 Live from Boston, it's theCUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. Welcome back to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Paul Gillum. We're joined by Inderpal Bandari. He is the Global Chief Data Officer at IBM. Thank you so much for coming back on theCUBE, Inderpal. It's my pleasure. Great to have you. Thank you for having me. So I want to start by talking a little bit about your own career journey. You start, your first CDO job was in the early 2000s. You were one of the first CDOs ever in the history of CUBE Data Officers. Talk a little bit about the evolution of the role and sort of set the scene for our viewers in terms of what you've seen in your own career. Yes, no, thank you. In December 2006, I became a Chief Data Officer of a major healthcare company. And it turned out at that time there were only four of us. Two in banking, one in the internet. I was the only one in healthcare. And now of course there are well over a thousand of us and the professions take it off. And I've had the fortune of actually doing this four times now. So leaving a legacy in four different organizations in terms of building that organizational capability. I think initially when I became Chief Data Officer, the culture was one of viewing data as exhaust. Something that you had to discard, it came out of the transactions that your business was doing. And then after that you would discard this data you didn't really care about it. And over the course of time, people have begun to realize that data is actually a strategic asset. And you can really use it to drive not just the data strategy, but the actual business strategy and enable the business to go to the next level. And that transition's been tremendous to watch and to see. I'm just being fortunate that I've been there for the full day. Are you seeing any consensus developing around what background makes for a good CDO? What are the skills that the CDO needs? Yeah, no, that's a very, very good question. My view has been evolving on that one too over the last few years as I've had these experiences. So I'll jump to the conclusion so that you can answer your question as opposed to what I started out with. The CDO has to be the change agent in chief for the organization. That's really the role of the CDO. So yes, there's the technical chops that you have to have. And you have to be able to deal with people who have advanced technical degrees and to get them to move forward. But you do have to change the entire organization. And you have to be adept at going off to the culture, changing it. You can't get frustrated with all the pushback that's inevitable. You have to almost develop it as an art as you move forward. And address it, not just bottom up and lateral but also top down. And I think that's probably where the art gets the most interesting because you've got to push for change even at the top. But you can push just so far without really derailing everything that you're trying to do. And so I think if I had to pick one afternoon, it would be that the CDO has to be the change agent in chief. They have to be adept at addressing the culture of the organization and moving it forward. You're laying out all of these sort of character traits that someone has to be indefatigable, inspirational, visionary. You also said during the keynote, you have six months to really make your first push. You have six months, the first six months are so important. When we talk about presidents, it's the first 100 days. Describe what do you mean by that? You have six months. So when you, and I'm talking here mainly about a large organization, like an IBM, a large enterprise. When you go in, the key observation is it's a functioning organization. It's a going concern. It's already making money, it's doing stuff like that. We have. And the people who are running that organization, they have their own needs and demands. So very quickly, you can just become somebody who ends up servicing multiple demands that come from different business units, different people. And so that's kind of one aspect of it, that the way the organization takes over. If you don't really come in with an overarching strategy. The other way the organizations take over is typically large organizations are very siloed. And even at the lower levels, you have people who develop little fiefdoms where they control that data. And they say this is mine, I'm not going to let anybody else have it. They're the only ones who really understand that turf. And so you're pretty much, unless you're able to get them to align to a much larger falls, you'll never be able to break down those siloes culturally, just because of the way it's set up. So it's a pervasive problem, goes across the board. And I think when you walk in, you've got that college honeymoon period or whatever, my estimate is based on my experience, six months. If you don't have it down in six months, in terms of that larger cause that you're going to push forward, which you can use to at least align everybody with the region, you're not going to really succeed. You'll succeed tactically, but not in a strategic sense. You're about to undertake the largest acquisition in IBM's history. And as the chief data officer, you must be thinking right now about what that's going to mean for data governance and data integration. How are you preparing for an acquisition that large? Yeah, so the acquisition has still got to work through all the regulations and so forth. So there's just so much we can do. It's much more from a planning standpoint that we can do things. I'll give you a sense of how I've been thinking about it. Now we've been doing acquisitions before. So in that sense, we do have a set process for how we go about it, in terms of evaluating the data, how we're going to manage the data and so forth. The interesting aspect that was different for me on this one is I also thought back on our data strategy itself and tried to understand now that there's going to be this big acquisition that moves forward from a planning standpoint, how should I be prepared to change with regard to that acquisition? And because we were so aligned with the overall IBM business strategy to pursue cognition, I think you could see that in my remarks that when you push forward AI in a large enterprise, you very quickly run into this multi-cloud issue where you've got not just different clouds but also on-prem and private clouds and you have to manage across all that and that becomes a pain point that you have to scale. The scale you have to get past that pain point. And so we were already thinking about that. We actually, I just did a check after the acquisition was announced, asking my team to figure out well, how standardized are we with Red Hat Linux? And I find that we're actually completely standardized across with Red Hat Linux. We pretty much will have use cases ready to go and I think that's a facet of the, because we were so aligned to the business strategy to begin with. So we were discovering that pain point just as all our customers were. And so when the cooperation acted as it did, in some extent, we're already ready to go with use cases that we can take directly to our clients and customers. I think it also has to do with the fact that we've had a partnership with Red Hat and it's been pretty speedy. Do you think people understand AI in a business context? I actually think that that's, people don't really understand that. That was the biggest, in my mind anyway, was the biggest barrier to the business strategy that we had embarked on several years ago to take AI or cognition to the enterprise. People never really understood it. And so our own data strategy became one of enabling IBM itself to become an AI enterprise and use that as a showcase for our clients and customers. And over the journey in the last two years that I've been with IBM, we've become more, we've been putting forward more and more collateral, but also technology, but also business process change ideas, organization change ideas, so that our clients and customers can see exactly how it's done. Not that it's perfect yet, but that too they benefit from, right? They don't make the same mistakes that we do. And so we've become, if your colleagues have been covering this conflict, so they will know that it's become more and more clear exactly what we're doing. You made an interesting comment. In the keynote this morning, you said nobody understands AI in a business context. What did you mean by that? So in a business context, what does it look like? What does AI look like from an AI enterprise standpoint? From a business context? So, excuse me, I'll just rubble them for tissue. Okay, all right, well, we can talk about this a little bit too, while he... Yeah, well I think we understand AI as Amazon Echo, right? We understand it as an interface medium, but I think what he was getting at is that impacting business processes is a lot more complicated. And so we tend to think of AI in terms of how we relate to technology rather than how technology changes the rules. Right, and clearly it's such a... On the consumer side, we've all grasped this and we all are excited by its possibilities, but in terms of the business context, it's the season, yes. Yeah, so, although it is the season, it's the enclosure. So, to your question, with regard to how... AI in a business context, yeah. Consumer context, everybody understands, but in a business context, what does it really mean? That's difficult for people to understand, but eventually it's all around making decisions. But in my mind, it's not the big decision. It's not the decisions that we're gonna acquire at hand. It's not those decisions. It's the thousands and thousands of little decisions that are made day in and night out by people who are working, the rank and file who are actually working the different processes. That's what you really need to go after. And if you're able to do that, it completely changes the process and you're going to get just such a lot more out of it, not just in terms of productivity, but also in terms of new ideas that need to revenue enhancement, new products, et cetera, et cetera. That's what a business AI enterprise looks like. And that's what we've been bringing forward and showcasing, right? Today's keynote, I actually had Sonia who was, who is none of our data governance people, SMEs who works on metadata generation. This is a very difficult manual problem, data about data, specifically labeling data so that a business person could understand it. It's all being done manually, but now it's done automatically using AI. And it's completely changed the process. But Sonia is the person who's at the forefront of that. And I don't think people really understand that. They think in terms of AI and business, and they think this is going to be somebody who's a data scientist, a technologist, somebody who's a very talented technical engineer, but it's not that. It's actually the rank and file people who've been working these business processes, now working with an intelligent system to take it to the next level. And that's why, as you said, it's so important that the CDO is a change agent in chief, because it is, it does require so much buy-in from, as you say, the rank and file. It's not just the top decision makers that you're trying to persuade. Yes, you are affecting change at all levels. Top-down, bottom-up, laterally. You have to go after it across the board. And then in terms of talking about the data, it's not just data for data sake. You need to talk about it in terms that a business person can understand. During the keynote, you described an earlier work that you were doing with the NBA. Can you tell our viewers a little bit about that and then sort of how the data had to tell a story? Yeah, so that was in my first go-around with IBM from 1990 through 97. I was with IBM Research at the Watson Research Lab as a research staff member. And I created this program called Advanced Scout for the National Basketball Association. Ended up being used by every team on the NBA. And it would essentially suggest who to put in the lineup and your matching lineups and so forth by looking at a lot of game data and it was particularly useful during the playoff games. The major lesson that came out of that experience for me at that time, this is before Moneyball and before all the stuff, it was like 1993, 92. I think if you Google it, you'll still see articles about this. But the main lesson that came out for me was the first time when the program identified a pattern and suggested that to a coach during a playoff game where they were down to zero, it suggested they start to back up players. And the coach was just completely flabbergasted and said, there's no way I'm going to do this. This is the kind of thing that would get me, not only get me fired, but make me look really silly. And it hit me then that there was context that was missing, that the coach could not really make a decision. And the way we solved it then was we tied it to the snippets of video when those two players were on coach and then they made the decision, they went on, they won that game and so forth. Today's AI system can actually fathom all that automatically from the video itself. And I think that's what's really advanced the technology and the approaches that we've got today to move forward as quickly as they have and that they can hold across the board in the sense of a consumer setting, but now also in the sense of a business setting where we're applying it pretty much to every business process that we have. Well, Inderpal, thank you so much for coming back on theCUBE, it was always a pleasure talking to you. It's my pleasure, thank you. I'm Rebecca Knight for Paul Gillan, we will have more from theCUBE's live coverage of IBM CDO coming up in just a little bit.