 Live from Boston, Massachusetts, it's theCUBE. Covering IBM Chief Data Officer Summit, brought to you by IBM. We are wrapping up theCUBE's coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with Dave Vellante. It's been a great day here in Boston at the CDO Strategy Summit. Yeah, I mean, I like these events. They're packed with content, very intimate. You know, not a lot of vendor push. Well, one vendor, I guess, is pushing. But I like the way, you know, we're talking to Chris Penn about earned media, and owned media, and paid media. This is all media. It's really the quality of the content that differentiates those media. And IBM always has really solid content here. A lot of practitioners, a lot of, not so much how-to, but hands-on stories, use cases, maturity models, things of that nature. And I think we are seeing the maturity of the CDO role from a back-office function to one that's sort of morphed into or evolved into data quality and part of the whole data warehouses, King, you know, push, and that meant a lot of reporting, a lot of compliance, a lot of governance, to one that is really supporting a monetization mission of the business. And when you think about monetization at the simplest level, there's two ways to get there. You cut costs and you grow revenue. And now, you should be careful. Not all of these companies are for-profit firms, but in a commercial sense, those are really the two levers that you can push in a lot of forms. Productivity, time to market, time to value, quality, things of that nature. But at the end of the day, it comes down to spending less, making more. Right, exactly. And I think you made a great point and that data was the back-office. It was sort of something we had to worry about, manage a bit, but now it's really front and center in the organization and thinking about using it to make money and to save money. And I think that that's what we're learning about too. And what I've appreciated is how candid IBM is being, frankly, about mistakes that it has made and it's saying, this is a blueprint because we've learned. We've learned where we went wrong and here's what we have to offer other companies to learn from us. Well, it's interesting too, if you take my little simple model of how to get value out of data, from IBM's standpoint, it's really a lot of opportunities to cut costs in a huge organization, 300,000 employees. So we heard from Jim Cavanaugh and Inderpal Bhandari today how they're applying a lot of their data-driven expertise to not only capture that data, but understand how they can become more efficient. We haven't seen the growth. The idea is, you know, everybody talks about the string of quarterly declines in terms of revenue. The good news is the pace of that decline has slowed. That's sort of the best you can say about IBM's top line. But the bottom line seems to be working. And IBM's such a huge machine that you can actually squeeze a lot of cash flow by saving some money. And there are a lot of stories about, you know, IBM in the supply chain and making that more efficient, which as we heard was a main focus of a lot of the CFOs, you know, or CXOs out there. You know, so it's, I mean, IBM is, we always talk about the steamship, you know, turning, and this has been a five to seven year turn. You know, it's going to be interesting to see, you know, if IBM really will be perceived as a data-driven company, you know, they're pushing cognitive, you know, there's a lot of blowback about Watson and how its very service is led. Having said that, IBM's trying to do things that Google and Facebook and Amazon aren't trying to do. IBM's trying to solve cancer, for example. Those other companies are trying to, you know, push ads in your face. So, you know, got to give props to IBM for that effort. The social innovation piece, I think. It's really, it's part of this company's DNA. Yeah, I mean, you know, again, frankly, the Silicon Valley crowd sort of poo-poo's Watson from a technological perspective. Honestly, I'm not really qualified to address that question, but, you know, IBM tends to take capital and pour it into long-term, you know, businesses and eventually gets there. So it's not there yet. And so, but if IBM can use the data to become a more efficient company, be more responsive to its customers, understand the needs of its clients better, that's going to yield results. And I think the other part that we've heard a lot about today is the cultural transformation that's needed to make these dramatic changes in your business. As you said, IBM is a huge company. Hundreds of thousands of employees dispersed across the globe. So teams working across time zones, across cultures, across languages, that is difficult to really say, no, this is where we're going. This is our blueprint for success. Everyone come on board. Well, and you've seen some real cultural, you know, shake-ups inside of IBM. I mean, I was mentioning just a very small example. You know, when you go to the third floor in Armok now, the big, you know, concrete building, it's now all open. This is a corporate executive office. It's an open area with open cubicles. They're nice cubes. Believe me, the cubes are nicer than your office, I guarantee it. But they're open. You know, you can see executives, you can talk to executives in an open way. That's not how IBM used to be. It was very closed off and compartmental. Or everyone was working from home. I mean, that's frankly. Well, and that's the other piece of it, right? They said, hey, guys, you know, time to create the beehive effect. And that's created a lot of, you know, dislocation, a lot of, you know, concerns and blowback. But personally, I like that approach. If you're trying to foster collaboration, you know, nothing beats face-to-face contact. That's why we still have events. And that's why the cube comes to these events, right? No, you're absolutely right. A growing body of research has really pointed to the value and the benefit of an open office to spur collaboration, spur creativity, to get colleagues really working in understanding the rhythms of each other's interpersonal lives and work lives. And really that's where the good ideas come from. Yeah, so, I mean, those decisions are tough ones for organizations to make. But I'm presuming that IBM had some data related to this. I hope they did and made that decision. And, you know, I mean, it's way too early to tell if that was the right or the wrong move. Again, I tend to lean toward the beehive approach as a positive, you know, potential outcome. Right, exactly. So the other piece that we've heard a little bit about today is this talent shortage, this skill shortage, because you made this great point when we were talking to Chris Penn of shift communications. So much of all of this stuff is now math and science. And that's not what you typically think of as someone who's in marketing, for example. We have a real shortage of people who know data science and analytics. And that's a big problem that a lot of these companies are facing and trying to deal with some more successfully than others. Yeah, I mean, I think that the industry is going to address that problem. Because all this deep learning stuff and this machine learning and AI, it is largely math. And it's math that's known. I mean, you know, you talk when you really peel the onion and get into the sort of the type of math, you hear things like, oh, support vector machines and probabilistic latent cement indexing. Right. Okay, but these are concepts in math and algorithms that have been proven over time. And so I guess my point is, I think organizations are going to bring people in with strong math and computer skills and people who like data and can hack data and say, okay, you're a data scientist and I'll figure it out. And over time, I think they will figure it out. They'll train people. The hard part about that is not necessarily the math. If you're good at math, you're good at math. It's applying that math to help your organization understand A, how to monetize data. B, how to have data that's trusted. We heard that a lot. So the quality of the data. C, who gets access to that data. How do you secure and protect that data? What are the sort of policies around that data? And then in parallel, how do you form relationships with the line of business? So you got geeks talking to wallets. Right. Like how do you deal with that? You need the intermediary who can speak both languages. And then ultimately the answer to that, I think is in skill sets and evolving those skill sets. So those are sort of the sort of five things that the chief data officer has to think about. Three are in parallel or three are in sequential and two are in parallel. Well you also mentioned the trust in the data. And you were talking about it from an internal standpoint of colleagues agreeing, all right, this is what the data is telling us, this is clearly the direction we go in. But then there's the trust on the other side too, which is the trust that the company has with customers and clients to feel okay about using our data, using my data to make decisions. Well I think it's a great point. It was interesting to hear Chris Penn's response to that. He was basically saying, well, we could switch suits, but it's not going to have the same impact. I'm not buying it. I'm really going to keep pushing on this issue because while I agree that IBM doesn't have the same proclivity to take data and push ads in front of your face, it's unclear to me how you train models and somehow those models don't seep out. Now, IBM has said, we heard some IBM executives say, no, they're the customer's models. But you know how ideas get in people's heads and things happen. And that's just one example. There are many, many other examples. So think about internet of things and the factory floor and you've got some widget on the floor that's capturing data. And that widget manufacturer wants to use data for predictive analytics, for predictive failures, sending data back home. And then who knows what other insights they're going to gather from that data. Whose data is that? Is that data owned by the widget manufacturer? Is that data owned by the factory? It's their process. It's their workflow. Now, of course, if I'm the factory owner, I'm going to say it's my data. If I'm the widget manufacturer, I'm going to say, well, that's my data. And you're both right. I mean, I think that's the problem here. So there's no real arbiter to make that determination. Yeah, and I don't think these things have been challenged in court and certainly not adequately. So there's a lot of learnings that are going to occur over the next decade and we'll watch that evolution. But Jim Cavanaugh's right. We are at a real seminal moment here for this explosion in data, which is really changing the role of the CDO and how it fits in with the rest of the organization. Yeah, and I think the other thing to watch is how everybody talks about data-driven organizations, digital businesses, cognitive businesses. What are those? Those are kind of buzzwords, but what do they mean? What they mean, in our view, is how well you leverage data to create competitive advantage. And that's what a digital business does. It uses data differentially to retain customers, or attract and retain customers. And so that's what a digital business is. That's what a cognitive business is. Most businesses really aren't digital businesses today or cognitive business today. They're really few and far between. So a lot of work has to be done before we reach that vision. Yeah, but it throws out the Ubers and the Airbnb. Those are sort of easy examples, but when you have giant logistic systems and supply chains and ERP systems and HR systems with all this stovepipe data, becoming a digital business ain't so easy. No, no, and we are really at early days, exactly. So that's something to discuss next CDO strategy summit. And I think there was a lot of discussion early on when the CDO role emerged that they're essentially going to replace the CIO. I don't see it that way. There's a lot of discussion about what's the growth path for the CIO? Is it technology or is it business? But I think the CIO is okay. I think the CDO, I think actually there's more overlap between the chief digital officer and the chief data officer because if you buy the argument that digital equals data, then the chief data officer and the chief digital officer kind of one in the same. So that to me is a more interesting dynamic than the CIO versus the CDO. I don't see those two roles as highly overlapping and full of friction. I really see that chief digital officer and the chief data officer should be more aligned and maybe even be the same role. And it gets back to the organizational politics that are involved with all of these massive changes taking place. Well, again, the starting point for a CDO in a for-profit company is how can we use data to create value and monetize that value? Not necessarily sell the data, but how does data contribute to our value creation as a company? So with that as the starting point, that leads to, okay, well, if you're going to be data-driven, then you better have measurements. You better have a system. I mean, do you use enterprise value? Do you use simple ROI? Do you use an IRR calculation? Do you use a more sophisticated options-based calculation? I mean, how do you measure value and how do you determine capital allocation as a function of those value measurements? The vast majority of the companies out there certainly can't answer that across the board. They might be able to, you know, the CFO's office might be able to answer some of that, but deep down in the line of business, in the field where decisions are being made, are they really data-driven? They're just starting. I mean, this is first, second inning. Right, right, right, right. So there's much more to come. Great. Well, you have watched the cubes coverage of the IBM CDO Summit. Thanks for tuning in for Rebecca Knight and Dave Vellante. We'll see you next time.