and they generally do very well with acquisitions. Do you think Hortonworks would fit that profile, that monetization profile? I wouldn't say that Hortonworks, in terms of monetization potential, would match, say, what IBM has achieved by acquiring the T's and then- Cognos or SPSS, SPSS has been an extraordinarily successful. Well, the day IBM acquired SPSS, they tripled the license fees as a customer, I know, ouch, but it worked. I mean, it was incredibly successful. Well, yeah, Cognos was, Natesa was and SPSS. Those three acquisitions in the last 10 years have been extraordinarily pivotal and successful for IBM to build what they now have, which is really the most comprehensive portfolio of fit-to-purpose data platforms. Second to, so in other words, all those acquisitions prepared IBM to duke it out now with their primary competitors in this new field, which are Microsoft, who's newly resurgent, and Amazon- Amazon, absolutely. In other words, the two Seattle vendors, Seattle has come on strong in a way that's almost Seattle now, and big data in the cloud is eclipsing Silicon Valley in terms of where, you know, it's like the locus of innovation and really of customer adoption in the cloud space. Quite amazing. Well, Google's still hanging in there. Oh, yeah. All right, Jim, really a pleasure working with you today. Thanks so much, really appreciate it. Thanks for bringing me on your team. And Munich crew, you guys did a great job, really. Well done. Chuck, Alex, Patrick, wherever he is, and our great makeup lady, thanks a lot, everybody back home. We're out, this is fast track your data. Go to ibm.go.com for all the replays, youtube.com slash Silicon Angle for all the shows. The cube.net is where we tell you where the cube's going to be. Go to wikibon.com for all the research. Thanks for watching everybody. This is Dave Vellante with Jim Cabales, we're out.