 from Fisherman's Wharf in San Francisco. It's theCUBE covering IBM Chief Data Officer Strategy Summit, Spring 2017, brought to you by IBM. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit, Spring 2017. It's a mouthful, it's a great event, and it's one of many CDO summits that IBM's putting in around the country and soon around the world. So check it out, we're happy to be here and really talk to some of the thought leaders about getting into the nitty-gritty detail of strategy and execution. So we're excited to be joined by our next guest, Rebecca Shockley. She's an analytics global research leader for the IBM Institute for Business Value. Welcome, Rebecca. I didn't know about the IBM Institute for Business Value. Thank you. Absolutely, and Priya V. She said Priya V's good, so you can see the whole name on the bottom, but Priya V is CTO of Cognitive IoT Watson Health at IBM. Welcome, Priya. Thank you. So first off, just impressions of the conference. It's been going on all day today. You've got 170 or some odd CDOs here, sharing best practices, listening into the sessions. Any surprising takeaways coming out of any of the sessions you've been at so far? On a daily basis, I live and breathe data. That's what I help our customers to get better at it, and today it's a day where we get to talk about how can we adopt something which is emerging in that space. We talk about data governance, what we need to look out in that space, and Cognitive has been the fabric that we are integrating into this data governance, actually. It's a great day, and I'm happy to talk to over, like you said, 170 CDOs across, representing different verticals. Excellent. And Rebecca, you do a lot of core research that feeds a lot of the statistics that we've seen on the keynote slides and stuff. And one of the interesting things we talked about off here was really, you guys are coming up with a playbook, which is really to help CDOs basically execute and be successful CDOs. Can you tell us about the playbook? Well, the playbook was born out of a Gartner statistic that came out, I guess, two or three years ago that said by 2016, you'll have 90% of organizations will have a CDO and 50% of them will fail. And we didn't think that was very optimistic. 90% will have them and 50% will fail. Yes, and so I can tell you based on our survey of 6,000 global executives last fall, the numbers at 41% in 2016, and I'm hoping that the playbook kept them from being a failure. So what we did with the playbook is basically laid out the six questions, key questions that an organization needs to think about as they're either putting in a CDO office or revamping their CDO offices because Gartner wasn't completely unfounded and thinking a lot of CDO offices weren't doing well when they made that prediction because it is very difficult to put in place mostly because of culture change, right? It's a very different kind of way to think. But we're certainly not seeing the turnover we were in the early years of CDOs or hopefully the failure rate that Gartner predicted. So what are the top two or three of those six that they need to be thinking about? So they need to think about their objectives and one of the things that we found was that when we look at CDOs, there's three different categories that you can really put them in. A data integrator, so is the CDO primarily focused on getting the data together, getting the quality of the data, really bringing the organization up to speed? The next thing that most organizations look at is being a business optimizer. So can they use that data to optimize their internal processes or their external relationships? And then the third category is market innovator. Can they use that data to really innovate, bring in new business models, new data monetization strategies, things like that? The biggest problem we found is that CDOs that we surveyed and we surveyed 800 CDOs, we're seeing that they're being assessed on all three of those things and it's hard to do all three at once, largely because if you're still having to focus on getting your data in a place where you can start doing real science against it, you're probably not going to be full-time market innovator either. You can't be full-time in two different places. That's not to say as a data integrator, you can't bring in data scientists, do some skunk works on some of the early work, find and we've seen organizations really like Bank Etau down in Brazil, really in that early stages, still come up with some very innovative things to do, but that's more of a one-off, right? If you're being judged on all three of those, that I think is where the failure rate comes in. But it sounds like those are kind of sequential, but you can't operate them sequentially because in theory you never finish the first phase, right? You're always doing, you're always keeping up with the data, but for some organizations they really need to, they're still operating with very dirty, very siloed data that you really can't bring together for analytics. Now, once you're able to look at that data, you can be doing the other two, optimizing and innovating at the same time, but your primary focus has to be on getting the data straight. Once you've got a functioning data ecosystem, then the level of attention that you have to put there is going to go down and you can start working on focusing on innovation and optimization more as your full-time role, but no, data integrator never goes away completely. And cleanser. Yeah. Then that's a great strategy then, as you said, then the rubber's got to hit the road. The rubber's got to hit the road. That's where you play on the execution point. Like I said, you like to get your hands dirty with the CDOs. So what are you seeing from your kind of point of view in terms of actually executing, finding early wins, easy paths to success, you know, how to get those early wins basically, right? To validate what you're doing. That's right. Like you said, it's become a universal fact that data governance and things, everything around consolidating data and the value of insights we get offered, that's been established fact. Now CDOs and rest of the organization, the CIOs and the CTOs have this mandate to start executing on them. And how do we go about it? That's part of my job at IBM as well as a CTO. I work with our customers to identify where are the German and business value? Where are those things which are completely data-driven? Be it as a cognitive forecasting or your business requirement could be how can I maximize a 40% of my service channel? Which in the end of the day, it could be a cognitive-enabled data-driven virtual assistant which is automating and bringing a TCO of huge incredible value. Those are some of the key execution elements we are trying to bring. But like we said, yes, we have to bring in the data, we have to hire the right talent and we have to have a strategy. All those great things happen. But I always start with a problem, a problem which actually anchors everything together. A problem is a business problem which demonstrate key business value. So we actually know what we're trying to solve and work backwards in terms of what is the data relevant to it? What are the technologies and toolkits that we can put on top of it? And who are the right people that we can involve? In parallel with the strategy that we have already established. So that's the way we've been going about. We have seen phenomenal successes, huge results which has been transformative in nature and not just this 170 CDOs. I mean, we want to make sure every one of our customers is able to take advantage of that. Right. But it's not just the CDO, it's the entire business. So the IBM Institute on Business Value looks at an enormous amount of research. Or does an enormous amount of research look at a lot of different issues? So for example, your CEO report is phenomenal. I think you do one for the CMO number of different users. How are other functions or other roles within business starting to acculturate to this notion of data as a driver of new behaviors? And then we can talk about and what are some of those new behaviors in the degree to which the leadership is ready to drive them? I think the executive suite is really starting to embrace data much more than it has in the past. Primarily because of the digitization of everything, right? Before the amount of data that you had was somewhat limited. Often it was internal data and the quality was suspect. As we've started digitizing all of the business processes and being able to bring in an enormous amount of external data, I think organizationally executives are getting much more comfortable with the ability to use that data to further their goals within the organization. So in general, the chief groups are starting to look at data as a way of doing things differently? Absolutely. How is that translating into then doing things differently? Yeah, so I was just at the session where we talked about how organizations and business units are even coming together because of data governance and the data itself, because they are having federated units where a certain part of business is enabled and having new insights because we are actually doing these things. And new businesses, like monetizing data is something which is happening new. Data is a service. Actually having data as a platform where people can build new applications. I mean, the whole new segment of people as data engineers, full-stack developers and data scientists actually, I mean, they are incubated and they end up building lots of new applications which has never been part of a typical business unit. So these are the cultural and the business changes we are starting to see in many organizations actually. Some of them are leading the way because they just did it without knowing it's actually, that's the way it should be doing it. But that's how it influences many organizations. I think you were looking for kind of an example as well. So in the keynote this morning, one of the gentlemen was talking about working with their CFO, their risk and compliance office, and we're able to take the ability to identify a threat within their ecosystem from two days down to three milliseconds. So that's what can happen once you really start being able to utilize the data that's available to an organization much more effectively is that kind of quantum leap change and being able to understand what's happening in the marketplace, being able to understand what's happening with consumers or customers or clients, whichever flavor you have. And we see that throughout the organization. So it's not just the CFO but the CMO and being able to do much more targeted, much more focused and on the consumer side or the client customer side, that's better for me, right? And the marketing teams are seeing 30, 40% increase in their ability to execute campaigns because they're more data driven now. So has the bit flipped where the business units are now coming to the CDO's office and pounding on the door saying, I need my team as opposed to being, try to coerce that you no longer use intuition? So it depends upon where the company is because what we call that is the snowball effect, right? It's one of the reasons you have to have the governance in place and get things going in parallel because what we see is that most organizations go in skeptically, they're used to running on their gut instinct, that's how they got their jobs, mostly, right? They had good instincts, they made good decisions, they got promoted. And so making that transition to being a data driven organization can be very difficult. What we find though is that once one section, one segment, one flavor, one good campaign happens, as soon as those results start to mount up in the organization, you start to see a snowball effect. And what I was hearing particularly last year when I was talking to CDO's was that it had taken them so long to get started, but now they had so much demand coming from the business that they want to look at this and they want to look at that and they want to look at the other thing because once you have results, everybody else in the organization wants those same kind of results. I don't- Just to add up to that is data is not anymore viewed as a commodity. If you've seen valuable organizations who know what their asset is, it's not just a commodity. So the parity of- Or even a liability is what it used to be, right? Yeah, exactly. It's expensive to hold it and store it and keep track of it. Exactly. So the parity of this is very different right now. So people are talking about how can I take advantage of the intelligence? So business units, they don't come and pound the door. Rather, they're trying to see what data that I can have or what intelligence that I can have to make my business differentiate or I can value add something more. That's a type of argument. So I feel based on the experiences that we work with our customers, it's bringing organizations together. And for certain times, yes, sometimes the smartness and the best practices come in place that how we can avoid some of the common mistakes that we do in terms of replicating it 100 times or not knowing who else is using. So some of the tools and techniques helps us to master those things. But pretty much it is bringing organizations and leveraging the intelligence that what you find might be useful to her and what she finds might be useful or what we all don't know that we go figure it out where we can get it. So what's the next step in the journey to increase the democratization of the utilization of that data? Because obviously, chief data officers, there aren't that many of them, their teams are relatively small. Well, 41% of businesses. 41%. So there's a large number from out there. Yeah, but these are running huge companies with a whole bunch of business units that have tremendous opportunity to optimize around things that they haven't done yet. So how do we continue to kind of move this democratization of both the access and the tools and the utilization of the insights that they're all sitting on? Yeah, I have some bolder expectations on this because data and the way in which data becomes an asset, not anymore a liability actually folds up many of the layers of applications that we have. I used to come from an enterprise background in the past. We had layers of application programming which just used data as a one single layer. In terms of opportunities for this, there is a lot more dissolving silos and dissolving layers of IT in a typical organization. When we build data-driven applications, this is all going to change. It's fascinating, this role is in the front center of everything actually around data-driven. And I mean, you also heard enough about cognitive computing these days because it is the key ingredient for cognitive computing. We talked about four E's of cognitive computing. It has to start first learning and data is the first step in terms of learning and then it goes into process re-engineering and then you reinvent things and you disrupt things and you bring new experiences or humanize your solution. So it's on a great trajectory. It's going to change the way we do things. It's going to give new and expected things both from a consumer point and from an enterprise point as well. It'll bring effects like consumerization of enterprises and whatnot. So I have bolder and broader expectations out of this fascinating data world. I think one of the things that made people hesitant before was an unfamiliarity with thinking about using data, say a CSR on the front line using data instead of the scripts he or she had been given or their own experience. And I think what we're seeing now is, A, everybody's personal life is much more digital than it was before. Therefore, everybody's somewhat more comfortable with interacting, right? And B, once you start to see those results and they realize that they can move from having to crunch numbers and do all the background work, once we can automate that through robotic process automation or cognitive process automation and let them focus on the more interesting, higher value parts of their job, we've seen that greatly impact the culture change. It's, the culture change question comes whether people are thinking they're going to lose their job because of the data or whether it's going to let them do more interesting things with their jobs. And I think, hopefully we're getting past that, I don't, you know, it's me or it, stage into the, how can I use data to augment the work that I'm doing and get more personal satisfaction, if not, you know, business satisfaction out of the work that I'm doing. Hopefully getting rid of some of the mundane. I think there's also going to be a lot of software that's created, that's going to be created in different ways and have different impacts. Absolutely. The reality is, we're creating data incredibly fast. We know that it has enormous value. People are not going to change that rapidly. The new types of algorithms are coming on, but many of the algorithms have been for years. So in many respects, it's how we render all of that in some of the new software that's not driven by process but driven by data. And the beauty of it is this software will be invisible. It will be self-feeling, regeneratable software. Invisible to some, but very, very highly visible to others. And I think that's one of the big challenges that IT organizations face, and businesses face, is how do they think through that new software? So you talked about today, or historically, you talked about your application stack, where you have stacks, which one has some little view of the data. And in many respects, we need to free that data up, remove it out of the application so we can do new things with it. So how is that going to, how is that process going to either be facilitated or impeded by the fact that in so many organizations, data is regarded as a commodity, something that's disposable. Do we need to become more explicit in articulating or talking about what it means to think of data as an asset, as something that's valuable? What do you think? Yeah, so in the typical application world, when we start, if you really look at it, data comes at the very end of it, because people start designing what is going to be the mock-ups, where are we going to integrate with what sources? Am I talking to the bank as an API and et cetera? So the data representation comes at the very end. In the current generation of applications, the cognitive applications that we are building, it is, first we start with the data, we understand what are we working on, and we start applying, taking advantage of machines and all these algorithms, which existed, like you said, many, many decades ago, and we take advantage of machines to automate them, to get the intelligence, and then we write applications. So you see the order has changed, actually. It's a complete reversal. Yes, we had three-pickle, three-tier, four-tier architecture, but the order of how we perceive and understand the problem is different. But we are very outcome-driven. We are trying to maximize 40% of your sales. We are trying to create a digital, connected dashboards for your CFO, where the entire board can make decisions on the fly. So we know the business outcome, but we are starting with the data. So the fundamental change in how software is built, and all these modules of software which you're talking about, why I mentioned Invisible, is somewhat generatable. The AI and cognitive is advanced in such a way that somewhat generatable, if it understands the data and domain, it can generate what it should do with the data. That's what we are teaching, that's what ontology and all this is about. So that's why I said it's limitless, it's pretty bold, and it's going to change the way we have done things in the past. And like she said, it's only going to compliment humans because we are always better decision makers, but we need so much of cognitive capability to aid and supplement our decision making. So that's going to be the way that we run our businesses. All right, Priya's paying a pretty picture, I like it. Right, some people see only the dark side, that's clearly a bright side, that's a terrific story. So thank you. So Priya and Rebecca, thanks for taking a few minutes, hope you enjoy the rest of the show, surrounded by all this big brain power, and I appreciate you stopping by. Thanks so much. All right, Jeffery with Peter Burris, you're watching theCUBE from the IBM Cheap Date Officer Summit Spring 2017, we'll be right back after this short break, thanks for watching. They crushed it. My name is Dave Vellante, and I'm a long time industry analyst. I was at IDC for a number of years and ran the company's largest and most profitable business. I focused on a lot of areas, infrastructure, software, organizations, the CIO community. Cut my teeth there.