>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officers Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. I'm Dave Vellante and you're watching theCUBE, the leader in live tech coverage. This is the end of the day panel at the IBM Chief Data Officer Summit. This is the 10th CDO event that IBM has held and we love to to gather these panels. This is a data all-star panel and I've recruited Seth Dobrin who is the CDO of the analytics group at IBM. Seth, thank you for agreeing to chip in and be my co-host in this segment. >> Yeah, thanks Dave. Like I said before we started, I don't know if this is a promotion or a demotion. (Dave laughing) >> We'll let you know after the segment. So, the data all-star panel and the data all-star awards that you guys are giving out a little later in the event here, what's that all about? >> Yeah so this is our 10th CDU Summit. So two a year, so we've been doing this for 5 years. The data all-stars are those people that have been to four at least of the ten. And so these are five of the 16 people that got the award. And so thank you all for participating and I attended these like I said earlier, before I joined IBM they were immensely valuable to me and I was glad to see 16 other people that think it's valuable too. >> That is awesome. Thank you guys for coming on. So, here's the format. I'm going to introduce each of you individually and then ask you to talk about your role in your organization. What role you play, how you're using data, however you want to frame that. And the first question I want to ask is, what's a good day in the life of a data person? Or if you want to answer what's a bad day, that's fine too, you choose. So let's start with Lucia Mendoza-Ronquillo. Welcome, she's the Senior Vice President and the Head of BI and Data Governance at Wells Fargo. You told us that you work within the line of business group, right? So introduce your role and what's a good day for a data person? >> Okay, so my role basically is again business intelligence so I support what's called cards and retail services within Wells Fargo. And I also am responsible for data governance within the business. We roll up into what's called a data governance enterprise. So we comply with all the enterprise policies and my role is to make sure our line of business complies with data governance policies for enterprise. >> Okay, good day? What's a good day for you? >> A good day for me is really when I don't get a call that the regulators are knocking on our doors. (group laughs) Asking for additional reports or have questions on the data and so that would be a good day. >> Yeah, especially in your business. Okay, great. Parag Shrivastava is the Director of Data Architecture at McKesson, welcome. Thanks so much for coming on. So we got a healthcare, couple of healthcare examples here. But, Parag, introduce yourself, your role, and then what's a good day or if you want to choose a bad day, be fun the mix that up. >> Yeah, sounds good. Yeah, so mainly I'm responsible for the leader strategy and architecture at McKesson. What that means is McKesson has a lot of data around the pharmaceutical supply chain, around one-third of the world's pharmaceutical supply chain, clinical data, also around pharmacy automation data, and we want to leverage it for the better engagement of the patients and better engagement of our customers. And my team, which includes the data product owners, and data architects, we are all responsible for looking at the data holistically and creating the data foundation layer. So I lead the team across North America. So that's my current role. And going back to the question around what's a good day, I think I would say the good day, I'll start at the good day. Is really looking at when the data improves the business. And the first thing that comes to my mind is sort of like an example, of McKesson did an acquisition of an eight billion dollar pharmaceutical company in Europe and we were creating the synergy solution which was based around the analytics and data. And actually IBM was one of the partners in implementing that solution. When the solution got really implemented, I mean that was a big deal for me to see that all the effort that we did in plumbing the data, making sure doing some analytics, is really helping improve the business. I think that is really a good day I would say. I mean I wouldn't say a bad day is such, there are challenges, constant challenges, but I think one of the top priorities that we are having right now is to deal with the demand. As we look at the demand around the data, the role of data has got multiple facets to it now. For example, some of the very foundational, evidentiary, and compliance type of needs as you just talked about and then also profitability and the cost avoidance and those kind of aspects. So how to balance between that demand is the other aspect. >> All right good. And we'll get into a lot of that. So Carl Gold is the Chief Data Scientist at Zuora. Carl, tell us a little bit about Zuora. People might not be as familiar with how you guys do software for billing et cetera. Tell us about your role and what's a good day for a data scientist? >> Okay, sure, I'll start by a little bit about Zuora. Zuora is a subscription management platform. So any company who wants to offer a product or service as subscription and you don't want to build your billing and subscription management, revenue recognition, from scratch, you can use a product like ours. I say it lets anyone build a telco with a complicated plan, with tiers and stuff like that. I don't know if that's a good thing or not. You guys'll have to make up your own mind. My role is an interesting one. It's split, so I said I'm a chief data scientist and we work about 50% on product features based on data science. Things like churn prediction, or predictive payment retries are product areas where we offer AI-based solutions. And then but because Zuora is a subscription platform, we have an amazing set of data on the actual performance of companies using our product. So a really interesting part of my role has been leading what we call the subscription economy index and subscription economy benchmarks which are reports around best practices for subscription companies. And it's all based off this amazing dataset created from an anonymized data of our customers. So that's a really exciting part of my role. And for me, maybe this speaks to our level of data governance, I might be able to get some tips from some of my co-panelists, but for me a good day is when all the data for me and everyone on my team is where we left it the night before. And no schema changes, no data, you know records that you were depending on finding removed >> Pipeline failures. >> Yeah pipeline failures. And on a bad day is a schema change, some crucial data just went missing and someone on my team is like, "The code's broken." >> And everybody's stressed >> Yeah, so those are bad days. But, data governance issues maybe. >> Great, okay thank you. Jung Park is the COO of Latitude Food Allergy Care. Jung welcome. >> Yeah hi, thanks for having me and the rest of us here. So, I guess my role I like to put it as I'm really the support team. I'm part of the support team really for the medical practice so, Latitude Food Allergy Care is a specialty practice that treats patients with food allergies. So, I don't know if any of you guys have food allergies or maybe have friends, kids, who have food allergies, but, food allergies unfortunately have become a lot more prevalent. And what we've been able to do is take research and data really from clinical trials and other research institutions and really use that from the clinical trial setting, back to the clinical care model so that we can now treat patients who have food allergies by using a process called oral immunotherapy. It's fascinating and this is really personal to me because my son as food allergies and he's been to the ER four times. >> Wow. >> And one of the scariest events was when he went to an ER out of the country and as a parent, you know you prepare your child right? With the food, he takes the food. He was 13 years old and you had the chaperones, everyone all set up, but you get this call because accidentally he ate some peanut, right. And so I saw this unfold and it scared me so much that this is something I believe we just have to get people treated. So this process allows people to really eat a little bit of the food at a time and then you eat the food at the clinic and then you go home and eat it. Then you come back two weeks later and then you eat a little bit more until your body desensitizes. >> So you build up that immunity >> Exactly. >> and then you watch the data obviously. >> Yeah. So what's a good day for me? When our patients are done for the day and they have a smile on their face because they were able to progress to that next level. >> Now do you have a chief data officer or are you the de facto CFO? >> I'm the de facto. So, my career has been pretty varied. So I've been essentially chief data officer, CIO, at companies small and big. And what's unique about I guess in this role is that I'm able to really think about the data holistically through every component of the practice. So I like to think of it as a patient journey and I'm sure you guys all think of it similarly when you talk about your customers, but from a patient's perspective, before they even come in, you have to make sure the data behind the science of whatever you're treating is proper, right? Once that's there, then you have to have the acquisition part. How do you actually work with the community to make sure people are aware of really the services that you're providing? And when they're with you, how do you engage them? How do you make sure that they are compliant with the process? So in healthcare especially, oftentimes patients don't actually succeed all the way through because they don't continue all the way through. So it's that compliance. And then finally, it's really long-term care. And when you get the long-term care, you know that the patient that you've treated is able to really continue on six months, a year from now, and be able to eat the food. >> Great, thank you for that description. Awesome mission. Rolland Ho is the Vice President of Data and Analytics at Clover Health. Tell us a little bit about Clover Health and then your role. >> Yeah, sure. So Clover is a startup Medicare Advantage plan. So we provide Medicare, private Medicare to seniors. And what we do is we're because of the way we run our health plan, we're able to really lower a lot of the copay costs and protect seniors against out of pocket. If you're on regular Medicare, you get cancer, you have some horrible accident, your out of pocket is infinite potentially. Whereas with Medicare Advantage Plan it's limited to like five, $6,000 and you're always protected. One of the things I'm excited about being at Clover is our ability to really look at how can we bring the value of data analytics to healthcare? Something I've been in this industry for close to 20 years at this point and there's a lot of waste in healthcare. And there's also a lot of very poor application of preventive measures to the right populations. So one of the things that I'm excited about is that with today's models, if you're able to better identify with precision, the right patients to intervene with, then you fundamentally transform the economics of what can be done. Like if you had to pa $1,000 to intervene, but you were only 20% of the chance right, that's very expensive for each success. But, now if your model is 60, 70% right, then now it opens up a whole new world of what you can do. And that's what excites me. In terms of my best day? I'll give you two different angles. One as an MBA, one of my best days was, client calls me up, says, "Hey Rolland, you know, "your analytics brought us over $100 million "in new revenue last year." and I was like, cha-ching! Excellent! >> Which is my half? >> Yeah right. And then on the data geek side the best day was really, run a model, you train a model, you get ridiculous AUC score, so area under the curve, and then you expect that to just disintegrate as you go into validation testing and actual live production. But the 98 AUC score held up through production. And it's like holy cow, the model actually works! And literally we could cut out half of the workload because of how good that model was. >> Great, excellent, thank you. Seth, anything you'd add to the good day, bad day, as a CDO? >> So for me, well as a CDO or as CDO at IBM? 'Cause at IBM I spend most of my time traveling. So a good day is a day I'm home. >> Yeah, when you're not in an (group laughing) aluminum tube. >> Yeah. Hurdling through space (laughs). No, but a good day is when a GDPR compliance just happened, a good day for me was May 20th of last year when IBM was done and we were, or as done as we needed to be for GDPR so that was a good day for me last year. This year is really a good day is when we start implementing some new models to help IBM become a more effective company and increase our bottom line or increase our margins. >> Great, all right so I got a lot of questions as you know and so I want to give you a chance to jump in. >> All right. >> But, I can get it started or have you got something? >> I'll go ahead and get started. So this is a the 10th CDO Summit. So five years. I know personally I've had three jobs at two different companies. So over the course of the last five years, how many jobs, how many companies? Lucia? >> One job with one company. >> Oh my gosh you're boring. (group laughing) >> No, but actually, because I support basically the head of the business, we go into various areas. So, we're not just from an analytics perspective and business intelligence perspective and of course data governance, right? It's been a real journey. I mean there's a lot of work to be done. A lot of work has been accomplished and constantly improving the business, which is the first goal, right? Increasing market share through insights and business intelligence, tracking product performance to really helping us respond to regulators (laughs). So it's a variety of areas I've had to be involved in. >> So one company, 50 jobs. >> Exactly. So right now I wear different hats depending on the day. So that's really what's happening. >> So it's a good question, have you guys been jumping around? Sure, I mean I think of same company, one company, but two jobs. And I think those two jobs have two different layers. When I started at McKesson I was a solution leader or solution director for business intelligence and I think that's how I started. And over the five years I've seen the complete shift towards machine learning and my new role is actually focused around machine learning and AI. That's why we created this layer, so our own data product owners who understand the data science side of things and the ongoing and business architecture. So, same company but has seen a very different shift of data over the last five years. >> Anybody else? >> Sure, I'll say two companies. I'm going on four years at Zuora. I was at a different company for a year before that, although it was kind of the same job, first at the first company, and then at Zuora I was really focused on subscriber analytics and churn for my first couple a years. And then actually I kind of got a new job at Zuora by becoming the subscription economy expert. I become like an economist, even though I don't honestly have a background. My PhD's in biology, but now I'm a subscription economy guru. And a book author, I'm writing a book about my experiences in the area. >> Awesome. That's great. >> All right, I'll give a bit of a riddle. Four, how do you have four jobs, five companies? >> In five years. >> In five years. (group laughing) >> Through a series of acquisition, acquisition, acquisition, acquisition. Exactly, so yeah, I have to really, really count on that one (laughs). >> I've been with three companies over the past five years and I would say I've had seven jobs. But what's interesting is I think it kind of mirrors and kind of mimics what's been going on in the data world. So I started my career in data analytics and business intelligence. But then along with that I had the fortune to work with the IT team. So the IT came under me. And then after that, the opportunity came about in which I was presented to work with compliance. So I became a compliance officer. So in healthcare, it's very interesting because these things are tied together. When you look about the data, and then the IT, and then the regulations as it relates to healthcare, you have to have the proper compliance, both internal compliance, as well as external regulatory compliance. And then from there I became CIO and then ultimately the chief operating officer. But what's interesting is as I go through this it's all still the same common themes. It's how do you use the data? And if anything it just gets to a level in which you become closer with the business and that is the most important part. If you stand alone as a data scientist, or a data analyst, or the data officer, and you don't incorporate the business, you alienate the folks. There's a math I like to do. It's different from your basic math, right? I believe one plus one is equal to three because when you get the data and the business together, you create that synergy and then that's where the value is created. >> Yeah, I mean if you think about it, data's the only commodity that increases value when you use it correctly. >> Yeah. >> Yeah so then that kind of leads to a question that I had. There's this mantra, the more data the better. Or is it more of an Einstein derivative? Collect as much data as possible but not too much. What are your thoughts? Is more data better? >> I'll take it. So, I would say the curve has shifted over the years. Before it used to be data was the bottleneck. But now especially over the last five to 10 years, I feel like data is no longer oftentimes the bottleneck as much as the use case. The definition of what exactly we're going to apply to, how we're going to apply it to. Oftentimes once you have that clear, you can go get the data. And then in the case where there is not data, like in Mechanical Turk, you can all set up experiments, gather data, the cost of that is now so cheap to experiment that I think the bottleneck's really around the business understanding the use case. >> Mm-hmm. >> Mm-hmm. >> And I think the wave that we are seeing, I'm seeing this as there are, in some cases, more data is good, in some cases more data is not good. And I think I'll start it where it is not good. I think where quality is more required is the area where more data is not good. For example like regulation and compliance. So for example in McKesson's case, we have to report on opioid compliance for different states. How much opioid drugs we are giving to states and making sure we have very, very tight reporting and compliance regulations. There, highest quality of data is important. In our data organization, we have very, very dedicated focus around maintaining that quality. So, quality is most important, quantity is not if you will, in that case. Having the right data. Now on the other side of things, where we are doing some kind of exploratory analysis. Like what could be a right category management for our stores? Or where the product pricing could be the right ones. Product has around 140 attributes. We would like to look at all of them and see what patterns are we finding in our models. So there you could say more data is good. >> Well you could definitely see a lot of cases. But certainly in financial services and a lot of healthcare, particularly in pharmaceutical where you don't want work in process hanging around. >> Yeah. >> Some lawyer could find a smoking gun and say, "Ooh see." And then if that data doesn't get deleted. So, let's see, I would imagine it's a challenge in your business, I've heard people say, "Oh keep all the, now we can keep all the data, "it's so inexpensive to store." But that's not necessarily such a good thing is it? >> Well, we're required to store data. >> For N number of years, right? >> Yeah, N number of years. But, sometimes they go beyond those number of years when there's a legal requirements to comply or to answer questions. So we do keep more than, >> Like a legal hold for example. >> Yeah. So we keep more than seven years for example and seven years is the regulatory requirement. But in the case of more data, I'm a data junkie, so I like more data (laughs). Whenever I'm asked, "Is the data available?" I always say, "Give me time I'll find it for you." so that's really how we operate because again, we're the go-to team, we need to be able to respond to regulators to the business and make sure we understand the data. So that's the other key. I mean more data, but make sure you understand what that means. >> But has that perspective changed? Maybe go back 10 years, maybe 15 years ago, when you didn't have the tooling to be able to say, "Give me more data." "I'll get you the answer." Maybe, "Give me more data." "I'll get you the answer in three years." Whereas today, you're able to, >> I'm going to go get it off the backup tapes (laughs). >> (laughs) Yeah, right, exactly. (group laughing) >> That's fortunately for us, Wells Fargo has implemented data warehouse for so many number of years, I think more than 10 years. So we do have that capability. There's certainly a lot of platforms you have to navigate through, but if you are able to navigate, you can get to the data >> Yeah. >> within the required timeline. So I have, astonished you have the technology, team behind you. Jung, you want to add something? >> Yeah, so that's an interesting question. So, clearly in healthcare, there is a lot of data and as I've kind of come closer to the business, I also realize that there's a fine line between collecting the data and actually asking our folks, our clinicians, to generate the data. Because if you are focused only on generating data, the electronic medical records systems for example. There's burnout, you don't want the clinicians to be working to make sure you capture every element because if you do so, yes on the back end you have all kinds of great data, but on the other side, on the business side, it may not be necessarily a productive thing. And so we have to make a fine line judgment as to the data that's generated and who's generating that data and then ultimately how you end up using it. >> And I think there's a bit of a paradox here too, right? The geneticist in me says, "Don't ever throw anything away." >> Right. >> Right? I want to keep everything. But, the most interesting insights often come from small data which are a subset of that larger, keep everything inclination that we as data geeks have. I think also, as we're moving in to kind of the next phase of AI when you can start doing really, really doing things like transfer learning. That small data becomes even more valuable because you can take a model trained on one thing or a different domain and move it over to yours to have a starting point where you don't need as much data to get the insight. So, I think in my perspective, the answer is yes. >> Yeah (laughs). >> Okay, go. >> I'll go with that just to run with that question. I think it's a little bit of both 'cause people touched on different definitions of more data. In general, more observations can never hurt you. But, more features, or more types of things associated with those observations actually can if you bring in irrelevant stuff. So going back to Rolland's answer, the first thing that's good is like a good mental model. My PhD is actually in physical science, so I think about physical science, where you actually have a theory of how the thing works and you collect data around that theory. I think the approach of just, oh let's put in 2,000 features and see what sticks, you know you're leaving yourself open to all kinds of problems. >> That's why data science is not democratized, >> Yeah (laughing). >> because (laughing). >> Right, but first Carl, in your world, you don't have to guess anymore right, 'cause you have real data. >> Well yeah, of course, we have real data, but the collection, I mean for example, I've worked on a lot of customer churn problems. It's very easy to predict customer churn if you capture data that pertains to the value customers are receiving. If you don't capture that data, then you'll never predict churn by counting how many times they login or more crude measures of engagement. >> Right. >> All right guys, we got to go. The keynotes are spilling out. Seth thank you so much. >> That's it? >> Folks, thank you. I know, I'd love to carry on, right? >> Yeah. >> It goes fast. >> Great. >> Yeah. >> Guys, great, great content. >> Yeah, thanks. And congratulations on participating and being data all-stars. >> We'd love to do this again sometime. All right and thank you for watching everybody, it's a wrap from IBM CDOs, Dave Vellante from theCUBE. We'll see you next time. (light music)

>> Live from Boston, it's theCUBE! Covering IBM Chief Data Officers Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Inderpal Bhandari, he is the Global Chief Data Officer at IBM. Thank you so much for coming back on theCUBE, Inderpal. >> It's my pleasure. >> It's great to have you. >> Thank you for having me. >> So I want to talk, I want to start by talking a little bit about your own career journey. Your first CDO job was in the early 2000s. You were one of the first CDOs, ever. In the history of Chief Data Officers. Talk a little bit about the evolution of the role and sort of set the scene for our viewers in terms of what you've seen, in your own career. >> Yes, no thank you, December 2006, I became a Chief Data Officer of a major healthcare company. And you know, it turned out at that time there were only four of us. Two in banking, one in the internet, I was the only one in healthcare. And now of course there are well over 1,999 of us and the professions taken off. And I've had the fortune of actually doing this four times now. So leading a legacy in four different organizations in terms of building that organizational capability. I think initially, when I became Chief Data Officer, the culture was one of viewing data's exhaust. Something that we had to discard, that came out of the transactions that you were, that your business was doing. And then after that you would discard this data, or you didn't really care about it. And over the course of time, people had begun to realize that data is actually a strategic asset and you can really use it to drive not just the data strategy, but the actual business strategy, and enable the business to go to the next level. And that transitions been tremendous to watch and to see. I've just been fortunate that I've been there for the full journey. >> Are you seeing any consensus developing around what background makes for a good CDO? What are the skills that a CDO needs? >> Yeah, no that's a very, very good question. My view has been evolving on that one too, over the last few years, right, as I've had these experiences. So, I'll jump to the conclusion, so that you kind of, to answer your question as opposed to what I started out with. The CDO, has to be the change agent in chief, for the organization. That's really the role of the CDO. So yes, there's the technical sharps that you have to have and you have to be able to deal with people who have advanced technical degrees and to get them to move forward. But you do have to change the entire organization and you have to be adept at going after the culture, changing it. You can't get frustrated with all the push back, that's inevitable. You have to almost develop it as an art, as you move forward. And address it, not just bottom up and lateral, but also top down. And I think that's probably where the art gets the most interesting. Because you've got to push a for change even at the top. But you can push just so far without really derailing everything that you are trying to do. And so, I think if I have to pick one attribute, it would be that the CDO has to be the change agent in chief and they have to be adept at addressing the culture of the organization, and moving it forward. >> You're laying out all of these sort of character traits that someone has to be indefatigable, inspirational, visionary. You also said during the keynote you have six months to really make your first push, the first six months are so important. When we talk about presidents, it's the first 100 days. Describe what you mean by that, you have six months? >> So if a new, and I'm talking here mainly about a large organization like an IBM, a large enterprise. When you go in, the key observation is it's a functioning organization. It's a growing concern. It's already making money, it's doing stuff like that. >> We hope. >> And the people who are running that organization, they have their own needs and demands. So very quickly, you can just become somebody who ends up servicing multiple demands that come from different business units, different people. And so that's kind of one aspect of it. The way the organization takes over if you don't really come in with an overarching strategy. The other way the organizations take over is typically large organizations are very siloed. And even at the lower levels you who have people who developed little fiefdoms, where they control that data, and they say this is mine, I'm not going to let anybody else have it. They're the only one's who really understand that curve. And so, pretty much unless you're able to get them to align to a much larger cause, you'll never be able to break down those silos, culturally. Just because of the way it's set up. So its a pervasive problem, goes across the board and I think, when you walk in you've got that, you call it honeymoon period, or whatever. My estimate is based on my experience, six months. If you don't have it down in six months, in terms of that larger cause that your going to push forward, that you can use to at least align everybody with the vision, or you're not going to really succeed. You'll succeed tactically, but not in a strategic sense. >> You're about to undertake the largest acquisition in IBM's history. And as the Chief Data Officer, you must be thinking right now about what that's going to mean for data governance and data integration. How are you preparing for an acquisition that large? >> Yeah so, the acquisition is still got to work through all the regulations, and so forth. So there's just so much we can do. It's much more from a planning stand point that we can do things. I'll give you a sense of how I've been thinking about it. Now we've been doing acquisitions before. So in that since we do have a set process for how we go about it, in terms of evaluating the data, how we're going to manage the data and so forth. The interesting aspect that was different for me on this one is I also talked back on our data strategy itself. And tried to understand now that there's going to be this big acquisition of move forward, from a planning standpoint how should I be prepared to change? With regard to that acquisition. And because we were so aligned with the overall IBM business strategy, to pursue cognition. I think you could see that in my remarks that when you push forward AI in a large enterprise, you very quickly run into this multi-cloud issue. Where you've got, not just different clouds but also unprime and private clouds, and you have to manage across all that and that becomes the pin point that you have to scale. To scale you have to get past that pin point. And so we were already thinking about that. Actually, I just did a check after the acquisition was announced, asking my team to figure out well how standardized are we with Red Hat Linux? And I find that we're actually completely standardized across with Red Hat Linux. We pretty much will have use cases ready to go, and I think that's the facet of the goal, because we were so aligned with the business strategy to begin with. So we were discovering that pinpoint, just as all our customers were. And so when the cooperation acted as it did, in some extent we're already ready to go with used cases that we can take directly to our clients and customers. I think it also has to do with the fact that we've had a partnership with Red Hat for some time, we've been pretty strategic. >> Do you think people understand AI in a business context? >> I actually think that that's, people don't really understand that. That's was the biggest, in my mind anyway, was the biggest barrier to the business strategy that we had embarked on several years ago. To take AI or cognition to the enterprise. People never really understood it. And so our own data strategy became one of enabling IBM itself to become an AI enterprise. And use that as a showcase for our clients and customers, and over the journey in the last two, three years that I've been with IBM. We've become more, we've been putting forward more and more collateral, but also technology, but also business process change ideas, organizational change ideas. So that our clients and customers can see exactly how it's done. Not that i'ts perfect yet, but that too they benefit from, right? They don't make the same mistakes that we do. And so we've become, your colleagues have been covering this conference so they will know that it's become more and more clear, exactly what we're doing. >> You made an interesting comment, in the keynote this morning you said nobody understands AI in a business context. What did you mean by that? >> So in a business context, what does it look like? What does AI look like from an AI enterprise standpoint? From a business context. So excuse me I just trouble them for a tissue, I don't know why. >> Okay, alright, well we can talk about this a little bit too while he-- >> Yeah, well I think we understand AI as an Amazon Echo. We understand it as interface medium but I think what he was getting at is that impacting business processes is a lot more complicated. >> Right. >> And so we tend to think of AI in terms of how we relate to technology rather than how technology changes the rules. >> Right and clearly its such, on the consumers side, we've all grasped this and we all are excited by its possibilities but in terms of the business context. >> I'm back! >> It's the season, yes. >> Yeah, it is the season, don't want to get in closer. So to your question with regard to how-- >> AI in a business context. >> AI in a business context. Consumer context everybody understands, but in a business context what does it really mean? That's difficult for people to understand. But eventually it's all around making decisions. But in my mind its not the big decisions, it's not the decisions we going to acquire Red Hat. It's not those decisions. It's the thousands and thousands of little decisions that are made day in and night out by people who are working the rank and file who are actually working the different processes. That's what we really need to go after. And if you're able to do that, it completely changes the process and you're going to get just such a lot more out of it, not just terms of productivity but also in terms of new ideas that lead to revenue enhancement, new products, et cetera, et cetera. That's what a business AI enterprise looks like. And that's what we've been bringing forward and show casing. In today's keynote I actually had Sonya, who is one of our data governance people, SMEs, who works on metadata generation. Really a very difficult manual problem. Data about data, specifically labeling data so that a business person could understand it. Its all been done manually but now it's done automatically using AI and its completely changed the process. But Sonya is the person who's at the forefront of that and I don't think people really understand that. They think in terms of AI and business and they think this is going to be somebody who's a data scientist, a technologist, somebody who's a very talented technical engineer, but it's not that. It's actually the rank and file people, who've been working these business processes, now working with an intelligent system, to take it to the next level. >> And that's why as you've said it's so important that the CDO is a change agent in chief. Because it is, it does require so much buy-in from, as you say, the rank and file, its not just the top decision makers that you're trying to persuade. >> Yes, you are affecting change at all levels. Top down, bottom up, laterally. >> Exactly. >> You have to go after it across the board. >> And in terms of talking about the data, it's not just data for data's sake. You need to talk about it in terms that a business person can understand. During the keynote, you described an earlier work that you were doing with the NBA. Can you tell our viewers a little bit about that? And sort of how the data had to tell a story? >> Yes, so that was in my first go 'round with IBM, from 1990 through '97. I was with IBM Research, at the Watson Research Lab, as a research staff member. And I created this program called Advanced Scout for the National Basketball Association. Ended up being used by every team on the NBA. And it would essentially suggest who to put in the line up, when you're matching lines up and so forth. By looking at a lot of game data and it was particularly useful during the Playoff games. The major lesson that came out of that experience for me, at that time, alright, this was before Moneyball, and before all this stuff. I think it was like '90, '93, '92. I think if you Google it you will still see articles about this. But the main lesson that came out for me was the first time when the program identified a pattern and suggested that to a coach during a playoff game where they were down two, zero, it suggested they start two backup players. And the coach was just completely flabbergasted, and said there's no way I'm going to do this. This is the kind of thing that would not only get me fired, but make me look really silly. And it hit me then that there was context that was missing, that the coach could not really make a decision. And the way we solved it then was we tied it to the snippets of video when those two players were on call. And then they made the decision that went on and won that game, and so forth. Today's AI systems can actually fathom all that automatically from the video itself. And I think that's what's really advanced the technology and the approaches that we've got today to move forward as quickly as they have. And they've taken hold across the board, right? In the sense of a consumer setting but now also in the sense of a business setting. Where we're applying it pretty much to every business process that we have. >> Exciting. Well Inderpal, thank you so much for coming back on theCUBE, it was always a pleasure talking to you. >> It's my pleasure, thank you. >> I'm Rebecca Knight for Paul Gillin, we will have more from theCUBE's live coverage of IBM CDO coming up in just a little bit. (upbeat music)

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and it's ecosystem partners. >> Welcome back to theCUBE's continuing coverage of our own event, Big Data SV. I'm Lisa Martin, with my cohost Dave Vellante. We're in downtown San Jose at this really cool place, Forager Eatery. Come by, check us out. We're here tomorrow as well. We're joined by, next, one of our CUBE alumni, Seth Dobrin, the Vice President and Chief Data Officer at IBM Analytics. Hey, Seth, welcome back to theCUBE. >> Hey, thanks for having again. Always fun being with you guys. >> Good to see you, Seth. >> Good to see you. >> Yeah, so last time you were chatting with Dave and company was about in the fall at the Chief Data Officers Summit. What's kind of new with you in IBM Analytics since then? >> Yeah, so the Chief Data Officers Summit, I was talking with one of the data governance people from TD Bank and we spent a lot of time talking about governance. Still doing a lot with governance, especially with GDPR coming up. But really started to ramp up my team to focus on data science, machine learning. How do you do data science in the enterprise? How is it different from doing a Kaggle competition, or someone getting their PhD or Masters in Data Science? >> Just quickly, who is your team composed of in IBM Analytics? >> So IBM Analytics represents, think of it as our software umbrella, so it's everything that's not pure cloud or Watson or services. So it's all of our software franchise. >> But in terms of roles and responsibilities, data scientists, analysts. What's the mixture of-- >> Yeah. So on my team I have a small group of people that do governance, and so they're really managing our GDPR readiness inside of IBM in our business unit. And then the rest of my team is really focused on this data science space. And so this is set up from the perspective of we have machine-learning engineers, we have predictive-analytics engineers, we have data engineers, and we have data journalists. And that's really focus on helping IBM and other companies do data science in the enterprise. >> So what's the dynamic amongst those roles that you just mentioned? Is it really a team sport? I mean, initially it was the data science on a pedestal. Have you been able to attack that problem? >> So I know a total of two people that can do that all themselves. So I think it absolutely is a team sport. And it really takes a data engineer or someone with deep expertise in there, that also understands machine-learning, to really build out the data assets, engineer the features appropriately, provide access to the model, and ultimately to what you're going to deploy, right? Because the way you do it as a research project or an activity is different than using it in real life, right? And so you need to make sure the data pipes are there. And when I look for people, I actually look for a differentiation between machine-learning engineers and optimization. I don't even post for data scientists because then you get a lot of data scientists, right? People who aren't really data scientists, and so if you're specific and ask for machine-learning engineers or decision optimization, OR-type people, you really get a whole different crowd in. But the interplay is really important because most machine-learning use cases you want to be able to give information about what you should do next. What's the next best action? And to do that, you need decision optimization. >> So in the early days of when we, I mean, data science has been around forever, right? We always hear that. But in the, sort of, more modern use of the term, you never heard much about machine learning. It was more like stats, math, some programming, data hacking, creativity. And then now, machine learning sounds fundamental. Is that a new skillset that the data scientists had to learn? Did they get them from other parts of the organization? >> I mean, when we talk about math and stats, what we call machine learning today has been what we've been doing since the first statistics for years, right? I mean, a lot of the same things we apply in what we call machine learning today I did during my PhD 20 years ago, right? It was just with a different perspective. And you applied those types of, they were more static, right? So I would build a model to predict something, and it was only for that. It really didn't apply it beyond, so it was very static. Now, when we're talking about machine learning, I want to understand Dave, right? And I want to be able to predict Dave's behavior in the future, and learn how you're changing your behavior over time, right? So one of the things that a lot of people don't realize, especially senior executives, is that machine learning creates a self-fulfilling prophecy. You're going to drive a behavior so your data is going to change, right? So your model needs to change. And so that's really the difference between what you think of as stats and what we think of as machine learning today. So what we were looking for years ago is all the same we just described it a little differently. >> So how fine is the line between a statistician and a data scientist? >> I think any good statistician can really become a data scientist. There's some issues around data engineering and things like that but if it's a team sport, I think any really good, pure mathematician or statistician could certainly become a data scientist. Or machine-learning engineer. Sorry. >> I'm interested in it from a skillset standpoint. You were saying how you're advertising to bring on these roles. I was at the Women in Data Science Conference with theCUBE just a couple of days ago, and we hear so much excitement about the role of data scientists. It's so horizontal. People have the opportunity to make impact in policy change, healthcare, etc. So the hard skills, the soft skills, mathematician, what are some of the other elements that you would look for or that companies, enterprises that need to learn how to embrace data science, should look for? Someone that's not just a mathematician but someone that has communication skills, collaboration, empathy, what are some of those, openness, to not lead data down a certain, what do you see as the right mix there of a data scientist? >> Yeah, so I think that's a really good point, right? It's not just the hard skills. When my team goes out, because part of what we do is we go out and sit with clients and teach them our philosophy on how you should integrate data science in the enterprise. A good part of that is sitting down and understanding the use case. And working with people to tease out, how do you get to this ultimate use case because any problem worth solving is not one model, any use case is not one model, it's many models. How do you work with the people in the business to understand, okay, what's the most important thing for us to deliver first? And it's almost a negotiation, right? Talking them back. Okay, we can't solve the whole problem. We need to break it down in discreet pieces. Even when we break it down into discreet pieces, there's going to be a series of sprints to deliver that. Right? And so having these soft skills to be able to tease that in a way, and really help people understand that their way of thinking about this may or may not be right. And doing that in a way that's not offensive. And there's a lot of really smart people that can say that, but they can come across at being offensive, so those soft skills are really important. >> I'm going to talk about GDPR in the time we have remaining. We talked about in the past, the clocks ticking, May the fines go into effect. The relationship between data science, machine learning, GDPR, is it going to help us solve this problem? This is a nightmare for people. And many organizations aren't ready. Your thoughts. >> Yeah, so I think there's some aspects that we've talked about before. How important it's going to be to apply machine learning to your data to get ready for GDPR. But I think there's some aspects that we haven't talked about before here, and that's around what impact does GDPR have on being able to do data science, and being able to implement data science. So one of the aspects of the GDPR is this concept of consent, right? So it really requires consent to be understandable and very explicit. And it allows people to be able to retract that consent at any time. And so what does that mean when you build a model that's trained on someone's data? If you haven't anonymized it properly, do I have to rebuild the model without their data? And then it also brings up some points around explainability. So you need to be able to explain your decision, how you used analytics, how you got to that decision, to someone if they request it. To an auditor if they request it. Traditional machine learning, that's not too much of a problem. You can look at the features and say these features, this contributed 20%, this contributed 50%. But as you get into things like deep learning, this concept of explainable or XAI becomes really, really important. And there were some talks earlier today at Strata about how you apply machine learning, traditional machine learning to interpret your deep learning or black box AI. So that's really going to be important, those two things, in terms of how they effect data science. >> Well, you mentioned the black box. I mean, do you think we'll ever resolve the black box challenge? Or is it really that people are just going to be comfortable that what happens inside the box, how you got to that decision is okay? >> So I'm inherently both cynical and optimistic. (chuckles) But I think there's a lot of things we looked at five years ago and we said there's no way we'll ever be able to do them that we can do today. And so while I don't know how we're going to get to be able to explain this black box as a XAI, I'm fairly confident that in five years, this won't even be a conversation anymore. >> Yeah, I kind of agree. I mean, somebody said to me the other day, well, it's really hard to explain how you know it's a dog. >> Seth: Right (chuckles). But you know it's a dog. >> But you know it's a dog. And so, we'll get over this. >> Yeah. >> I love that you just brought up dogs as we're ending. That's my favorite thing in the world, thank you. Yes, you knew that. Well, Seth, I wish we had more time, and thanks so much for stopping by theCUBE and sharing some of your insights. Look forward to the next update in the next few months from you. >> Yeah, thanks for having me. Good seeing you again. >> Pleasure. >> Nice meeting you. >> Likewise. We want to thank you for watching theCUBE live from our event Big Data SV down the street from the Strata Data Conference. I'm Lisa Martin, for Dave Vellante. Thanks for watching, stick around, we'll be rick back after a short break.

(pulsating music) >> Live 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 the 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 CDO's here sharing best practices, listening to 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 is the 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 at in that space, and cognitive as being 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 CDO's 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, this and that. And one of the interesting things we talked about off air, was really you guys are coming up with a playbook which is really to help CDO's basically execute and be successful CDO's. 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. >> Jeff: 90% will have them and 50% will fail? >> Yes, and so I can tell you that based on our survey of 6,000 global executives last fall, the number is 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 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 in 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. So, but we're certainly not seeing the turnover we were in the early years of CDO's 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 CDO's, 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 CDO's that we surveyed, and we surveyed 800 CDO's, 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 Itau 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 cause in theory you never finish the first phase, right? >> You never finish, 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. Then, that's a great strategy. Then, as you said, then the rubber's got to hit the road. And Priya, that's where you play in, the execution point. Like you say, you like to get your hands dirty with the CDO's. So what are you seeing from your 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 off it, that's been established fact. Now CDO's and the rest of the organization, the CIO's and the CTO's, 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 dominant business value? Where are those things which is completely data-driven? Maybe it is cognitive forecasting, or your business requirement could be how can I maximize 40% of my service channel? Which in the end of the day 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 demonstrates key business values, so we actually know what we are trying to solve, and work backwards in terms of what is the data element 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 these 170 CDO's. I mean, we want to make sure every one of our customers is able to take advantage of that. >> 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 and looks at a lot of different issues. So for example, your CDO report is phenomenal, I think you do one for the CMO, a number of different chief officers. 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, what are some of those new behaviors? The degree to which the leadership is ready to drive that? >> 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 started digitizing all 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. >> And 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 now. Data as 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 actually that's the way they 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 were 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 in being able to understand what's happening in the marketplace, bing 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 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 trying to coerce that you no longer use intuition? >> So it depends upon where you are, where the company is. Because what we call that is the snowball effect. It's one of the reasons you have to have the governance in place and get things going kind of 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. >> Just to add to that, data is not anymore viewed as a commodity. If you have seen valuable organizations who know what their asset is, it's not just a commodity. So the parity of... >> Peter: Or even a liability is what it used to be, right? >> Exactly. >> Peter: 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 are trying to see what data that I can have, or what intelligence that I can have to make my business different shade, or I can value add something more. That's a type of... 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 800 times or not knowing who else is using. So some of the tools and techniques help us to master those things. 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, so there's a large number of them out there. >> Yeah, but these are 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? >> 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 one single layer. In terms of opportunities for this, there is a lot more deserving silos and deserving 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 and center of everything actually, around data-driven. And you also heard enough about cognitive computing these days, because it is the key ingredient for cognitive computing. We talked about full ease 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 tochange the way we do things. It's going to give new and unexpected things both from a consumer point and from an enterprise point as well. It'll bring effects like consumerization of enterprises and what-not. 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. 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. 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 "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 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. The reality is, we're creating data incredibly fast. We know that is has enormous value. People are not going to change that rapidly. New types of algorithms are coming on, but many of the algorithms are algorithms we've had 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-healing, regeneratable software. >> Invisible to some, but very very highly visible to others. 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 would have 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 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 their mockups, where are they going to integrate with what sources, am I talking to the bank as an API, et cetera. So the data representation comes at the very end. In the current generation of applications, the cognitive applications that we are building, 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 typical three-tier, four-tier architecture. But the order of how we perceive and understand the problem is different. But we are very confident. We are trying to maximize 40% of your sales. We are trying to create 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 are talking about, why I mentioned invisible, is some are generatable. The AI and cognitive is advanced in such a way that some are generatable. If it understands the data underlying, 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 complement 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 painting a pretty picture. I like it. You know, some people see only the dark side. That's clearly the 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. >> Thank you. >> All right. Jeff Frick and Peter Burris. You're watching theCUBE from the IBM Chief Data Officers Summit, Spring 2017. We'll be right back after this short break. Thanks for watching. (drums pound) (hands clap rhythmically) >> [Computerized Voice] You really crushed it. (quiet synthesizer music) >> 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.

>> It's the Cube covering IBM cheap Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts Day villain day and still minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We here at the Chief Data Officers Summit that IBM is hosting in Boston. I'm joined by Courtney Abercrombie. According your your title's too long. I'm just gonna call you a cognitive rockstar on >> Alec Crane is >> here from Yusa. System by President, Vice President at that firm. Welcome to the Cube. Great to see you guys. Thank you. So this event I love it. I mean, we first met at the, uh, the mighty chief data officer conference. You were all over that networking with the CEO's helping him out and just really, I think identified early on the importance of this constituency. Why? How did you sort of realize and where have you taken it? >> It's more important than it's ever been. And we're so grateful every time that we see a new chief data officer coming in because you just can't govern and do data by committee. Um, if you really hope to be transformational in your company. All these huge, different technologies that are out there, All this amazing, rich data like weather data and the ability to leverage, you know, social media information, bringing that all together and really establishing an innovation platform for your company. You can't do that by committee. You really have to have a leader in charge of it. and that’s what chief data officers are here to do. And so every time we see one, we're so grateful >> that just so >> that we just heard from Inderpal Bhandari on his recommendation for how you get started. It was pretty precise and prescriptive. But I wonder, Alan. So tell us about the chief data officer role at USAA. Hasn't been around for a while. Of course, it's a regulated business. So probably Maur, data oriented are cognizant than most businesses. But tell us about your journey. >> We started probably about 4 or 5 years ago, and it was a combination of trying to consolidate data and analytics operations and then decentralized them, and we found that there was advantages and pros and cons of doing both. You'd get the efficiencies, but once you got the efficiencies, you'd lose the business expertise, and then we'd have to tow decentralize. So we ended up landing a couple of years ago. What we call a hub and spoke system where we have centralized governance and management of key data assets, uh, data modelling data science type work. And then we still allow the, uh, various lines of business to have their own data offices. And the one I run for USAA is our distribution channels office for all of the data and analytics. And we take about 100,000,000 phone calls a year. About 2,000,000,000 webb interactions. Mobile interactions. We take about 18,000 hours. That's really roughly two years of phone conversation data in per day. Uh, we take about 50,000,000 lines of, uh, Web analytic traffic per day as well. So trying to make sense of that to nurture remember, relationships, reinforce trust and remove obstacles >> for your supporting the agent systems. Is that right? >> I support the agent systems as well as the, um, digital >> systems. Okay. And so the objective is obviously toe to grow the business, keep it running, keep the customers happy. Very operate, agent Just efficient. Okay. Um and so when you that's really interesting. This sort of hub and spoke of decentralization gets you speed and closer to the business. Centralization get you that that efficiency. Do you feel like you found that right balance? I mean, if you think so. I >> think you know, early on, we it was mme or we had more cerebral alignment, you know, meaning that it seemed logical to us. But actually, once the last couple of years, we've had some growing pains with roles, responsibilities, overlaps, some redundancy, those types of things. But I think we've landed in a good place. And that's that's what I'm pretty proud of because we've been able to balance the agility with the governance necessary toe, have good governance and put in place, but then also be able to move at the speed the businessmen. >> So Courtney, one of things we heard one of the themes this morning within IBM it's of the role of the chief Data officer's office is to really empower the lines of business with data so that you can empower your customers is what Bob Tatiana was telling us, right? With data. So how are you doing? That is you have new services. You have processes or how is that all working >> right? We dio We have a lot of things, actually, because we've been working so much with people like Allen's group who have been leaders at, quite frankly, in establishing best practices on even how to set up these husbands votes. A lot of people are, you know, want to talk, Teo, um, the CDO and they've spun off even a lot of CEOs into other organizations, in fact, but I mean, they're really a leader in this area. So one of the things that we've noticed is you know, the thing that gives everybody the biggest grief is trying to figure out how to work with unstructured data. Um, and all this volume of data, it's just insane. And just like I was saying in the panel earlier, only about 5% of your actual internal data is enough to actually create a context around your customers. You really have to be able to go with all this exogenous data to understand what were the bigger ramifications that were going on in any customer event, whether it's a call in or whether it's, uh, you know, I'm not happy today with something that you tried to sell me or something that you didn't respond too fast enough, which I'm sure Alan could, you know, equate to. But so we have this new data as a service that we've put together based on the way the weather data has, the weather company has put their platform together. We're using a lot of the same kind of like micro services that you saw Bob put on the screen. You know, everything from, I mean, open source. As much open sources we can get, get it. And it's all cloud based. So and it's it's ways to digest and mix up both that internal data with all of that big, voluminous external data. >> So I'm interested in. So you get the organizational part down. Least you've settled on approach. What are some of the other big challenges that you face in terms of analytics and cognitive projects? Your organization? How are you dealing with those? >> Well, uh, >> to take a step back, use a We're, uh, financial services company that supports the military and their families. We now have 12 million members, and we're known for our service. And most of the time, those moments of truth, if you will, where our service really shines has been when someone talks to you, us on the phone when those member service reps are giving that incredible service that they're known for on the reason being is that the MSR is the aggregator of all that data. When you call in, it's all about you. There's two screens full of your information and the MSR is not interested in anything else but just serving you, our digital experiences more transactional in orientation. And it was It's more utilitarian, and we're trying to make it more personal, trying to make it more How do we know about you? And so one of the cues that were that were taking from the MSR community through cognitive learning is we like to say the only way to get into the call is to get into the call, and that is to truly get into the speech to text, Then do the text mining on that to see what are the other topics that are coming out that could surface that we're not actually capturing. And then how do we use those topics at a member level two then help inform the digital experience to make it more personal. How do I detect life events? Our MSR's are actually trained to listen for things like words like fiance, marriage moving, maybe even a baby crying in the background. How do we take that knowledge and turn that into something that machine learning can give us insights that can feedback into our digital transact actions. So >> this's what our group. >> It's a big task. So So how are >> you doing that? I mean, it's obviously we always talk about people processing technology. Yeah, break that down for us. I mean, how are you approaching that massive opportunity? >> Part of it is is, uh, you know, I look at it. It is like a set of those, you know, Russian nesting dolls. You know, every time you solve one problem, there's another problem inside of it. The first problem is getting access to the data. You know, where and where do you store? We're taking in two years of data per day of phone call data into a system where you put all that right and then you're where you put a week's worth a month's worth a quarter's worth of data like that. Then once you solve that problem, how do you read Act all that personal information So that that private information that you really don't need that data exhaust that would actually create a liability for you in our in our world so that you can really stay focused on what of the key themes that the member needs? And then the third thing is now had. Now that you've got access to the data, it's transcribed for you. It's been redacted from its P I I type work well, now you need the horse power and of analysts on, we're exploring partnerships with IBM, both locally and in in the States as well as internationally to look at data science as a service and try to understand How can we tap into this huge volume of data that we've got to explore those types of themes that are coming up The biggest challenges in typical transaction logging systems. You have to know what your logging You have to know what you're looking for before you know what to put the date, where to put the data. And so it's almost like you kind of have to already know that it's there to know how much you're acquiring for it and what we need to do more as we pivot more towards machine learning is that we need the data to tell us what's important to look at. And that's really the vat on the value of working with these folks. >> So obviously, date is increasingly on structure we heard this morning and whatever, 80 90% is structured. So here you're no whatever. You're putting it into whatever data fake swamp, ocean, everything center everywhere, and you're using sort of machine learning toe both find signal, but also protected yourself from risk. Right. So you've got a T said you gotta redact private information. So much of that information could be and not not no schema? Absolutely. Okay, So you're where are you in terms of solving that problem in the first inning or you deeper than that, >> we're probably would say beyond the first inning, but we so we've kind of figured out what that process is to get the data and all the piece parts working together. We've made some incredible insights already. Things that people, you know, I had no idea that was there. Um, but, uh, I'd say we still have a long way to go. Is particularly terms of scaling scaling the process, scaling the thie analytics, scaling the partnerships, figuring out how do we get the most throughput? I would say it's It's one of those things. We're measuring it on, maybe having a couple of good wins this year. A couple of really good projects that have come across. We want to kind of take that tube out 10 projects next year in this space. And that's how we're kind of measuring the velocity and the success >> data divas. I walked away and >> there was one of them Was breakfast this morning. Data divas. You hold this every year. >> D'oh! It's growing. Now we got data, >> dudes. So I was one of the few data dudes way walked in >> one of the women chief date officers. I got no problem with people calling me a P. >> I No. Yeah, I just sell. Sit down. Really? Bath s o. But also, >> what's the intent of that? What learning is that you take out of those? >> I think it's >> more. It's You know, you could honestly say this isn't just a data Debo problem. This is also, you know, anybody who feels like they're not being heard. Um, it's really easy to get drowned out in a lot of voices when it comes to data and analytics. Um, everybody has an opinion. I think. Remember, Ursula is always saying, Ah, all's fair in love, war and data. Um and it feels like, you know, sometimes you go, I'll come to the table and whoever has the loudest voice and whoever bangs their test the loudest, um, kind of wins the game. But I think in this case, you know, a lot of women are taking these roles. In fact, we saw, you know, a while back from Gardner that number about 25% of chief data officers are actually women because the role is evolving out of the business lines as opposed Thio more lines. And so I mean, it makes sense that, you know, were natural collaborators. I mean, like the biggest struggle and data governance isn't setting up frameworks. It's getting people to actually cooperate and bring data to the table and talk about their business processes that support that. And that's something that women do really well. But we've got to find our voice and our strength and our resolve. And we've got to support each other in trying to bring more diverse thinking to the table, you know? So it's it's all those kinds of issues and how do you balance family? I mean, >> we're seeing >> more and more. You know, I don't know if you know this, but there's actual statistics around millennials and that males are actually starting to take on more more role of being the the caregiver in the family. So I mean as we see that it's an interesting turnabout because now all the sudden, it's no longer, you know, women having that traditional role of, you know, I gotta always be home. Now we're actually starting to see a flip of that, which is which is, >> You know, I think it's kind of welcome. My husband's definitely >> I say he's a better parent than me. >> Friday. It's >> honest he'll watch this and he >> can thank me later that it was >> a great discussion this morning. Alan, I want to get your feedback on this event and also you participate in a couple of sessions yesterday. Maybe you could share with our audience Some of the key takeaways in the event of general and specific ones that you worked on yesterday. >> Well, I've been fortunate to come to the event for a couple of years now. And when we were just what 50 or so of us that were showing up? So, you know, I see that the evolution just in a couple of years time conversations have really changed. First meeting that we had people were saying, Where do you report in the organization? Um, how many people do you have? What do you do for your job? They were very different answers to any of that everywhere. From I'm an independent contributor that's a data evangelist to I run legions of data analysts and reporting shops, you know, and so forth and everything in between. And so what I see what it's offers in first year was really kind of a coalescing of what it really means to be a data officer in the company that actually happened pretty quickly in my mind, Um, when by seeing it through through the lens of my peers here, the other thing was when you when you think about the topics the topics are getting a lot more pointed. They're getting more pointed around the monetization of data communicating data through visualization, storytelling, key insights that you, you know, using different technologies. And we talked a lot yesterday about storytelling and storytelling is not through visual days in storytelling is not just about like who has the most, you know, colors on on a slide or or ah you know, animation of your bubble charts and things like that. But sometimes the best stories are told with the most simple charts because they resonate with your customers. And so what I think is it's almost like kind of getting a back to the basics when it comes to taking data and making it meaningful. We're only going to grow our organizations and data and data scientists and analysts. If we can communicate to the rest of the organization, our value and the key to creating that value is they can see themselves in our data. >> Yeah, the visit is we like to call it sometimes is critical to that to that storytelling. Sometimes I worry and we go onto these conferences and you go into a booth and look what we can do with machine learning, and we would just be looking at just this data. So what do I do? What >> I do with all this? Yeah. >> I don't know how it would make sense of it. So So is there a special storyteller role within your organization or you all storytellers? Do you cross train on that? Or >> it's funny you'd ask that one of the gentlemen of my team. He actually came to me about six months ago, and he says I'm really good at at the analysis part, but I really have a passion for things like Photoshopped things like, uh uh, uh the various, uh, video and video editing type software. He says I want to be your storyteller. I want to be creating a team of data and analytics storytellers for the rest of the organization. So we pitched the idea to our central hub and spoke leadership group. They loved it. They loved the idea. And he is now, um, oversubscribed. You would say in terms of demand for how do you tell the data? How do you tell the data story and how it's moving the business forward? And that takes the form kind of everything from infographics tell you also about how do you make it personal when, when? Now 7,000 m s. Ours have access to their own data. You know, really telling that at a at a very personal level, almost like a vignette of animus are who's now able to manage themselves using the data that they were not able able tto have before we're in the past, only managers had access to their performance results. This video, actually, you know, pulls on the heartstrings. But it it not only does that, but it really tells the story of how doing these types of things and creating these different data assets for the rest of your organization can actually have a very meaningful benefit to how they view work and how they view autonomy and how they view their own personal growth. >> That's critical, especially in a decentralized organization. Leased a quasi decentralized organization, getting everybody on the same page and understand You know what the vision is and what the direction is. It s so often if you don't have that storytelling capability, you have thousands of stories, and a lot of times there's dissonance. I mean, I'm not saying there's not in your in your organization, but have you seen the organization because of that storytelling capability become Mohr? Yeah, Joe. At least Mohr sort of effective and efficient, moving forward to the objectives. Well, >> you know, as a as a data person, I'm always biased thatyou know data, you know, can win an argument if presented the right way. It's the The challenge is when you're trying to overcome or go into a direction. And in this case, it was. We wanted to give more autonomy. Toothy MSR community. Well, the management of that call center were 94 year old company. And so the management of that of that call center has been doing things a certain way for many, many, many, many years. And the manager's having access to the data. The reps not That was how we did things, you know. And so when you make a change like that, there's a lot of hesitation of what is this going to do to us? How is this going to change? And what we're able to show with data and with through these visualizations is you really don't have anything to worry about? You're only gonna have upside, you know, in this conversation because at the end of the day, what's going to empower people this having access and power of >> their own destiny? Yeah, access is really the key isn't because we've all been in the meetings where somebody stands up and they've got some data point in there pounding the table, >> right? Oftentimes it's a man, all right. It >> is a powerful pl leader on jamming data down your throats, and you don't necessarily know the poor sap that he's, you know, beating up. Doesn't think Target doesn't have access to the data. This concept of citizen data scientists begins to a level that playing field doesn't want you seeing that >> it does. And I want to actually >> come back to what you're saying because there's a larger thought there, which is that we don't often address, and that's this change banishment concept. I mean, we we look at all these. I mean, everybody looks at all these technologies and all this information, and how much data can you possibly get your >> hands on? But at the end of >> the day, it's all about trying to create an outcome. A some joint outcome for the business and it could be threatening. It could be threatening to the C suite people who are actually deploying the use of these data driven tools because >> it may go >> against their gut. And, you >> know, oftentimes the poor messenger of that, >> When when you have to be the one that stands up and go against that, that senior vice presidents got it, the one who's pounding and saying No, but I know better >> That could be a >> tough position to be in without having some sort of change management philosophy going on with the introduction of data and analytics and with the introduction of tools, because there's a whole reframing that, Hey, my gut instinct that got me here all the way to the top doesn't necessarily mean that it's going to continue to scale in this new world with all of all of our competitors and all these, you know, massive changes going on in the market place right now. My guts not going to get me there anymore. So it's hard, it's hard, and I think a lot of executives don't really know to invest in that change management, if you know that goes with it that you need to change philosophies and mindsets and slowly introduced visualizations and things that get people slowly onboard, as opposed to just throwing it at him and saying here, believe it. >> Think I mean, it wasn't that >> long ago. Certainly this this millennium, where you know, publications like Harvard Business Review had, uh, cover stories on why gut feel, you know, beats, you know, analysis by paralysis. >> That seems to be changing. And >> the data purists would say the data doesn't lie. It was long as you could interpret it correctly. Let the data tell us what to do, as opposed to trying to push an agenda. But they're still politics. >> There's just things out >> there that you can't even perceive of that air coming your way. I mean, like, Blockbuster Netflix, Alibaba versus standard retailers. I mean, >> there's just things out >> there that without the use of things like machine learning and being comfortable with the use, the things like mission learning a lot of people think of that kind of stuff is >> Well, don't get your >> hoodoo voodoo into my business. You know, I don't know what that algorithm stuff does. It's >> going Yeah, I mean, e. I mean to say, What the hell is this? And now, yeah, it's coming and >> you need to get ready. >> There's an >> important role, though I think instinct, you know, you don't want to dismiss a 20 year leader in a particular operations because they've they've they've getting themselves where they're at because in large part, maybe they didn't have all the data. But they learned through a lot of those things, and I think it's when you marry those things up. And if you kenbrell in a kind of humble way to that kind of leader and win them over and show how it may be validating some of their, um uh yeah, that some of their points Or maybe how it explains it in a different way. Maybe it's not exactly what they want to see, but it's helping to inform their business, and you come into him as a partner, as opposed to gotcha, you know. Then then you know you can really change the business that way. And >> what is it? Was Linda Limbic brain is it just doesn't feel right. Is that the part of the brain that informs you that? And so It's hard to sometimes put, but you're right. Uh, there there is a component of this which is gut feel instinct and probably relates to to experience. So it's It's like, uh, when, when, uh, Deep blue beat Garry Kasparov. We talk about this all the time. It turns out that the best chess player in the world isn't a machine. It's a It's a human in the machine. >> That's right. That's exactly right. It's always the training that people training these things, that's where it gets its information. So at the end of the day, you're right. It's always still instinct to some >> level. I could We gotta go. All right. Last word on the event. You know what's next? >> Don't love my team. Data officer. Miss, you guys. It is good >> to be here. We appreciate it. All right, We'll leave it there. Thank you, guys. Thank you. All right, keep right. Everybody, this is Cuba. Live from IBM Chief Data Officer, Summit in Boston Right back. My name is Dave Volante.

>> live from Boston, Massachusetts. >> It's the Cube >> covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day villain Day and >> stew minimum. Welcome back to Boston, everybody. This is the IBM Chief Data Officer Summit. And this is the Cube, the worldwide leader in live tech coverage. Caitlin Lepic is here. She's an executive within the chief data officer office at IBM. And she's joined by Dave Shoot Mel, who's a research director at, uh D. C. And he covers cognitive systems and content analytics. Folks, welcome to the Cube. Good to see you. Thank you. Can't. Then we'll start with you. You were You kicked off the morning and I referenced the Forbes article or CDOs. Miracle workers. That's great. I hadn't read that article. You put up their scanned it very quickly, but you set up the event. It started yesterday afternoon at noon. You're going through, uh, this afternoon? What's it all about? This is evolved. Since, what, 2014 >> it has, um, we started our first CDO summit back in 2014. And at that time, we estimated there were maybe 200 or so CDOs worldwide, give or take and we had 30, 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 30 2014. And we've really grown. So this year we have about 170 folks joining us, 70 of which are CEOs, more acting, the studios in the organization. And so we've really been able to grow the community over the last two years and are really excited to see to see how we can continue to do that moving forward. >> And IBM has always had a big presence at the conference that we've covered the CDO event. So that's nice that you can leverage that community and continue to cultivate it. Didn't want to ask you, so it used that we were talking when we first met this morning. It used to be dated was such a wonky topic, you know, data was data value. People would try to put a value on data, and but it was just a really kind of boring but important topic. Now it's front and center with cognitive with analytics. What are you seeing in the marketplace. >> Yeah, I think. Well, what we're seeing in the market is this emphasis on predictive applications, predictive analytics, cognitive applications, artificial intelligence of deep learning. All of those 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, you know, 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, you know, IBM is really talking about what you know, Bob Picciotto talked about this morning. But also, we're seeing thie 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, you know, they're going to take us hopefully, you know, far into the future. >> Okay, so, Caitlin square the circle for us. So the CDO roll generally is not perceived. Is it technology role? Correct. Yet as Davis to saying, we're talking about machine learning cognitive. Aye, aye. These air 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 is. It's a very good point, you know, Traditionally, Seo's really have a business background, and we find that the most successful CDO sit in the business organization. So they report somewhere in a line of business. Um, and there are certainly some that have a technical background, but far more come from business background and sit in the business. I can't tell you how we are setting up our studio office at IBM. Um, so are new. And our first global chief date officer joined in December of last year. Interpol Bhandari, um 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 sign. So getting that team in place next, it's information, governance and policy. How are we going to govern access, manage, work with data, both data that we own within our organization as well as the long list of of external data sources that that we bring in and then third is the business integration filler. So the idea is CDOs are going to be most successful when they deliver those data Science data engineering. Um, they manage and govern the data, but they pull it through the business, so ensuring that were really, you know, grounded in business unit and doing this. And so those there are three primary pillars at this point. So prior >> to formalizing the CDO role at I b m e mean remnants of these roles existed. There was a date, equality, you know, function. There was certainly governance in policy, and somebody was responsible to integrate between, you know, from the i t. To the applications, tow the business. Were those part of I t where they sort of, you know, by committee and and how did you bring all those pieces together? That couldn't have been trivial, >> and I would say it's filling. It's still going filling ongoing process. But absolutely, I would say they typically resided within particular business units, um, 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 the studio really comes in and what we're What we're doing now is we are partnering very closely with business units. One example is IBM analytic. Seen it. So we're here with Bob Luciano and other business units to ensure that, as they provide us, you know, their data were able to create the single trusted source of data across the organization across the enterprise. And so I agree with you, I think, ah, lot of those capabilities and functions quite mature, they, you know, 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 on 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 you know, there's that governance peace, but a lot of CEOs are put into that role with a mandate for innovation on. That's something that you know a lot of times it has been accused of not being all that innovative. Is that what you're seeing? You know what? Because some of the kind of is it project based or, you know, best initiatives that air driving forward with CEOs. I think what we're seeing is that enterprises they're 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 the 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, you know, looking at how data and predictive models and innovation all become part of the operational fabric of a company. Uh, you know, and if you think about the companies that are there, you know, just beating it together. You know Amazon, for example. I mean, Amazon is a completely data driven company. When you get your recommendations for, you know what to buy, or that's all coming from the data when they set up these logistics centers where they're, you know, shipping the latest supplies. They're doing that because they know where their customers are. You know, they have all this data, so they're they're integrating data into their day to day decision making. And I think that's what we're seeing, You know, throughout industry is this this idea of integrating decision data into the decision making process and elevating it? And I think that's why the CDO rule has become so much more important over the last 2 to 3 years. >> We heard this morning at 88% percent of data is dark data. Papa Geno talked about that. So thinking about the CEOs scope roll agenda, you've got data sources. You've gotto identify those. You gotta 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 they may not be the best predictive models in the world. You've got iterated them. So how does an organization, because not every organizations like Amazon with virtually unlimited resource is capital? How does an organization balance What are you seeing in terms of getting new data sources? Refining those data source is putting my emphasis on the data vs refining and calibrating the predictive models. How organizations balancing that Maybe we start with how IBM is doing. It's what you're seeing in the field. >> So 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 monitor monetization strategy? Not how your mind monetizing data. How are how are you? What's your strategy? Moving forward, Um, for Mance station. And so with IBM we've talked about it is moved 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 talking examples with contracts, for example. Every organization has a lot of contracts, and right now it's, you know, 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 in 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? What are longer term vision is, is we want to build the intelligence that pulls in that internal data and then really does pull in the external data that we've that we've all talked about. You know, the social data, the sentiment analysis, analysis, the weather. You know, all of that sort of external data to help us. Ultimately, in our value proposition, our mission is, you know, data driven enablement cognition. So helps us achieve our our strategy there. >> Thank you, Dad, to that. Yeah, >> I mean, I think I mean, you could take a number of examples. I mean, there's there's ah, uh, small insurance company in Florida, for example. Uh, 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 Tex analytics to identify where problems are occurring when hurricane happens. So they're what they're doing is they're they're organizing that kind of data and >> there and there were >> relatively small insurance company. And a lot of this is being done to the cloud, but they're basically getting that kind of sentiment analysis being ableto interpret that and add that to their decision making process. About where should I land a person? Where should I land? You know, an insurance adjuster and agent, you know, based on the tweets, that air coming in rather than than just the phone calls that air coming into the into the organization, you know? So that's a That's a simple example. And you were talking about Not everybody has the resources of an Amazon, but, you know, certainly small insurance companies, small manufacturers, small retail organizations, you, Khun 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? Uh, you know, we're seeing ah whole range of solutions coming out, and and IBM actually has a broad range of solutions for things like that. But, you know, they're not the only points out there. There's there's a lot of folks do it that kind of thing, you know, in terms of the dark data analysis and barely providing that, you know, as part of the solution to help people make better decisions. >> So the answers to the questions both You're doing both new sources of data and trying to improve the the the analytics and the models. But it's a balancing act, and you could come back to the E. R. A. Y question. It sounds like IBM strategies to supercharge your existing businesses by infusing them with new data and new insights. Is >> that correctly? I would say that is correct. >> Okay, where is in many cases, the R A. Y of analytics projects that date have been a reduction on investment? You know, I'm going to move stuff from my traditional W two. A dupe is cheaper, and we feels like Dave, we're entering a new wave now maybe could talk about that a little bit. >> Yeah. I mean, I think I think there's a desk in 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. You know what 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, you know, through these mechanisms? And I think that's what we're starting to see in the organization. And we're starting to see start ups that are dedicated to providing this level of disruption and helping address new markets. You know, by using these kinds of technologies, uh, in in new and interesting ways. I mean, everybody uses the airbnb example. Everybody uses uber example. You know that these are people who don't own cars. They don't know what hotel rooms. But, you know, they provide analytics to disrupt the hotel industry and disrupt the taxi industry. It's not just limited to those two industries. It's, you know, virtually everything you know. And I think that's what we're starting to see is this height of, uh, virtual disruption based on the dark data, uh, that people can actually begin to analyze >> within IBM. Uh, the chief data officer reports to whom. >> So the way we've set up in our organization is our CBO reports to our senior vice president of transformation and operations, who then reports to our CEO our recommendation as we talked with clients. I mean, we see this as a CEO level reporting relationship, and and oftentimes we advocate, you know, for that is where we're talking with customers and clients. It fits nicely in our organization within transformation 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 process is to bring new folks on board s. So it made sense to fit within, uh, organization that the mandate is really transformation of the company of the >> and the CDO was a peer of the CIA. Is that right? Yes. >> Yes, that's right. That's right. Um, and then in our organization, the role of split and that we have a chief data officer as well as a chief analytics officer. Um, but, you know, we often see one person serving both of those roles as well. So that's kind of, you know, depend on the organizational structure of the company. >> So you can't run the business. So to grow the business, which I guess is the P and L manager's role and transformed the business, which is where the CDO comes. >> Right? Right, right. Exactly. >> I can't give you the last word. Sort of Put a bumper sticker on this event. Where do you want to see it go? In the future? >> Yes. Eso last word. You know, we try Tio, we tried a couple new things. Uh, this 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 their governance or is innovation being able to talk? How do you get started in the 1st 90 days? What? What do you do first? You know, we we have sort of a five steps that we talk through around, you know, getting your data strategy and your plan together and how you execute against that. Um And I have to tell you, those topics continue to be of interest to our to our participants every year. So we're going to continue to have those, um, and I just I love to see the community grow. I saw the first Chief data officer University, you know, announced earlier this year. I did notice a lot of PR and media around. Role of studio is miracle workers, As you mentioned, doing a lot of great work. So, you know, we're really supportive. Were big supporters of the role we'll continue to host in person events. Uh, do virtual events continue to support studios? To be successful on our big plug is will be world of Watson. Eyes are big IBM Analytics event in October, last week of October in Vegas. So we certainly invite folks to join us. There >> will be, >> and he'll be there. Right? >> Get still, try to get Jimmy on. So, Jenny, if you're watching, talking to come on the Q. >> So we do a second interview >> and we'll see. We get Teo, And I saw Hillary Mason is going to be the oh so fantastic to see her so well. Excellent. Congratulations. on being ahead of the curve with the chief date officer can theme. And I really appreciate you coming to Cube, Dave. Thank you. Thank you. All right, Keep right there. Everybody stew and I were back with our next guest. We're live from the Chief Data Officers Summit. IBM sze event in Boston Right back. My name is Dave Volante on DH. I'm a longtime industry analysts.