>> Announcer: Live, 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 Parc 55, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, and I'm here with Krishna Venkatraman, who is with IBM, he's the Vice President of Data Science and Data Governance. Krishna, thanks for coming on. >> Thank you, thank you for this opportunity. >> Oh, 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. That's a good passion to have these days. So what's your role at IBM? >> So I work for Inderpal, who's 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 enterprise scale. And in my career, 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 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 when I was at On Deck, we built all of it from scratch, and I thought 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, a company doesn't have to 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 was kind of a blank sheet of paper, right? And so now you have a complex organization, as Inderpal was describing this morning, so big challenge. Ginni Rometty at IBM Think talked about incumbent disruptors, so that's essentially what IBM is, right? >> 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 lifecycle 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 lifecycle worked out, and you know how to deploy it into a business process, because you understand the business process really well. And I think, you know, 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. >> And everybody's trying to get, I often say, get digital right, you know, your customers, your clients, everyone 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, you know, we can talk about the CDO and all the great things that are happening and 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, alright? Because if you have trust without the data value, then okay. And you do see a lot of organizations just 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, and at the same time, you're trying to protect your organization. >> Yeah. I think it's a virtuous 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. >> Let's talk 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 does 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, you know? 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 seem them as, you know, they're twins, separated at some point, but... >> What's interesting, too, is you think about, we were sort of talking about this off-camera, but now, you're one step away from going to a user or 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 own, 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. I wonder if we can talk about disruption, nobody talks 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, music, taxis, hotels have been disrupted. 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? >> I think sometimes disruption happens and then you look back and you say, that was disrupted after all, and 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, where's there's blockchain or quantum computing or 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. >> Orange Bank was an example that came up this morning, an all-digital bank, you can't call them, right? You can't walk into their branch. You think banks will lose control of the payment systems? They've always done a pretty good job of hanging onto them, but... >> 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 you've given 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... Which banks are going to win, is the way I would look at it. >> Maybe the existing banks might get trouble, but there's so many different interesting disruption scenarios, I mean, you think about Watson in healthcare, maybe we're at the point already where machines can make better diagnoses than doctors. You think about retail, and 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, 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 Inderpal 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, with its strategy, with its 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, you know, let's slow 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, you know, 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 are 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 will implement it, how they will 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. >> 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 are 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, right? So that's the third piece. And the last piece is, we're building out this platform that Inderpal showed this morning, that platform needs product management, so we are also working on, what are the fundamental pieces of functionality we want in the platform, and how do we make sure they're on the roadmap and they're prioritized in the right way. >> Excellent. Well, Krishna, thanks very much for coming to theCUBE, it was a pleasure meeting you. >> Thanks. >> Alright, keep it right there everybody, 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. (funky electronic music) (phone dialing)