>> Announcer: Live from New York City, it's theCUBE. Covering IBM Data Science For All. Brought to you by IBM. >> Welcome to the big apple, John Walls and Dave Vellante here on theCUBE we are live at IBM's Data Science For All. Going to be here throughout the day with a big panel discussion wrapping up our day. So be sure to stick around all day long on theCUBe for that. Dave always good to be here in New York is it not? >> Well you know it's been kind of the data science weeks, months, last week we're in Boston at an event with the chief data officer conference. All the Boston Datarati were there, bring it all down to New York City getting hardcore really with data science so it's from chief data officer to the hardcore data scientists. >> The CDO, hot term right now. Daniel Hernandez now joins as our first guest here at Data Science For All. Who's a VP of IBM Analytics, good to see you. David thanks for being with us. >> Pleasure. >> Alright well give us first off your take, let's just step back high level here. Data science it's certainly been evolving for decades if you will. First off how do you define it today? And then just from the IBM side of the fence, how do you see it in terms of how businesses should be integrating this into their mindset. >> So the way I describe data science simply to my clients is it's using the scientific method to answer questions or deliver insights. It's kind of that simple. Or answering questions quantitatively. So it's a methodology, it's a discipline, it's not necessarily tools. So that's kind of the way I approach describing what it is. >> Okay and then from the IBM side of the fence, in terms of how wide of a net are you casting these days I assume it's as big as you can get your arms out. >> So when you think about any particular problem that's a data science problem, you need certain capabilities. We happen to deliver those capabilities. You need the ability to collect, store, manage, any and all data. You need the ability to organize that data so you can discover it and protect it. You got to be able to analyze it. Automate the mundane, explain the past, predict the future. Those are the capabilities you need to do data science. We deliver a portfolio of it. Including on the analyze part of our portfolio, our data science tools that we would declare as such. >> So data science for all is very aspirational, and when you guys made the announcement of the Watson data platform last fall, one of the things that you focused on was collaboration between data scientists, data engineers, quality engineers, application development, the whole sort of chain. And you made the point that most of the time that data scientists spend is on wrangling data. You're trying to attack that problem, and you're trying to break down the stovepipes between those roles that I just mentioned. All that has to happen before you can actually have data science for all. I mean that's just data science for all hardcore data people. Where are we in terms of sort of the progress that your clients have made in that regard? >> So you know, I would say there's two majors vectors of progress we've made. So if you want data science for all you need to be able to address people that know how to code and people that don't know how to code. So if you consider kind the history of IBM in the data science space especially in SPSS, which has been around for decades. We're mastering and solving data science problems for non-coders. The data science experience really started with embracing coders. Developers that grew up in open source, that lived and learned Jupiter or Python and were more comfortable there. And integration of these is kind of our focus. So that's one aspect. Serving the needs of people that know how to code and don't in the kind of data science role. And then for all means supporting an entire analytics life cycle from collecting the data you need in order to answer the question that you're trying to answer to organizing that information once you've collected so you can discover it inside of tools like our own data science experience and SPSS, and then of course the set of tools that around exploratory analytics. All integrated so that you can do that end to end life cycle. So where clients are, I think they're getting certainly much more sophisticated in understanding that. You know most people have approached data science as a tool problem, as a data prep problem. It's a life cycle problem. And that's kind of how we're thinking about it. We're thinking about it in terms of, alright if our job is answer questions, delivering insights through scientific methods, how do we decompose that problem to a set of things that people need to get the job done, serving the individuals that have to work together. >> And when you think about, go back to the days where it's sort of the data warehouse was king. Something we talked about in Boston last week, it used to be the data warehouse was king, now it's the process is much more important. But it was very few people had access to that data, you had the elapsed time of getting answers, and the inflexibility of the systems. Has that changed and to what degree has it changed? >> I think if you were to go ask anybody in business whether or not they have all the data they need to do their job, they would say no. Why? So we've invested in EDW's, we've invested in Hadoop. In part sometimes, the problem might be, I just don't have the data. Most of the time it is I have the data I just don't know where it is. So there's a pretty significant issue on data discoverability, and it's important that I might have data in my operational systems, I might have data inside my EDW, I don't have everything inside my EDW, I've standed up one or more data lakes, and to solve my problem like customer segmentation I have data everywhere, how do I find and bring it in? >> That seems like that should be a fundamental consideration, right? If you're going to gather this much more information, make it accessible to people. And if you don't, it's a big flaw, it's a big gap is it not? >> So yes, and I think part of the reason why is because governance professionals which I am, you know I spent quite a bit of time trying to solve governance related problems. We've been focusing pretty maniacally on kind of the compliance, and the regulatory and security related issues. Like how do we keep people from going to jail, how do we ensure regulatory compliance with things like e-discovery, and records for instance. And it just so happens the same discipline that you use, even though in some cases lighter weight implementations, are what you need in order to solve this data discovery problem. So the discourse around governance has been historically about compliance, about regulations, about cost takeout, not analytics. And so a lot of our time certainly in R&D is trying to solve that data discovery problem which is how do I discover data using semantics that I have, which as a regular user is not physical understandings of my data, and once I find it how am I assured that what I get is what I should get so that it's, I'm not subject to compliance related issues, but also making the company more vulnerable to data breach. >> Well so presumably part of that anyway involves automating classification at the point of creation or use, which is actually was a technical challenge for a number of years. Has that challenge been solved in your view? >> I think machine learning is, and in fact later on today I will be doing some demonstrations of technology which will show how we're making the application of machine learning easy, inside of everything we do we're applying machine learning techniques including to classification problems that help us solve the problem. So it could be we're automatically harvesting technical metadata. Are there business terms that could be automatically extracted that don't require some data steward to have to know and assert, right? Or can we automatically suggest and still have the steward for a case where I need a canonical data model, and so I just don't want the machine to tell me everything, but I want the machine to assist the data curation process. We are not just exploring the application of machine learning to solve that data classification problem, which historically was a manual one. We're embedding that into most of the stuff that we're doing. Often you won't even know that we're doing it behind the scenes. >> So that means that often times well the machine ideally are making the decisions as to who gets access to what, and is helping at least automate that governance, but there's a natural friction that occurs. And I wonder if you can talk about the balance sheet if you will between information as an asset, information as a liability. You know the more restrictions you put on that information the more it constricts you know a business user's ability. So how do you see that shaping up? >> I think it's often a people process problem, not necessarily a technology problem. I don't think as an industry we've figured it out. Certainly a lot of our clients haven't figured out that balance. I mean there are plenty of conversation I'll go into where I'll talk to a data science team in a same line of business as a governance team and what the data science team will tell us is I'm building my own data catalog because the stuff that the governance guys are doing doesn't help me. And the reason why it doesn't help me is because it's they're going through this top down data curation methodology and I've got a question, I need to go find the data that's relevant. I might not know what that is straight away. So the CDO function in a lot of organizations is helping bridge that. So you'll see governance responsibilities line up with the CDO with analytics. And I think that's gone a long way to bridge that gaps. But that conversation that I was just mentioning is not unique to one or two customers. Still a lot of customers are doing it. Often customers that either haven't started a CDO practice or are early days on it still. >> So about that, because this is being introduced to the workplace, a new concept right, fairly new CDOs. As opposed to CIO or CTO, you know you have these other. I mean how do you talk to your clients about trying to broaden their perspective on that and I guess emphasizing the need for them to consider putting somebody of a sole responsibility, or primary responsibility for their data. Instead of just putting it lumping it in somewhere else. >> So we happen to have one of the best CDO's inside of our group which is like a handy tool for me. So if I go into a client and it's purporting to be a data science problem and it turns out they have a data management issue around data discovery, and they haven't yet figured out how to install the process and people design to solve that particular issue one of the key things I'll do is I'll bring in our CDO and his delegates to have a conversation around them on what we're doing inside of IBM, what we're seeing in other customers to help institute that practice inside of, inside of their own organization. We have forums like the CDO event in Boston last week, which are designed to, you know it's not designed to be here's what IBM can do in technology, it's designed to say here's how the discipline impacts your business and here's some best practices you should apply. So if ultimately I enter into those conversations where I find that there's a need, I typically am like alright, I'm not going to, tools are part of the problem but not the only issue, let me bring someone in that can describe the people process related issues which you got to get right. In order for, in some cases to the tools that I deliver to matter. >> We had Seth Dobrin on last weekend in Boston, and Inderpal Bhandari as well, and he put forth this enterprise, sort of data blueprint if you will. CDO's are sort of-- >> Daniel: We're using that in IBM by the way. >> Well this is the thing, it's a really well thought out sort of structure that seems to be trickling down to the divisions. And so it's interesting to hear how you're applying Seth's expertise. I want to ask you about the Hortonworks relationship. You guys have made a big deal about that this summer. To me it was a no brainer. Really what was the point of IBM having a Hadoop distro, and Hortonworks gets this awesome distribution channel. IBM has always had an affinity for open source so that made sense there. What's behind that relationship and how's it going? >> It's going awesome. Perhaps what we didn't say and we probably should have focused on is the why customers care aspect. There are three main by an occasion use cases that customers are implementing where they are ready even before the relationship. They're asking IBM and Hortonworks to work together. And so we were coming to the table working together as partners before the deeper collaboration we started in June. The first one was bringing data science to Hadoop. So running data science models, doing data exploration where the data is. And if you were to actually rewind the clock on the IBM side and consider what we did with Hortonworks in full consideration of what we did prior, we brought the data science experience and machine learning to Z in February. The highest value transactional data was there. The next step was bring data science to where the, often for a lot of clients the second most valuable set of data which is Hadoop. So that was kind of part one. And then we've kind of continued that by bringing data science experience to the private cloud. So that's one use case. I got a lot data, I need to do data science, I want to do it in resident, I want to take advantage of the compute grid I've already laid down, and I want to take advantage of the performance benefits and the integrated security and governance benefits by having these things co-located. That's kind of play one. So we're bringing in data science experience and HDP and HDF, which are the Hortonworks distributions way closer together and optimized for each other. Another component of that is not all data is going to be in Hadoop as we were describing. Some of it's in an EDW and that data science job is going to require data outside of Hadoop, and so we brought big SQL. It was already supporting Hortonworks, we just optimized the stack, and so the combination of data science experience and big SQL allows you to data science against a broader surface area of data. That's kind of play one. Play two is I've got a EDW either for cost or agility reasons I want to augment it or some cases I might want to offload some data from it to Hadoop. And so the combination of Hortonworks plus big SQL and our data integration technologies are a perfect combination there and we have plenty of clients using that for kind of analytics offloading from EDW. And then the third piece that we're doing quite a bit of engineering, go-to-market work around is govern data lakes. So I want to enable self service analytics throughout my enterprise. I want self service analytics tools to everyone that has access to it. I want to make data available to them, but I want that data to be governed so that they can discover what's in it in the lake, and whatever I give them is what they should have access to. So those are the kind of the three tracks that we're working with Hortonworks on, and all of them are making stunning results inside of clients. >> And so that involves actually some serious engineering as well-- >> Big time. It's not just sort of a Barney deal or just a pure go to market-- >> It's certainly more the market texture and just works. >> Big picture down the road then. Whatever challenges that you see on your side of the business for the next 12 months. What are you going to tackle, what's that monster out there that you think okay this is our next hurdle to get by. >> I forgot if Rob said this before, but you'll hear him say often and it's statistically proven, the majority of the data that's available is not available to be Googled, so it's behind a firewall. And so we started last year with the Watson data platform creating an integrating data analytics system. What if customers have data that's on-prem that they want to take advantage of, what if they're not ready for the public cloud. How do we deliver public benefits to them when they want to run that workload behind a firewall. So we're doing a significant amount of engineering, really starting with the work that we did on a data science experience. Bringing it behind the firewall, but still delivering similar benefits you would expect if you're delivering it in the public cloud. A major advancement that IBM made is run IBM cloud private. I don't know if you guys are familiar with that announcement. We made, I think it's already two weeks ago. So it's a (mumbles) foundation on top of which we have micro services on top of which our stack is going to be made available. So when I think of kind of where the future is, you know our customers ultimately we believe want to run data and analytic workloads in the public cloud. How do we get them there considering they're not there now in a stepwise fashion that is sensible economically project management-wise culturally. Without having them having to wait. That's kind of big picture, kind of a big problem space we're spending considerable time thinking through. >> We've been talking a lot about this on theCUBE in the last several months or even years is people realize they can't just reform their business and stuff into the cloud. They have to bring the cloud model to their data. Wherever that data exists. If it's in the cloud, great. And the key there is you got to have a capability and a solution that substantially mimics that public cloud experience. That's kind of what you guys are focused on. >> What I tell clients is, if you're ready for certain workloads, especially green field workloads, and the capability exists in a public cloud, you should go there now. Because you're going to want to go there eventually anyway. And if not, then a vendor like IBM helps you take advantage of that behind a firewall, often in form facts that are ready to go. The integrated analytics system, I don't know if you're familiar with that. That includes our super advanced data warehouse, the data science experience, our query federation technology powered by big SQL, all in a form factor that's ready to go. You get started there for data and data science workloads and that's a major step in the direction to the public cloud. >> Alright well Daniel thank you for the time, we appreciate that. We didn't get to touch at all on baseball, but next time right? >> Daniel: Go Cubbies. (laughing) >> Sore spot with me but it's alright, go Cubbies. Alright Daniel Hernandez from IBM, back with more here from Data Science For All. IBM's event here in Manhattan. Back with more in theCUBE in just a bit. (electronic music)

>> Announcer: Live from Munich, Germany; it's The Cube. Covering IBM; fast-track your data. Brought to you by IBM. (upbeat techno music) >> For you here at the show, generally; and specifically, what are you doing here today? >> There's really three things going on at the show, three high level things. One is we're talking about our new... How we're repositioning our hybrid data management portfolio, specifically some announcements around DB2 in a hybrid environment, and some highly transactional offerings around DB2. We're talking about our unified governance portfolio; so actually delivering a platform for unified governance that allows our clients to interact with governance and data management kind of products in a more streamlined way, and help them actually solve a problem instead of just offering products. The third is really around data science and machine learning. Specifically we're talking about our machine learning hub that we're launching here in Germany. Prior to this we had a machine learning hub in San Francisco, Toronto, one in Asia, and now we're launching one here in Europe. >> Seth, can you describe what this hub is all about? This is a data center where you're hosting machine learning services, or is it something else? >> Yeah, so this is where clients can come and learn how to do data science. They can bring their problems, bring their data to our facilities, learn how to solve a data science problem in a more team oriented way; interacting with data scientists, machine learning engineers, basically, data engineers, developers, to solve a problem for their business around data science. These previous hubs have been completely booked, so we wanted to launch them in other areas to try and expand the capacity of them. >> You're hosting a round table today, right, on the main tent? >> Yep. >> And you got a customer on, you guys going to be talking about sort of applying practices and financial and other areas. Maybe describe that a little bit. >> We have a customer on from ING, Heinrich, who's the chief architect for ING. ING, IBM, and Horton Works have a consortium, if you would, or a framework that we're doing around Apache Atlas and Ranger, as the kind of open-source operating system for our unified governance platform. So much as IBM has positioned Spark as a unified, kind of open-source operating system for analytics, for a unified governance platform... For a governance platform to be truly unified, you need to be able to integrate metadata. The biggest challenge about connecting your data environments, if you're an enterprise that was not internet born, or cloud born, is that you have proprietary metadata platforms that all want to be the master. When everyone wants to be the master, you can't really get anything done. So what we're doing around Apache Atlas is we are setting up Apache Atlas as kind of a virtual translator, if you would, or a dictionary between all the different proprietary metadata platforms so that you can get a single unified view of your data environment across hybrid clouds, on premise, in the cloud, and across different proprietary vendor platforms. Because it's open-sourced, there are these connectors that can go in and out of the proprietary platforms. >> So Seth, you seem like you're pretty tuned in to the portfolio within the analytics group. How are you spending your time as the Chief Data Officer? How do you balance it between customer visits, maybe talking about some of the products, and then you're sort of day job? >> I actually have three days jobs. My job's actually split into kind of three pieces. The first, my primary mission, is really around transforming IBM's internal business unit, internal business workings, to use data and analytics to run our business. So kind of internal business unit transformation. Part of that business unit transformation is also making sure that we're compliant with regulations like GDBR and other regulations. Another third is really around kind of rethinking our offerings from a CDO perspective. As a CDO, and as you, Dave, I've only been with IBM for seven months. As a former client recently, and as a CDO, what is it that I want to see from IBM's offerings? We kind of hit on it a little bit with the unified governance platform, where I think IBM makes fantastic products. But as a client, if a salesperson shows up to me, I don't want them selling me a product, 'cause if I want an MDM solution, I'll call you up and say, "Hey, I need an MDM solution. "Give me a quote." What I want them showing up is saying, "I have a solution that's going to solve "your governance problem across your portfolio." Or, "I'm going to solve your data science problem." Or, "I'm going to help you master your data, "and manage your data across "all these different environments." So really working with the offering management and the Dev teams to define what are these three or four, kind of business platforms that we want to settle on? We know three of them at least, right? We know that we have a hybrid data management. We have unified governance. We have data science and machine learning, and you could think of the Z franchise as a fourth platform. >> Seth, can you net out how governance relates to data science? 'Cause there is governance of the statistical models, machine learning, and so forth, version control. I mean, in an end to end machine learning pipeline, there's various versions of various artifacts they have to be managed in a structured way. Is your unified governance bundle, or portfolio, does it address those requirements? Or just the data governance? >> Yeah, so the unified governance platform really kind of focuses today on data governance and how good data governance can be an enabler of rapid data science. So if you have your data all pre-governed, it makes it much quicker to get access to data and understand what you can and can't do with data; especially being here in Europe, in the context of the EU GDPR. You need to make sure that your data scientists are doing things that are approved by the user, because basically your data, you have to give explicit consent to allow things to be done with it. But long term vision is that... essentially the output of models is data, right? And how you use and deploy those models also need to be governed. So the long term vision is that we will have a governance platform for all those things, as well. I think it makes more sense for those things to be governed in the data science platform, if you would. And we... >> We often hear separate from GDPR and all that, is something called algorithmic accountability; that more is being discussed in policy circles, in government circles around the world, as strongly related to everything you're describing. Being able to trace the lineage of any algorithmic decision back to the data, the metadata, and so forth, and the machine learning models that might have driven it. Is that where IBM's going with this portfolio? >> I think that's the natural extension of it. We're thinking really in the context of them as two different pieces, but if you solve them both and you connect them together, then you have that problem. But I think you're absolutely right. As we're leveraging machine learning and artificial intelligence, in general, we need to be able to understand how we got to a decision, and that includes the model, the data, how the data was gathered, how the data was used and processed. So it is that entire pipeline, 'cause it is a pipeline. You're not doing machine learning or AI in a vacuum. You're doing it in the context of the data, and you're doing it in the context about the individuals or the organizations that you're trying to influence with the output of those models. >> I call it Dev ops for data science. >> Seth, in the early Hadoop days, the real headwind was complexity. It still is, by the way. We know that. Companies like IBM are trying to reduce that complexity. Spark helps a little bit So the technology will evolve, we get that. It seems like one of the other big headwinds right now is that most companies don't have a great understanding of how they can take data and monetize it, turn it into value. Most companies, many anyway, make the mistake of, "Well, I don't really want to sell my data," or, "I'm not really a data supplier." And they're kind of thinking about it, maybe not in the right way. But we seem to be entering a next wave here, where people are beginning to understand I can cut costs, I can do predictive maintenance, I can maybe not sell the data, but I can enhance what I'm doing and increase my revenue, maybe my customer retention. They seem to be tuning, more so; largely, I think 'cause of the chief data officer roles, helping them think that through. I wonder if you would give us your point of view on that narrative. >> I think what you're describing is kind of the digital transformation journey. I think the end game, as enterprises go through a digital transformation, the end game is how do I sell services, outcomes, those types of things. How do I sell an outcome to my end user? That's really the end game of a digital transformation in my mind. But before you can get to that, before you transform your business's objectives, there's a couple of intermediary steps that are required for that. The first is what you're describing, is those kind of data transformations. Enterprises need to really get a handle on their data and become data driven, and start then transforming their current business model; so how do I accelerate my current business leveraging data and analytics? I kind of frame that, that's like the data science kind of transformation aspect of the digital journey. Then the next aspect of it is how do I transform my business and change my business objectives? Part of that first step is in fact, how do I optimize my supply chain? How do I optimize my workforce? How do I optimize my goals? How do I get to my current, you know, the things that Wall Street cares about for business; how do I accelerate those, make those faster, make those better, and really put my company out in front? 'Cause really in the grand scheme of things, there's two types of companies today; there's the company that's going to be the disruptor, and there's companies that's going to get disrupted. Most companies want to be the disruptors, and it's a process to do that. >> So the accounting industry doesn't have standards around valuing data as an asset, and many of us feel as though waiting for that is a mistake. You can't wait for that. You've got to figure out on your own. But again, it seems to be somewhat of a headwind because it puts data and data value in this fuzzy category. But there are clearly the data haves and the data have-nots. What are you seeing in that regard? >> I think the first... When I was in my former role, my former company went through an exercise of valuing our data and our decisions. I'm actually doing that same exercise at IBM right now. We're going through IBM, at least in the analytics business unit, the part I'm responsible for, and going to all the leaders and saying, "What decisions are you making?" "Help me understand the decisions that you're making." "Help me understand the data you need "to make those decisions." And that does two things. Number one, it does get to the point of, how can we value the decisions? 'Cause each one of those decisions has a specific value to the company. You can assign a dollar amount to it. But it also helps you change how people in the enterprise think. Because the first time you go through and ask these questions, they talk about the dashboards they want to help them make their preconceived decisions, validated by data. They have a preconceived notion of the decision they want to make. They want the data to back it up. So they want a dashboard to help them do that. So when you come in and start having this conversation, you kind of stop them and say, "Okay, what you're describing is a dashboard. "That's not a decision. "Let's talk about the decision that you want to make, "and let's understand the real value of that decision." So you're doing two things, you're building a portfolio of decisions that then becomes to your point, Jim, about Dev ops for data science. It's your backlog for your data scientists, in the long run. You then connect those decisions to data that's required to make those, and you can extrapolate the data for each decision to the component that each piece of data makes up to it. So you can group your data logically within an enterprise; customer, product, talent, location, things like that, and you can assign a value to those based on decisions they support. >> Jim: So... >> Dave: Go ahead, please. >> As a CDO, following on that, are you also, as part of that exercise, trying to assess the value of not just the data, but of data science as a capability? Or particular data science assets, like machine learning models? In the overall scheme of things, that kind of valuation can then drive IBM's decision to ramp up their internal data science initiatives, or redeploy it, or, give me a... >> That's exactly what happened. As you build this portfolio of decisions, each decision has a value. So I am now assigning a value to the data science models that my team will build. As CDOs, CDOs are a relatively new role in many organizations. When money gets tight, they say, "What's this guy doing?" (Dave laughing) Having a portfolio of decisions that's saying, "Here's real value I'm adding..." So, number one, "Here's the value I can add in the future," and as you check off those boxes, you can kind of go and say, "Here's value I've added. "Here's where I've changed how the company's operating. "Here's where I've generated X billions of dollars "of new revenue, or cost savings, or cost avoidance, "for the enterprise." >> When you went through these exercises at your previous company, and now at IBM, are you using standardized valuation methodologies? Did you kind of develop your own, or come up with a scoring system? How'd you do that? >> I think there's some things around, like net promoter score, where there's pretty good standards on how to assign value to increases in net promoter score, or decreases in net promoter score for certain aspects of your business. In other ways, you need to kind of decide as an enterprise, how do we value our assets? Do we use a three year, five year, ten year MPV? Do we use some other metric? You need to kind of frame it in the reference that your CFO is used to talking about so that it's in the context that the company is used to talking about. Most companies, it's net present value. >> Okay, and you're measuring that on an ongoing basis. >> Seth: Yep. >> And fine tuning as you go along. Seth, we're out of time. Thanks so much for coming back in The Cube. It was great to see you. >> Seth: Yeah, thanks for having me. >> You're welcome, good luck this afternoon. >> Seth: Alright. >> Keep it right there, buddy. We'll be back. Actually, let me run down the day here for you, just take a second to do that. We're going to end our Cube interviews for the morning, and then we're going to cut over to the main tent. So in about an hour, Rob Thomas is going to kick off the main tent here with a keynote, talking about where data goes next. Hilary Mason's going to be on. There's a session with Dez Blanchfield on data science as a team sport. Then the big session on changing regulations, GDPRs. Seth, you've got some customers that you're going to bring on and talk about these issues. And then, sort of balancing act, the balancing act of hybrid data. Then we're going to come back to The Cube and finish up our Cube interviews for the afternoon. There's also going to be two breakout sessions; one with Hilary Mason, and one on GDPR. You got to go to IBMgo.com and log in and register. It's all free to see those breakout sessions. Everything else is open. You don't even have to register or log in to see that. So keep it right here, everybody. Check out the main tent. Check out siliconangle.com, and of course IBMgo.com for all the action here. Fast track your data. We're live from Munich, Germany; and we'll see you a little later. (upbeat techno 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)

>> Announcer: Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2017, brought to you by Hortonworks. >> Hi, welcome to theCUBE. We are live in San Jose, in the heart of Silicon Valley at the DataWorks Summit, day one. I'm Lisa Martin, with my co-host, George Gilbert. And we're very excited to be talking to two Robs. With Rob squared on the program this morning. Rob Bearden, the CEO of Hortonworks. Welcome, Rob. >> Thank you for having us. >> And Rob Thomas, the VP, GM rather, of IBM Analytics. So, guys, we just came from this really exciting, high energy keynote. The laser show was fantastic, but one of the great things, Rob, that you kicked off with was really showing the journey that Hortonworks has been on, and in a really pretty short period of time. Tremendous inertia, and you talked about the four mega-trends that are really driving enterprises to modernize their data architecture. Cloud, IOT, streaming data, and the fourth, next leg of this is data science. Data science, you said, will be the transformational next leg in the journey. Tell our viewers a little bit more about that. What does that mean for Hortonworks and your partnership with IBM? >> Well, what I think what IBM and Hortonworks now have the ability to do is to bring all the data together across a connected data platform. The data in motion, the data at rest, now have in one common platform, irrespective of the deployment architecture, whether it's on prim across multiple data centers or whether deployed in the cloud. And now that the large volume of data and we have access to it, we can now start to begin to drive the analytics in the end as that data moves through each phase of its life cycle. And what really happens now, is now that we have visibility and access to the inclusive life cycle of the data we can now put a data science framework over that to really now understand and learn those patterns and what's the data telling us, what's the pattern behind that. And we can bring simplification to the data science and turn data science actually into a team sport. Allow them to collaborate, allow them to have access to it. And sort of take the black magic out of doing data science with the framework of the tool and the power of DSX on top of the connected data platform. Now we can advance rapidly the insights in the end of the data and what that really does is drive value really quickly back into the customer. And then we can then begin to bring smart applications via the data science back into the enterprise. So we can now do things like connected car in real time, and have connected car learn as it's moving and through all the patterns, we can now, from a retail standpoint really get smart and accurate about inventory placement and inventory management. From an industrial standpoint, we know in real time, down to the component, what's happening with the machine, and any failures that may happen and be able to eliminate downtime. Agriculture, same kind of... Healthcare, every industry, financial services, fraud detection, money laundering advances that we have but it's all going to be attributable to how machine learning is applied and the DSX platform is the best platform in the world to do that with. >> And one of the things that I thought was really interesting, was that, as we saw enterprises start to embrace Hadoop and Big Data and Segano this needs to co-exist and inter-operate with our traditional applications, our traditional technologies. Now you're saying and seeing data science is going to be strategic business differentiator. You mentioned a number of industries, and there were several of them on stage today. Give us some, maybe some, one of your favorite examples of one of your customers leveraging data science and driving a pretty significant advantage for their business. >> Sure. Yeah, well, to step back a little bit, just a little context, only ten companies have out performed the S&P 500 in each of the last five years. We start looking at what are they doing. Those are companies that have decided data science and machine learning is critical. They've made a big bet on it, and every company needs to be doing that. So a big part of our message today was, kind of, I'd say, open the eyes of everybody to say there is something happening in the market right now. And it can make a huge difference in how you're applying data analytics to improve your business. We announced our first focus on this back in February, and one of our clients that spoke at that event is a company called Argus Healthcare. And Argus has massive amounts of data, sitting on a mainframe, and they were looking for how can we unleash that to do better care of patients, better care for our hospital networks, and they did that with data they had in their mainframe. So they brought data science experience and machine learning to their mainframe, that's what they talked about. What Rob and I have announced today is there's another great trove of data in every organization which is the data inside Hadoop. HDP, leading distribution for that, is a great place to start. So the use case that I just shared, which is on the mainframe, that's going to apply anywhere where there's large amounts of data. And right now there's not a great answer for data science on Hadoop, until today, where data science experience plus HDP brings really, I'd say, an elegant approach to it. It makes it a team sport. You can collaborate, you can interact, you can get education right in the platform. So we have the opportunity to create a next generation of data scientists working with data and HDP. That's why we're excited. >> Let me follow up with this question in your intro that, in terms of sort of the data science experience as this next major building block, to extract, or to build on the value from the data lake, the two companies, your two companies have different sort of, better markets, especially at IBM, but the industry solutions and global business services, you guys can actually build semi-custom solutions around this platform, both the data and the data science experience. With Hortonworks, what are those, what's your go to market motion going to look like and what are the offerings going to look like to the customer? >> They'll be several. You just described a great example, with IBM professional services, they have the ability to take those industry templates and take these data science models and instantly be able to bring those to the data, and so as part of our joint go to market motion, we'll be able now partner, bring those templates, bring those models to not only our customer base, but also part of the new sales go to market motion in the light space, in new customer opportunities and the whole point is, now we can use the enterprise data platforms to bring the data under management in a mission critical way that then bring value to it through these kinds of use case and templates that drive the smart applications into quick time to value. And just increase that time to value for the customers. >> So, how would you look at the mix changing over time in terms of data scientists working with the data to experiment on the model development and the two hard parts that you talked about, data prep and operationalization. So in other words, custom models, the issue of deploying it 11 months later because there's no real process for that that's packaged, and then packaged enterprise apps that are going to bake these models in as part of their functionality that, you know, the way Salesforce is starting to do and Workday is starting to do. How does that change over time? >> It'll be a layering effect. So today, we now have the ability to bring through the connected data platforms all the data under management in a mission critical manner from point of origination through the entire stream till it comes at rest. Now with the data science, through DSX, we can now, then, have that data science framework to where, you know, the analogy I would say, is instead of it being a black science of how you do data access and go through and build the models and determine what the algorithms are and how that yields a result, the analogy is you don't have to be a mechanic to drive a car anymore. The common person can drive a car. So, now we really open up the community business analyst that can now participate and enable data science through collaboration and then we can take those models and build the smart apps and evolve the smart apps that go to that very rapidly and we can accelerate that process also now through the partnership with IBM and bringing their core domain and value that, drivers that they've already built and drop that into the DSX environments and so I think we can accelerate the time to value now much faster and efficient than we've ever been able to do before. >> You mentioned teamwork a number of times, and I'm curious about, you also talked about the business analyst, what's the governance like to facilitate business analysts and different lines of business that have particular access? And what is that team composed of? >> Yeah, well, so let's look at what's happening in the big enterprises in the world right now. There's two major things going one. One is everybody's recognizing this is a multi-cloud world. There's multiple public cloud options, most clients are building a private cloud. They need a way to manage data as a strategic asset across all those multiple cloud environments. The second piece is, we are moving towards, what I would call, the next generation data fabric, which is your warehousing capabilities, your database capabilities, married with Hadoop, married with other open source data repositories and doing that in a seamless fashion. So you need a governance strategy for all of that. And the way I describe governance, simple analogy, we do for data what libraries do for books. Libraries create a catalog of books, they know they have different copies of books, some they archive, but they can access all of the intelligence in the library. That's what we do for data. So when we talk about governance and working together, we're both big supporters of the Atlas project, that will continue, but the other piece, kind of this point around enterprise data fabric is what we're doing with Big SQL. Big SQL is the only 100% ANSI-SQL compliant SQL engine for data across Hadoop and other repositories. So we'll be working closely together to help enterprises evolve in a multi-cloud world to this enterprise data fabric and Big SQL's a big capability for that. >> And an immediate example of that is in our EDW optimization suite that we have today we be loading Big SQL as the platform to do the complex query sector of that. That will go to market with almost immediately. >> Follow up question on the governance, there's, to what extent is end to end governance, meaning from the point of origin through the last mile, you know, if the last mile might be some specialized analytic engine, versus having all the data management capabilities in that fabric, you mentioned operational and analytic, so, like, are customers going to be looking for a provider who can give them sort of end to end capabilities on both the governance side and on all the data management capabilities? Is that sort of a critical decision? >> I believe so. I think there's really two use cases for governance. It's either insights or it's compliance. And if you're focus is on compliance, something like GDPR, as an example, that's really about the life cycle of data from when it starts to when it can be disposed of. So for compliance use case, absolutely. When I say insights as a governance use case, that's really about self-service. The ideal world is you can make your data available to anybody in your organization, knowing that they have the right permissions, that they can access, that they can do it in a protected way and most companies don't have that advantage today. Part of the idea around data science on HDP is if you've got the right governance framework in place suddenly you can enable self-service which is any data scientist or any business analyst can go find and access the data they need. So it's a really key part of delivering on data science, is this governance piece. Now I just talked to clients, they understand where you're going. Is this about compliance or is this about insights? Because there's probably a different starting point, but the end game is similar. >> Curious about your target markets, Tyler talked about the go to market model a minute ago, are you targeting customers that are on mainframes? And you said, I think, in your keynote, 90% of transactional data is in a mainframe. Is that one of the targets, or is it the target, like you mention, Rob, with the EDW optimization solution, are you working with customers who have an existing enterprise data warehouse that needs to be modernized, is it both? >> The good news is it's both. It's about, really the opportunity and mission, is about enabling the next generation data architecture. And within that is again, back to the layering approach, is being able to bring the data under management from point of origination through point of it reg. Now if we look at it, you know, probably 90% of, at least transactional data, sits in the mainframe, so you have to be able to span all data sets and all deployment architectures on prim multi-data center as well as public cloud. And that then, is the opportunity, but for that to then drive value ultimately back, you've got to be able to have then the simplification of the data science framework and toolset to be able to then have the proper insights and basis on which you can bring the new smart applications. And drive the insights, drive the governance through the entire life cycle. >> On the value front, you know, we talk about, and Hortonworks talks about, the fact that this technology can really help a business unlock transformational value across their organization, across lines of business. This conversation, we just talked about a couple of the customer segments, is this a conversation that you're having at the C-suite initially? Where are the business leaders in terms of understanding? We know there's more value here, we probably can open up new business opportunities or are you talking more the data science level? >> Look, it's at different levels. So, data science, machined learning, that is a C-suite topic. A lot of times I'm not sure the audience knows what they're asking for, but they know it's important and they know they need to be doing something. When you go to things like a data architecture, the C-suite discussion there is, I just want to become more productive in how I'm deploying and using technology because my IT budget's probably not going up, if anything it may be going down, so I've got to become a lot more productive and efficient to do that. So it depends on who you're talking to, there's different levels of dialogue. But there's no question in my mind, I've seen, you know, just look at major press Financial Times, Wallstreet Journal last year. CEOs are talking about AI, machine learning, using data as a competitive weapon. It is happening and it's happening right now. What we're doing together, saying how do we make data simple and accessible? How do we make getting there really easy? Because right now it's pretty hard. But we think with the combination of what we're bringing, we make it pretty darn easy. >> So one quick question following up on that, and then I think we're getting close to the end. Which is when the data lakes started out, it was sort of, it seemed like, for many customers a mandate from on high, we need a big data strategy, and that translated into standing up a Hadoop cluster, and that resulted in people realizing that there's a lot to manage there. It sounds like, right now people know machine learning is hot so they need to get data science tools in place, but is there a business capability sort of like the ETL offload was for the initial Hadoop use cases, where you would go to a customer and recommend do this, bite this off as something concrete? >> I'll start and then Rob can comment. Look, the issue's not Hadoop, a lot of clients have started with it. The reason there hasn't been, in some cases, the outcomes they wanted is because just putting data into Hadoop doesn't drive an outcome. What drives an outcome is what do you do with it. How do you change your business process, how do you change what the company's doing with the data, and that's what this is about, it's kind of that next step in the evolution of Hadoop. And that's starting to happen now. It's not happening everywhere, but we think this will start to propel that discussion. Any thoughts you had, Rob? >> Spot on. Data lake was about releasing the constraints of all the silos and being able to bring those together and aggregate that data. And it was the first basis for being able to have a 360 degree or wholistic centralized insight about something and, or pattern, but what then data science does is it actually accelerates those patterns and those lessons learned and the ability to have a much more detailed and higher velocity insight that you can react to much faster, and actually accelerate the business models around this aggregate. So it's a foundational approach with Hadoop. And it's then, as I mentioned in the keynote, the data science platforms, machine learning, and AI actually is what is the thing that transformationally opens up and accelerates those insights, so then new models and patterns and applications get built to accelerate value. >> Well, speaking of transformation, thank you both so much for taking time to share your transformation and the big news and the announcements with Hortonworks and IBM this morning. Thank you Rob Bearden, CEO of Hortonworks, Rob Thomas, General Manager of IBM Analytics. I'm Lisa Martin with my co-host, George Gilbert. Stick around. We are live from day one at DataWorks Summit in the heart of Silicon Valley. We'll be right back. (tech music)

>> Narrator: Live from Boston, Massachusetts, this is theCUBE! Covering Spark Summit East 2017. Brought to you by, Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. >> Welcome back to Boston, everybody, Seth Dobrin is here, he's the vice president and chief data officer of the IBM Analytics Organization. Great to see you, Seth, thanks for coming on. >> Great to be back, thanks for having me again. >> You're welcome, so chief data officer is the hot title. It was predicted to be the hot title and now it really is. Many more of you around the world and IBM's got an interesting sort of structure of chief data officers, can you explain that? >> Yeah, so there's a global chief data officer, that's Inderpal Bhandari and he's been on this podcast or videocast a view times. Then he's set up structures within each of the business units in IBM. Where each of the major business units have a chief data officer, also. And so I'm the chief data officer for the analytics business unit. >> So one of Interpol's things when I've interviewed them is culture. The data culture, you've got to drive that in. And he talks about the five things that chief data officers really need to do to be successful. Maybe you could give us your perspective on how that flows down through the organization and what are the key critical success factors for you and how are you implementing them? >> I agree, there's five key things and maybe I frame a little differently than Interpol does. There's this whole cloud migration, so every chief data officer needs to understand what their cloud migration strategy is. Every chief data officer needs to have a good understanding of what their data science strategy is. So how are they going to build the posable data science assets. So not data science assets that are delivered through spreadsheets. Every chief data officer needs to understand what their approach to unified governance is. So how do I govern all of my platforms in a way that enables that last point about data science. And then there's a piece around people. How do I build a pipeline for me today and the future? >> So the people piece is both the skills, and it's presumably a relationship with the line of business, as well. There's sort of two vectors there, right? >> Yeah the people piece when I think of it, is really about skills. There's a whole cultural component that goes across all of those five pieces that I laid out. Finding the right people, with the right skillset, where you need them, is hard. >> Can you talk about cloud migration, why that's so critical and so hard? >> If you look at kind of where the industry's been, the IT industry, it's been this race to the public cloud. I think it's a little misguided, all along. If you look at how business is run, right? Today, enterprises that are not internet born, make their money from what's running their businesses today. So this business critical assets. And just thinking that you can pick those up and move them to the cloud and take advantage of cloud, is not realistic. So the race really, is to a hybrid cloud. Our future's really lie in how do I connect these business critical assets to the cloud? And how do I migrate those things to the cloud? >> So Seth, the CIO might say to you, "Okay, let's go there for a minute, I kind of agree with what you're saying, I can't just shift everything in to the cloud. But what I can do in a hybrid cloud that I can't do in a public cloud?" >> Well, there's some drivers for that. I think one driver for hybrid cloud is what I just said. You can't just pick everything up and move it overnight, it's a journey. And it's not a six month journey, it's probably not a year journey, it's probably a multi year journey. >> Dave: So you can actually keep running your business? >> So you can actually keep running your business. And then other piece is there's new regulations that are coming up. And these regulations, EUGDPR is the biggest example of them right now. There are very stiff fines, for violations of those policies. And the party that's responsible for paying those fines, is the party that who the consumer engaged with. It's you, it's whoever owns the business. And as a business leader, I don't know that I would be, very willingly give up, trust a third party to manage that, just any any third party to manage that for me. And so there's certain types of data that some enterprises may never want to move to the cloud, because they're not going to trust a third party to manage that risk for them. >> So it's more transparent from a government standpoint. It's not opaque. >> Seth: Yup. >> You feel like you're in control? >> Yeah, you feel like you're in control and if something goes wrong, it's my fault. It's not something that I got penalized for because someone else did something wrong. >> So at the data layer, help us sort of abstract one layer up and the applications. How would you partition the applications. The ones that are managing that critical data that has to stay on premises. What would you build up potentially to compliment it in the public cloud? >> I don't think you need to partition applications. The way you build modern applications today, it's all API driven. You can reduce some of the costs of latency, through design. So you don't really need to partition the applications, per say. >> I'm thinking more along the lines of that the systems of record are not going to be torn out and those are probably the last ones if ever to go to the public cloud. But other applications leverage them. If that's not the right way of looking at it, where do you add value in the public cloud versus what stays on premise? >> So some of the system of record data, there's no reason you can't replicate some of it to the cloud. So if it's not this personal information, or highly regulated information, there's no reason that you can't replicate some of that to the cloud. And I think we get caught up in, we can't replicate data, we can't replicate data. I don't think that's the right answer, I think the right answer is to replicate the data if you need to, or if the data and system of record is not in the right structure, for what I need to do, then let's put the data in the right structure. Let's not have the conversation about how I can't replicate data. Let's have the conversation about where's the right place for the data, where does it make most sense and what's the right structure for it? And if that means you've got 10 copies of a certain type of data then you've got 10 copies of a certain type of data. >> Would you be, on that data, would it typically be, other parts of the systems of record that you might have in the public cloud, or would they be new apps, sort of green field apps? >> Seth: Yes. >> George: Okay. >> Seth: I think both. And that's part of, i think in my mind, that's kind of how you build, that question you just asked right there. Is one of the things that guide how you build your cloud migration strategy. So we said you can't just pick everything up and move it. So how do you prioritize? You look at what you need to build to run your business differently. And you start there and you start thinking about how do I migrate information to support those to the cloud? And maybe you start by building a local private cloud. So that everything's close together until you kind of master it. And then once you get enough, critical mass of data and applications around it, then you start moving stuff to the cloud. >> We talked earlier off camera about reframing governance steps. I used to head a CIO consultancy and we worked with a number of CIOs that were within legal IT, for example. And were worried about compliance and governance and things of that nature. And their ROI was always scare the board. But the holy grail, was can we turn governance into something of value? For the organization? Can we? >> I think in the world we live in today, with ever increasing regulations. And with a need to be agile and with everyone needing to and wanting to apply data science at scale. You need to reframe governance, right? Governance needs to be reframed from something that is seen as a roadblock. To something that is truly an enabler. And not just giving it lip service. And what do I mean by that? For governance to be an enabler, you really got to think about, how do I upfront, classify my data so that all data in my organization is bucketed in to some version of public, propietary and confidential. Different enterprises may have 30 scales and some may only have two. Or some may have one. and so you do that up front and so you know what can be done with data, when it can be done and who it can by done with. You need to capture intent. So what are allowed intended uses of data? And as a data scientist, what am I intending to do with this data? So that you can then mesh those two things together? Cause that's important in these new regulations I talked about, is people give you access to data, their personal data for an intended purpose. And then you need to be able to apply these governance, policies, actively. So it's not a passive, after the fact. Or you got to stop and you got to wait, it's leveraging services. Leveraging APIs. And building a composable system of polices that are delivered through APIs. So if I want to create a sandbox. To run some analytics on. I'm going to call an API. To get that data. That API is going to call a policy API that's going to say, "Okay, does Seth have permission to see this data? Can Seth use this data for this intended purpose?" if yes, the sandbox is created. If not, there's a conversation about really why does Seth need access to this data? It's really moving governance to be actively to enable me to do things. And it changes the conversation from, hey it's your data, can I have it? To there's really solid reasons as to why I can and can't have data. >> And then some potential automation around a sandbox that creates value. >> Seth: Absolutely. >> But it's still, the example you gave, public prop6ietary or confidential. Is still very governance like, where I was hoping you were going with the data classification and I think you referenced this. Can I extend that, that schema, that nomenclature to include other attributes of value? And can i do it, automate it, at the point of creation or use and scale it? >> Absolutely, that is exactly what I mean. I just used those three cause it was the three that are easy to understand. >> So I can give you as a business owner some areas that I would like to see, a classification schema and then you could automate that for me at scale? In theory? >> In theory, that's where we're hoping to go. To be able to automate. And it's going to be different based on what industry vertical you're in. What risk profile your business is willing to take. So that classification scheme is going to look very different for a bank, than it will for a pharmaceutical company. Or for a research organization. >> Dave: Well, if I can then defensively delete data. That's of real value to an organization. >> With new regulations, you need to be able to delete data. And you need to be able to know where all of your data is. So that you can delete it. Today, most organizations don't know where all their data is. >> And that problem is solved with math and data science, or? >> I think that problem is solved with a combination of governance. >> Dave: Sure. >> And technology. Right? >> Yeah, technology kind of got us into this problem. We'll say technology can get us out. >> On the technology subject, it seems like, with the explosion of data, whether it's not just volume, but also, many copies of the truth. You would need some sort of curation and catalog system that goes beyond what you had in a data warehouse. How do you address that challenge? >> Seth: Yeah and that gets into what I said when you guys asked me about CDOs, what do they care about? One of the things is unified governance. And so part of unified governance, the first piece of unified governance is having a catalog of your data. That is all of your data. And it's a single catalog for your data whether it's one of your business critical systems that's running your business today. Whether it's a public cloud, or it's a private cloud. Or some combination of both. You need to know where all your data is. You also need to have a policy catalog that's single for both of those. Catalogs like this fall apart by entropy. And the more you have, the more likely they are to fall apart. And so if you have one. And you have a lot of automation around it to do a lot of these things, so you have automation that allows you to go through your data and discover what data is where. And keep track of lineage in an automated fashion. Keep track of provenance in an automated fashion. Then we start getting into a system of truly unified governance that's active like I said before. >> There's a lot of talk about digital transformations. Of course, digital equals data. If it ain't data, it ain't digital. So one of the things that in the early days of the whole big data theme. You'd hear people say, "You have to figure out how to monetize the data." And that seems to have changed and morphed into you have to understand how your organization gets value from data. If you're a for profit company, it's monetizing. Something and feeding how data contributes to that monetization if you're a health care organization, maybe it's different. I wonder if you could talk about that in terms of the importance of understanding how an organization makes money to the CDO specifically. >> I think you bring up a good point. Monetization of data and analytics, is often interpreted differently. If you're a CFO you're going to say, "You're going to create new value for me, I'm going to start getting new revenue streams." And that may or may not be what you mean. >> Dave: Sell the data, it's not always so easy. >> It's not always so easy and it's hard to demonstrate value for data. To sell it. There's certain types, like IBM owns a weather company. Clearly, people want to buy weather data, it's important. But if you're talking about how do you transform a business unit it's not necessarily about creating new revenue streams, it's how do I leverage data and analytics to run my business differently. And maybe even what are new business models that I could never do before I had data and data science. >> Would it be fair to say that, as Dave was saying, there's the data side and people were talking about monetizing that. But when you talk about analytics increasingly, machine learning specifically, it's a fusion of the data and the model. And a feedback loop. Is that something where, that becomes a critical asset? >> I would actually say that you really can't generate a tremendous amount of value from just data. You need to apply something like machine learning to it. And machine learning has no value without good data. You need to be able to apply machine learning at scale. You need to build the deployable data science assets that run your business differently. So for example, I could run a report that shows me how my business did last quarter. How my sales team did last quarter. Or how my marketing team did last quarter. That's not really creating value. That's giving me a retrospective look on how I did. Where you can create value is how do I run my marketing team differently. So what data do I have and what types of learning can I get from that data that will tell my marketing team what they should be doing? >> George: And the ongoing process. >> And the ongoing process. And part of actually discovering, doing this catalog your data and understanding data you find data quality issues. And data quality issues are not necessarily an issue with the data itself or the people, they're usually process issues. And by discovering those data quality issues you may discover processes that need to be changed and in changing those processes you can create efficiencies. >> So it sounds like you guys got a pretty good framework. Having talked to Interpol a couple times and what you're saying makes sense. Do you have nightmares about IOT? (laughing) >> Do I have nightmares about IOT? I don't think I have nightmares about IOT. IOT is really just a series of connected devices. Is really what it is. On my talk tomorrow, I'm going to talk about hybrid cloud and connect a car is actually one of the things I'm going to talk about. And really a connected car you're just have a bunch of connected devices to a private cloud that's on wheels. I'm less concerned about IOT than I am, people manually changing data. IOT you get data, you can track it, if something goes wrong, you know what happened. I would say no, I don't have nightmares about IOT. If you do security wrong, that's a whole nother conversation. >> But it sounds like you're doing security right, sounds like you got a good handle on governance. Obviously scale is a key part of that. Could break the whole thing if you can't scale. And you're comfortable with the state of technology being able to support that? At least with IBM. >> I think at least with an IBM I think I am. Like I said, a connected car which is basically a bunch of IOT devices, a private cloud. How do we connect that private cloud to other private clouds or to a public cloud? There's tons of technologies out there to do that. Spark, Kafka. Those two things together allow you to do things that we could never do before. >> Can you elaborate? Like in a connected car environment or some other scenario where, other people called it a data center on wheels. Think of it as a private cloud, that's a wonderful analogy. How does Spark and Kafka on that very, very, smart device, cooperate with something like on the edge. Like the cities, buildings, versus in the clouds? >> If you're a connected car and you're this private cloud on wheels. You can't drive the car just on that information. You can't drive it just on the LIDAR knowing how well the wheels are in contact, you need weather information. You need information about other cars around you. You need information about pedestrians. You need information about traffic. All of this information you get from that connection. And the way you do that is leveraging Spark and Kafka. Kafka's a messaging system, you could leverage Kafka to send the car messages. Or send pedestrian messages. "This car is coming, you shouldn't cross." Or vice versa. Get a car to stop because there's a pedestrian in the way before even the systems on the car can see it. So if you can get that kind of messaging system in near real time. If I'm the pedestrian I'm 300 feet away. A half a second that it would take for that to go through, isn't that big of a deal because you'll be stopped before you get there. >> What about the again, intelligence between not just the data, but the advanced analytics. Where some of that would live in the car and some in the cloud. Is it just you're making realtime decisions in the car and you're retraining the models in the cloud, or how does that work? >> No I think some of those decisions would be done through Spark. In transit. And so one of the nice things about something about Spark is, we can do machine learning transformations on data. Think ETL. But think ETL where you can apply machine learning as part of that ETL. So I'm transferring all this weather data, positioning data and I'm applying a machine learning algorithm for a given purpose in that car. So the purpose is navigation. Or making sure I'm not running into a building. So that's happening in real time as it's streaming to the car. >> That's the prediction aspect that's happening in real time. >> Seth: Yes. >> But at the same time, you want to be learning from all the cars in your fleet. >> That would happen up in the cloud. I don't think that needs to happen on the edge. Maybe it does, but I don't think it needs to happen on the edge. And today, while I said a car is a data center, a private cloud on wheels, there's cost to the computation you can have on that car. And I don't think the cost is quite low enough yet where you could do all that where it makes sense to do all that computation on the edge. So some of it you would want to do in the cloud. Plus you would want to have all the information from as many cars in the area as possible. >> Dave: We're out of time, but some closing thoughts. They say may you live in interesting times. Well you can sum up the sum of the changes that are going on the business. Dell buys EMC, IBM buys The Weather Company. And that gave you a huge injection of data scientists. Which, talk about data culture. Just last thoughts on that in terms of the acquisition and how that's affected your role. >> I've only been at IBM since November. So all that happened before my role. >> Dave: So you inherited? >> So from my perspective it's a great thing. Before I got there, the culture was starting to change. Like we talked about before we went on air, that's the hardest part about any kind of data science transformation is the cultural aspects. >> Seth, thanks very much for coming back in theCUBE. Good to have you. >> Yeah, thanks for having me again. >> You're welcome, all right, keep it right there everybody, we'll be back with our next guest. This is theCUBE, we're live from Spark Summit in Boston. Right back. (soft rock music)

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. Welcome everybody. You watching the cube, the leader in live tech coverage. We're here at the IBM data and AI forum. Wow. What a day. 1700 customers. A lot of hands on labs sessions. What used to be the IBM analytics university is sort of morphed into this event. Now you see the buzz is going on. At least the Galean is here. She's the vice president of global sales for IBM data and AI. Welcome to the cube. Thank you for coming on. So this event is buzzing the double from last year almost. And uh, congratulations. >>Well, thank you very much. We have con, uh, lots of countries represented here. We have customers from small to large, every industry represented. And a, it's a, I can see a marked difference in the conversations in just a year around our, how customers want to figure out how to embark on this journey to AI. >>So yeah. So why are they come here? What's the, what's the primary motivation? >>Well, I think one IBM is recognized as the leader in AI and we just came out in the IDC survey as the three time w you know, leader, a recognized leader in AI. And when they come here they know they're going to hear from other clients who have embarked on similar journeys. They know they're going to have access to experts, hands on labs, and we bring our entire IBM team that's focused on data and AI to this event. So it's intimate, it's high skilled, it's high energy and they are learning a ton while they're. >>Yeah, a lot of content and you're educating but you're also trying to inspire people. I mean a raise. I was the hub this morning, he wrote this book, but he's this extreme, extreme, extreme like ultra marathoner. Uh, which I thought was a great talk this morning. And then you did a, I thought a good job of sort of connecting, you know, his talk of anything's possible to now bringing AI into the equation. What are you hearing from customers in terms of what they want to make possible and, and what's that conversation like in the field? >>Well, it's interesting because there is a huge recognition that every client that I talked to you, and they all want to understand this, that they have to be transforming their businesses on this journey to AI. So they all recognize that they need to start. Now. What I find when I talk to clients is that they're all coming in at different entry points. There's a maturity curve. So some are figuring out, you know, how do I move away from just Excel spreadsheets? I'm still running my business on Excel, right? And these are no banks in major that are operating on Excel spreadsheets and they're looking at niche competitors, you know, digital banks that are entering the scene. And if they don't change the way they operate, they're not going to survive. So a lot of companies are coming in knowing that they're low on the maturity curve and they better do something to move up that curve pretty fast. >>Some are in almost the second turn of the crank where they've invested in a lot of the AI technologies, they've built data science platforms, and now they're figuring out how do they get that next rev of productivity improvement? How do they come up with that next business idea that's going to give them that competitive advantage? So what I find is every client is embarking on this journey, which is a big difference where I think we were even a year, 18 months ago, where they were sort of just, okay, this is interesting. Now there I better do something. >>Okay, so you're a resource, you know, as the head of global sales for this group. So when you talk to customers that are immature, if I hear you right, they're saying, help us get started because we're going to fall behind. Uh, we're inefficient right now. We're drowning in spreadsheets, data. Our data quality is not where it needs to be. Help. Where do we start? What do you tell them? >>Well, one, we have a formula that we've proven works with clients. Um, we bring them into our garages where we will do design thinking, architectural workshops, and we figure out a use case because what we try not to do with our clients is boil the ocean. We want them to sh to have something that they can prove success around very quickly, create that minimal viable product, bring it back to the business so that the business can see, Oh, I understand. And then evolve that use case. So we will bring technical specialists, we will bring folks that are our own data scientists to these garage environments and we will work with them on building out this first use case. >>Explain the garage a little bit more. Is that those, those are sort of centers of excellence around the world or how do I tap them as a customer? Is it, is it a freebie? Is it for pay? Isn't it like the data science elite team? How does it all work? >>Well, it is. There are a number of physical locations and it's open to all clients. We have created these with co-leadership from across the entire IBM company. So our services organization, our cloud cognitive organization, all play a role in these garages. So we have a formal structure where a team can engage through a request process into the garages. We will help them define the use case they want to bring into the garage. We will bring them in for a period of time and provide the resources and capabilities and skills and that's not charged to the client. So we're trying to get them started now that they'll take that back to their company and then they will look at follow on opportunities and those may, you know, work out to be different services opportunities as they move forward. But we're on that get started phase. >>Yeah. Yeah. I mean you're a fraud for profit company, so it's great to have a loss leader, but the line outside the door at the garage must be huge for people that want to get in. Hi. How are you managing the dominion? >>Yes, well we're increasing obviously our capacity around the garages. Um, and we're still making customers aware of the garages. So there's still, because it's a commitment on their side, like they just can't come in and kick the tires. We ask them to bring their line of business along with their technical teams into the garages because that's where you get the best product coming out of it. When you know you've got something that's going to solve a business problem, but you have to have buy in from both sides. >>I want to ask you about the AI ladder. You know, Rob Thomas has been using this construct for awhile. It didn't just come out of thin air. I'm sure there was a lot of customer input and a lot of debate about what should be on the ladder. We went, when I first heard of the day AI ladder, it was, there was data in IAA analytics, ML and AI, sort of the building, the technical or technology building blocks. It's now become verbs, which I love, which is collect, organize, analyze and infuse, which is all about operationalizing and scaling. How is that resonating with customers and how do they fit into that methodology or framework? >>Well, I'll tell you, I use that framework with every single client and I described that there is a set of steps and you know, obviously to the ladder that every customer has to embark upon. And it starts with some very basic principles and as soon as you start with the very basic principles, every client is like, of course like it seems so obvious that first and foremost you have to date as the foundation, right? AI is not created out of, you know, someone in a back room. The foundation to AI is, is information and data. Yet every customer, every customer struggles with that data is coming from multiple systems, multiple sources that they can't get to the data fast enough. They're shipping data around an organization. It's not managed. And yet that they know that in five years, the data they think they need today is going to be completely different. >>It could be 12 months, but certainly in the future. So how do you build out that architecture that allows them to build that now, but have the agility to grow as the requirements change? You start with that basic discussion and they're like, well of course. So that's collect and then you bring it up and you talk about how do you govern that data? How do you know where that data originated? Who is the owner? How do you know what that data means? What system did it come from? What's the, you know, who has access to it? How do you create that set of govern data? And we'll of course every client recognizes they have that set of issues. So I could continue working my way up the ladder and every client realizes that, okay, I re I'm, here's where I am today. What you just painted for me is absolutely what I need to focus on and address. Now help me get from a to B. >>So I'm really interested in this discussion because it sounds like you're a very disciplined sales leaders and you said you use the ladder with virtually every client and I presume your sales teams use the ladder. So you train your salespeople how to converse the ladder. And then the other observation I'd love your thoughts on this is every step of the ladder has these questions. So you're asking customers questions and I'm sure it catalyzes conversation, the, the answers to which you have solutions presumably from any of them. But I wonder if you could talk about that. >>Well, let me tell about the ladder and how we're using it with our Salesforce because it was a unifying approach, not just within our own team, our data and AI team, but outside of data and AI. Because not only did we explain it to clients this way, but to the rest of IBM, our business partners, our whole ecosystem. So unifying in that we started every single conversation with our sales team on enabling them on how do they talk to their clients, our materials, our use cases, our references, our marketing campaigns. We tied everything to this unified approach and it's made a huge difference in how we communicate our value to clients and explain this journey to AI in in comprehensive steps that everyone could understand and relate to. >>Love it. How is the portfolio evolving to map into that framework? And what can we expect going forward? What can you share with us at least? >>Well, the other amazing feat I'll call it that we produced around this is I'll talk to a client and I'll describe these capabilities and then I will say to a customer, you don't have to do every one of these things that I've just described, but you can implement what you need when you need it. Because we have built all of this into a unified platform called cloud pack for data and it's a modern data platform. It's built on an open infrastructure built on red hat OpenShift so that you can run it on your own premises as a private cloud or on public clouds, whether that be IBM or Amazon or Zohre. It allows you to have a framework, a platform built on this open modern infrastructure with access to all these capabilities I've just described as services and you decide completely open what services you need to deploy when you grow the platform as you need it. And, Oh, by the way, if you don't have the red hat OpenShift environment set up, we'll package that in a system and I will roll in the system to you and allow you to have access to the capabilities in ours. >>How's the red hat conversation going? I would imagine a lot of the traditional IBM customers are stoked. He just picked up red hat, you know, a very innovative company, open source mindset. Um, at the same time I would imagine a lot of red hat customers saying, is IBM really gonna? Let them keep their culture. How's that conversation going in the field? >>Well, I will tell you we've been a hundred percent consistent in terms of everything that you've heard Jenny and Arvin Krishna talk about in the fact that we are going to maintain their culture, keep them as that separate entity inside of IBM. It's absolutely perpetrated throughout the entire IBM company. Um, we have a lot to learn from, from them as I'm sure they have to learn from us, but it truly is operating and I see it in the clients that I'm working with as a real win-win. >>If you had to take one thing away from this event that you want customers to, to remember, what would it be? >>Start now. Um, because if you don't begin on this journey to AI, you will find yourselves, you know, fighting against new competitors, uh, increasing costs, you know, you have to improve productivity. Every client is embarking on this journey to AI start now. >>And when you were talking about, uh, the maturity model and, and one of those levels was folks that had started already and they wanted to get to the next level, when you go into those clients, do you discern a different sort of attitude? We've started, we're down the path. Did they have more of a spring in their step? Are they like chomping at the bit to really go faster and extend their lead relative to the competition competition? What's the dynamic like in those accounts? >>That's a great question because I was with a client this afternoon, um, a large manufacturer of, uh, of goods and they are at this turning point where they did kind of phase one, they implemented cloud pack for data and they did it to just join some of their disparate systems. Now, I mean, I, I barely got a word in because he was so excited cause he's, now what I'm going to do is I'm going to figure out where my factories should go based on where my products are selling. So he's now looking at how he can change his whole distribution process as a result of getting access to this data and analytics that he never had before. Um, and I was like, okay, well just tell me how I can help you. And he was like, no way ahead. >>So this was the big kickoff day. I know yesterday there was sort of deep learning hands on stuff, the big keynotes. Today we're only here for one day. What are we going to miss? What's, what's happening tomorrow? >>Well, it's a bit of a repeat of today. So we'll have another keynote tomorrow from Beth Smith who runs our Watson, uh, business for IBM. We'll have more hands on labs. We have a lot of customer presentations where they're sharing their best practices. Um, lots of fun. >>Where do you want to see this event go? And what kind of, what's next in an IBM event land? >>Well, the feedback from last year this year says we have to do this again next year. It's, it's, it will be bigger because I think this year approves that it's already doubled and we'll probably see a similar dynamic. Um, so I fully expect us to be here. Well, maybe not here. We're sort of outgrowing this hotel. Um, but doing this event again next year, >>AI machine learning automation, uh, I'll throw in cloud. These are the hottest topics going. Elise, thanks very much for coming to the cube was great to have you. >>It's great. It's great meeting with you. >>It. Thank you for watching everybody. That's a wrap from Miami. Go to siliconangle.com check out all the news of the cube.net is where you'll find all these videos and follow the, uh, the Twitter handles at the cube at the cube three 65. I'm Dave Volante. We're out. We'll see you next time.

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome everybody to the port of Miami. My name is Dave Vellante and you're watching the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM data and AI form. The hashtag is data AI forum. This is IBM's. It's formerly known as the, uh, IBM analytics university. It's a combination of learning peer network and really the focus is on AI and data. And there are about 1700 people here up from, Oh, about half of that last year, uh, when it was the IBM, uh, analytics university, about 600 customers, a few hundred partners. There's press here, there's, there's analysts, and of course the cube is covering this event. We'll be here for one day, 128 hands-on sessions or ER or sessions, 35 hands on labs. As I say, a lot of learning, a lot of technical discussions, a lot of best practices. >>What's happening here. For decades, our industry has marched to the cadence of Moore's law. The idea that you could double the processor performance every 18 months, doubling the number of transistors, you know, within, uh, the footprint that's no longer what's driving innovation in the it and technology industry today. It's a combination of data with machine intelligence applied to that data and cloud. So data we've been collecting data, we've always talked about all this data that we've collected and over the past 10 years with the advent of lower costs, warehousing technologies in file stores like Hadoop, um, with activity going on at the edge with new databases and lower cost data stores that can handle unstructured data as well as structured data. We've amassed this huge amount of, of data that's growing at a, at a nonlinear rate. It's, you know, this, the curve is steepening is exponential. >>So there's all this data and then applying machine intelligence or artificial intelligence with machine learning to that data is the sort of blending of a new cocktail. And then the third piece of that third leg of that stool is the cloud. Why is the cloud important? Well, it's important for several reasons. One is that's where a lot of the data lives too. It's where agility lives. So cloud, cloud, native of dev ops, and being able to spin up infrastructure as code really started in the cloud and it's sort of seeping to to on prem, slowly and hybrid and multi-cloud, ACC architectures. But cloud gives you not only that data access, not only the agility, but also scale, global scale. So you can test things out very cheaply. You can experiment very cheaply with cloud and data and AI. And then once your POC is set and you know it's going to give you business value and the business outcomes you want, you can then scale it globally. >>And that's really what what cloud brings. So this forum here today where the big keynotes, uh, Rob Thomas kicked it off. He uh, uh, actually take that back. A gentleman named Ray Zahab, he's an adventure and ultra marathon or kicked it off. This Jude one time ran 4,500 miles in 111 days with two ultra marathon or colleagues. Um, they had no days off. They traveled through six countries, they traversed Africa, the continent, and he took two showers in a 111 days. And his whole mission is really talking about the power of human beings, uh, and, and the will of humans to really rise above any challenge would with no limits. So that was the sort of theme that, that was set for. This, the, the tone that was set for this conference that Rob Thomas came in and invoked the metaphor of superheroes and superpowers of course, AI and data being two of those three superpowers that I talked about in addition to cloud. >>So Rob talked about, uh, eliminating the good to find the great, he talked about some of the experiences with Disney's ward. Uh, ward Kimball and Stanley, uh, ward Kimball went to, uh, uh, Walt Disney with this amazing animation. And Walter said, I love it. It was so funny. It was so beautiful, was so amazing. Your work 283 days on this. I'm cutting it out. So Rob talked about cutting out the good to find, uh, the great, um, also talking about AI is penetrated only about four to 10% within organizations. Why is that? Why is it so low? He said there are three things that are blockers. They're there. One is data and he specifically is referring to data quality. The second is trust and the third is skillsets. So he then talked about, you know, of course dovetailed a bunch of IBM products and capabilities, uh, into, you know, those, those blockers, those challenges. >>He talked about two in particular, IBM cloud pack for data, which is this way to sort of virtualize data across different clouds and on prem and hybrid and and basically being able to pull different data stores in, virtualize it, combine join data and be able to act on it and apply a machine learning and AI to it. And then auto AI a way to basically machine intelligence for artificial intelligence. In other words, AI for AI. What's an example? How do I choose the right algorithm and that's the best fit for the use case that I'm using. Let machines do that. They've got experience and they can have models that are trained to actually get the best fit. So we talked about that, talked about a customer, a panel, a Miami Dade County, a Wunderman Thompson, and the standard bank of South Africa. These are incumbents that are using a machine intelligence and AI to actually try to super supercharge their business. We heard a use case with the Royal bank of Scotland, uh, basically applying AI and driving their net promoter score. So we'll talk some more about that. Um, and we're going to be here all day today, uh, interviewing executives, uh, from, uh, from IBM, talking about, you know, what customers are doing with a, uh, getting the feedback from the analysts. So this is what we do. Keep it right there, buddy. We're in Miami all day long. This is Dave Olanta. You're watching the cube. We'll be right back right after this short break..

>> Live from San Francisco, California, it's the theCUBE, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM Chief Data Officer Summit, 10th anniversary. Seth Dobrin is here, he's the Vice President and Chief Data Officer of the IBM Analytics Group. Seth, always a pleasure to have you on. Good to see you again. >> Yeah, thanks for having me back Dave. >> You're very welcome. So I love these events you get a chance to interact with chief data officers, guys like yourself. We've been talking a lot today about IBM's internal transformation, how IBM itself is operationalizing AI and maybe we can talk about that, but I'm most interested in how you're pointing that at customers. What have you learned from your internal experiences and what are you bringing to customers? >> Yeah, so, you know, I was hired at IBM to lead part of our internal transformation, so I spent a lot of time doing that. >> Right. >> I've also, you know, when I came over to IBM I had just left Monsanto where I led part of their transformation. So I spent the better part of the first year or so at IBM not only focusing on our internal efforts, but helping our clients transform. And out of that I found that many of our clients needed help and guidance on how to do this. And so I started a team we call, The Data Science an AI Elite Team, and really what we do is we sit down with clients, we share not only our experience, but the methodology that we use internally at IBM so leveraging things like design thinking, DevOps, Agile, and how you implement that in the context of data science and AI. >> I've got a question, so Monsanto, obviously completely different business than IBM-- >> Yeah. >> But when we talk about digital transformation and then talk about the difference between a business and a digital business, it comes down to the data. And you've seen a lot of examples where you see companies traversing industries which never used to happen before. You know, Apple getting into music, there are many, many examples, and the theory is, well, it's 'cause it's data. So when you think about your experiences of a completely different industry bringing now the expertise to IBM, were there similarities that you're able to draw upon, or was it a completely different experience? >> No, I think there's tons of similarities which is, which is part of why I was excited about this and I think IBM was excited to have me. >> Because the chances for success were quite high in your mind? >> Yeah, yeah, because the chance for success were quite high, and also, you know, if you think about it there's on the, how you implement, how you execute, the differences are really cultural more than they're anything to do with the business, right? So it's, the whole role of a Chief Data Officer, or Chief Digital Officer, or a Chief Analytics Officer, is to drive fundamental change in the business, right? So it's how do you manage that cultural change, how do you build bridges, how do you make people, how do you make people a little uncomfortable, but at the same time get them excited about how to leverage things like data, and analytics, and AI, to change how they do business. And really this concept of a digital transformation is about moving away from traditional products and services, more towards outcome-based services and not selling things, but selling, as a Service, right? And it's the same whether it's IBM, you know, moving away from fully transactional to Cloud and subscription-based offerings. Or it's a bank reimagining how they interact with their customers, or it's oil and gas company, or it's a company like Monsanto really thinking about how do we provide outcomes. >> But how do you make sure that every, as a Service, is not a snowflake and it can scale so that you can actually, you know, make it a business? >> So underneath the, as a Service, is a few things. One is, data, one is, machine learning and AI, the other is really understanding your customer, right, because truly digital companies do everything through the eyes of their customer and so every company has many, many versions of their customer until they go through an exercise of creating a single version, right, a customer or a Client 360, if you will, and we went through that exercise at IBM. And those are all very consistent things, right? They're all pieces that kind of happen the same way in every company regardless of the industry and then you get into understanding what the desires of your customer are to do business with you differently. >> So you were talking before about the Chief Digital Officer, a Chief Data Officer, Chief Analytics Officer, as a change agent making people feel a little bit uncomfortable, explore that a little bit what's that, asking them questions that intuitively they, they know they need to have the answer to, but they don't through data? What did you mean by that? >> Yeah so here's the conversations that usually happen, right? You go and you talk to you peers in the organization and you start having conversations with them about what decisions are they trying to make, right? And you're the Chief Data Officer, you're responsible for that, and inevitably the conversation goes something like this, and I'm going to paraphrase. Give me the data I need to support my preconceived notions. >> (laughing) Yeah. >> Right? >> Right. >> And that's what they want to (voice covers voice). >> Here's the answer give me the data that-- >> That's right. So I want a Dashboard that helps me support this. And the uncomfortableness comes in a couple of things in that. It's getting them to let go of that and allow the data to provide some inkling of things that they didn't know were going on, that's one piece. The other is, then you start leveraging machine learning, or AI, to actually help start driving some decisions, so limiting the scope from infinity down to two or three things and surfacing those two or three things and telling people in your business your choices are one of these three things, right? That starts to make people feel uncomfortable and really is a challenge for that cultural change getting people used to trusting the machine, or in some instances even, trusting the machine to make the decision for you, or part of the decision for you. >> That's got to be one of the biggest cultural challenges because you've got somebody who's, let's say they run a big business, it's a profitable business, it's the engine of cashflow at the company, and you're saying, well, that's not what the data says. And you're, say okay, here's a future path-- >> Yeah. >> For success, but it's going to be disruptive, there's going to be a change and I can see people not wanting to go there. >> Yeah, and if you look at, to the point about, even businesses that are making the most money, or parts of a business that are making the most money, if you look at what the business journals say you start leveraging data and AI, you get double-digit increases in your productivity, in your, you know, in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business, or highly, contributing the most revenue is really what we could do better, right? You could get better margins on this revenue you're driving, you could, you know, that's the whole point is to get better leveraging data and AI to increase your margins, increase your revenue, all through data and AI. And then things like moving to, as a Service, from single point to transaction, that's a whole different business model and that leads from once every two or three or five years, getting revenue, to you get revenue every month, right? That's highly profitable for companies because you don't have to go in and send your sales force in every time to sell something, they buy something once, and they continue to pay as long as you keep 'em happy. >> But I can see that scaring people because if the incentives don't shift to go from a, you know, pay all up front, right, there's so many parts of the organization that have to align with that in order for that culture to actually occur. So can you give some examples of how you've, I mean obviously you ran through that at IBM, you saw-- >> Yeah. >> I'm sure a lot of that, got a lot of learnings and then took that to clients. Maybe some examples of client successes that you've had, or even not so successes that you've learned from. >> Yeah, so in terms of client success, I think many of our clients are just beginning this journey, certainly the ones I work with are beginning their journey so it's hard for me to say, client X has successfully done this. But I can certainly talk about how we've gone in, and some of the use cases we've done-- >> Great. >> With certain clients to think about how they transformed their business. So maybe the biggest bang for the buck one is in the oil and gas industry. So ExxonMobile was on stage with me at, Think, talking about-- >> Great. >> Some of the work that we've done with them in their upstream business, right? So every time they drop a well it costs them not thousands of dollars, but hundreds of millions of dollars. And in the oil and gas industry you're talking massive data, right, tens or hundreds of petabytes of data that constantly changes. And no one in that industry really had a data platform that could handle this dynamically. And it takes them months to get, to even start to be able to make a decision. So they really want us to help them figure out, well, how do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly? And so the aim was really to cut this down from 90 days to less than a month. And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. Now this is before, we haven't even started thinking about helping them with AI, oil and gas industry has been doing this type of thing for decades, but they really were struggling with this platform. So that's a big success where, at least for the pilot, which was a small subset of their fields, we were able to help them reduce that timeframe by a lot to be able to start making a decision. >> So an example of a decision might be where to drill next? >> That's exactly the decision they're trying to make. >> Because for years, in that industry, it was boop, oh, no oil, boop, oh, no oil. >> Yeah, well. >> And they got more sophisticated, they started to use data, but I think what you're saying is, the time it took for that analysis was quite long. >> So the time it took to even overlay things like seismic data, topography data, what's happened in wells, and core as they've drilled around that, was really protracted just to pull the data together, right? And then once they got the data together there were some really, really smart people looking at it going, well, my experience says here, and it was driven by the data, but it was not driven by an algorithm. >> A little bit of art. >> True, a lot of art, right, and it still is. So now they want some AI, or some machine learning, to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million and billion dollar decisions they're making so it's really about how do we help them. >> And that's just one example, I mean-- >> Yeah. >> Every industry has it's own use cases, or-- >> Yeah, and so that's on the front end, right, about the data foundation, and then if you go to a company that was really advanced in leveraging analytics, or machine learning, JPMorgan Chase, in their, they have a division, and also they were on stage with me at, Think, that they had, basically everything is driven by a model, so they give traders a series of models and they make decisions. And now they need to monitor those models, those hundreds of models they have for misuse of those models, right? And so they needed to build a series of models to manage, to monitor their models. >> Right. >> And this was a tremendous deep-learning use case and they had just bought a power AI box from us so they wanted to start leveraging GPUs. And we really helped them figure out how do you navigate and what's the difference between building a model leveraging GPUs, compared to CPUs? How do you use it to accelerate the output, and again, this was really a cost-avoidance play because if people misuse these models they can get in a lot of trouble. But they also need to make these decisions very quickly because a trader goes to make a trade they need to make a decision, was this used properly or not before that trade is kicked off and milliseconds make a difference in the stock market so they needed a model. And one of the things about, you know, when you start leveraging GPUs and deep learning is sometimes you need these GPUs to do training and sometimes you need 'em to do training and scoring. And this was a case where you need to also build a pipeline that can leverage the GPUs for scoring as well which is actually quite complicated and not as straight forward as you might think. In near real time, in real time. >> Pretty close to real time. >> You can't get much more real time then those things, potentially to stop a trade before it occurs to protect the firm. >> Yeah. >> Right, or RELug it. >> Yeah, and don't quote, I think this is right, I think they actually don't do trades until it's confirmed and so-- >> Right. >> Or that's the desire as to not (voice covers voice). >> Well, and then now you're in a competitive situation where, you know. >> Yeah, I mean people put these trading floors as close to the stock exchange as they can-- >> Physically. >> Physically to (voice covers voice)-- >> To the speed of light right? >> Right, so every millisecond counts. >> Yeah, read Flash Boys-- >> Right, yeah. >> So, what's the biggest challenge you're finding, both at IBM and in your clients, in terms of operationalizing AI. Is it technology? Is it culture? Is it process? Is it-- >> Yeah, so culture is always hard, but I think as we start getting to really think about integrating AI and data into our operations, right? As you look at what software development did with this whole concept of DevOps, right, and really rapidly iterating, but getting things into a production-ready pipeline, looking at continuous integration, continuous development, what does that mean for data and AI? And these concept of DataOps and AIOps, right? And I think DataOps is very similar to DevOps in that things don't change that rapidly, right? You build your data pipeline, you build your data assets, you integrate them. They may change on the weeks, or months timeframe, but they're not changing on the hours, or days timeframe. As you get into some of these AI models some of them need to be retrained within a day, right, because the data changes, they fall out of parameters, or the parameters are very narrow and you need to keep 'em in there, what does that mean? How do you integrate this for your, into your CI/CD pipeline? How do you know when you need to do regression testing on the whole thing again? Does your data science and AI pipeline even allow for you to integrate into your current CI/CD pipeline? So this is actually an IBM-wide effort that my team is leading to start thinking about, how do we incorporate what we're doing into people's CI/CD pipeline so we can enable AIOps, if you will, or MLOps, and really, really IBM is the only company that's positioned to do that for so many reasons. One is, we're the only one with an end-to-end toolchain. So we do everything from data, feature development, feature engineering, generating models, whether selecting models, whether it's auto AI, or hand coding or visual modeling into things like trust and transparency. And so we're the only one with that entire toolchain. Secondly, we've got IBM research, we've got decades of industry experience, we've got our IBM Services Organization, all of us have been tackling with this with large enterprises so we're uniquely positioned to really be able to tackle this in a very enterprised-grade manner. >> Well, and the leverage that you can get within IBM and for your customers. >> And leveraging our clients, right? >> It's off the charts. >> We have six clients that are our most advanced clients that are working with us on this so it's not just us in a box, it's us with our clients working on this. >> So what are you hoping to have happen today? We're just about to get started with the keynotes. >> Yeah. >> We're going to take a break and then come back after the keynotes and we've got some great guests, but what are you hoping to get out of today? >> Yeah, so I've been with IBM for 2 1/2 years and I, and this is my eighth CEO Summit, so I've been to many more of these than I've been at IBM. And I went to these religiously before I joined IBM really for two reasons. One, there's no sales pitch, right, it's not a trade show. The second is it's the only place where I get the opportunity to listen to my peers and really have open and candid conversations about the challenges they're facing and how they're addressing them and really giving me insights into what other industries are doing and being able to benchmark me and my organization against the leading edge of what's going on in this space. >> I love it and that's why I love coming to these events. It's practitioners talking to practitioners. Seth Dobrin thanks so much for coming to theCUBE. >> Yeah, thanks always, Dave. >> Always a pleasure. All right, keep it right there everybody we'll be right back right after this short break. You're watching, theCUBE, live from San Francisco. Be right back.

>> Live from San Francisco, it's theCUBE! Covering IBM Think 2019. Brought to you by IBM. >> Welcome back, live here in San Francisco, it's theCUBE's coverage of IBM Think 2019. I'm John Furrier--Stu Miniman. Stu, four days, we're on our fourth day, the sun's shining, they've shut down Howard Street here at IBM. Big event for IBM, in San Francisco, not Las Vegas. Lot of great cloud action, lot of great AI data developers. Great story, good to see you again. Our next two guests, Julie Lockner, Director, Offering Management, Portfolio Operations at IBM, Data+AI, great to see you. >> Thank you, it's great to see you too, thank you. >> And Jay Limburn, Director of Offering Management, IBM Data+AI, thanks for coming on. >> Hey guys, great to be here. >> So, we've chatted many times at events, the role of data. So, we're religious about data, data flows through our blood, but IBM has put it all together now. All the reorgs are over, everyone's kind of, the table is set for IBM. The data path is clear, it's part of applications. It's feeding the apps. AI's the key workload inside the application. This is now a fully set-up group, give us the update, what's the focus? >> Yeah, it's really exciting because, if you think about it, before, we were called IBM Analytics, and that really is only a part of what we do. Now that we're Data+AI, that means that not only are we responsible for delivering data assets, and technology that supports those data assets to our customers, but infusing AI, not only in the technologies that we have, but also helping them build applications so they can fuse AI into their business processes. >> It's pretty broad, I mean, data's very much a broad swath of things. Analytics, you know, wrangling data, setting things up, cataloging them. Take me through how you guys set this up. How do you present it to the marketplace? How are clients engaged with it? Because it's pretty broad. But it could be, it needs to be specific. Take us through the methodology. >> So, you probably heard a lot of people today talk about the ladder to AI, right? This is IBM's view of how we explain our client's journey towards AI. It really starts at the bottom rung of the ladder, where we've got the collection of information. Collect your data. Once you've collected your data, you move up to the next rung, which is the Organize. And this is really where all the governance stuff comes in. This is how we can provide a view across that data, understand that data, provide trust to that data, and then serve that up to the consumers of that information, so they can actually use that in AI. That's where all the data science capabilities come in, allowing people to actually be able to consume that information. >> So, the bottom set is just really all the hard and heavy lifting that data scientists actually don't want to do. >> And writing algorithms, the collecting, the ingesting of data from any source, that's the bottom? And then, tell me about that next layer up, from the collection-- >> So, Collect is the physical assets or the collection of the data that you're going to be using for AI. If you don't get that foundation right, it doesn't really make sense. You have to have the data first. The piece in the middle that Jay was referring to, that's called Organize, our whole divisions are actually organized around these ladders to AI, so, Collect, Organize, Analyze, Infuse. On the Organize side, as Jay was mentioning, it's all about inventorying the data assets, knowing what data you have, then providing data quality rules, governance, compliance-type offerings, that allow organizations to not just know your data, trust your data, but then make it available so you can use your data, and the users are those data scientists, they're the analytics teams, they're the operation organizations that need to be able to build their solutions on top of trusted data. >> So, where does the Catalog fit in? Which level does that come into? >> Yeah, so, think of the Data Catalog as the DNS for data, all right? It's the way in which you can provide a full view of all of your information. Whether it's structured information, unstructured information, data you've got on PRAM and data you've got in a cloud somewhere. >> That's in the Organize layer, right? >> That's all in the Organize layer. So, if you can collect that information, you can then provide capabilities that allow you to understand the quality of that data, know where that data's come from, and then, finally, if you serve that up inside a compelling, business-friendly experience, so that a data scientist can go to one place, quickly make a decision on if that's the right data for them, and allow them to go and be productive by building a data science model, then we're really able to move the needle on making those data science organizations efficient, allowing us to build better models to transform their business. >> Yeah, and a big part of that is, if you think about what makes Amazon successful, it's because they know where all their products are, from the vendor, to when it shows up on the doorstep. What the Catalog provides is really the similar capability of, I would call it inventory management of your data assets, where we know where the data came from, its source--in that Collect layer-- who's transformed it, who's accessed it, if they're even allowed to see it, so, data privacy policies are part of that, and then being able to just serve up that data to those users. Being able to see that whole end-to-end lineage is a key point, critical point of the ladder to AI. Especially when you start to think about things like bias detection, which is a big part of the Analyze layer. >> But one of the things we've been digging into on theCUBE is, is data the next flywheel of innovation? You know, it used to be I just had my information, many years ago we started talking about, "Okay, I need to be able to access all that other information." We hear things like 80% of the data out there isn't really searchable today. So, how do you see data, data gravity, all those pieces, as the next flywheel of innovation? >> Yeah, I think it's key. I mean, we've talked a lot about how, you can't do AI without information architecture. And it's absolutely true. And getting that view of that data in a single location, so it is like the DNS of the internet. So you know exactly where to search, you can get hold of that data, and then you've got tools that give you self-service access to actually get hold of the data without any need of support from IT to get access to it. It's really a key-- >> Yeah, but to the point you were just asking about, data gravity? I mean, being able to do this where the data resides. So, for example, we have a lot of our customers that are mergers and acquisitions. Some teams have a lot of data assets that are on-premises, others have large data lakes in AWS or Azure. How do you inventory those assets and really have a view of what you have available across that landscape? Part of what we've been focusing on this year is making our technology work across all of those clouds. And having a single view of your assets but knowing where it resides. >> So, Julie, this environment is a bit more complicated than the old data warehousing, or even what we were looking at with big data and Hadoop and all those pieces. >> Isn't that the truth? >> Help explain why we're actually going to be able to get the information, leverage and drive new business value out of data today, when we've struggled so many times in the past. >> Well, I think the biggest thing that's changed is the adoption of DevOps, and when I say adoption of DevOps and things like containerization and Docker containers, Kubernetes, the ability to provision data assets very quickly, no matter where they are, build these very quick value-producing applications based on AI, Artificial Intelligence APIs, is what's allowing us to take advantage of this multi-cloud landscape. If you didn't have that DevOps foundation, you'd still be building ETL jobs in data warehouses, and that was 20 years ago. Today, it's much more about these microservices-based architecture, building up these AI-- >> Well, that's the key point, and the "Fuse" part of the stack, I think, or ladder. Stack? Ladder? >> Ladder. (laughs) >> Ladder to success! Is key, because you're seeing the applications that have data native into the app, where it has to have certain characteristics, whether it's a realtime healthcare app, or retail app, and we had the retail folks on earlier, it's like, oh my god, this now has to be addressable very fast, so, the old fenced-off data warehouse-- "Hey, give me that data!"--pull it over. You need a sub-second latency, or milliseconds. So, this is now a requirement. >> That's right. >> So, how are people getting there? What are some use cases? >> Sure. I'll start with the healthcare 'cause you brought that up. One of the big use cases for technology that we provide is really around taking information that might be realtime, or batch data, and providing the ability to analyze that data very quickly in realtime to the point where you can predict when someone might potentially have a cardiac arrest. And yesterday's keynote that Rob Thomas presented, a demonstration that showed the ability to take data from a wearable device, combine it with data that's sitting in an Amazon... MySQL database, be able to predict who is the most at-risk of having a potential cardiac arrest! >> That's me! >> And then present that to a call center of cardiologists. So, this company that we work with, iCure, really took that entire stack, Organize, Collect, Organize, Analyze, Infuse, and built an application in a matter of six weeks. Now, that's the most compelling part. We were able to build the solution, inventory their data assets, tie it to the industry model, healthcare industry model, and predict when someone might potentially-- >> Do you have that demo on you? The device? >> Of course I do. I know, I know. So, here is, this is called a BraveHeart Life Sensor. And essentially, it's a Bluetooth device. I know! If you put it on! (laughs) >> If I put it on, it'll track... Biometric? It'll start capturing information about your heart, ECG, and on Valentine's Day, right? My heart to yours, happy Valentine's Day to my husband, of course. The ability to be able to capture all this data here on the device, stream it to an AI engine that can then immediately classify whether or not someone has an anomaly in their ECG signal. You couldn't do that without having a complete ladder to AI capability. >> So, realtime telemetry from the heart. So, I see timing's important if you're about to have a heart attack. >> Yeah. >> Pretty important. >> And that's a great example of, you mentioned the speed. It's all about being able to capture that data in whatever form it's coming in, understand what that data is, know if you can trust that data, and then put it in the hands of the individuals that can do something valuable with the analysis from that data. >> Yeah, you have to able to trust it. Especially-- >> So, you brought up earlier bias in data. So, I want to bring that up in context of this. This is just one example of wearables, Fitbits, all kinds of things happening. >> New sources of tech, yeah. >> In healthcare, retail, all kinds of edge, realtime, is bias of data. And the other one's privacy because now you have a new kind of data source going into the cloud. And then, so, this fits into what part of the ladder? So, the ladder needs a secure piece. >> Tell me about that. >> Yeah, it does. So, that really falls into that Organize piece of that ladder, the governance aspects around it. If you're going to make data available for self-service, you've got to still make sure that that data's protected, and that you're not going to go and break any kind of regulatory law around that data. So, we actually can use technology now to understand what that data is, whether it contains sensitive information, credit card numbers, and expose that information out to those consumers, yet still masking the key elements that should be protected. And that's really important, because data science is a hugely inefficient business. Data scientists are spending too much time looking for information. And worse than that, they actually don't have all the information available that they need, because certain information needs to be protected. But what we can do now is expose information that wasn't previously available, but protect just the key parts of that information, so we're still ensuring it's safe. >> That's a really key point. It's the classic iceberg, right? What you see: "Oh, data science is going to "change the game of our business!" And then when they realize what's underneath the water, it's like, all this set-up, incompatible data, dirty data, data cleaning, and then all of a sudden it just doesn't work, right? This is the reality. Are you guys seeing this? Do you see that? >> Yeah, absolutely. I think we're only just really at the beginning of a crest of a wave, here. I think organizations know they want to get to AI, the ladder to AI really helps explain and it helps to understand how they can get there. And we're able then to solve that through our technology, and help them get there and drive those efficiencies that they need. >> And just to add to that, I mean, now that there's more data assets available, you can't manually classify, tag and inventory all that data, determine whether or not it contains sensitive data. And that's where infusing machine learning into our products has really allowed our customers to automate the process. I mentioned, the only way that we were able to deploy this application in six weeks, is because we used a lot of the embedded machine learning to identify the patient data that was considered sensitive, tag it as patient data, and then, when the data scientists were actually building the models in that same environment, it was masked. So, they knew that they had access to the data, but they weren't allowed to see it. It's perfectly--especially with HIMSS' conference this week as well! You were talking about this there. >> Great use case with healthcare. >> Love to hear you speak about the ecosystem being built around this. Everything, open APIs, I'm guessing? >> Oh, yeah. What kind of partners are-- >> Jay, talk a little bit-- >> Yeah, so, one of the key things we're doing is ensuring that we're able to keep this stuff open. We don't want to curate a proprietary system. We're already big supporters of open source, as you know, in IBM. One of the things that we're heavily-invested in is our open metadata strategy. Open metadata is part of the open source ODPi Foundation. Project Egeria defines a standard for common metadata interchange. And what that means is that, any of these metadata systems that adopt this standard can freely share and exchange metadata across that landscape, so that wherever your data is, whichever systems it's stored in, wherever that metadata is harvested, it can play part of that network and share that metadata across those systems. >> I'd like to get your thoughts on something, Julie. You've been on the analyst side, you're now at IBM. Jay, if you can weigh in on this too, that'd be great. We, here, we see all the trends and go to all the events and one of the things that's popping up that's clear within the IBM ecosystem because you guys have a lot of business customers, is that a new kind of business app developer's coming in. And we've seen data science highlight the citizen data scientist, so if data is code, part of the application, and all the ladder stuff kind of falls into place, that means we're going to see new kinds of applications. So, how are you guys looking at, this is kind of a, not like the cloud-native, hardcore DevOps developer. It's the person that says, "Hey, I can innovate "a business model." I see a business model innovation that's not so much about building technology, it's about using insight and a unique... Formula or algorithm, to tweak something. That's not a lot of programming involved. 'Cause with Cloud and Cloud Private, all these back end systems, that's an ecosystem partner opportunity for you guys, but it's not your classic ISV. So, there's a new breed of business apps that we see coming, your thoughts on this? >> Yeah, it's almost like taking business process optimization as a discipline, and turning it into micro-applications. You want to be able to leverage data that's available and accessible, be able to insert that particular Artificial Intelligence machine learning algorithm to optimize that business process, and then get out of the way. Because if you try to reinvent your entire business process, culture typically gets in the way of some of these things. >> I thought, as an application value, 'cause there's value creation here, right? >> Absolutely. >> You were talking about, so, is this a new kind of genre of developer, or-- >> It really is, I mean... If you take the citizen data scientist, an example that you mentioned earlier. It's really about lowering the entry point to that technology. How can you allow individuals with lower levels of skills to actually get in and be productive and create something valuable? It shouldn't be just a practice that's held away for the hardcore developer anymore. It's about lowering the entry point with the set of tools. One of the things we have in Watson Studio, for example, our data science platform, is just that. It's about providing wizards and walkthroughs to allow people to develop productive use models very easily, without needing hardcore coding skills. >> Yeah, I also think, though, that, in order for these value-added applications to be built, the data has to be business-ready. That's how you accelerate these application development life cycles. That's how you get the new class of application developers productive, is making sure that they start with a business-ready foundation. >> So, how are you guys going to go after this new market? What's the marketing strategy? Again, this is like, forward-pioneering kind of things happening. What's the strategy, how are you going to enable this, what's the plan? >> Well, there's two parts of it. One is, when Jay was mentioning the Open Metadata Repository Services, our key strategy is embedding Catalog everywhere and anywhere we can. We believe that having that open metadata exchange allows us to open up access to metadata across these applications. So, really, that's first and foremost, is making sure that we can catalog and inventory data assets that might not necessarily be in the IBM Cloud, or in IBM products. That's really the first step. >> Absolutely. The second step, I would say, is really taking all of our capabilities, making them, from the ground up, microservices-enabled, delivering them through Docker containers and making sure that they can port across whatever cloud deployment model our customers want to be able to execute on. And being able to optimize the runtime engines, whether it's data integration, data movement, data virtualization, based on data gravity, that you had mentioned-- >> So, something like a whole new developer program opportunity to bring to the market. >> Absolutely. I mean, there is, I think there is a huge opportunity for, from an education perspective, to help our customers build these applications. But it starts with understanding the data assets, understanding what they can do with it, and using self-service-type tools that Jay was referring to. >> And all of that underpinned with the trust. If you don't trust your data, the data scientist is not going to know whether or not they're using the right thing. >> So, the ladder's great. Great way for people to figure out where they are, it's like looking in the mirror, on the organization. How early is this? What inning are we in? How do you guys see the progression? How far along are we? Obviously, you have some data, examples, some people are doing it end-to-end. What's the maturity look like? What's the uptake? >> Go ahead, Jay. >> So, I think we're at the beginning of a crest of a wave. As I say, there's been a lot of discussion so far, even if you compare this year's conference to last year's. A lot of the discussion last year was, "What's possible with AI?" This year's conference is much more about, "What are we doing with AI?" And I think we're now getting to the point where people can actually start to be productive and really start to change their business through that. >> Yeah and, just to add to that, I mean, the ladder to AI was introduced last year, and it has gained so much adoption in the marketplace and our customers, they're actually organizing their business that way. So, the Collect divisions are the database teams, are now expanding to Hadoop and Cloudera, and Hortonworks and Mongo. They're organizing their data governance teams around the Organize pillar, where they're doing things like data integration, data replication. So, I feel like the maturity of this ladder to AI is really enabling our customers to achieve it much faster than-- >> I was talking to Dave Vellante about this, and we're seeing that, you know, we've been covering IBM since, it's the 10th year of theCUBE, all ten years. It's been, watching the progression. The past couple of years has been setting the table, everyone seems to be pumping, it makes sense, everything's hanging together, it's in one group. Data's not one, "This group, that group," it's all, Data, AI, all Analytics, all Watson. Smart, and the ladder just allows you to understand where a customer is, and then-- >> Well, and also, we mentioned the emphasis on open source. It allows our customers to take an inventory of, what do they have, internally, with IBM assets, externally, open source, so that they can actually start to architect their information architecture, using the same kind of analogy. >> And an opportunity for developers too, great. Julie, thanks for coming on. Jay, appreciate it. >> Thank you so much for the opportunity, happy Valentine's Day! Happy Valentine's Day, we're theCUBE. I'm John Furrier, Stu Miniman here, live in San Francisco at the Moscone Center, and the whole street's shut down, Howard Street. Huge event, 30,000 people, we'll be back with more Day Four coverage after this short break.

(digital music) >> From Yorktown Heights, New York It's theCUBE! Covering IBM Cloud Innovation Day. Brought to you by IBM. >> Hi, it's Wikibon's Peter Burris again. We're broadcasting on The Cube from IBM Innovation Day at the Thomas J Watson Research Laboratory in Yorktown Heights, New York. Have a number of great conversations, and we got a great one right now. Rob Thomas, who's the General Manager of IBM Analytics, welcome back to theCUBE. >> Thanks Peter, great to see you. Thanks for coming out here to the woods. >> Oh, well it's not that bad. I actually live not to far from here. Interesting Rob, I was driving up the Taconic Parkway and I realized I hadn't been on it in 40 years, so. >> Is that right? (laugh) >> Very exciting. So Rob let's talk IBM analytics and some of the changes that are taking place. Specifically, how are customers thinking about achieving their AI outcomes. What's that ladder look like? >> Yeah. We call it the AI ladder. Which is basically all the steps that a client has to take to get to get to an AI future, is the best way I would describe it. From how you collect data, to how you organize your data. How you analyze your data, start to put machine learning into motion. How you infuse your data, meaning you can take any insights, infuse it into other applications. Those are the basic building blocks of this laddered AI. 81 percent of clients that start to do something with AI, they realize their first issue is a data issue. They can't find the data, they don't have the data. The AI ladder's about taking care of the data problem so you can focus on where the value is, the AI pieces. >> So, AI is a pretty broad, hairy topic today. What are customers learning about AI? What kind of experience are they gaining? How is it sharpening their thoughts and their pencils, as they think about what kind of outcomes they want to achieve? >> You know, its... For some reason, it's a bit of a mystical topic, but to me AI is actually quite simple. I'd like to say AI is not magic. Some people think it's a magical black box. You just, you know, put a few inputs in, you sit around and magic happens. It's not that, it's real work, it's real computer science. It's about how do I put, you know, how do I build models? Put models into production? Most models, when they go into production, are not that good, so how do I continually train and retrain those models? Then the AI aspect is about how do I bring human features to that? How do I integrate that with natural language, or with speech recognition, or with image recognition. So, when you get under the covers, it's actually not that mystical. It's about basic building blocks that help you start to achieve business outcomes. >> It's got to be very practical, otherwise the business has a hard time ultimately adopting it, but you mentioned a number of different... I especially like the 'add the human features' to it of the natural language. It also suggests that the skill set of AI starts to evolve as companies mature up this ladder. How is that starting to change? >> That's still one of the biggest gaps, I would say. Skill sets around the modern languages of data science that lead to AI: Python, AR, Scala, as an example of a few. That's still a bit of a gap. Our focus has been how do we make tools that anybody can use. So if you've grown up doing SPSS or SaaS, something like that, how do you adopt those skills for the open world of data science? That can make a big difference. On the human features point, we've actually built applications to try to make that piece easy. Great example is with Royal Bank of Scotland where we've created a solution called Watson Assistant which is basically how do we arm their call center representatives to be much more intelligent and engaging with clients, predicting what clients may do. Those types of applications package up the human features and the components I talked about, makes it really easy to get AI into production. >> Now many years ago, the genius Turing, noted the notion of the Turing machine where you couldn't tell the difference between the human and a machine from an engagement standpoint. We're actually starting to see that happen in some important ways. You mentioned the call center. >> Yep. >> How are technologies and agency coming together? By that I mean, the rate at which businesses are actually applying AI to act as an agent for them in front of customers? >> I think it's slow. What I encourage clients to do is, you have to do a massive number of experiments. So don't talk to me about the one or two AI projects you're doing, I'm thinking like hundreds. I was with a bank last week in Japan, and they're comment was in the last year they've done a hundred different AI projects. These are not one year long projects with hundreds of people. It's like, let's do a bunch of small experiments. You have to be comfortable that probably half of your experiments are going to fail, that's okay. The goal is how do you increase your win rate. Do you learn from the ones that work, and from the ones that don't work, so that you can apply those. This is all, to me at this stage, is about experimentation. Any enterprise right now, has to be thinking in terms of hundreds of experiments, not one, not two or 'Hey, should we do that project?' Think in terms of hundreds of experiments. You're going to learn a lot when you do that. >> But as you said earlier, AI is not magic and it's grounded in something, and it's increasingly obvious that it's grounded in analytics. So what is the relationship between AI analytics, and what types of analytics are capable of creating value independent of AI? >> So if you think about how I kind of decomposed AI, talked about human features, I talked about, it kind of starts with a model, you train the model. The model is only as good as the data that you feed it. So, that assumes that one, that your data's not locked into a bunch of different silos. It assumes that your data is actually governed. You have a data catalog or that type of capability. If you have those basics in place, once you have a single instantiation of your data, it becomes very easy to train models, and you can find that the more that you feed it, the better the model's going to get, the better your business outcomes are going to get. That's our whole strategy around IBM Cloud Private for Data. Basically, one environment, a console for all your data, build a model here, train it in all your data, no matter where it is, it's pretty powerful. >> Let me pick up on that where it is, 'cause it's becoming increasingly obvious, at least to us and our clients, that the world is not going to move all the data over to a central location. The data is going to be increasingly distributed closer to the sources, closer to where the action is. How does AI and that notion of increasing distributed data going to work together for clients. >> So we've just released what's called IBM Data Virtualization this month, and it is a leapfrog in terms of data virtualization technology. So the idea is leave your data where ever it is, it could be in a data center, it could be on a different data center, it could be on an automobile if you're an automobile manufacturer. We can federate data from anywhere, take advantage of processing power on the edge. So we're breaking down that problem. Which is, the initial analytics problem was before I do this I've got to bring all my data to one place. It's not a good use of money. It's a lot of time and it's a lot of money. So we're saying leave your data where it is, we will virtualize your data from wherever it may be. >> That's really cool. What was it called again? >> IBM Data Virtualization and it's part of IBM Cloud Private for Data. It's a feature in that. >> Excellent, so one last question Rob. February's coming up, IBM Think San Francisco thirty plus thousand people, what kind of conversations do you anticipate having with you customers, your partners, as they try to learn, experiment, take away actions that they can take to achieve their outcomes? >> I want to have this AI experimentation discussion. I will be encouraging every client, let's talk about hundreds of experiments not 5. Let's talk about what we can get started on now. Technology's incredibly cheap to get started and do something, and it's all about rate and pace, and trying a bunch of things. That's what I'm going to be encouraging. The clients that you're going to see on stage there are the ones that have adopted this mentality in the last year and they've got some great successes to show. >> Rob Thomas, general manager IBM Analytics, thanks again for being on theCUBE. >> Thanks Peter. >> Once again this is Peter Buriss of Wikibon, from IBM Innovation Day, Thomas J Watson Research Center. We'll be back in a moment. (techno beat)

>> [Announcer] Live from Times Square in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to theCUBE's special presentation. We're covering IBM's announcements today around AI. IBM, as theCUBE does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on theCUBE, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching theCUBE.

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)

(upbeat music) >> Live from Time Square in New York City, it's The Cube. Covering IBM's change the game, winning with AI. Brought to you by IBM. >> Hi everybody, welcome back to The Big Apple. My name is Dave Vellante. We're here in the Theater District at The Westin Hotel covering a Special Cube event. IBM's got a big event today and tonight, if we can pan here to this pop-up. Change the game: winning with AI. So IBM has got an event here at The Westin, The Tide at Terminal 5 which is right up the Westside Highway. Go to IBM.com/winwithAI. Register, you can watch it online, or if you're in the city come down and see us, we'll be there. Uh, we have a bunch of customers will be there. We had Rob Thomas on earlier, he's kind of the host of the event. IBM does these events periodically throughout the year. They gather customers, they put forth some thought leadership, talk about some hard dues. So, we're very excited to have John Thomas here, he's a distinguished engineer and Director of IBM Analytics, long time Cube alum, great to see you again John >> Same here. Thanks for coming on. >> Great to have you. >> So we just heard a great case study with Niagara Bottling around the Data Science Elite Team, that's something that you've been involved in, and we're going to get into that. But give us the update since we last talked, what have you been up to?? >> Sure sure. So we're living and breathing data science these days. So the Data Science Elite Team, we are a team of practitioners. We actually work collaboratively with clients. And I stress on the word collaboratively because we're not there to just go do some work for a client. We actually sit down, expect the client to put their team to work with our team, and we build AI solutions together. Scope use cases, but sort of you know, expose them to expertise, tools, techniques, and do this together, right. And we've been very busy, (laughs) I can tell you that. You know it has been a lot of travel around the world. A lot of interest in the program. And engagements that bring us very interesting use cases. You know, use cases that you would expect to see, use cases that are hmmm, I had not thought of a use case like that. You know, but it's been an interesting journey in the last six, eight months now. >> And these are pretty small, agile teams. >> Sometimes people >> Yes. use tiger teams and they're two to three pizza teams, right? >> Yeah. And my understanding is you bring some number of resources that's called two three data scientists, >> Yes and the customer matches that resource, right? >> Exactly. That's the prerequisite. >> That is the prerequisite, because we're not there to just do the work for the client. We want to do this in a collaborative fashion, right. So, the customers Data Science Team is learning from us, we are working with them hand in hand to build a solution out. >> And that's got to resonate well with customers. >> Absolutely I mean so often the services business is like kind of, customers will say well I don't want to keep going back to a company to get these services >> Right, right. I want, teach me how to fish and that's exactly >> That's exactly! >> I was going to use that phrase. That's exactly what we do, that's exactly. So at the end of the two or three month period, when IBM leaves, my team leaves, you know, the client, the customer knows what the tools are, what the techniques are, what to watch out for, what are success criteria, they have a good handle of that. >> So we heard about the Niagara Bottling use case, which was a pretty narrow, >> Mm-hmm. How can we optimize the use of the plastic wrapping, save some money there, but at the same time maintain stability. >> Ya. You know very, quite a narrow in this case. >> Yes, yes. What are some of the other use cases? >> Yeah that's a very, like you said, a narrow one. But there are some use cases that span industries, that cut across different domains. I think I may have mentioned this on one of our previous discussions, Dave. You know customer interactions, trying to improve customer interactions is something that cuts across industry, right. Now that can be across different channels. One of the most prominent channels is a call center, I think we have talked about this previously. You know I hate calling into a call center (laughter) because I don't know Yeah, yeah. What kind of support I'm going to get. But, what if you could equip the call center agents to provide consistent service to the caller, and handle the calls in the best appropriate way. Reducing costs on the business side because call handling is expensive. And eventually lead up to can I even avoid the call, through insights on why the call is coming in in the first place. So this use case cuts across industry. Any enterprise that has got a call center is doing this. So we are looking at can we apply machine-learning techniques to understand dominant topics in the conversation. Once we understand with these have with unsupervised techniques, once we understand dominant topics in the conversation, can we drill into that and understand what are the intents, and does the intent change as the conversation progress? So you know I'm calling someone, it starts off with pleasantries, it then goes into weather, how are the kids doing? You know, complain about life in general. But then you get to something of substance why the person was calling in the first place. And then you may think that is the intent of the conversation, but you find that as the conversation progresses, the intent might actually change. And can you understand that real time? Can you understand the reasons behind the call, so that you could take proactive steps to maybe avoid the call coming in at the first place? This use case Dave, you know we are seeing so much interest in this use case. Because call centers are a big cost to most enterprises. >> Let's double down on that because I want to understand this. So you basically doing. So every time you call a call center this call may be recorded, >> (laughter) Yeah. For quality of service. >> Yeah. So you're recording the calls maybe using MLP to transcribe those calls. >> MLP is just the first step, >> Right. so you're absolutely right, when a calls come in there's already call recording systems in place. We're not getting into that space, right. So call recording systems record the voice calls. So often in offline batch mode you can take these millions of calls, pass it through a speech-to-text mechanism, which produces a text equivalent of the voice recordings. Then what we do is we apply unsupervised machine learning, and clustering, and topic-modeling techniques against it to understand what are the dominant topics in this conversation. >> You do kind of an entity extraction of those topics. >> Exactly, exactly, exactly. >> Then we find what is the most relevant, what are the relevant ones, what is the relevancy of topics in a particular conversation. That's not enough, that is just step two, if you will. Then you have to, we build what is called an intent hierarchy. So this is at top most level will be let's say payments, the call is about payments. But what about payments, right? Is it an intent to make a late payment? Or is the intent to avoid the payment or contest a payment? Or is the intent to structure a different payment mechanism? So can you get down to that level of detail? Then comes a further level of detail which is the reason that is tied to this intent. What is a reason for a late payment? Is it a job loss or job change? Is it because they are just not happy with the charges that I have coming? What is a reason? And the reason can be pretty complex, right? It may not be in the immediate vicinity of the snippet of conversation itself. So you got to go find out what the reason is and see if you can match it to this particular intent. So multiple steps off the journey, and eventually what we want to do is so we do our offers in an offline batch mode, and we are building a series of classifiers instead of classifiers. But eventually we want to get this to real time action. So think of this, if you have machine learning models, supervised models that can predict the intent, the reasons, et cetera, you can have them deployed operationalize them, so that when a call comes in real time, you can screen it in real time, do the speech to text, you can do this pass it to the supervise models that have been deployed, and the model fires and comes back and says this is the intent, take some action or guide the agent to take some action real time. >> Based on some automated discussion, so tell me what you're calling about, that kind of thing, >> Right. Is that right? >> So it's probably even gone past tell me what you're calling about. So it could be the conversation has begun to get into you know, I'm going through a tough time, my spouse had a job change. You know that is itself an indicator of some other reasons, and can that be used to prompt the CSR >> Ah, to take some action >> Ah, oh case. appropriate to the conversation. >> So I'm not talking to a machine, at first >> no no I'm talking to a human. >> Still talking to human. >> And then real time feedback to that human >> Exactly, exactly. is a good example of >> Exactly. human augmentation. >> Exactly, exactly. I wanted to go back and to process a little bit in terms of the model building. Are there humans involved in calibrating the model? >> There has to be. Yeah, there has to be. So you know, for all the hype in the industry, (laughter) you still need a (laughter). You know what it is is you need expertise to look at what these models produce, right. Because if you think about it, machine learning algorithms don't by themselves have an understanding of the domain. They are you know either statistical or similar in nature, so somebody has to marry the statistical observations with the domain expertise. So humans are definitely involved in the building of these models and claiming of these models. >> Okay. >> (inaudible). So that's who you got math, you got stats, you got some coding involved, and you >> Absolutely got humans are the last mile >> Absolutely. to really bring that >> Absolutely. expertise. And then in terms of operationalizing it, how does that actually get done? What tech behind that? >> Ah, yeah. >> It's a very good question, Dave. You build models, and what good are they if they stay inside your laptop, you know, they don't go anywhere. What you need to do is, I use a phrase, weave these models in your business processes and your applications. So you need a way to deploy these models. The models should be consumable from your business processes. Now it could be a Rest API Call could be a model. In some cases a Rest API Call is not sufficient, the latency is too high. Maybe you've got embed that model right into where your application is running. You know you've got data on a mainframe. A credit card transaction comes in, and the authorization for the credit card is happening in a four millisecond window on the mainframe on all, not all, but you know CICS COBOL Code. I don't have the time to make a Rest API call outside. I got to have the model execute in context with my CICS COBOL Code in that memory space. >> Yeah right. You know so the operationalizing is deploying, consuming these models, and then beyond that, how do the models behave over time? Because you can have the best programmer, the best data scientist build the absolute best model, which has got great accuracy, great performance today. Two weeks from now, performance is going to go down. >> Hmm. How do I monitor that? How do I trigger a loads map for below certain threshold. And, can I have a system in place that reclaims this model with new data as it comes in. >> So you got to understand where the data lives. >> Absolutely. You got to understand the physics, >> Yes. The latencies involved. >> Yes. You got to understand the economics. >> Yes. And there's also probably in many industries legal implications. >> Oh yes. >> No, the explainability of models. You know, can I prove that there is no bias here. >> Right. Now all of these are challenging but you know, doable things. >> What makes a successful engagement? Obviously you guys are outcome driven, >> Yeah. but talk about how you guys measure success. >> So um, for our team right now it is not about revenue, it's purely about adoption. Does the client, does the customer see the value of what IBM brings to the table. This is not just tools and technology, by the way. It's also expertise, right? >> Hmm. So this notion of expertise as a service, which is coupled with tools and technology to build a successful engagement. The way we measure success is has the client, have we built out the use case in a way that is useful for the business? Two, does a client see value in going further with that. So this is right now what we look at. It's not, you know yes of course everybody is scared about revenue. But that is not our key metric. Now in order to get there though, what we have found, a little bit of hard work, yes, uh, no you need different constituents of the customer to come together. It's not just me sending a bunch of awesome Python Programmers to the client. >> Yeah right. But now it is from the customer's side we need involvement from their Data Science Team. We talk about collaborating with them. We need involvement from their line of business. Because if the line of business doesn't care about the models we've produced you know, what good are they? >> Hmm. And third, people don't usually think about it, we need IT to be part of the discussion. Not just part of the discussion, part of being the stakeholder. >> Yes, so you've got, so IBM has the chops to actually bring these constituents together. >> Ya. I have actually a fair amount of experience in herding cats on large organizations. (laughter) And you know, the customer, they've got skin in the IBM game. This is to me a big differentiator between IBM, certainly some of the other technology suppliers who don't have the depth of services, expertise, and domain expertise. But on the flip side of that, differentiation from many of the a size who have that level of global expertise, but they don't have tech piece. >> Right. >> Now they would argue well we do anybodies tech. >> Ya. But you know, if you've got tech. >> Ya. >> You just got to (laughter) Ya. >> Bring those two together. >> Exactly. And that's really seems to me to be the big differentiator >> Yes, absolutely. for IBM. Well John, thanks so much for stopping by theCube and explaining sort of what you've been up to, the Data Science Elite Team, very exciting. Six to nine months in, >> Yes. are you declaring success yet? Still too early? >> Uh, well we're declaring success and we are growing, >> Ya. >> Growth is good. >> A lot of lot of attention. >> Alright, great to see you again, John. >> Absolutely, thanks you Dave. Thanks very much. Okay, keep it right there everybody. You're watching theCube. We're here at The Westin in midtown and we'll be right back after this short break. I'm Dave Vellante. (tech music)

>> Live from Times Square in New York City, it's The Cube covering IBM's Change the Game: Winning with AI, brought to you by IBM. >> Hello everybody, welcome to The Cube's special presentation. We're covering IBM's announcements today around AI. IBM, as The Cube does, runs of sessions and programs in conjunction with Strata, which is down at the Javits, and we're Rob Thomas, who's the General Manager of IBM Analytics. Long time Cube alum, Rob, great to see you. >> Dave, great to see you. >> So you guys got a lot going on today. We're here at the Westin Hotel, you've got an analyst event, you've got a partner meeting, you've got an event tonight, Change the game: winning with AI at Terminal 5, check that out, ibm.com/WinWithAI, go register there. But Rob, let's start with what you guys have going on, give us the run down. >> Yeah, it's a big week for us, and like many others, it's great when you have Strata, a lot of people in town. So, we've structured a week where, today, we're going to spend a lot of time with analysts and our business partners, talking about where we're going with data and AI. This evening, we've got a broadcast, it's called Winning with AI. What's unique about that broadcast is it's all clients. We've got clients on stage doing demonstrations, how they're using IBM technology to get to unique outcomes in their business. So I think it's going to be a pretty unique event, which should be a lot of fun. >> So this place, it looks like a cool event, a venue, Terminal 5, it's just up the street on the west side highway, probably a mile from the Javits Center, so definitely check that out. Alright, let's talk about, Rob, we've known each other for a long time, we've seen the early Hadoop days, you guys were very careful about diving in, you kind of let things settle and watched very carefully, and then came in at the right time. But we saw the evolution of so-called Big Data go from a phase of really reducing investments, cheaper data warehousing, and what that did is allowed people to collect a lot more data, and kind of get ready for this era that we're in now. But maybe you can give us your perspective on the phases, the waves that we've seen of data, and where we are today and where we're going. >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the question is, how do you get there? So you think about the steps, it's about, a lot of people started with, we're going to reduce the cost of our operations, we're going to use data to take out cost, that was kind of the Hadoop thrust, I would say. Then they moved to, well, now we need to see more about our data, we need higher performance data, BI data warehousing. So, everybody, I would say, has dabbled in those two area. The next leap forward is self-service analytics, so how do you actually empower everybody in your organization to use and access data? And the next step beyond that is, can I use AI to drive new business models, new levers of growth, for my business? So, I ask clients, pin yourself on this journey, most are, depends on the division or the part of the company, they're at different areas, but as I tell everybody, if you don't know where you are and you don't know where you want to go, you're just going to wind around, so I try to get them to pin down, where are you versus where do you want to go? >> So four phases, basically, the sort of cheap data store, the BI data warehouse modernization, self-service analytics, a big part of that is data science and data science collaboration, you guys have a lot of investments there, and then new business models with AI automation running on top. Where are we today? Would you say we're kind of in-between BI/DW modernization and on our way to self-service analytics, or what's your sense? >> I'd say most are right in the middle between BI data warehousing and self-service analytics. Self-service analytics is hard, because it requires you, sometimes to take a couple steps back, and look at your data. It's hard to provide self-service if you don't have a data catalog, if you don't have data security, if you haven't gone through the processes around data governance. So, sometimes you have to take one step back to go two steps forward, that's why I see a lot of people, I'd say, stuck in the middle right now. And the examples that you're going to see tonight as part of the broadcast are clients that have figured out how to break through that wall, and I think that's pretty illustrative of what's possible. >> Okay, so you're saying that, got to maybe take a step back and get the infrastructure right with, let's say a catalog, to give some basic things that they have to do, some x's and o's, you've got the Vince Lombardi played out here, and also, skillsets, I imagine, is a key part of that. So, that's what they've got to do to get prepared, and then, what's next? They start creating new business models, imagining this is where the cheap data officer comes in and it's an executive level, what are you seeing clients as part of digital transformation, what's the conversation like with customers? >> The biggest change, the great thing about the times we live in, is technology's become so accessible, you can do things very quickly. We created a team last year called Data Science Elite, and we've hired what we think are some of the best data scientists in the world. Their only job is to go work with clients and help them get to a first success with data science. So, we put a team in. Normally, one month, two months, normally a team of two or three people, our investment, and we say, let's go build a model, let's get to an outcome, and you can do this incredibly quickly now. I tell clients, I see somebody that says, we're going to spend six months evaluating and thinking about this, I was like, why would you spend six months thinking about this when you could actually do it in one month? So you just need to get over the edge and go try it. >> So we're going to learn more about the Data Science Elite team. We've got John Thomas coming on today, who is a distinguished engineer at IBM, and he's very much involved in that team, and I think we have a customer who's actually gone through that, so we're going to talk about what their experience was with the Data Science Elite team. Alright, you've got some hard news coming up, you've actually made some news earlier with Hortonworks and Red Hat, I want to talk about that, but you've also got some hard news today. Take us through that. >> Yeah, let's talk about all three. First, Monday we announced the expanded relationship with both Hortonworks and Red Hat. This goes back to one of the core beliefs I talked about, every enterprise is modernizing their data and application of states, I don't think there's any debate about that. We are big believers in Kubernetes and containers as the architecture to drive that modernization. The announcement on Monday was, we're working closer with Red Hat to take all of our data services as part of Cloud Private for Data, which are basically microservice for data, and we're running those on OpenShift, and we're starting to see great customer traction with that. And where does Hortonworks come in? Hadoop has been the outlier on moving to microservices containers, we're working with Hortonworks to help them make that move as well. So, it's really about the three of us getting together and helping clients with this modernization journey. >> So, just to remind people, you remember ODPI, folks? It was all this kerfuffle about, why do we even need this? Well, what's interesting to me about this triumvirate is, well, first of all, Red Hat and Hortonworks are hardcore opensource, IBM's always been a big supporter of open source. You three got together and you're proving now the productivity for customers of this relationship. You guys don't talk about this, but Hortonworks had to, when it's public call, that the relationship with IBM drove many, many seven-figure deals, which, obviously means that customers are getting value out of this, so it's great to see that come to fruition, and it wasn't just a Barney announcement a couple years ago, so congratulations on that. Now, there's this other news that you guys announced this morning, talk about that. >> Yeah, two other things. One is, we announced a relationship with Stack Overflow. 50 million developers go to Stack Overflow a month, it's an amazing environment for developers that are looking to do new things, and we're sponsoring a community around AI. Back to your point before, you said, is there a skills gap in enterprises, there absolutely is, I don't think that's a surprise. Data science, AI developers, not every company has the skills they need, so we're sponsoring a community to help drive the growth of skills in and around data science and AI. So things like Python, R, Scala, these are the languages of data science, and it's a great relationship with us and Stack Overflow to build a community to get things going on skills. >> Okay, and then there was one more. >> Last one's a product announcement. This is one of the most interesting product annoucements we've had in quite a while. Imagine this, you write a sequel query, and traditional approach is, I've got a server, I point it as that server, I get the data, it's pretty limited. We're announcing technology where I write a query, and it can find data anywhere in the world. I think of it as wide-area sequel. So it can find data on an automotive device, a telematics device, an IoT device, it could be a mobile device, we think of it as sequel the whole world. You write a query, you can find the data anywhere it is, and we take advantage of the processing power on the edge. The biggest problem with IoT is, it's been the old mantra of, go find the data, bring it all back to a centralized warehouse, that makes it impossible to do it real time. We're enabling real time because we can write a query once, find data anywhere, this is technology we've had in preview for the last year. We've been working with a lot of clients to prove out used cases to do it, we're integrating as the capability inside of IBM Cloud Private for Data. So if you buy IBM Cloud for Data, it's there. >> Interesting, so when you've been around as long as I have, long enough to see some of the pendulums swings, and it's clearly a pendulum swing back toward decentralization in the edge, but the key is, from what you just described, is you're sort of redefining the boundary, so I presume it's the edge, any Cloud, or on premises, where you can find that data, is that correct? >> Yeah, so it's multi-Cloud. I mean, look, every organization is going to be multi-Cloud, like 100%, that's going to happen, and that could be private, it could be multiple public Cloud providers, but the key point is, data on the edge is not just limited to what's in those Clouds. It could be anywhere that you're collecting data. And, we're enabling an architecture which performs incredibly well, because you take advantage of processing power on the edge, where you can get data anywhere that it sits. >> Okay, so, then, I'm setting up a Cloud, I'll call it a Cloud architecture, that encompasses the edge, where essentially, there are no boundaries, and you're bringing security. We talked about containers before, we've been talking about Kubernetes all week here at a Big Data show. And then of course, Cloud, and what's interesting, I think many of the Hadoop distral vendors kind of missed Cloud early on, and then now are sort of saying, oh wow, it's a hybrid world and we've got a part, you guys obviously made some moves, a couple billion dollar moves, to do some acquisitions and get hardcore into Cloud, so that becomes a critical component. You're not just limiting your scope to the IBM Cloud. You're recognizing that it's a multi-Cloud world, that' what customers want to do. Your comments. >> It's multi-Cloud, and it's not just the IBM Cloud, I think the most predominant Cloud that's emerging is every client's private Cloud. Every client I talk to is building out a containerized architecture. They need their own Cloud, and they need seamless connectivity to any public Cloud that they may be using. This is why you see such a premium being put on things like data ingestion, data curation. It's not popular, it's not exciting, people don't want to talk about it, but we're the biggest inhibitors, to this AI point, comes back to data curation, data ingestion, because if you're dealing with multiple Clouds, suddenly your data's in a bunch of different spots. >> Well, so you're basically, and we talked about this a lot on The Cube, you're bringing the Cloud model to the data, wherever the data lives. Is that the right way to think about it? >> I think organizations have spoken, set aside what they say, look at their actions. Their actions say, we don't want to move all of our data to any particular Cloud, we'll move some of our data. We need to give them seamless connectivity so that they can leave their data where they want, we can bring Cloud-Native Architecture to their data, we could also help move their data to a Cloud-Native architecture if that's what they prefer. >> Well, it makes sense, because you've got physics, latency, you've got economics, moving all the data into a public Cloud is expensive and just doesn't make economic sense, and then you've got things like GDPR, which says, well, you have to keep the data, certain laws of the land, if you will, that say, you've got to keep the data in whatever it is, in Germany, or whatever country. So those sort of edicts dictate how you approach managing workloads and what you put where, right? Okay, what's going on with Watson? Give us the update there. >> I get a lot of questions, people trying to peel back the onion of what exactly is it? So, I want to make that super clear here. Watson is a few things, start at the bottom. You need a runtime for models that you've built. So we have a product called Watson Machine Learning, runs anywhere you want, that is the runtime for how you execute models that you've built. Anytime you have a runtime, you need somewhere where you can build models, you need a development environment. That is called Watson Studio. So, we had a product called Data Science Experience, we've evolved that into Watson Studio, connecting in some of those features. So we have Watson Studio, that's the development environment, Watson Machine Learning, that's the runtime. Now you move further up the stack. We have a set of APIs that bring in human features, vision, natural language processing, audio analytics, those types of things. You can integrate those as part of a model that you build. And then on top of that, we've got things like Watson Applications, we've got Watson for call centers, doing customer service and chatbots, and then we've got a lot of clients who've taken pieces of that stack and built their own AI solutions. They've taken some of the APIs, they've taken some of the design time, the studio, they've taken some of the Watson Machine Learning. So, it is really a stack of capabilities, and where we're driving the greatest productivity, this is in a lot of the examples you'll see tonight for clients, is clients that have bought into this idea of, I need a development environment, I need a runtime, where I can deploy models anywhere. We're getting a lot of momentum on that, and then that raises the question of, well, do I have expandability, do I have trust in transparency, and that's another thing that we're working on. >> Okay, so there's API oriented architecture, exposing all these services make it very easy for people to consume. Okay, so we've been talking all week at Cube NYC, is Big Data is in AI, is this old wine, new bottle? I mean, it's clear, Rob, from the conversation here, there's a lot of substantive innovation, and early adoption, anyway, of some of these innovations, but a lot of potential going forward. Last thoughts? >> What people have to realize is AI is not magic, it's still computer science. So it actually requires some hard work. You need to roll up your sleeves, you need to understand how I get from point A to point B, you need a development environment, you need a runtime. I want people to really think about this, it's not magic. I think for a while, people have gotten the impression that there's some magic button. There's not, but if you put in the time, and it's not a lot of time, you'll see the examples tonight, most of them have been done in one or two months, there's great business value in starting to leverage AI in your business. >> Awesome, alright, so if you're in this city or you're at Strata, go to ibm.com/WinWithAI, register for the event tonight. Rob, we'll see you there, thanks so much for coming back. >> Yeah, it's going to be fun, thanks Dave, great to see you. >> Alright, keep it right there everybody, we'll be back with our next guest right after this short break, you're watching The Cube.

>> Live from Times Square in New York City, it's theCUBE, covering IBM's Change the Game, Winning with AI, brought to you by IBM. >> Hi everybody, welcome back to theCUBE's special presentation. We're here at the Western Hotel and the theater district covering IBM's announcements. They've got an analyst meeting today, partner event. They've got a big event tonight. IBM.com/winwithAI, go to that website, if you're in town register. You can watch the webcast online. You'll see this very cool play of Vince Lombardy, one of his famous plays. It's kind of a power sweep right which is a great way to talk about sort of winning and with X's and O's. So anyway, Daniel Hernandez is here the vice president of IBM analytics, long time Cube along. It's great to see you again, thanks for coming on. >> My pleasure Dave. >> So we've talked a number of times. We talked earlier this year. Give us the update on momentum in your business. You guys are doing really well, we see this in the quadrants and the waves, but your perspective. >> Data science and AI, so when we last talked we were just introducing something called IBM Club Private for data. The basic idea is anybody that wants to do data science, data engineering or building apps with data anywhere, we're going to give them a single integrated platform to get that done. It's going to be the most efficient, best way to do those jobs to be done. We introduced it, it's been a resounding success. Been rolling that out with clients, that's been a whole lot of fun. >> So we talked a little bit with Rob Thomas about some of the news that you guys have, but this is really your wheelhouse so I'm going to drill down into each of these. Let's say we had Rob Beerden on yesterday on our program and he talked a lot about the IBM Red Hat and Hortonworks relationship. Certainly they talked about it on their earnings call and there seems to be clear momentum in the marketplace. But give us your perspective on that announcement. What exactly is it all about? I mean it started kind of back in the ODPI days and it's really evolved into something that now customers are taking advantage of. >> You go back to June last year, we entered into a relationship with Hortonworks where the basic primacy, was customers care about data and any data driven initiative was going to require data science. We had to do a better job bringing these eco systems, one focused on kind of Hadoop, the other one on classic enterprise analytical and operational data together. We did that last year. The other element of that was we're going to bring our data science and machine learning tools and run times to where the data is including Hadoop. That's been a resounding success. The next step up is how do we proliferate that single integrated stack everywhere including private Cloud or preferred Clouds like Open Shift. So there was two elements of the announcement. We did the hybrid Cloud architecture initiative which is taking the Hadoop data stack and bringing it to containers and Kubernetes. That's a big deal for people that want to run the infrastructure with Cloud characteristics. And the other was we're going to bring that whole stack onto Open Shift. So on IBM's side, with IBM Cloud Private for data we are driving certification of that entire stack on OpenShift so any customer that's betting on OpenShift as their Cloud infrastructure can benefit from that and the single integrated data stack. It's a pretty big deal. >> So OpenShift is really interesting because OpenShift was kind of quiet for awhile. It was quiest if you will. And then containers come on the scene and OpenShift has just exploded. What are your perspectives on that and what's IBM's angle on OpenShift? >> Containers of Kubernetes basically allow you to get Cloud characteristics everywhere. It used to be locked in to kind of the public Cloud or SCP providers that were offering as a service whether PAS OR IAS and Docker and Kubernetes are making the same underline technology that enabled elasticity, pay as you go models available anywhere including your own data center. So I think it explains why OpenShift, why IBM Cloud Private, why IBM Club Private for data just got on there. >> I mean the Core OS move by Red Hat was genius. They picked that up for the song in our view anyway and it's really helped explode that. And in this world, everybody's talking about Kubernetes. I mean we're here at a big data conference all week. It used to be Hadoop world. Everybody's talking about containers, Kubernetes and Multi cloud. Those are kind of the hot trends. I presume you've seen the same thing. >> 100 percent. There's not a single client that I know, and I spend the majority of my time with clients that are running their workloads in a single stack. And so what do you do? If data is an imperative for you, you better run your data analytic stack wherever you need to and that means Multi cloud by definition. So you've got a choice. You can say, I can port that workload to every distinct programming model and data stack or you can have a data stack everywhere including Multi clouds and Open Shift in this case. >> So thinking about the three companies, so Hortonworks obviously had duped distro specialists, open source, brings that end to end sort of data management from you know Edge, or Clouds on Prim. Red Hat doing a lot of the sort of hardcore infrastructure layer. IBM bringing in the analytics and really empowering people to get insights out of data. Is that the right way to think about that triangle? >> 100 percent and you know with the Hortonworks and IBM data stacks, we've got our common services, particularly you're on open meta data which means wherever your data is, you're going to know about it and you're going to be able to control it. Privacy, security, data discovery reasons, that's a pretty big deal. >> Yeah and as the Cloud, well obviously the Cloud whether it's on Prim or in the public Cloud expands now to the Edge, you've also got this concept of data virtualization. We've talked about this in the past. You guys have made some announcements there. But let's put a double click on that a little bit. What's it all about? >> Data virtualization been going on for a long time. It's basic intent is to help you access data through whatever tools, no matter where the data is. Traditional approaches of data virtualization are pretty limiting. So they work relatively well when you've got small data sets but when you've got highly fragmented data, which is the case in virtually every enterprise that exists a lot of the undermined technology for data virtualization breaks down. Data coming through a single headnote. Ultimately that becomes the critical issue. So you can't take advantage of data virtualization technologies largely because of that when you've got wide scale deployments. We've been incubating technology under this project codename query plex, it was a code name that we used internally and that we were working with Beta clients on and testing it out, validating it technically and it was pretty clear that this is a game changing method for data virtualization that allows you to drive the benefits of accessing your data wherever it is, pushing down queries where the data is and getting benefits of that through highly fragmented data landscape. And so what we've done is take that extremely innovated next generation data virtualization technology include it in our data platform called IBM Club Private for Data, and made it a critical feature inside of that. >> I like that term, query plex, it reminds me of the global sisplex. I go back to the days when actually viewing sort of distributed global systems was very, very challenging and IBM sort of solved that problem. Okay, so what's the secret sauce though of query plex and data virtualization? How does it all work? What's the tech behind it? >> So technically, instead of data coming and getting funneled through one node. If you ever think of your data as kind of a graph of computational data nodes. What query plex does is take advantage of that computational mesh to do queries and analytics. So instead of bringing all the data and funneling it through one of the nodes, and depending on the computational horsepower of that node and all the data being able to get to it, this just federates it out. It distributes out that workload so it's some magic behind the scenes but relatively simple technique. Low computing aggregate, it's probably going to be higher than whatever you can put into that single node. >> And how do customers access these services? How long does it take? >> It would look like a standard query interface to them. So this is all magic behind the scenes. >> Okay and they get this capability as part of what? IBM's >> IBM's Club Private for Data. It's going to be a feature, so this project query plex, is introduced as next generation data virtualization technology which just becomes a part of IBM Club Private for Data. >> Okay and then the other announcement that we talked to Rob, I'd like to understand a little bit more behind it. Actually before we get there, can we talk about the business impact of query plex and data virtualization? Thinking about it, it dramatically simplifies the processes that I have to go through to get data. But more importantly, it helps me get a handle on my data so I can apply machine intelligence. It seems like the innovation sandwich if you will. Data plus AI and then Cloud models for scale and simplicity and that's what's going to drive innovation. So talk about the business impact that people are excited about with regard to query plex. >> Better economics, so in order for you to access your data, you don't have to do ETO in this particular case. So data at rest getting consumed because of this online technology. Two performance, so because of the way this works you're actually going to get faster response times. Three, you're going to be able to query more data simply because this technology allows you to access all your data in a fragmented way without having to consolidate it. >> Okay, so it eliminates steps, right, and gets you time to value and gives you a bigger corporate of data that you can the analyze and drive inside. >> 100 percent. >> Okay, let's talk about stack overflow. You know, Rob took us through a little bit about what that's, what's going on there but why stack overflow, you're targeting developers? Talk to me more about that. >> So stack overflow, 50 million active developers each month on that community. You're a developer and you want to know something, you have to go to stack overflow. You think about data science and AI as disciplines. The idea that that is only dermained to AI and data scientists is very limiting idea. In order for you to actually apply artificial intelligence for whatever your use case is instead of a business it's going to require multiple individuals working together to get that particular outcome done including developers. So instead of having a distinct community for AI that's focused on AI machine developers, why not bring the artificial intelligence community to where the developers already are, which is stack overflow. So, if you go to AI.stackexchange.com, it's going to be the place for you to go to get all your answers to any question around artificial intelligence and of course IBM is going to be there in the community helping out. >> So it's AI.stackexchange.com. You know, it's interesting Daniel that, I mean to talk about digital transformation talking about data. John Furrier said something awhile back about the dots. This is like five or six years ago. He said data is the new development kit and now you guys are essentially targeting developers around AI, obviously a data centric. People trying to put data at the core of the organization. You see that that's a winning strategy. What do you think about that? >> 100 percent, I mean we're the data company instead of IBM, so you're probably asking the wrong guy if you think >> You're biased. (laughing) >> Yeah possibly, but I'm acknowledged. The data over opinions. >> Alright, tell us about tonight what we can expect? I was referencing the Vince Lombardy play here. You know, what's behind that? What are we going to see tonight? >> We were joking a little bit about the old school power eye formation, but that obviously works for your, you're a New England fan aren't you? >> I am actually, if you saw the games this weekend Pat's were in the power eye for quite a bit of the game which I know upset a lot of people. But it works. >> Yeah, maybe we should of used it as a Dallas Cowboy team. But anyways, it's going to be an amazing night. So we're going to have a bunch of clients talking about what they're doing with AI. And so if you're interested in learning what's happening in the industry, kind of perfect event to get it. We're going to do some expert analysis. It will be a little bit of fun breaking down what those customers did to be successful and maybe some tips and tricks that will help you along your way. >> Great, it's right up the street on the west side highway, probably about a mile from the Javis Center people that are at Strata. We've been running programs all week. One of the themes that we talked about, we had an event Tuesday night. We had a bunch of people coming in. There was people from financial services, we had folks from New York State, the city of New York. It was a great meet up and we had a whole conversation got going and one of the things that we talked about and I'd love to get your thoughts and kind of know where you're headed here, but big data to do all that talk and people ask, is that, now at AI, the conversation has moved to AI, is it same wine, new bottle, or is there something substantive here? The consensus was, there's substantive innovation going on. Your thoughts about where that innovation is coming from and what the potential is for clients? >> So if you're going to implement AI for let's say customer care for instance, you're going to be three wrongs griefs. You need data, you need algorithms, you need compute. With a lot of different structure to relate down to capture data wasn't captured until the traditional data systems anchored by Hadoop and big data movement. We landed, we created a data and computational grid for that data today. With all the advancements going on in algorithms particularly in Open Source, you now have, you can build a neuro networks, you can do Cisco machine learning in any language that you want. And bringing those together are exactly the combination that you need to implement any AI system. You already have data and computational grids here. You've got algorithms bringing them together solving some problem that matters to a customer is like the natural next step. >> And despite the skills gap, the skill gaps that we talked about, you're seeing a lot of knowledge transfer from a lot of expertise getting out there into the wild when you follow people like Kirk Born on Twitter you'll see that he'll post like the 20 different models for deep learning and people are starting to share that information. And then that skills gap is closing. Maybe not as fast as some people like but it seems like the industry is paying attention to this and really driving hard to work toward it 'cause it's real. >> Yeah I agree. You're going to have Seth Dulpren, I think it's Niagara, one of our clients. What I like about them is the, in general there's two skill issues. There's one, where does data science and AI help us solve problems that matter in business? That's really a, trying to build a treasure map of potential problems you can solve with a stack. And Seth and Niagara are going to give you a really good basis for the kinds of problems that we can solve. I don't think there's enough of that going on. There's a lot of commentary communication actually work underway in the technical skill problem. You know, how do I actually build these models to do. But there's not enough in how do I, now that I solved that problem, how do we marry it to problems that matter? So the skills gap, you know, we're doing our part with our data science lead team which Seth opens which is telling a customer, pick a hard problem, give us some data, give us some domain experts. We're going to be in the AI and ML experts and we're going to see what happens. So the skill problem is very serious but I don't think it's most people are not having the right conversations about it necessarily. They understand intuitively there's a tech problem but that tech not linked to a business problem matters nothing. >> Yeah it's not insurmountable, I'm glad you mentioned that. We're going to be talking to Niagara Bottling and how they use the data science elite team as an accelerant, to kind of close that gap. And I'm really interested in the knowledge transfer that occurred and of course the one thing about IBM and companies like IBM is you get not only technical skills but you get deep industry expertise as well. Daniel, always great to see you. Love talking about the offerings and going deep. So good luck tonight. We'll see you there and thanks so much for coming on theCUBE. >> My pleasure. >> Alright, keep it right there everybody. This is Dave Vellanti. We'll be back right after this short break. You're watching theCUBE. (upbeat music)