 Live from Boston, Massachusetts. It's theCUBE, covering IBM Chief Data Officer Strategy Summit, brought to you by IBM. And now here are your hosts, Dave Vellante and Stu Miniman. Welcome back to Boston, everybody. This is the IBM Chief Data Officer Summit. And this is theCUBE, the worldwide leader in live tech coverage. Caitlin Lepic is here. She's an executive within the Chief Data Officer Office at IBM. She's joined by Dave Schubamel, who's a research director at IDC. And he covers cognitive systems and content analytics. Folks, welcome to theCUBE. Good to see you. Thank you. So Caitlin, we'll start with you. You kicked off the morning. I referenced the Forbes article, our CDO's Miracle Workers. It's great. I hadn't read that article until you put it up there. I scanned it very quickly. But set up the event. It started yesterday afternoon at noon. You're going through this afternoon. What's it all about? This has evolved since what, 2014? It has. We started our first CDO Summit back in 2014. And at that time, we estimated there were maybe 200 or so CDO's worldwide, give or take. And we had 30 people at our first event. And we joked that we had one small corner of the conference room. And we were really quite excited to start the event in 2014. And we've really grown. So this year, we have about 170 folks joining us, 70 of which are CDO's or acting as CDO's in their organization. And so we've really been able to grow the community over the last two years and are really excited to see how we can continue to do that moving forward. And IBM's always had a big presence at a conference that we've covered, the MIT CDO event. So it's nice that you can leverage that community and continue to cultivate it. Dave, I want to ask you. It used to be when we were talking when we first met this morning, it used to be data was such a wonky topic. Data was data value. People would try to put value on data. But it was just a really boring but important topic. Now it's front and center with cognitive, with analytics. What are you seeing in the marketplace? Yeah, I think what we're seeing in the market is this emphasis on predictive applications, predictive analytics, cognitive applications, artificial intelligence, and deep learning. All of those types of applications are derived and really run by data. So unless you have really good authoritative data to actually make these models work, the systems aren't going to be effective. So we're seeing an emerging marketplace in both people looking at how they can leverage their first party data, which IBM is really talking about what Bob Picciano talked about this morning. But also we're seeing the emergency of a second party and third party data market to help build these models out even further. So that, I think that's what we're really seeing is the combination of the third party data along with the first party data, really being the instrument for building these kind of predictive models that are going to take us hopefully far into the future. So okay, so Caitlin, square the circle for us. So the CDO role generally is not perceived as a technology role, yet, as Dave was just saying, we're talking about machine learning, cognitive, AI, these are like heavy technical topics. So how does the miracle worker deal with all this stuff generally and how does IBM deal with it inside the CDO office specifically? Sure, so it's a very good point. Traditionally CDOs really have a business background and we find that the most successful CDOs sit in the business organization. So they report somewhere in a line of business. And there are certainly some that have a technical background, but far more come from a business background and sit in the business. I can tell you how we are setting up our CDO office at IBM. So our new and our first global Chief Data Officer joined in December of last year, Inder Palbandari. And I started working for him shortly thereafter. And the way he's setting up his office is really three pillars. So first and foremost, we focused on the data engineering data science. So getting that team in place. Next, it's information governance and policy. How are we gonna govern, access, manage, work with data? Both data that we own within our organization as well as the long list of external data sources that we bring in. And then third is the business integration pillar. So the idea is CDOs are gonna be most successful when they deliver those data science, data engineering, they manage and govern the data, but they pull it through the business. So ensuring that we're really grounded in business unit and doing this. And so those are our three primary pillars at this point. So prior to formalizing the CDO role at IBM, I mean remnants of these roles existed. There was a data quality function. There was certainly a governance and policy and somebody was responsible to integrate from the IT to the applications to the business. Were those part of IT? Were they sort of by committee? And how did you bring all those pieces together? That couldn't have been trivial. And I would say it's still an ongoing process, but absolutely. I would say they typically resided within particular business units. And so certainly have mature functions within the unit, but when we're looking for enterprise wide answers to questions about certain customers, certain business opportunities, that's where I think the role of the CDO really comes in. And what we're doing now is we are partnering very closely with business units. One example is our IBM analytics unit. So we're here with Bob Ticciano and other business units to ensure that as they provide us their data, we're able to create that single trusted source of data across the organization, across the enterprise. And so I agree with you. I think a lot of those capabilities and functions quite mature. They existed within units. And now it's about pulling that up to the enterprise level. And then our next step, the next vision is starting to make that cognitive and starting to add some of those capabilities, in particular data science engineering, the deep learning and starting to move toward cognitive. Dave, I think Caitlin brought up something really interesting. We've been digging into the last couple of years is there's that governance piece, but a lot of CDOs are put into that role with a mandate for innovation. And that's something that a lot of times IT has been accused of not being all that innovative. Is that what you're seeing? What are some of the kind of the, is it project-based or best initiatives that are driving forward with CDOs? I think what we're seeing is that enterprises are beginning to recognize that it's not just enough to be a manufacturer. It's not just enough to be a retail organization. You need to be one of the best, one of the top two or the top three. And the only way to get to that top two or top three is to have that innovation that you're talking about. And that innovation relies on having accurate data for decision-making. It also relies on having accurate data for operations. So we're seeing a lot of organizations that are really looking at how data and predictive models and innovation all become part of the operational fabric of a company. And if you think about the companies that are just beating it together, Amazon, for example. I mean, Amazon is a completely data-driven company. When you get your recommendations for what to buy or that's all coming from the data. When they set up these logistics centers where they're shipping the latest supply, they're doing that because they know where their customers are. They have all this data. So they're integrating data into their day-to-day decision-making. And I think that's what we're seeing throughout industry, is this idea of integrating decision, data into the decision-making process and elevating it. And I think that's why the CDO role has become so much more important over the last two to three years. We heard this morning that 88% of data is dark data. Bob Pacino talked about that. So thinking about the CDO's scope role agenda. You've got data sources. You've got to identify those. You've got to deal with data quality. And then, Dave, with some of the things you've been talking about, you've got predictive models that out of the box may not be the best predictive models in the world. You've got to iterate them. So how does an organization, because not every organization's like Amazon with virtually unlimited resources at capital, how does an organization balance? What are you seeing in terms of getting new data sources, refining those data sources, putting my emphasis on the data versus refining and calibrating the predictive models? How are organizations balancing that? Maybe we start with how IBM's doing it and Dave, what you're seeing in the field. Sure. So I would say from what we're doing from a setting up the Chief Data Office role, we've taken a step back to say what's the company's monetization strategy? Not how you're monetizing data, but how are you, what's your strategy moving forward for monetization? And so with IBM we've talked about it as move to enabling cognition throughout the enterprise. And so we've really talked about taking all of your standard business processes, whether they be procurement, HR, finance, and infusing those with cognitive and figuring out how to make those smarter. We talk examples with contracts, for example. Every organization has a lot of contracts and right now it's quite a manual process to go through and try and discern the sorts of information you need to make better decisions and optimize the contract process. And so the idea is you start with that strategy, for us, IBM, it's cognitive, and that then dictates what sort of data sources you need because that's the problem you're trying to solve and the opportunity you're chasing down. And so then we talk about, okay, we've got some of that data currently residing today internally, typically in silos, typically in business units, you know, some different databases, and then what our longer term vision is is we wanna build the intelligence that pulls in that internal data and then really does pull in the external data that we've all talked about, you know, the social data, the sentiment analysis, the weather, you know, all of that sort of external data to help us, ultimately in our value proposition, our mission is data-driven enablement of cognition. So helps us achieve our strategy there. Anything you'd add to that, Dave? Yeah, I mean, I think, I mean, you can take a number of examples. I mean, there's a small insurance company in Florida, for example, and what they've done is they have organized their emergency situation, their emergency processing to be able to deal with tweets and to be able to deal with, you know, SMS messages and things like that. They're using sentiment analysis, they're using text analytics to identify where problems are occurring, when a hurricane happens. So what they're doing is they're organizing that kind of data and they're a relatively small insurance company and a lot of this is being done through the cloud, but they're basically getting that kind of sentiment analysis, being able to interpret that and add that to their decision-making process about where should I land a person? Where should I land an insurance adjuster, an agent, you know, based on the tweets that are coming in, rather than just the phone calls that are coming into the organization? You know, so that's a simple example and you were talking about, not everybody has the resources on Amazon, but you know, certainly small insurance companies, small manufacturers, small retail organizations, you can get started by, you know, analyzing your, you know, what people are saying about you. You know, what are people saying about me on Twitter? What are people saying about me on Facebook? You know, how can I use that to improve my customer service? You know, we're seeing a whole range of solutions coming out and IBM actually has a broad range of solutions for things like that, but you know, they're not the only ones out there. There's a ton of folks doing that kind of thing, you know, in terms of the dark data analysis and really providing that, you know, as part of the solution to help people make better decisions. So the answers to the question is really both. You're doing both new sources of data and trying to improve the analytics and the models, but it's a balancing act. And you go, coming back to the ROI question, it sounds like IBM strategy is to supercharge your existing businesses by infusing them with new data and new insights. Is that correct? Absolutely, I would say that is correct. Okay, whereas in many cases, the ROI of analytics projects to date have been a reduction on investment. You know, I'm going to move stuff from my traditional EDW to Hadoop, because it's cheaper. And it feels like Dave, we're entering a new wave now. Maybe you could talk about that a little bit. Yeah, I mean, I think there's a, that's been the traditional way of measuring ROI. And I think what people are trying to do now is look at how, you mentioned disruption, for example. And I think disruption is a huge opportunity. How can I increase my sales? How can I increase my revenue? How can I find new customers through these mechanisms? And I think that's what we're starting to see in the organization. And we're starting to even see startups that are dedicated to providing this level of disruption and helping address new markets by using these kinds of technologies in new and interesting ways. I mean, everybody uses the Airbnb example. Everybody uses the Uber example. That these are people that don't own cars, they don't own hotel rooms, but they provide analytics to disrupt the hotel industry and to disrupt the taxi industry. It's not just limited to those two industries. It's virtually everything. And I think that's what we're starting to see is this kind of virtual disruption based on the dark data that people can actually begin to analyze. Within IBM, the chief data officer reports to whom? So the way we've set up in our organization is our CDO reports to our senior vice president of transformation and operations who then reports to our CEO. Our recommendation and as we talk with clients, I mean, we see this as a CEO level reporting relationship. And oftentimes we advocate for that as we're talking with customers and clients. It fits nicely in our organization within transformation and operations because this line is really responsible for transforming IBM. And so they're really charged with a number of initiatives throughout the organization to have better skills alignment with some of the new opportunities, to really improve processes, to bring new folks on board. So it made sense to fit within an organization the mandate is really transformation of the company of the organization. And the CDO is a peer of the CIO, is that right? CIO reports to the same individual? Yes, yes, that's right, that's right. And then in our organization, the role is split in that we have a chief data officer as well as a chief analytics officer. But we often see one person serving both of those roles as well. So that can kind of depend on the organizational structure of the company. So you gotta run the business, sort of grow the business which I guess is the P and L manager's role and then transform the business which is where the CDO comes in. Right, right, right, exactly, exactly. All right, Caitlin, I'll give you the last word, sort of put a bumper sticker on this event. Where do you wanna see it go in the future? Yes, so last word, we tried a couple new things this year. We had our deep dive breakout sessions yesterday and the feedback I've been hearing from folks is the opportunity to talk about certain topics they really care about. Is it governance or is it innovation? Being able to talk, how do you get started in the first 90 days? What do you do first? We have sort of the five steps that we talk through around getting your data strategy and your plan together and how you execute against that. And I have to tell you those topics continue to be of interest to our participants every year. So we're gonna continue to have those. And I just, I love to see the community grow. I saw the first chief data officer University announced earlier this year. I did notice a lot of PR and media around role CDOs, miracle workers as you mentioned, doing a lot of great work. So we're really supportive, we're big supporters of the role. We'll continue to host in-person events, do virtual events, continue to support CDOs to be successful. And our big plug is we'll be World of Watson is our big IBM analytics event in October, last week of October in Vegas. So we certainly invite folks to join us there. And you'll be there, right? Okay, good. Still trying to get Ginny on. So Ginny, if you're watching, welcome to come on theCUBE. So can we do a second interview and then we'll see if we can get Ginny. And I saw Hilary Mason's going to be there as well. Cube alum, so that'd be fantastic to see her. So, well, excellent. Congratulations on being ahead of the curve with the chief data officer theme. And I really appreciate you coming to theCUBE. Dave, thank you too. Thank you. All right, keep it right there, everybody. Stu and I will be back with our next guest. We're live from the Chief Data Officer Summit, IBM's event in Boston, right back. My name is Dave Vellante and I'm a long-time industry analyst.