 from downtown San Francisco. It's theCUBE, covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. We're back at the IBM CDO Strategy Summit in San Francisco. We're at the Park 55, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, and I'm here with Kristina Venkatraman, who is with IBM. He's the Vice President of Data Science and Data Governance. Kristina, thanks for coming on. Thank you. Thank you for this opportunity. You're very welcome. So let's start with your role. Your passion is really creating value from data. That's something you told me off camera. It's kind of, that's a good passion to have these days. So what's your role at IBM? So I work for Indapal, who's a GCDO. He's the CDO for the company. And I joined IBM about a year ago. And what I was intrigued by when I talked to him early on was, you know, IBM has so many assets. It's got a huge history and legacy of technology, enormous copious amounts of data. But most importantly, it also has a lot of experience helping customers solve problems at an enterprise scale. And in my career, you know, I started at HP Labs many, many years ago. I've been in a few startups. Most recently, before I joined IBM, I was at an on deck. What I've always found is that it's very hard to extract information and insights from data unless you have the end to end pieces in place. And, you know, when I was at on deck, we built all of it from scratch. And I thought, you know, this would be a great opportunity to come to IBM, leverage all that great history and legacy and skill to build something that would allow data to almost be taken for granted. So in a sense, you know, a company doesn't have to, you know, think about the pain of getting value extracted from data. They could just say, you know, I trust data just as I trust the other things in life. Like when I go buy a book, I know all the backend stuff is done for me. I can trust the product I get. And I was interested in that. And that's the role that Inderpal offered to me. So the opposite of on deck, really, on deck is kind of a blank sheet of paper, right? And so now you have complex organization as Inderpal was describing this morning. So big challenge, Ginni Rometti at IBM Think talked about incumbent disruptors. So that's essentially what IBM is, right? Yes, exactly, exactly. The fact is IBM has a history and a culture of making their customers successful. So they understand business problems really well. They have a huge legacy in innovation around technology. And I think now is the right time to put all of those pieces together, right? To string together a sort of life cycle for how data can work for you. So when you embark on a data project, it doesn't have to take six months. It could be done in two or three days because you've cobbled together how to manage data at the backend. You've got the data science and the data science life cycle worked out. And you know how to deploy it into a business process because you understand the business process really well. And I think those are the mismatches that I've seen happen over and over again. Data isn't ready for the application of machine learning. The machine learning model really isn't well-suited to the eventual environment in which it's deployed. But I think IBM has all of that expertise. And I feel like it's an opportunity for us to tie that together. Well, and everybody's trying to get, often say, get digital right. All your customers, your clients, everybody talks about digital transformation. But it's really all about the data, isn't it? Getting the data right. Getting the data right, that's where it starts. Tomorrow I'm doing a panel on trust. We can talk about the CDO and all the great things that are happening in the extracting value. But unless you have trust at the beginning and you're doing good data governance and you're able to understand your data, all of the rest will never happen. But you have to have both. Right, because if you have trust without the data value then okay. And you do see a lot of organizations is focusing maybe over-rotating on that privacy and trust and security. For good reason, how do you balance that information as an asset versus liability equation? Because you're trying to get value out of it at the same time you're trying to protect your organization. Yeah, I think it's a virtue cycle. I think they build on each other. If customers trust you with their data, they're going to give you more of it because they know you're going to use it responsibly. And I think that's a very positive thing. So I actually look at privacy and trust as enablers to create value rather than somehow they're in competition. Not a zero-sum game. Not at all, it's a- Let's talk about some more about that. I mean, when you think about it, because I've heard this before, GDPR comes up. Hey, we can turn GDPR into an opportunity. It's not just this onerous, even though it is, regulatory imposition. So maybe some examples or maybe talk through how organizations can take the privacy and trust part of the equation and turn it into value. So very simply, what is GDPR promise, right? It's restoring the fundamental rights of data subjects in terms of their ownership of their data and the processing of their data and the ability to know how that data is used at any point in time. Now imagine if you're a data scientist and you could, for a problem that you're trying to solve, have the same kind of guarantees. You know all about the data. You know where it resides. You know exactly what it contains. They're very similar. They both are asking for the same type of information. So in a sense, if you solve the GDPR problem well, you have to really understand your data assets very well. And you have to have it governed really well, which is exactly the same need for data scientists. So in a way, I see them as their twins, separated at some point, but... It was interesting too, when you think about, and we were sort of talking about this off camera, but now you're one step away from going to a user or a customer and saying here, here's your data, do what you like with it. Now okay, in the one case GDPR, you control it sort of, but the other is if you want to monetize your own data, why pay the search company for clicking on an ad? Why not monetize your own data based on your reputation? Or do you see a day where consumers will actually be able to truly own their own data? I think as a consumer as well as a data professional, I think that the technologies are falling into place for that model to possibly become real. So if you have something that's very valuable that other people want, there should be a way for you to get some remuneration for that, right? And maybe it's something like a blockchain, you contribute your data, and then when that data is used, you get some little piece of it as your reward for that. I don't know, I think it's possible, I haven't really... Nirvana. Yeah. I wonder if we could talk about disruption. Yeah. Maybe we could talk about that. We haven't had a ton of conversations here about disruption, it seems to be more applying disciplines to create data value, but coming from the financial services industry, there's an industry that really hasn't been highly disrupted. You know, on deck in a way was trying to disrupt, healthcare is another one that hasn't been disrupted, aerospace really hasn't been disrupted. Other industries like publishing and music, taxis, you know, hotels have been disrupted. And the premise is it's the data that enables that disruption. Thoughts on disruption from the standpoint of your clients and how you're helping them become incumbent disruptors. Yeah, I think sometimes disruption happens and then you look back and you say that was disrupted after all, and then you don't notice it when it happens. So even if I look at financial services and I look at small business lending, the expectations of businesses have changed on how they would access capital in that case. Even though the early providers of that service may not be the ones who win in the end, that's a different matter. So I think the idea that, you know, and I feel like this confluence of technologies whether it's blockchain or quantum computing or, you know, the even regulation that's coming in that's sort of forcing certain types of activities around cleaning up data, they're all happening simultaneously. I think we will see certain industries and certain processes transform dramatically. So I, yeah. Orange Bank was an example that came up this morning and all digital bank, you can't call them. Right? You can't walk into their branch and you think banks will lose control of the payment systems? They've always done a pretty good job of hanging onto them, but, you know. I don't know. I think, ultimately, customers are going to go to institutions they trust. So it's all going to end up with, do you trust the entity of giving this your precious commodities to, right? Your data, your information. I think companies that really take that seriously and not take it as a burden are the ones who are going to find that customers are going to reach out to them. So it's more about not necessarily whether banks are going to lose control or whether it's, which banks are going to win is the way I would look at it. All right, well, maybe the existing banks might get troubled. But there's so many different interesting disruption scenarios. I mean, you think about Watson and healthcare. I mean, well, maybe we're at the point already where machines can make better diagnoses than doctors. You think about retail. I mean, certain retail won't go away. Obviously, grocery and maybe high-end luxury malls won't go away. But you wonder about the future of retail as a result of this data disruption. Your thoughts? On retail, I do feel like because the data is getting more... People are going to have more access to their own information. It will lead to a change in business models in certain cases. And the friction or the forces that used to keep customers with certain businesses may dissolve. And so if you don't have friction, then it's going to end up with value and loyalty and service. And those are the ones I think that will thrive. Client comes to you, says, Krishna, I'm really struggling with my overall data strategy, my data platform, governance, skills, all the things that Interpol talked about this morning. Where do I start? I would start with making sure that the client has really thought about the questions they need answered. What is it that you really want to answer with data or it doesn't even have to be with data? For the business, whether it's strategy, whether it's tactics, there have to be a set of questions framed up that are truly important to that business. And then starting from there, you can say, let's flow it down and see what technologies, what types of data will help support answering those questions. So there has to be an overarching value proposition that you're trying to solve for. And I see, that's why when the way we work in our organization is we look at use cases as a way to drive the technology adoption. What are the big business processes you're trying to transform? What's the value you expect to create? So we have a very robust discovery process where we ask people to answer those types of questions, we help them with it. We ask them to think through what they would do if they had the perfect answer, how they would implement it, how they would measure it. And then we start working on the technology. I often think technology is an easier question to answer once you know what you want to ask. Yeah, totally. Is that how you spend your time, mostly working with the lines of business, trying to help them sort of answer those questions? That is one part of my charter. So my charter involves basically four areas. The first is data governance, just making sure that we're creating all the tools and processes so that we can guarantee that when data is used, it is trusted, it is certified, and that it's always going to be reliable. The second piece is building up a real data competency and data science competency in the organization. So we know how to use data for different types of business value. And then the third is actually taking these client engagements internally and making sure that they are successful. So our model is what we call co-creation. We ask business teams to contribute their own resources, data engineers, data scientists, business experts. We contribute specialized skills as well. And so we're jointly in the game together. So that's the third piece. And the last piece is we're building out this platform that Indipal showed this morning. That platform needs product management. So we are also working on what are the fundamental pieces of functionality we want on the platform and how do we make sure they're on the roadmap and they're prioritized in the right way. Excellent. Well, Kirsten, thanks very much for coming on theCUBE. It's a pleasure meeting you. Thanks. All right, keep it right there, buddy. We'll be back with our next guest. You're watching theCUBE live from IBM CDO Summit in San Francisco. We'll be right back.