 Live from Munich, Germany, it's theCUBE. Covering IBM, fast track your data. Brought to you by IBM. For you here at the show, you know, generally and specifically what are you doing here today? Yeah, so 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. And then 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. So 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. So Seth, can you describe what this hub is all about? This is a data center where you're hosting the machine learning services or is it something else? Yeah, so this is where clients can come and learn how to do data science. So 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. So interacting with data scientists, machine learning engineers basically, data engineers, developers to solve a problem for their business around data science. And these previous hubs have been completely booked. And so we wanted to launch them in other areas to try and expand the capacity of them. Now you're hosting a round table today, right? On the main tent. Yep. Right, and you got a customer on, you guys are going to be talking about sort of applying practices and financial services in other areas, maybe describe that a little bit. Yeah, so we have a customer on from ING, Heinrich, who's the chief architect for ING. And ING, IBM and Hortonworks have a contortion, if you would, or a framework that we're doing around Apache Atlas and Ranger as the kind of open source operating system for a unified governance platform. So much as IBM has positioned Spark as the 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. And whenever wants to be the master, you can't really get anything done. And 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 dictionary between all the different proprietary metadata platforms so that you can get a single unified view of your data environment across hybrid clouds, so on-premise, in the cloud, and across different proprietary vendor platforms. And because it's open source, there's these connectors that can go in and out of the proprietary platforms. So Seth, you seem like you're pretty tuned into the portfolio within the analytics group. How are you spending your time as the chief data officer? How do you balance it between sort of customer visits, maybe talking about some of the products, and then your sort of day job? Yeah, so I actually have three day jobs. So my job's actually split into kind of three pieces. So 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, and part of that business unit transformation is also making sure that we're compliant with regulations like GDPR and other regulations. Another third is really around kind of rethinking our offerings from a CDO perspective. So as a CDO, and as you know, Dave, I've only been with IBM for seven months. So as a former client recently, and as a CDO, what is it that I want to see from IBM's offerings? And we kind of hit on 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, because 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, manage your data across all these different environments. And 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, and we know three of them at least, right? So we know that we have a hybrid data management, we have unified governance, we have data science and machine learning, and you can think of the Z franchise as yet another as a fourth platform. So can you net out how governance relates to data science? Because 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 that 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 say, give explicit consent to allow things to be done with it. But long-term vision is that data governance, essentially the output of models is data, right? And how you use and deploy those models also needs to be governed. And so the long-term vision is that we will have a governance platform for all those things as well. But I think it makes more sense for those things to be governed in the data science platform, if you would. In fact, we often hear separate from GDPR and all that is something called algorithmic accountability that more is being discussed in policy circles and government circles around the world as strongly related to everything you were 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? So I think that's a natural extension of it. I think 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. And so it is that entire pipeline, right? Because 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 DevOps for Data Science. Yeah. So set them in the early Hadoop days, the real headwind was complexity. And it still is, by the way, and we know that. And companies like IBM are trying to reduce that complexity, Spark helps a little bit. Okay, so the technology will evolve. We get that. It seems like one of the other big headwinds right now is that companies don't have a, most companies don't have a great understanding of how they could 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've seemed to be entering a next wave here where people are trying to, beginning to understand, okay, 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. And there seem to be tuning in more so. Largely, I think, because of the chief data officer roles, helping them think that through. I wonder if you could give us your point of view on that narrative. Yeah, so 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. So 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 this kind of data transformation. So companies, 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. And I kind of frame that, that's like the data science kind of transformation aspect of a digital journey. And then the next aspect of it is how do I transform my business and change my business objectives? And 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? So how do I get to my current, my current, 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? Because really in the grand scheme of things there's two types of companies today. There's a 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 counting 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? So I think the first, so when I was in my former role we went through an exercise, and 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 where 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, okay, how can we value the decisions? Because 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. And so they want a dashboard to help them do that. And 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. And so you're doing two things. You're building a portfolio of decisions that then becomes to your point, Jim, about DevOps for data science. It's your backlog for your data scientist in the long run. And then 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. So 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 can drive, that kind of valuation can then drive IBM's decision to ramp up their internal data science initiatives or redeploy it or... Yeah, so that's exactly what happens. So 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. And so, you know, and as CDOs, right? CDOs are a relatively new role and in many organizations when money gets tight they say, what's this guy doing? Right? And having a portfolio of decisions that's saying, here's real value I'm adding, right? 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, you know, 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? So 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 types of aspects of your business. In other ways, you need to kind of decide as an enterprise, how do we value our assets, right? Do we use a three-year, five-year, 10-year NPV? Do we use some other metric? You need to kind of shape it, 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. Yeah, okay, so you're measuring that on an ongoing basis. Okay, and fine tuning as you go along. All right, Seth, we're out of time. Thanks so much for coming back in the Cube. Is it great to see you? Yeah, thank you for having me. You're welcome, good luck this afternoon. All right. All right, keep it right there, buddy. We'll be back, actually. Let me run down the day here for you. Just take a second and do that. So 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, and talking about where data goes next. Hillary Mason's going to be on. There's a session with Des Blanchfield on data science as a team sport. And then the big session on changing regulations, GDPR. 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. And then we're going to come back to the Cube and finish up our Cube interviews for the afternoon. We also, there's also going to be a breakout, two breakout sessions, one with Hillary 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.