 Live from downtown San Francisco, it's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. Welcome to San Francisco everybody. My name is Dave Vellante and you're watching theCUBE, the leader in live tech coverage. And we're at the IBM CDO Strategy Summit, hashtag IBM CDO. The Chief Data Officer role emerged about a decade ago and it was typically focused in regulated industries, healthcare, financial services, and government. And it sort of emerged from sort of a dark back office role of governance and compliance and data quality. But increasingly as the big data wave came to the market, people realized there was an opportunity to take that sort of wonky back office governance compliance discipline and really point it toward generating value, whether that was with direct monetization of data or contributing to an organization's data strategy. And over the next five to seven years, that Chief Data Officer role, a couple of things happened. One is it got much, much deeper into those regulated industries, but also permeated other non-regulated industries, you know, beyond those three that I mentioned. IBM is an organization that has targeted the Chief Data Officer role as a key constituency as part of the cognitive, what IBM calls the cognitive enterprise. And IBM hosts shows in Boston and San Francisco each year gathering Chief Data Officers, about 100 to 150 Chief Data Officers in each city. These are very focused and targeted events that comprise Chief Data Officers, data analytics officers, and the like. People focused sometimes on compliance and governance. And they're, I say, very intimate events. And today we heard from a number of IBM experts, Inderpal Bandari, who's been on theCUBE a number of times, who is IBM's global Chief Data Officer, laying out sort of a blueprint, an enterprise blueprint for data strategy. So the audience is filled with practitioners who are really sort of lapping up sort of how to implement some of these techniques and ultimately platforms. IBM has put together solutions that not only involve, of course, Watson, but also some of the other components, whether it's cognitive systems, governance systems, compliance systems, to create a solution that Chief Data Officers and their colleagues can implement. So this morning we heard about the cognitive enterprise, Blueprint, what IBM calls the AI Enterprise or the Cognitive Enterprise. Talking about organizational issues, how do you break down silos of data? If you think about most incumbent organizations, the data lives in silos. It's maybe data in the marketing department, data in the sales department, data in the customer service department, data in the maintenance department. So these are sort of separate silos of data. How do you break those down? How do you bring those together so you can compete with some of these born digital AI oriented companies? You know, the likes of just the perfect examples, Facebook, Google, LinkedIn, et cetera, who have these sort of centralized data models. How do you take an existing organization, break down those silos and deal with a data model that is accessible by everyone who needs to access that data? And as well, very importantly, make it secure, make it enterprise ready. The other thing that IBM talked about was process. We always talk about in theCUBE, people process and technology. Technology is the easiest piece of that. It's the people and process components of that matrix that you need to really focus on before you even bring in the technology. And then of course there is the technology component. IBM is a technology company. We've heard about Watson. IBM has a number of hardware and software components that it brings to bear to try to help organizations affect their data strategy and be more effective in the marketplace. So as I say, this is about 130, 150 chief data officers we heard from Caitlin Lafferty who's going to come on a little later. She's going to be my quasi co-host which should be interesting. Beth Smith, who is the GM of Watson data. She talked a lot about use cases. She gave an example of Orange Bank, a totally digital bank using Watson to service customers that you can't call this bank. And they've got some interesting measurements that they'll share with us in terms of customer satisfaction and a born digital or all digital bank. She also talked about partnerships that they're doing. Not directly, sort of indirectly I inferred. She talked about IT service management embedding Watson into the IT service management from an HR perspective. I believe that she was referring to, even though she didn't mention it a deal that IBM struck with service now. IBM's got similar deals with Watson, with Salesforce. Salesforce Einstein is based on Watson. So what you're seeing is embedding AI into different applications. And we've talked about this a lot on SiliconANGLE and theCUBE and Wikibon. It's really those embedded use cases for AI that are going to drive adoption as opposed to generalized horizontal AI. That seems to be not the recipe for adoption success. Really more so specific use cases. I mean the obvious ones are some consumer ones and even in the enterprise as well. Security, facial recognition, natural language processing for example, very specific use cases for AI. We also heard from Indapal Bandari the global chief data officer of IBM talking about the AI enterprise. Really showcasing IBM as a company that is bringing this AI enterprise to itself and then teaching, sharing that knowledge with its clients and with its customers. I really like talking to Indapal Bandari and learn a lot from him. This is his fourth CDO gig. He was the very first CDO ever in healthcare. I mean I think he was first of four or one of four first CDOs in healthcare. Now there are thousands. So this is his fourth gig as a CDO. He talks about what a CDO has to do to get started. Starting with a clear data strategy. When I've talked to him before he said, he mentioned how does data contribute to the monetization of your organization? Now it's not always monetization if it's a non-public company or a healthcare company for example that's not for profit. It's not necessarily a monetization component. It's more of a how does it affect your strategy? But that's number one is sort of how does data drive value for your organization? The second is how do you implement the system that's based on governance and security? What's the management system look like? Who has access to that data? How do you affect privacy? And then how do you become a central source for that AI framework being a service organization essentially to the entire organization? And then developing deep analytics partnerships with lines of business. That's critical because the domain expertise for the business is obviously going to live in the line of business not in some centralized data organization and then finally very importantly skills. What skills do you need? Identify those skills and then how do you get those people? How do you both train internally and find those people externally? Very hard to find those skills. He talked about AI systems having four attributes. Number one is expertise, domain knowledge. AI systems have to be smart about the problem that they're trying to solve. Natural human interaction. IBM talks about natural language processing. A lot of companies do. Everybody's familiar with the likes of Alexa and Google Home and Siri. Well IBM Watson also has an NLP capability that's quite powerful. So that's very important and interestingly he talked about it and I'll ask him about this. The black box phenomenon. Most AI is a black box. If you think about it, AI can tell you if you're looking at a dog but think about your own human brain. How do you know when you're actually seeing a dog? Try to explain to somebody someday how you go about recognizing that animal. It's sort of hard to do and systems today can tell you that if it's a dog or for you Silicon Valley watchers, hot dog. But it's a black box. What IBM is saying is no, we can't live with a black box in the enterprise. We have to open up that black box, make it a white box and share with our customers exactly how that decision is being made. That's an interesting problem that I want to talk to him about. And then next, the third piece is learning through education. How do you learn at scale? And then the fourth piece was how do you evolve? How do you iterate? How do you become autodidactic or self learning with regard to the system and getting better and better and better over time? And that sets a foundation for this AI enterprise or cognitive enterprise blueprints where the subject matter expert can actually interact with the system. We had some questions from the audience. One came up on cloud and security concerns, not surprising, data exposure. How do you automate a lot of this stuff and provide access at the same time at ensuring privacy and security? So IBM's going to be addressing that today. So we're here all day, wall-to-wall coverage of the IBM CDO strategy summit, hashtag IBM CDO. Of course, we're running multiple live programs today. I'm covering this show in San Francisco. John Furrier is in Copenhagen at KubeCon with the Linux Foundation. Stu Beniman is holding down the fort with a very large crew at Dell Technologies World. So keep it right there, buddy. This is theCUBE at IBM's CDO strategy summit in San Francisco. We'll be right back after this short break.