>> Live from Boston, Massachusetts, it's the CUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. (soft electronic music) >> Welcome to theCUBE's coverage of IBM Chief Data Strategy Officer Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, co-hosting here today with Dave Vellante. >> Hey, Rebecca. >> Great to be working with you again. >> Good to see you again. It's been a while. >> It has. >> Last summer, in the heat of New York. >> That's right, and now here we are in the dreariness of Boston. Dave, we just finished up the keynote. As you said, it's a meaty keynote. It's a seminal time for Chief Data Officers at companies. What did you hear? What most interested you about what Joe Kavanaugh said? >> Well, a couple things. I think it's worthwhile going back a few years. The ascendancy of the Chief Data Officer as a role and a title kind of emerged from the back-office records management side of the house. It really started in regulated industries. Financial services, healthcare, and government. For obvious reasons. These are data-oriented companies. They're highly regulated. There's a lot of risk. So, there's really sort of a risk-first approach. Then, that sort of coincided with the big data meme exploding. Then, this whole discussion of is data an asset or a liability? Increasingly, organizations are looking at it, as we know, as an asset. So, the Chief Data Officer has emerged as the individual who is responsible for the data architecture of the company, trying to figure out how to monetize data. Not necessarily monetize explicitly the data, but how data contributes to the monetization of the organization. That has a lot of ripple effects, Rebecca, in terms of technology implications, skillsets, obviously security, relationships with line of business, and fundamentally the organization and the mission of the company. So, IBM has been pretty leading and aggressive about going after the Chief Data Officer role, and has events like this, the Chief Data Officer Summit. They do them, kind of signature moments, and these little its and bit events. I don't know how many people you think are here. >> 150, I think. >> 150? Okay. And they're the data-rowdy of the Boston community. They're chartered with figuring out what the data strategy is. How to value data and how to put data front and center. Everybody talks about being a data-driven organization, but most organizations-- Everybody talks about becoming a digital business, but a digital business means that you are data driven. The data is first. You understand how to monetize data. You know how to value data. Your decisions are data-driven. I would say that less than 10% of the organizations that we work with are of that ilk. So, it's early days still. What was interesting about what Jim Kavanaugh says, they put forth this cognitive blueprint that Inderpal Bhandari, who'll be on theCUBE later, envisioned and has brought to life in his two years as the Chief Data Officer here at IBM. Now, what I like about what IBM is doing is they're sharing their dog food experience with their clients. He talked about that enterprise blueprint architecture but he also talked about what IBM is doing to transform. So, James Kavanaugh is the Senior Vice President of Transformation at IBM, and works directly for Jenny Remetti. He fundamentally talked about IBM as an organization that is data-first, cloud, and consumerization was the other big trend. Now, I don't know if IBM's hit on all three of those yet but they're certainly working to get there. The other thing that was interesting is they talked about the data warehouse as the former king, and now process is king. What I think is interesting about that, I want to explore this with those guys, is that technology largely is well known today. People have access to technology. You can get security from-- You can log in with Twitter linked in our Facebook. You can-- Look at Uber and Waze. They're really software companies but they're built on other platforms, like the cloud, for example. These horizontal platforms. It's the processes that are new and unknown. You know, when you look at these emerging companies like Air BnB and Uber and Waze, and so forth, the processes by which consumers interact with businesses are totally changed. >> Exactly. That is what Jim and James and Inderpal were saying is that this explosion in data is really forcing companies to rethink their business models. And it's-- Their reporting structures, how they innovate, the kinds of things that they're working on, the kinds of risks that are keeping them up at night. >> Yeah, Kavanaugh cited a study for 4,000 CXOs and they said the number one factor impacting business sustainability in the next five years are technology-related. Which again, I want to poke at that a little bit, because to me technology is not the problem. It's process and skill sets and people are the really big challenges. But, I think really what I interpret from that data, what the CXOs are saying, the challenge is applying technology to create a business capability that involves all the process changes, the organizational changes, the people and skills set issues. Of course, they threw in a little fear, uncertainty, and doubt with GDPR, the recent breaches. The other big thing that you hear from IBM at these events is that IBM is a steward of your data. That it's your data, we're not going to-- They have this notion of data responsibility. He didn't mention-- He said the unnamed west coast companies. Of course, he's talking about Google and Amazon, who are sucking in our data and then advertising to us and telling us, hey there's a special and what to buy and what movie to watch, and so forth. That's not IBM's business. But, there's a nuance there that again, I want to explore with these guys if we have time is, while IBM is not taking your data and then turning it into business through advertising, IBM is training models. I'm interested in hearing IBM's response about where's the dividing line between the model-- sorry, the data, and the model. If the data is informing the model, the model then becomes IP. What happens to that IP? Does it get shared across the client base within an industry? So, I really want to understand that better. >> Right, and that is one thing that Jim Kavanaugh will talk about, definitely, is the responsibility that IBM has in terms of our data and protecting it and keeping it private. >> Yeah, so what I like about these events is they're intimate. We get into it with the CDOs. We got CDOs at banks, we have the influencer panel coming on, a lot are data practitioners. And, so much has changed over the last three or four years that we're happy to be here with the CUBE. >> It is. It's going to be a great day. So, we will have much more here at the IBM Chief Data Officer Strategy Summit. I'm Rebecca Knight for Dave Vallante. Stay tuned. (soft electronic music)

>> Live from Boston, Massachusetts, it's theCUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. (techno music) >> We are wrapping up theCUBE's coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with Dave Vellante. It's been a great day here in Boston at the CDO Strategy Summit. >> Yeah, I like these events, they're packed with content, very intimate. You know, not a lot of vendor push -- well, one vendor I guess is pushing. >> (laughs) >> But I like the way, we were talking to Chris Penn about earned media and owned media and paid media - this is all media. It's really the quality of the content that differentiates those media, and IBM always has really solid content here. A lot of practitioners, a lot of, not so much how to but hands on stories, use cases. >> Right. >> Maturity models, things of that nature. And I think we are seeing the maturity of the CDO role from a back office function to one that's sort of morphed into or evolved into data quality and part of the whole data-warehouse-as-king push, and that meant a lot of reporting, a lot of compliance, a lot of governance, to one that is really supporting a monetization mission of the business. And when you think about monetization at the simplest level, there's two ways to get there. You cut costs and you grow revenue. Now you should be careful, not all of these companies are for-profit firms, but in a commercial sense those are really the two levers that you can push, in a lot of forms. Productivity, time to market, time to value, quality, things of that nature, but at the end of the day it comes down to spending less, making more. >> Right, exactly, and I think that you made a great point in that data was the back office, it was sort of something we had to worry about, manage a bit, but now it's really front and center in the organization, and then thinking about using it to make money and to save money. And I think that's what we're learning about too, and what I've appreciated is how candid IBM is being, frankly, about mistakes that it has made, and it's saying this is a blueprint because we've learned. We've learned where we went wrong, and here's what we have to offer other companies to learn from us. >> Well, it's interesting too, if you take my little simple model of how to get value out of data, from IBM's standpoint, it's really a lot of opportunities to cut costs. A huge organization, 300,000 employees so we heard, from Jim Cavanaugh and Indabal Bendari today, how they're applying a lot of their data driven expertise to not only capture that data but understand how they can become more efficient. We haven't seen the growth from IBM. >> That's true. >> Everybody talks about the string of quarterly declines in terms of revenue. The good news is the pace of that decline is slow, that's the best you could say about IBM's top line, but the bottom line seems to be working. And IBM's such a huge machine that you can actually squeeze a lot of cash flow by saving some money. And there are a lot of stories about IBM and the supply chain and making that more efficient, which as we heard was a main focus of a lot of the CFOs, or CXOs out there. So, I mean IBM, we always talk about the steamship, you know, turning, and this has been a five- to seven-year turn, it's going to be interesting to see if IBM really will be perceived as a data driven company. They're pushing cognitive, there's a lot of blow back about Watson and how it's very services-led. Having said that, IBM's trying to do things that Google and Facebook and Amazon aren't trying to do. IBM's trying to solve cancer, for example. >> Right, right. >> Those other companies are trying to push ads in your face. So, got to give props to IBM for that effort. >> The social innovation piece I think is really a part of this company's DNA. >> Yeah, I mean, you know, again, frankly the Silicon Valley crowd sort of poo poos Watson from a technological perspective, honestly I'm not really qualified to address that question, but IBM tends to take capital and pour it into long-term businesses and eventually gets there. So, it's not there yet, and so, but if IBM can use the data to become a more efficient company, be more responsive to its customers, understand the needs of its clients better, that's going to yield results. >> And I think the other part that we've heard a lot about today is the cultural transformation that's needed to make these dramatic changes in your business. As you said, IBM is a huge company, hundreds of thousands of employees dispersed across the globe, so teams working across time zones, across cultures, across languages. That is difficult to really say, no, this is where we're going, this is our blueprint for success. Everyone come on board. >> Well, and you've seen some real cultural shakeups inside of IBM. I mean I was mentioning just a very small example, when you go to the third floor at Armonk now, the big concrete building, it's now all open, this is a corporate executive office. It's an open area with open cubicles, they're nice cubes, believe me, the cubes are nicer than your office, I guarantee it. But they're open, you can see executives, you can talk to executives in an open way. That's not how IBM used to be, it was very closed off and compartmentalized. >> Or everyone was working from home. That frankly... >> Well, that's the other piece of it, right? >> Yeah. >> They said, hey, guys, time to create the beehive effect. And that's created a lot of dislocation, a lot of concerns and blow back, but personally I like that approach. If you're trying to foster collaboration, nothing beats face to face contact. That's why we still have events and that's why theCUBE... >> That's why we're here. >> ...comes to these events, right? >> No, you're absolutely right, a growing body of research has really pointed to the value and the benefit of an open office to spur collaboration, spur creativity, to get colleagues really working and understanding the rhythms of each other's interpersonal lives and work lives, and really that's where the good ideas come from. >> Yeah, so I mean those decisions are tough ones for organizations to make, but I'm presuming that IBM had some data... >> Yeah. >> ...related to this, I hope they did, and made that decision. You know, and it's way too early to tell if that was the right or the wrong move. Again, I tend to lean toward the beehive approach as a positive potential outcome. >> Right, exactly. So, the other piece that we've heard a little bit about today is this talent shortage, the skills shortage because you made this great point when we were talking to Chris Penn of Shift Communications. So much of all of this stuff is now math and science, and that's not what you typically think of as someone who's in marketing, for example. We have a real shortage of people who know data science and analytics, and that's a big problem that a lot of these companies are facing and trying to deal with, some more successfully than others. >> Yeah, I mean I think that the industry is going to address that problem because all this deep learning stuff and this machine learning and AI, it is largely math and it's math that's known. When you really peel the onion and get into the sort of the type of math, you hear things like, oh, support vector machines and probabilistic latent cement tech indexing. >> (laughs) >> Okay, but these are concepts in math and algorithms that have been proven over time, and so I guess my point is, I think organizations are going to bring people in with strong math and computer skills and people who like data and can hack data, and say, okay, you're a data scientist, now figure it out. And over time I think they will figure it out, they'll train people. The hard part about that is, not necessarily the math, if you're good at math you're good at math, it's applying that math to help your organization understand A. How to monetize data, B. How to have data that's trusted. We heard that a lot. >> Yeah. >> So the quality of the data. C. Who gets access to that data, how do you secure and protect that data, what are some of the policies around that data. And then in parallel, how do you form relationships with the line of business? You got geeks talking to wallets. >> Right, yeah. >> How do you deal with that? >> You need the intermediary who can speak both languages. >> And then ultimately the answer to that I think is in skill sets and evolving those skill sets. So those are sort of the five things that the chief data officer has to think about, three are in parallel, or, three are in sequential and two are in parallel. >> Yeah, you also mentioned the trust in the data, and you were talking about it from an internal standpoint of colleagues agreeing, alright, this is what the data is telling us, this is clearly the direction we go in, but then there's the trust on the other side too, which is the trust that the company has with customers and clients to feel okay about using our data, using my data to make decisions. >> Well, I think it's a great point. It was interesting to hear Chris Penn's response to that. He was basically saying, well, we could switch suits, but it's not going to have the same impact. I'm not buying it. I'm really going to keep pushing on this issue because, while I agree that IBM doesn't have the same proclivity to take data and push ads in front of your face, it's unclear to me how you train models and somehow those models don't seep out. Now, IBM has said, we heard some IBM executives say, no, they're the customers' models. But you know, ideas get in people's heads and things happen. And that's just one example. There are many, many other examples. So think about internet of things and the factory floor, and you've got some widget on the floor that's capturing data, and that widget manufacturer wants to use data for predictive analytics, for predictive failures, sending data back home, and then who knows what other insights they're going to gather from that data? Whose data is that? Is that data owned by the widget manufacturer, is that data owned by the factory? >> Right. >> It's their process, it's their work flow. Now of course if I'm the factory owner I'm going to say it's my data, if I'm the widget manufacturer I'm going to say that's my data, so... >> And you're both right. >> And you're both right. >> That's the problem here, is that there's no real arbiter to say, to make that determination. >> Yeah, and I don't think these things have been challenged in court and certainly not adequately, and so there's a lot of learnings that are going to occur over the next decade, and we'll watch that evolution. >> But Jim Cavanaugh is right, we are at a real seminal moment here for this explosion in data, which is really changing the role of the CDO and how it fits in with the rest of the organization. >> Yeah, and I think the other thing to watch is how (mumbles) talks about data driven organizations, digital businesses, cognitive businesses, what are those? Those are kind of buzzwords, but what do they mean? What they mean, in our view, is how well you leverage data to create a competitive advantage, and that's what a digital business does. It uses data differentially (chuckles) to retain customers, attract and retain customers. And so that's what a digital business is, that's what a cognitive business is. Most businesses really aren't digital businesses today, or cognitive businesses today, they're really few and far between. So a lot of work has to be done before we reach that vision. Yeah, everybody throws out the Ubers and the Airbnb's, those are sort of easy examples, but when you have giant logistic systems and supply chains and ERP systems and HR systems with all this stovepipe data, becoming a "digital business" ain't so easy. >> No, and we are really in early days, exactly. So that's something to discuss at the next CDO Strategy Summit. >> And I think there was a lot of discussion early on when the CDO role emerged that they're essentially going to replace the CIO, I don't see it that way. There's a lot of discussion about what's the growth path for the CIO, is it technology or is it business? But I think the CIO's okay. >> Yeah? >> I think the CDO, I think actually there's more overlap between the chief digital officer and the chief data officer, because if you buy the argument that digital equals data, then the chief data officer and the chief digital officer are kind of one in the same. >> Right, right. >> So that to me is a more interesting dynamic than the CIO versus the CDO. I don't see those two roles as highly overlapping and full of friction. I really see that the chief digital officer and the chief data officer are more, should be more aligned and maybe even be the same role. >> And it gets back to the organizational politics that are involved, with all of these massive changes taking place. >> Well, again, first, the starting point for a CDO in a for-profit company is, how can we use data to create value and monetize that value? Not necessarily sell the data, but how does data contribute to our value creation as a company? So, with that as the starting point, that leads to, okay, well, if you're going to be data driven, then you better have measurements, you better have a system. I mean do you use enterprise value, do you use simple ROI, do you use an IOR calculation, do you use a more sophisticated options-based calculation? I mean, how do you measure value and how do you determine capital allocation as a function of those value measurements? The vast majority of the companies out there certainly can't answer that across the board, the CFO's office might be able to answer some of that, but deep down the line of business in the field where decisions are being made, are they really data driven? They're just starting, I mean this is first, second inning. >> Right, right, right. So there's much more to come. Great. Well, you have watched theCUBE's coverage of the IBM CDO Summit. Thanks for tuning in. For Rebecca Knight and Dave Vellante, we'll see you next time. (techno music)

>> Live from Boston, Massachusetts, it's the CUBE covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to the CUBE's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Dave Vellante. We're joined by Mark Lack. He is the Strategy Analytics and Business Intelligence Manager at Mueller Inc. Thanks so much for joining us, Mark. >> Thank you for the invite. >> So why don't you tell our viewers a little bit about Mueller and about what you do there. >> Sure, Mueller Inc. is based in the southwest. Ballinger, Texas, to be specific. And, I don't expect anybody, unless they Google it right now, would be able to find that city. But that's where our corporate headquarters and our main manufacturing plant has been. And, we are a company that manufactures and retails steel building products. So, if you think of a warehouse, or a backyard building or even a metal roof, or even I was looking downtown, or downstairs, earlier today, this building is made out of big steel girders. We take those and form them into a product that a customer can use for storage or for living or for any of whatever their use happens to be. Typically, it might be agricultural, but you also find it in very, very large buildings. Mueller is a retailer that happens to manufacture its products. Now, that's a very important distinction, because the company, up until about 15, 20 years ago, viewed itself as a manufacturer that just happened to retail its products. And so when you take the change in the emphasis, your business changes. The way you approach your customers, the way you approach your products, the way you market yourself, is completely different from one side to the other. We've been in business since 1930s, been around for a very long time. It's a family owned business that has it's culture and it's success rooted in West Texas. We have 40 locations all over the southwest. We're headquartered in Ballinger, Texas. We're as far east as Oak Grove, Louisiana and as far west with locations as Albuquerque, New Mexico. >> So you do cognitive analytics for Mueller, so tell our viewers a little bit about what you do there. >> Sure. Mueller has always been on the forefront of technology. Not for technology's sake, but really for effectiveness and efficiency's sake. So Mueller did business process reengineering when it was common for much larger organizations to do. But Mueller took it under as the reality for us to manage our business in the future. We need to have the professional tools to be able to do this. So we set on in our industry using technology in novel ways that our competition just doesn't do. So with the implementation of technology, what you have is a lot of data that comes along. And so we've been very effective using it for our balance scorecard to report metrics and keep the organization on track with that. Giving information back to various parts of the organization and then also creating an analytics platform and program that allows us to really dive deep into the organization and the data and everything that's being thrown off from modern technology. So cognitive analytics. This is something, as you hear about in technology today is, from the robots to artificial intelligence. Cognitive analytics, I think is for us a better way of looking at it of augmented intelligence. We have all of this data, we have these wonderful systems that help give us information to give us the answers we need on our business processes. We have some predictive analytics that help us to identify the challenges going ahead. What we don't have is the deep dive into using these technologies of cognitive to take all of this big data and find answers to situations that it would take a hundred people a hundred years to find out to be able to mine through. So the cognitive analytics is our new direction of analytics, and to be honest with you it's really the natural progression from our traditional analytic system. So as I said before, we have the regular analytics, we have the predictive analytics. As we get into cognitive, this is the next generation of how do we take this data that we have, that's coming at a volume and a velocity and a variety that is so difficult to look at as it is in a spreadsheet, and offload this onto system that can help us to interpret, give us some answers that we can then judge and then make decisions from. >> So, as you said, you have a lot of data. You got customer data, you got supply chain data, you got product data, you got sales data, retail location data. What's the data architecture look like? I mean, some data is more important than other data. How did you approach this opportunity? >> So, a few years ago I went to the first World of Watson, which was in New York. There was about a thousand attendees and Ginni Rometty had had this great presentation and it was very inspiring and she asked, "What will you do with Watson?" And at the time I had no idea what we were going to do with Watson, and so I sat on the plane on the way back and I thought through what are the business case scenarios that we can use to use artificial intelligence in a steel building company in Ballinger, Texas. Don't forget the irony of that part. As we're going to to go back to start using cognitive. So I thought through this and I went to our owner and we had many, many conversations on cognitive. You had the jeopardy, the Watson championship and you started thinking about all of these systems. But the real question was how could we take a new technology and apply it to our existing business to make a difference? And I'm getting to the answer to your question on how it got structured. So we went down the path of investigating Watson, and we've realized that the cognitive is part of our future. And so we plan on leveraging cognitive in many ways. We'd like to see it sales effectiveness, operations effectiveness, transportation effectiveness. There are all sorts of great ideas that we have. One of the challenges we have, and the reason I'm here at the CDO Summit, is when we start to look at our data, the question is are we cognitive ready? And I'll be honest to you, we are for today for a sliver of what cognitive capability is. As you've always heard the numbers 80% of your data is in unstructured format. So we have lots and lots of unstructured data. We have a lot of structured data. When it comes to the analytics around our structured data, we're pretty good, but when you start talking about unstructured data, how do we now take this to add to our structured data and then have a more complete picture of the problem that we're searching? So what I'm hoping to gain here at the CDO Summit is talking to some of these world-class leaders in data operations and data management to help understand what their pain points were. Learn from them so I can take that back and help to architect what our needs are so that we can take advantage of this entire cognitive future that's... >> So you're precognitive. So cognitive ready, let's unpack that a little bit. That means, that what you've got a level of confidence in the data quality? You've got an understanding of how to secure it, govern it, who gets access to it? What does that mean, being cognitive ready? >> So it's going to to be all of those. All of the above. First is, do you have the data? And we all have data, whether it's in spreedsheet on our systems, whether it's in our mobile phone, whether it's on our websites, whether it's in our EIP systems, and I can keep going on >> You got data. >> We have data, but the question is, do we have access to the data? And if you talk to some people, well sure, we have access to the data. Just tell me what data you want and I'll get you access. Okay, well, that is one answer to a much larger problem, because that's only going to give you what your asking for. What the cognitive future is promising for us is we may not know the questions to ask. I think that's the difference between traditional analytics and then the cognitive analytics. One of the benefits of cognitive will be the fact that cognitive will give answers to questions that we're never asked. And so now that this happens, what do we do with it? You know, when we start thinking about having attacking a problem, you know, being data ready, having the data there, that's part of the problem. And I think most companies say we're pretty good with our data. But with the 80% that we don't have access to, the real question is, are we missing that crucial piece of information that prevents us from making the right decision at the right time? And so our approach, and what I'm going to go back with, is understanding the data architecture that those who have gone before me that I can pick up and bring back to my organization and help us to implement that in a way that will make it cognitive ready for the future. You know, it's not just the access to the data; it's having the data. And I had lunch a few years ago with Steve Mills who was a senior executive for IBM, and one of the people at lunch was bold enough to ask him, "How do we know what data to capture?" And he said, very bluntly, "All of it." Now this was about five years ago. So, back then, you're shaking your heads saying, "We don't have storage capabilities. "We don't have the ability to store all these data." But he had already seen the future, and what he was telling us right then was all of it is going to be valuable. So where we are today, we think we know what data's valuable. But cognitive's going to help us to understand what other data might me valuable as well. >> So I'm interested in your job from the perspective of the organizational change. And you work for, as you said, a small family-owned company. Smallish of family-owned company. And we've heard a lot of today about the business transformation, the technology involved, and how that has really changed dramatically over the last decade. But then, there's also this other piece which is the social and cultural change within these organizations. Can you describe your experience in terms of how your colleagues interpret your world? >> You're asking me those questions 'cause you can see the bruises from whatever I have to accomplish. (laughter) You know, within an organization, one of the benefits of working that I found at Muller, and it's a family organization, is that those who work there, and I've been there for 18 years, and I'm still considered a newcomer to the organization right after 18 years. But we're not there unless we have a strong commitment to the organization and to the culture of the company. So, while we may not always agree as to what the future needs to hold, okay? We all understand we need to do what's best for this company for its long term survival. At the end of the day, that's what we're there to do. So culturally, when you first come up with saying you're going to do artificial intelligence, you know, you got a lot of head-scratching, especially in West Texas. I have a hard time explaining even to those around me what it is that I do. But, once you start telling the story that we have data, we have lots of data, and that there might be information in that data that we don't know now but in the future we may have, and so, it's important for us to capture that data and store it. Whether or not we know that there's immediate value, we know there's some value, okay? And if we can take that leap that there's going to be some value, and we're here with the help of the organization faces, we know that there are challenges to every organization. We're a still building company in Ballinger, Texas. Now I know I keep saying that, but what if a company like Uber comes up with metal building and all of a sudden, we have new challenges that we never thought we'd face? Many organizations that have been up, industries that have been in upheaval from these changes in either technology access or a new idea that splits the difference. We want to make sure we can stay ahead, and so when we start talking about that from a culture, we're here for the long term value of the company. We're committed to this organization, so what it do we need to do? And so, you know, the term "out of the box thinking" is something that sometimes we have to do. That doesn't mean it's easy. It doesn't mean that we all immediately say, "Aha! This is what we're going to do." It takes convincing. It takes a lot of conversation, and it takes a lot of political capital to show that what it is that we're going to do is going to make sense and use a lot of good examples. >> Well, and you come to tongue-in-cheek about people rolling their eyes about AI and so forth, but any manufacturer who sees 3D printing and the way it's evolved goes "Wow!" And then the data that you can capture from that, so, I wanted to ask you, when you talk to your colleagues and people are afraid that robots are going to take over the world and so forth, but what are the things that when you think about augmented intelligence that, you know, where do the machines leave off and the humans pick up? What kinds of things do humans do in your world that machines don't do that well? >> So, you know, if I go back and think about analytics, for example, there's a lot of time collecting data, storing data, translating data, creating contract to retrieve that data, putting that data into a beautiful report and then handing it out. Think of all that time that it takes to get there, right? A lot of people who are in analytics think that they're adding value by doing it. But to be honest with you, they're not. There's no value in the construct. And so, what the value is in the interpretation of that data. So what do computers do well and what do we do well? We do well at interpreting what those findings tell us. If we can offload those transactions back to a machine that can set the data for us, automatically construct the data, put it into a situation for us that can then allow us to then interpret the results? Then we're spending the majority of our time adding value by interpreting and making changes with the company versus spending that same time going back and constructing something that may or may not be something that may add value. So we spend 80% of our time creating data for a report. The report, now we have to test the report to determine, can I communicate this the right way? You have machine learning now and you have tools that will then take this data and say, "Oh, this is numerical data. "This looks like general ledger data. This is the type of way this data should be displayed." So I don't have to think of a graph. It suggests one for me. So what it does is then allow me to interpret the results, not worry about the construct. >> So you can focus on the things that humans do well. But the other thing I want to talk to you about is the talent issue. I mean you guys, you've mentioned before that you're based in West Texas and you are working on a real vanguard in your industry. As I said, you were someone who is thinking about whether or not Uber is going to say, "Let's make steel buildings." I mean, is that a problem that you're facing, that your company is facing? >> Well, there is no joke, right, that the fact of the future's going to have a man and a dog. And the man's job is to feed the dog, and the dog's job is to bite the man if he tries to touch any of the machinery, right? So, I don't think that we're there. The jobs aren't going to be eliminated to where people are not able to add value. But finding a talent, back to your question, is the expectation that we have of talent, it is scarce. Finding people that have the skills to now interpret the data, so you can find people that have a lot of time that can do any of those steps in between. But now, what's happened is, you want people to add value, not create constructs that don't add the value. So the type of talent that you look for are people who can interpret this information to give us the better answers that we need for the organization to thrive. And that's really where I see the talent shifting is on more forward-looking, outcome-based, value-based decision making, not as much on the development of items that could be offloaded to a machine. >> Yeah, I mean, interpretation, creativity, ideation. I mean, machines have always replaced humans. We've talked about this on The Cube before, but the first time in human history, machines are replacing humans in cognitive functions. I mean, you gave an example of the workflow of developing a report, which... >> Kenney Company can relate to, yeah. >> But yeah, 10 years ago, that was like super valuable. Today it's like, "Let's automate that." >> Well, but the challenge I think where people have is where do they add value? What is the problem that we're trying to solve? It's where do we add value. If we add value creating the construct, you aren't going to be employed, because something else is going to do that. >> But if you add value on focusing on the output and being able to interpret that output in a way that adds value to your company, you'll be employed forever. So, you know, people that can solve problems, take the information, make decisions, make suggestions that are going to make the company better, will always be employed. But it's the people who think they add value flipping a switch or programming a lever, now, they think their value's very important there, but I think what we have to do and it behooves us, is to translate those jobs into where do you add value? Where is the most important thing you need to be doing for the success of this company? And that I think is really the future. >> Are you... We haven't asked any IoT questions today. I want to ask you, are you sort of digitizing, instrumenting for your customers the end products of what you guys produce, and how was that creating data? >> You know, we haven't, we talked about it. We don't have products that, we're not selling things that are machinery that might break down and give us information, and so, we're building final products that are there, that people will then do different things with. So, IoT hasn't worked for us from a product standpoint, but we are looking at our various machinery and making sure that we have understanding as to those events that are causing a break down. One of the challenges we have in our industry is if we have a line that manufactures apart, if it goes down, okay, now it shuts everything down. So we have a duplicate, which can get very expensive. We have duplicates of everything, and how many duplicates do you need to have to make sure you have duplicates of the duplicates? So if we can start to look at the state of this coming from our machinery, and use that as a predictor, then we can use that, and so you have sort of an IoT thing there by looking at the data that's there. But is it feeding back into our normal reporting systems? It's not necessarily like it is from a smartphone are enabled like that. >> No, but it's anticipating a potential outage. >> Sure. >> And avoiding that. Yeah, great. >> Well Mark, thanks so much for coming on The Cube. It was wonderful conversation. >> Thank you. >> I'm Rebecca Knight with Dave Vellante. We will have more from the CDO Summit just after this. (upbeat music)

>> Live from Boston, Massachusetts, it's the Cube, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to the Cube's live coverage of the IBM CDO Summit here in Boston Massachusetts. I'm your host, Rebecca Knight, along with my co-host Dave Vellante. We are joined by Caitlin Halferty. She is the Chief of Staff IBM Data Office, and also John Backhouse, the chief information officer and senior VP at CareEnroll. Thank you both so much for coming on the Cube. >> Great to be here. >> Thank you, good to see you. >> So before the cameras were rolling, John, we were talking about how you have this very unique vantage point and perspective on the role of the CIO and CDO. Can you tell our viewers a bit about your background? >> Sure. I started off in the military. I was in the army for 12 years as a military intelligence officer. I then moved to the NHS, which is a national health service in England and where I wrote the Clinical Care Pathways for myocardial infraction and diabetes pre-hospital. I then moved to the USA and became Chief Data Officer for Envision Healthcare, one of the largest hybrid providers of insurance and clinical care. And then I became a CIO for a multi-state Medicare program. >> So you've been around, so to speak (laughter) But the last two roles, CIO and CDO, so how would you describe them? I mean obviously two different places, but is it adversarial? Is it cooperative? What is the relationship like? >> I think its, the last couple of years, CDO role has matured, and it's become a direct competition between a CIO and a CDO. As my experiences I've been fighting for the same budget. I've been fighting for the same bind, I've been fighting for the same executives to sponsor my programs and projects. I think now as the maturity of the CDO has stepped out, especially in health, the CDO has a lot more power between the conduit between the business and IT. If the CDO sits in IT he's doomed for failure because it's a direct competition of a CIO role. But I also think the CIO role has changed in the way that the innovation has stepped up. The CIO role used to be "Your career is over, CIO." (laughter) Now it's the innovational aspect of infrastructure, cloud cognitive analysts, cognitive solutions and analytics so that the way the data is monetized and sold and reused, in the way that the business makes decisions. So I see a big difference. >> How much of that, sort-of authority, if I can use that term, of the chief data officer inside of a regulated company versus you're in the office of the chief data officer in an unregulated company, compare and contrast. >> Well, the chief data officer's got all the new regulatory compliancies coming down the GDC, the security, safe harbor, and as the technology moves in to cloud it becomes even harder. As you get PCI, HIPPA and etc. So, everything you do is scrutinized to a point where you have to justify, why, what, and when. And then you have to have the custodian of who is responsible. So then no longer can you say, "I got the data for this reason." You have to justify why you have that information about anything. And I think that regulatory component is getting stronger and stronger. >> And you know, we've often talked about the rise of the CDO role and how it's changed over the last few years. Primarily it started in response to regulatory and compliance concerns within financial services industries as we know banking and insurance, healthcare. And we're seeing more and more retail consumer products. Other industries saying look, "We don't really have enterprise-wide management of data across the organization" Investing in that leadership role to drive that transformation. So I'm seeing that spread beyond the regulated industries. >> Well Caitlin, in the keynote you really kicked off this conference by reminding us of why we're all here and that is to bring chief data officers together, to share those practices, to share what they've learned in their own organizations. Hearing John talk about the fight for resources, the fight to justify its existence. What do you think, how would you tease out the best practices around that? >> The way we've approached it, you know, I've mentioned this cognitive enterprise blueprint that we highlighted and released this morning. And this has been an 18-month project for us. And we've done it in close partnership with folks like John, giving a lot of great insight and feedback. And essentially the way we see it is there's these four pillars. So it's the technology piece and getting the technology right. It's the business process, both CDO-owned processes as well as enterprise-wide. And then the new piece we've added is around data, understanding the data part of it is so important. And so we've delivered the blueprint and then taking it to the next level to figure out what are the top used cases. How do we prioritize to your question, where prioritized-used cases. >> So, come back to the overlap between the CIO and CDO. I remember when I first met Ender Paul, we had him on the Cube and he's seared into my brain he's five points that the CDO has to do, the imperative. And three were sequential two were in parallel. One was figure out how to monetize, how you're data can contribute to the monetization of your company. Second was data trust and sources, third was access to that data and those were sequential >> Right. Processes and then he said "Line of business and skill sets were the other two that you kind of do in parallel, >> Absolutely. forge relationships with a lot of businesses and re-skill. Okay, so with that as the Ender Paul framework for what a CDO's job was... I loved it, I wrote a blog about it, (laughter) I clipped it. >> That's very good >> But the CIO hits a lot of those areas, certainly data access, of trust and security, the skill sets. Thinking about that framework, first of all do you buy it? I presume it's pretty valid, but where do you see the overlap and the collaboration? >> So I think that the framework works out and what IBM has produced is very tangible, it means you can take the pieces and you can action them. So, before you have to reflect on one: building the team, getting the right numbers in the team, getting the right skill sets in the team. That was always a challenge because you're building a team but you're not quite sure what the skill set is until you've started the plan and the math and you've started down that pathway, so with that blueprint it helps you to understand what you're trying to recruit for, is one aspect, and then two is the monetization or getting the data or making it fit for purpose, that's a real challenge and there's no magic wand for this, you know it depends on what the business problem is, the business process and understanding it. I'm very unique cause not only have I understand the data and the technology I actually give it the clinical care as well, so I've got the translations in the clinical speak into data, into business value. So, I can take information and translate it into value very quickly, and create a solution but it comes back to that you must have a designer and the designer must be an innovator, and an innovator must stay within the curve and the object is the business problems. That enables, that blueprint to be taken and run with, and hit the ground very quickly in an actionable manner. for me information in health is about insights, everybody's already doing the medical record, the electronic record, the debtor exchange. It's a little immature in health and a proper interoperability but it there and it's coming it's the actually use of and the visualization of population analysis. It used to be population health, as in we knew what we were doing after the fact, now we need to know what we are doing before the fact so we can target the outreach and to move the right people in the right place at the right time for the right care, is a bigger insight and that's what cognitive and the blueprint enables. >> So Caitlin, it feels like these two worlds are really coming together, you know, in the early days it was just really regulated businesses. >> Correct. >> Now with GDPR now everybody is a regulated business, >> Right. >> And given that EMR, and Meaningful Use and things like that are kind of rote now. >> Yeah. >> Regulated industries are really driving for that value holy grail. >> Yeah. >> So, I wonder if you could share your perspectives on those two worlds coming together. >> Yeah I do see them coming together, as well as the leadership. >> Right, yeah. >> Across the C-sweep, it's interesting we host these two in-person summits, one in the spring in San Francisco one here in Boston in the fall and we get about 120 or so CDOs that join us. We pull for, what are top topics and we always get ones around data monetization, talent, the one again that came up this year was changing nature of to the point on building those deep analytics partnerships within the organization, changing the relationships between CDOs and C-sweep peers. We do a virtual call with about 25 CDO's and we had John as our guest speaker, recently >> Yeah. And it was our best attended call, (laughter) it was solely focused on how CDOs and CIOs can partner together to drive business critical cross-enterprise initiatives, like GDPR in ways that they haven't in the past. >> Yeah. >> It was a reinforcement to me that building those relationships, that analytic partnership piece, is still top of mind to our CDO community. >> Yeah, and I think that the call itself was like sun because I invited the chief of their office and now he's the innovator and the chief information officer used to be the guy who kept the lights on, that's no longer the fact. The chief information officer is the innovator of the infrastructure, the design, the monetization, the value, the business and the chief in their office now has become the chief designer of information to make it fit for purpose, for presentation, for analytics, for the cognitive use of the business. Those roles now, when you bring them together, is extremely powerful and as the maturity comes of these chief there officer roles with the modern approach to chief information then you have a powerful, powerful dynamic. >> Well let's talk about the chief innovator, it reminds me of 1999. (laughter) >> If you want to be a CEO you've got to go the CEO's office and then Y2K on the whole thing blew up. (laughter) >> What's different now though, is the data >> Yeah. - [Caitlin] Absolutely. >> There certainly was a lot of data back then but not nearly like it is today and the technology underneath it, the whole cloud piece, but I wonder if you could talk about the innovation piece of that a little bit more >> Sure. and it's relationship to the data. >> So, I mean we've always been let's all go to the data warehouse, let's have a data lake, let's get the data scientist to fix the data lake. (laughter) >> Yeah. >> And then he's like " Whoa, well what did he do?" "Does it do anything? Show me." And you know now that physical massive environment of big service and big cages and big rooms with big overhead expenses is no longer necessary. I've just put 91 servers for an entire state's data and population in a cloud environment, multiple security levels with multiple methods of new innovational cloud management. And I've been able to standup 91 server in six and a half minutes. I couldn't even procure that... (laughter) - Right. >> Before >> I'd be months, and months >> Yeah, to put physical architecture together like that but now I can do it in six and a half minutes, I can create DR rapidly, I can do flip over active-active and I can really make the sure of it. Not only can I use the infrastructure I can enable people to get information at the point where it's needed now, far easier than I ever did before. >> So talking about how the technology has moved and evolved and changed so rapidly for the better but yet there is still a massive talent shortage of the people who, as you said - [John] Yeah >> Who can speak the language and take the data and immediately translate it into business value. What are you doing now about this talent shortage? What's your take on it and what are we doing to fix it? >> Yeah >> I would say, in one of the morning keynotes, Jim Cavanaugh our SVP for transportation operations got that question around how do you educate internally what it means to be a cognitive enterprise when there are so many questions about what does that really mean? And then how do you access skill against those new capabilities? He spoke about some of the internal hackathons that we did and ran sort of an internal shark tank-like to see how those top projects rise, align resources against it and build those skills and we've invested quite a lot internally as I know many of our clients have around what we call cognitive academy to ensure that we've one: figured out and defined what it means in this new...what type of new skills and then make sure that we're able to retrain and then keep and retain some of our new talents. So I think we're trying that multi-prong approach to retrain and retain as well. >> You guys use the term cognitive business we use the term digital business cause we can't use IBM's terms (laughter) But to us there the same thing >> Why not? >> Cause it's all about... (laughter) >> Cause were independent - [Caitlin] Dave's upset here >> But to us it's all about how you leverage data >> Yeah. >> And how you use data to >> Yeah. >> Maintain and to get and maintain costumers. So since we're playing CX bingo >> Yeah right. >> Chief digital officer, Bob Lord >> Right >> Bob Lord and Ender Paul Endario are two totally different people and there roles are quite different, but if it's all about the data and you buy that premise what is the chief digital officer do? they are largely driving revenue >> Absolutely >> That's understandable but it's part of your job too >> Right >> Or former job as a CDO and now as an innovation officer. Where do those roles fit? >> I think there's a clear demarcation line and especially when you get into EIM solutions as in Enterprise Information Management. And you start breaking those down and you've got to break them down into master data management and you start putting the domains together, the multi-master domains, and one of them is media, and media needs someone to own it, be the custodian, manage it, and present it to the business for consumption, the other's are pure data driven. >> Yeah. >> Master patient, master member, master costumer, master product, they all need data driven analytics to present information to the business. You can't just show them a sequel schemer and say "There you go." >> Yeah. (laughter) >> It doesn't work so there is different demarcations of specialist skills and the presentation and it got to be that hybrid between the business and IT. The business and the data, the business and the consumer and that is, I think the maturity of way this X-sweet is going these days >> Yeah. >> One thing we've seen internally to that point, I agree there's a clear demarcation there, is when we do partner with the digital office it can be to aid say digital sellers so we have a joint project going where we are responsible for the data piece of it >> Yeah. >> And then we are enabling our digital sellers, we're calling it cognitive sales advisor to pull dispersed pieces of costumer data that are currently housed in cylos across the organization, pull that into a digital, user friendly app, that can really enable those sellers, so I think there's some nice opportunities just as there are CDOs and CIOs to partner, for a data officer and a digital officer as well. >> One of our earlier guests was talking about some of the things that he's hearing in the break out sessions and he said "You know they could have been talking about the same stuff ten years ago, these intractable organizations that aren't quite there yet." What do you think we will be talking about next CDO summit? Do you think there will come a point where were not talking about is data important? Or does data have a role in the organization? When do you think that will happen? (laughter) >> Every time I say we're done with governance right? >> Yeah >> We're done and then governance >> Comes right back - Top topic (laughter) >> If you get the answer to that can I have the locker notes? (laughter) >> Sure >> Exactly, Exactly >> I think in the next ten years we're not going to ask anymore about what did we do, we're going to be told what we did. As in we're going to be looking forward, thing are going to be coming out and saying this is the projected for the next minute, second, hour, month, year and that's the big change. We are all looking back, what did we do? How did we do? What was the goals we tried to achieve? I don't think that's going to be what we ask next month, next year, next week. It's going to be you're going to tell me what I did and you're going to tell me what I'm doing. And that's going to change, and also the healthcare market, the way that health is prescriptive, they're not prescribed anymore. They way that we diagnose things against the prognosis, I think that the way we manage that information is going to change dramatically. I would say too, I've been working quite a bit with a client in Vegas, a casino, and their current issue or problem is they have all this data on what their guest do from the moment they check in, they get their hotel key, they know where spend, where they go to dinner, what type of trip they're on, is it business is is pleasure. Are the kids in town, different behaviors, spending patterns accordingly. >> Yeah. >> And the main concern they relate to us is I can't do anything about it until my guest has exited the property and then I'm sending them outreach emails trying to get them back, or trying to offer a coupon. >> Yeah. >> You know post - [John] Yeah, yeah. >> And they're gone. >> And what if I could do some real time analysis and deliver something of value to my guest while they are on site and we are starting to see some of that with Disney and some other companies. - [John] Yeah. >> But I think we will see the ability to take all this data that we already have and deliver it. >> In real time. -[John] Yeah. >> Influence behavior >> Right >> And spending patterns in real time that's what I'm excited about. >> Yeah and these machines will actually start making decisions, certain decisions for the brand. >> Yeah >> Right >> At the point where it can affect an outcome. >> Right, right, Which I think is hard >> It's starting >> Yeah >> No question, you certainly see it in fraud detection today, you mentioned Disney. >> The magic bands >> Right >> And the ability to track >> Yeah >> Where you are and that type of thing, yeah >> Great >> We're starting cyber security cause cyber security, an aspect of user log, server log, network, are looking for behavioral patterns and those behavioral patterns are telling us where the risks and the vulnerabilities are coming from. >> Thing that humans >> Yep >> Would not see that >> People don't see the patterns, yep. >> You're absolutely right, >> right >> They just wouldn't see the patterns of the risk. >> Excellent, well John, Caitlin, thanks so much for coming on the Cube it's always a pleasure to talk to you. >> Thank you - Great, thank you. >> I'm Rebecca Knight for Dave Vellante we'll have more just after this.

>> Live from Boston, Massachusetts. It's The Cube! Covering IBM Chief Data Officer's Summit. Brought to you by IBM. (techno music) >> Welcome back to The Cube's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host Rebecca Knight along with my Co-host Dave Vellante. We're joined by Jennifer Gibbs, the VP Enterprise Data Management of TD Bank, and Seth Dobrin who is VP and Chief Data Officer of IBM Analytics. Thanks for joining us Seth and Jennifer. >> Thanks for having us. >> Thank you. >> So Jennifer, I want to start with you can you tell our viewers a little about TD Bank, America's Most Convenient Bank. Based, of course, in Toronto. (laughs). >> Go figure. (laughs) >> So tell us a little bit about your business. >> So TD is a, um, very old bank, headquartered in Toronto. We do have, ah, a lot of business as well in the U.S. Through acquisition we've built quite a big business on the Eastern seaboard of the United States. We've got about 85 thousand employees and we're servicing 42 lines of business when it comes to our Data Management and our Analytics programs, bank wide. >> So talk about your Data Management and Analytics programs a little bit. Tell our viewers a little bit about those. >> So, we split up our office of the Chief Data Officer, about 3 to 4 years ago and so we've been maturing. >> That's relatively new. >> Relatively new, probably, not unlike peers of ours as well. We started off with a strong focus on Data Governance. Setting up roles and responsibilities, data storage organization and councils from which we can drive consensus and discussion. And then we started rolling out some of our Data Management programs with a focus on Data Quality Management and Meta Data Management, across the business. So setting standards and policies and supporting business processes and tooling for those programs. >> Seth when we first met, now you're a long timer at IBM. (laughs) When we first met you were a newbie. But we heard today, about,it used to be the Data Warehouse was king but now Process is king. Can you unpack that a little bit? What does that mean? >> So, you know, to make value of data, it's more than just having it in one place, right? It's what you do with the data, how you ingest the data, how you make it available for other uses. And so it's really, you know, data is not for the sake of data. Data is not a digital dropping of applications, right? The whole purpose of having and collecting data is to use it to generate new value for the company. And that new value could be cost savings, it could be a cost avoidance, or it could be net new revenue. Um, and so, to do that right, you need processes. And the processes are everything from business processes, to technical processes, to implementation processes. And so it's the whole, you need all of it. >> And so Jennifer, I don't know if you've seen kind of a similar evolution from data warehouse to data everywhere, I'm sure you have. >> Yeah. >> But the data quality problem was hard enough when you had this sort of central master data management approach. How are you dealing with it? Is there less of a single version of the truth now than there ever was, and how do you deal with the data quality challenge? >> I think it's important to scope out the work effort in a way that you can get the business moving in the right direction without overwhelming and focusing on the areas that are most important to the bank. So, we've identified and scoped out what we call critical data. So each line of business has to identify what's critical to them. Does relate very strongly to what Seth said around what are your core business processes and what data are you leveraging to provide value to that, to the bank. So, um, data quality for us is about a consistent approach, to ensure the most critical elements of data that used for business processes are where they need to be from a quality perspective. >> You can go down a huge rabbit whole with data quality too, right? >> Yeah. >> Data quality is about what's good enough, and defining, you know. >> Right. >> Mm-hmm (affirmative) >> It's not, I liked your, someone, I think you said, it's not about data quality, it's about, you know it's, you got to understand what good enough is, and it's really about, you know, what is the state of the data and under, it's really about understanding the data, right? Than it is perfection. There are some cases, especially in banking, where you need perfection, but there's tons of cases where you don't. And you shouldn't spend a lot of resources on something that's not value added. And I think it's important to do, even things like, data quality, around a specific use case so that you do it right. >> And what you were saying too, it that it's good enough but then that, that standard is changing too, all the time. >> Yeah and that changes over time and it's, you know, if you drive it by use case and not just, we have get this boil the ocean kind of approach where all data needs to be perfect. And all data will never be perfect. And back to your question about processes, usually, a data quality issue, is not a data issue, it's a process issue. You get bad data quality because a process is broken or it's not working for a business or it's changed and no one's documented it so there's a work around, right? And so that's really where your data quality issues come from. Um, and I think that's important to remember. >> Yeah, and I think also coming out of the data quality efforts that we're making, to your point, is it central wise or is it cross business? It's really driving important conversations around who's the producer of this data, who's the consumer of this data? What does data quality mean to you? So it's really generating a lot of conversation across lines of business so that we can start talking about data in more of a shared way versus more of a business by business point of view. So those conversations are important by-products I would say of the individual data quality efforts that we're doing across the bank. >> Well, and of course, you're in a regulated business so you can have the big hammer of hey, we've got regulations, so if somebody spins up a Hadoop Cluster in some line of business you can reel 'em in, presumably, more easily, maybe not always. Seth you operate in an unregulated business. You consult with clients that are in unregulated businesses, is that a bigger challenge for you to reel in? >> So, I think, um, I think that's changing. >> Mm-hmm (affirmative) >> You know, there's new regulations coming out in Europe that basically have global impact, right? This whole GDPR thing. It's not just if you're based in Europe. It's if you have a subject in Europe and that's an employee, a contractor, a customer. And so everyone is subject to regulations now, whether they like it or not. And, in fact, there was some level of regulation even in the U.S., which is kind of the wild, wild, west when it comes to regulations. But I think, um, you should, even doing it because of regulation is not the right answer. I mean it's a great stick to hold up. It's great to be able to go to your board and say, "Hey if we don't do this, we need to spend this money 'cause it's going to cost us, in the case of GDPR, four percent of our revenue per instance.". Yikes, right? But really it's about what's the value and how do you use that information to drive value. A lot of these regulation are about lineage, right? Understanding where your data came from, how it's being processed, who's doing what with it. A lot of it is around quality, right? >> Yep. >> And so these are all good things, even if you're not in a regulated industry. And they help you build a better connection with your customer, right? I think lots of people are scared of GDPR. I think it's a really good thing because it forces companies to build a personal relationship with each of their clients. Because you need to get consent to do things with their data, very explicitly. No more of these 30 pages, two point font, you know ... >> Click a box. >> Click a box. >> Yeah. >> It's, I am going to use your data for X. Are you okay with that? Yes or no. >> So I'm interested from, to hear from both of you, what are you hearing from customers on this? Because this is such a sensitive topic and, in particularly, financial data, which is so private. What are you, what are you hearing from customers on this? >> Um, I think customers are, um, are, especially us in our industry, and us as a bank. Our relationship with our customer is top priority and so maintaining that trust and confidence is always a top priority. So whenever we leverage data or look for use cases to leverage data, making sure that that trust will not be compromised is critically important. So finding that balance between innovating with data while also maintaining that trust and frankly being very transparent with customers around what we're using it for, why we're using it, and what value it brings to them, is something that we're focused on with, with all of our data initiatives. >> So, big part of your job is understanding how data can affect and contribute to the monetization, you know, of your businesses. Um, at the simplest level, two ways, cut costs, increase revenue. Where do you each see the emphasis? I'm sure both, but is there a greater emphasis on cutting costs 'cause you're both established, you know, businesses, with hundreds of thousands, well in your case, 85 thousand employees. Where do you see the emphasis? Is it greater on cutting costs or not necessarily? >> I think for us, I don't necessarily separate the two. Anything we can do to drive more efficiency within our business processes is going to help us focus our efforts on innovative use of data, innovative ways to interact with our customers, innovative ways to understand more about out customers. So, I see them both as, um, I don't see them mutually exclusive, I see them as contributing to each. >> Mm-hmm (affirmative) >> So our business cases tend to have an efficiency slant to them or a productivity slant to them and that helps us redirect effort to other, other things that provide extra value to our clients. So I'd say it's a mix. >> I mean I think, I think you have to do the cost savings and cost avoidance ones first. Um, you learn a lot about your data when you do that. You learn a lot about the gaps. You learn about how would I even think about bringing external data in to generate that new revenue if I don't understand my own data? How am I going to tie 'em all together? Um, and there's a whole lot of cultural change that needs to happen before you can even start generating revenue from data. And you kind of cut your teeth on that by doing the really, simple cost savings, cost avoidance ones first, right? Inevitably, maybe not in the bank, but inevitably most company's supply chain. Let's go find money we can take out of your supply chain. Most companies, if you take out one percent of the supply chain budget, you're talking a lot of money for the company, right? And so you can generate a lot of money to free up to spend on some of these other things. >> So it's a proof of concept to bring everyone along. >> Well it's a proof of concept but it's also, it's more of a cultural change, right? >> Mm-hmm (affirmative) It's not even, you don't even frame it up as a proof of concept for data or analytics, you just frame it up, we're going to save the company, you know, one percent of our supply chain, right? We're going to save the company a billion dollars. >> Yes. >> And then there's gain share there 'cause we're going to put that thing there. >> And then there's a gain share and then other people are like, "Well, how do I do that?". And how do I do that, and how do I do that? And it kind of picks up. >> Mm-hmm (affirmative) But I don't think you can jump just to making new revenue. You got to kind of get there iteratively. >> And it becomes a virtuous circle. >> It becomes a virtuous circle and you kind of change the culture as you do it. But you got to start with, I don't, I don't think they're mutually exclusive, but I think you got to start with the cost avoidance and cost savings. >> Mm-hmm (affirmative) >> Great. Well, Seth, Jennifer thanks so much for coming on The Cube. We've had a great conversation. >> Thanks for having us. >> Thanks. >> Thanks you guys. >> We will have more from the IBM CDO Summit in Boston, Massachusetts, just after this. (techno music)

>> Live from Boston, Massachusetts, it's theCube, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to theCube's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We are here with Joseph Selle, he is the Cognitive Transformation Lead at IBM. Thanks so much for joining us, Joe. >> Hi, Rebecca, thank you. Hi, Dave. >> Good to see you, Joe. >> You, too. >> So, your job is to help drive the internal transformation of IBM. Tell our viewers what that means and then talk about your approach. >> Right, it a very exciting, frankly, it's one of the best jobs I've ever had personally. It's wonderful. We're transforming the company from the inside out. We're engaging with all of the functional areas within IBM's operations, and we're challenging those functional teams to breakdown their business process and reinvent it using some new tooling. And in this case, it's cognitive approaches to data analysis, and to crowd sourcing information, and systems that learn. We've talked a lot about at this conference, machine learning and deep learning. We're providing all of these tools to these functional teams so they can go reinvent HR and procurement, and even our M&A process, everything is fair game. So, it's very exciting and it really allows us to reinvent IBM. >> So, reinventing all of these individual functions, I mean, where to do you start? How do you begin to build the blueprint? >> Well, in our case, where we started was we had to get the whole company thinking about a large-scale enterprise, cultural transformation. We have a company of 300-some odd thousand people, employees, speaking all languages, all over the globe. So, how do you move that mass? So, we had cognitive jam, that's basically a technology enabled brainstorm session that spreads across the entire globe. And, by engaging about 300,000 IBM'ers, we were able to call and bring together all kinds of very disruptive, interesting ideas to remake all these business processes. We culled those ideas, and through some prioritization, almost a shark tank-like process, we ended up with a few that were really worthy, we felt, of investment. We've put money in, and our cognitive reinvention was born. Just like that. >> That's a lot of brain power. (laughs) >> Well, that's why it's wonderful to be at IBM, 'cause we have hundreds of thousands of brainy people working for us. >> You have talked about, when he was a controller during the Gerstner transformation, I don't know were you there back then? >> Yes, I was. >> Okay, so you guys were young pups back then, still young pups, I guess. But, he talked about, as the controller, he was an unhappy customer because he didn't have the data. So, can you talk about, sort of, what's different today? I mean, it's a lot different, obviously, the state of the industry, the technology, the amount of the data, et cetera. But, maybe talk about data as the starting point and how that was different from, maybe, the Gerstner transformation. >> The early days. >> Which was epic, by the way. You know, took IBM to new levels and be part of what the company is today. >> And this story that I'm going to tell you, is generally applicable to most any company that's global in nature. The data are not visible and they're not easy to see and discern any value from in the early stages of your transformation. So, when Jim was controller, he had data that was one, hard to get, and two, he had no tools to organize it except for, maybe, some smart people with Excel and, whatever it was back then, LotusPro, or something, I can't remember the name of that. (laughter) >> Something that ran on OS/2. >> There was no tooling, no approach. And, the whole idea of big data was not even around at that point. Because the data was organized and disorganized in little towers and databases all around, but there wasn't a flood of data. So, what's different between those days and this time period that we're in is, you can see data now and data are everywhere. And they're coming at us in high, high volumes and at high speeds. If you think about The Weather Company, one of the acquisitions we made two years ago, that is a stream of huge, big data, coming at us very fast. You can think about The Weather Company as a giant internet of things, device, which is pulling data from the sky and from people interacting with the environment, and bringing that all together. And now, what can we do with that data? Well, we can use it to help predict when we're going to have a supply chain disruption, or, I mean in an almost obvious sense, or we can use it when we're trying to respond to some sort of operational disturbance. If we're looking at where we can reroute things, or if we're trying to anticipate some sort of blockage on our supply chain, incoming supply chain, or outgoing supply chain of products. Very important, and we just see much more now then Jim ever could when he was a controller. >> In the scope of your data initiative, is everything, I mean, he's mentioned supply chain, you got customer data? >> It is, it is. But, I'll say that, you know, if a company's going to embark down this path, you don't want to try to boil the ocean at the start. You want to try to go after some selective business challenges, that are persistent challenges that you wish you had a way to solve because a lot of value's at play. So, you go in there and you solve a few problems. You deal with a data integrity and access problem, on a, sort of a, confined basis. And you do this, maybe, several times across different parts of your company. Then, once you've done that four or five times, or some small number of times, you begin to learn how to handle the problem more generally, and you can distill approaches and tools that can then be applied broadly. And where we are in our evolution, is that Inderpal and Jim, and the internal workings of IBM, were building a cognitive enterprise data platform. So, we're taking all of these point solutions that I just referred to, bringing them together onto a platform, and applying some common tooling to all of these common types of problems around data organization, and governance, and meta-data tagging, and all this geeky stuff that you have to be able to do if you're going to make any value. You know, if you're going to make an important, valuable business decision, based on a stream of data. >> So, where has it had tangible, measurable, business impact, this sort of cognitive initiative? >> Well, a couple of the areas where we're most mature, one would be in supply chain and procurement. We've been able to take jobs that, frankly, involve a lot of churning analysis, and be able to say to a procurement specialist, okay, what used to take you six hours, or an hour, or what ever the task was, we can shrink that down using a cognitive tool, down to just a few minutes. So, procurement, we've been able to get staffing efficiencies, and we've been able, even more importantly, to make sure that we're buying things at the best possible price. Because those same analysts want to know what's happening in the market, where's the market sentiment going? Is this market tightening or loosening? Is it a buyer or a seller market? If we're trolling the web, bringing back information on the micro-movements of all the regional markets in various electronics commodities, we know an aggregate, whether we should be hard bargainers or easy bargainers, essentially. So, that's procurement. But, you could talk about human resources, where the Watson tool can recommend a game plan for how you would manage the career of a person. You don't want to lose your star people. And it's wonderful that deep, subject matter experts in HR know how to anticipate what you're thinking, and those are the people you want in charge of HR. But, there's a lot of other people who aren't, maybe, as good as that one person at HR, now the system can help you by giving you a playbook, making you a better HR manager. So, that's HR, but I got one more that's really exciting that I'm working on right now in the area of M&A. So, IBM and any large company that has multiple offerings and geographies is involved in M&A. We're using cognition and big data to speed up our M&A process. Now, we have a small team of M&A, so we're not going to make millions of dollars of staffing efficiencies, but, if we can capture a company, if we can be the first one to make an offer on a company, rather than the third one, then we're going to get the best company. And if you can bring the best company in, like The Weather Company as an example in that space, or like any other type of data-mining company or something, you want the best company. And if you can use cognition to enhance your process to move very quickly, that's going to really help you. >> So, this is a huge transformation of the business model, but then you've also talked about the cultural transformation of IBM. How would you describe this new IBM, going through this transformation? How would you describe the culture and collaboration? >> So, luckily, we're pretty far along in the transformation and we're at a stage where we actually have a data platform that's been deployed internally. And, people know about the potential of cognition to redefine and remake their business processing, create all this value. So, now we're getting people to come on to the platform as citizen analysts, if you want to call them that, they're not operations PhD's, they're not necessarily data scientists, they're regular business analysts. They're coming onto the platform and they're finding data and they're finding tools to manipulate that data. They're coming in on a self-service model and being able to gain insights to bring back into their business decisions without the CIO office being involved. >> So that's a workbench on the Cloud, essentially, is that right? >> Yes, that it a good way to put it, yep. >> Workbench, we out of trademark that. (laughs) >> Let's do that. >> Good descriptor, I think. >> Well, Joe, thanks so much for joining us, it's been a pleasure talking to you. >> My pleasure, thank you. >> Thanks, thanks a lot. >> I'm Rebecca Knight, for Dave Vellante, we will have more from IBM CDO Summit just after this.

>> Live from Boston, Massachusetts, it's theCUBE, Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of IBM Chief Data Strategy Summit. My name is Rebecca Knight, and I'm here with my co-host Dave Vellante, we are joined by Christopher Penn, the VP of Marketing Technology at SHIFT Communications, here in Boston. >> Yes. >> Thanks so much for joining us. >> Thank you for having me. >> So we're going to talk about cognitive marketing. Tell our viewers: what is cognitive marketing, and what your approach to it is. >> Sure, so cognitive marketing essentially is applying machine learning and artificial intelligence strategies, tactics and technologies to the discipline of marketing. For a really long time marketing has been kind of known as the arts and crafts department, which was fine, and there's certainly, creativity is an essential part of the discipline, that's never going away. But we have been tasked with proving our value. What's the ROI of things, is a common question. Where's the data live? The chief data officer would be asking, like, who's responsible for this? And if we don't have good answers to those things, we kind of get shown the door. >> Well it sort of gets back to that old adage in advertising, I know half my marketing budget is wasted, I just don't know which half. >> Exactly. >> So now we're really able to know which half is working. >> Yeah, so I mean, one of the more interesting things that I've been working on recently is using what's called Markov chains, which is a type of very primitive machine learning, to do attribution analysis, to say what actually caused someone to become a new viewer of theCUBE, for example. And you would take all this data that you have from your analytics. Most of it that we have, we don't really do anything with. You might pull up your Google Analytics console, and go, "Okay, I got more visitors today than yesterday." but you don't really get a lot of insights from the stock software. But using a lot of tools, many of which are open source and free of financial cost, if you have technical skills you can get much deeper insights into your marketing. >> So I wonder, just if we can for our audience... When we talk about machine learning, and deep learning, and A.I., we're talking about math, right, largely? >> Well so let's actually go through this, because this is important. A.I. is a bucket category. It means teaching a machine to behave as though it had human intelligence. So if your viewers can see me, and disambiguate me from the background, they're using vision, right? If you're hearing sounds coming out of my mouth and interpreting them into words, that's natural language processing. Humans do this naturally. It is now trying to teach machines to do these things, and we've been trying to do this for centuries, in a lot of ways, right? You have the old Mechanical Turks and stuff like that. Machine learning is based on algorithms, and it is mostly math. And there's two broad categories, supervised and unsupervised. Supervised is you put a bunch of blocks on the table, kids blocks, and you hold the red one, and you show the machine over and over again this is red, this is red, and eventually you train it, that's red. Unsupervised is- >> Not a hot dog. (Laughter) >> This is an apple, not a banana. Sorry CNN. >> Silicon Valley fans. >> Unsupervised is there's a whole bunch of blocks on the table, "Machine, make as many different sequences as possible," some are big, some are small, some are red, some are blue, and so on, and so forth. You can sort, and then you figure out what's in there, and that's a lot of what we do. So if you were to take, for example, all of the comments on every episode of theCUBE, that's a lot, right? No humans going to be able to get through that, but you can take a machine and digest through, just say, what's in the bag? And then there's another category, beyond machine learning, called deep learning, and that's where you hear a lot of talk today. Deep learning, if you think of machine learning as a pancake, now deep learnings like a stack of pancakes, where the data gets passed from one layer to the next, until what you get at the bottom is a much better, more tuned out answer than any human can deliver, because it's like having a hundred humans all at once coming up with the answer. >> So when you hear about, like, rich neural networks, and deep neural networks, that's what we're talking about. >> Exactly, generative adversarial networks. All those things are ... Any kind of a lot of the neural network stuff is deep learning. It's tying all these piece together, so that in concert, they're greater than the sum of any one. >> And the math, I presume, is not new math, right? >> No. >> SVM and, it's stuff that's been around forever, it's just the application of that math. And why now? Cause there's so much data? Cause there's so much processing power? What are the factors that enable this? >> The main factor's cloud. There's a great shirt that says: "There's no cloud, it's just somebody else's computer." Well it's absolutely true, it's all somebody else's computer but because of the scale of this, all these tech companies have massive server farms that are kind of just waiting for something to do. And so they offer this as a service, so now you have computational power that is significantly greater than we've ever had in human history. You have the internet, which is a major contributor, the ability to connect machines and people. And you have all these devices. I mean, this little laptop right here, would have been a supercomputer twenty years ago, right? And the fact that you can go to a service like GitHub or Stack Exchange, and copy and paste some code that someone else has written that's open source, you can run machine learning stuff right on this machine, and get some incredible answers. So that's why now, because you've got this confluence of networks, and cloud, and technology, and processing power that we've never had before. >> Well with this emphasis on math and science in marketing, how does this change the composition of the marketing department at companies around the world? >> So, that's a really interesting question because it means very different skill sets for people. And a lot of people like to say, well there's the left brain and then there's a right brain. The right brains the creative, the left brains the quant, and you can't really do that anymore. You actually have to be both brained. You have to be just as creative as you've always been, but now you have to at least have an understanding of this technology and what to do with it. You may not necessarily have to write code, but you'd better know how to think like a coder, and say, how can I approach this problem systematically? This is kind of a popular culture joke: Is there an app for that, right? Well, think about that with every business problem you face. Is there an app for that? Is there an algorithm for that? Can I automate this? And once you go down that path of thinking, you're on the path towards being a true marketing technologist. >> Can you talk about earned, paid, and owned media? How those lines are blurring, or not, and the relationship between sort of those different forms of media, and results in PR or advertising. >> Yeah, there is no difference, media is media, because you can take a piece of content that this media, this interview that we're doing here on theCUBE is technically earned media. If I go and embed this on my website, is that owned media? Well it's still the same thing, and if I run some ads to it, is it technically now paid media? It's the thing, it's content that has value, and then what we do with it, how we distribute it, is up to us, and who our audience is. One of the things that a lot of veteran marketing and PR practitioners have to overcome is this idea that the PR folks sit over there, and they just smile and dial and get hits, go get another hit. And then the ad folks are over here... No, it's all the same thing. And if we don't, as an industry realize that those silos are artificially imposed, basically to keep people in certain jobs, we will eventually end up turning over all of it to the machines, because the machines will be able to cross those organizational barriers much faster. When you have the data, and whatever the data says that's what you do. So if the data says this channels going to be more effective, yes it's a CUBE interview, but actually it's better off as a paid YouTube video. So the machine will just go do that for us. >> I want to go back to something you were talking about at the very beginning of the conversation, which is really understanding, companies understanding, how their marketing campaigns and approaches are effectively working or not working. So without naming names of clients, can you talk about some specific examples of what you've seen, and how it's really changed the way companies are reaching customers? >> The number one thing that does not work, is for any business executive to have a pre-conceived idea of the way things should be, right? "Well we're the industry leader in this, we should have all the market share." Well no, the world doesn't work like that anymore. This lovely device that we all carry around in our pockets is literally a slot-machine for your attention. >> I like it, you've got to copyright that. A slot machine for your attention. >> And there's a million and a half different options, cause that's how many apps there are in the app store. There's a million and half different options that are more exciting than your white paper. (Laughter) Right, so for companies that are successful, they realize this, they realize they can't boil the ocean, that you are competing every single day with the Pope, the president, with Netflix, you know, all these things. So it's understanding: When is my audience interested in something? Then, what are they interested in? And then, how do I reach those people? There was a story on the news relatively recently, Facebook is saying, "Oh brand pages, we're not going to show "your stuff in the regular news feed anymore, "there will be a special feed over here "that no one will ever look at, unless you pay up." So understanding that if we don't understand our audiences, and recruit these influencers, these people who have the ability to reach these crowds, our ability to do so through the "free" social media continues to dwindle, and that's a major change. >> So the smart companies get this, where are we though, in terms of the journey? >> We're in still very early days. I was at major Fortune 50, not too long ago, who just installed Google Analytics on their website, and this is a company that if I named the name you would know it immediately. They make billions of dollars- >> It would embarrass them. >> They make billions of dollars, and it's like, "Yeah, we're just figuring out this whole internet thing." And I'm like, "Cool, we'd be happy to help you, but why, what took so long?" And it's a lot of organizational inertia. Like, "Well, this is the way we've always done it, and it's gotten us this far." But what they don't realize is the incredible amount of danger they're in, because their more agile competitors are going to eat them for lunch. >> Talking about organizational inertia, and this is a very big problem, we're here at a CDO summit to share best practices, and what to learn from each other, what's your advice for a viewer there who's part of an organization that isn't working fast enough on this topic? >> Update your LinkedIn profile. (Laughter) >> Move on, it's a lost cause. >> One of the things that you have to do an honest assessment of, is whether the organization you're in is capable of pivoting quickly enough to outrun its competition. And in some cases, you may be that laboratory inside, but if you don't have that executive buy in, you're going to be stymied, and your nearest competitor that does have that willingness to pivot, and bet big on a relatively proven change, like hey data is important, yeah, you make want to look for greener pastures. >> Great, well Chris thanks so much for joining us. >> Thank you for having me. >> I'm Rebecca Knight, for Dave Vellante, we will have more of theCUBE's coverage of the IBM Chief Data Strategy Officer Summit, after this.

>> Live from Boston, Massachusetts, it's The Cube! Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to The Cube's live coverage of IBM's Chief Data Strategy Summit, I'm your host Rebecca Knight, along with my cohost Dave Vellante. We have a big panel today, these are our social influencers. Starting at the top, we have Christopher Penn, VP Marketing of Shift Communications, then Tripp Braden, Executive Coach and Growth Strategist at Strategic Performance Partners, Mike Tamir, Chief Data Science Officer at TACT, Bob Hayes, President of Business Over Broadway. Thanks so much for joining us. >> Thank you. >> So we're talking about data as a way to engage customers, a way to engage employees. What business functions would you say stand to benefit the most from using data? >> I'll take a whack at that. I don't know if it's the biggest function, but I think the customer experience and customer success. How do you use data to help predict what customers will do, and how do you then use that information to kind of personalize that experience for them and drive up recommendations, retention, upselling, things like that. >> So it's really the customer experience that you're focusing on? >> Yes, and I just released a study. I found that analytical-leading companies tend to use analytics to understand their customers more than say analytical laggards. So those kind of companies who can actually get value from data, they focus their efforts around improving customer loyalty by just gaining a deeper understanding about their customers. >> Chris, you want to jump in here with- >> I was just going to say, as many of us said, we have three things we really care about as business people, right? We want to save money, save time, or make money. So any function that meets those qualifications, is a functional benefit from data. >> I think there's also another interesting dimension to this, when you start to look at the leadership team in the company, now having the ability to anticipate the future. I mean now, we are no longer just looking at static data. We are now looking at anticipatory capability and seeing around corners, so that the person comes to the team, they're bringing something completely different than the team has had in the past. This whole competency of being able to anticipate the future and then take from that, where you take your organization in the future. >> So follow up on that, Tripp, does data now finally trump gut feel? Remember the HBR article of 10, 15 years ago, can't beat gut feel? Is that, we hit a new era now? >> Well, I think we're moving into an era where we have both. I think it's no longer an either or, we have intuition or we have data. Now we have both. The organizations who can leverage both at the same time and develop that capability and earn the trust of the other members by doing that. I see the Chief Data Officer really being a catalyst for organizational change. >> So Dr. Tamir I wonder if I could ask you a question? Maybe the whole panel, but so we've all followed the big data trend and the meme, AI, deep learning, machine learning, same wine, new bottle, or is there something substantive behind it? >> So certainly our capabilities are growing, our capabilities in machine learning, and I think that's part of why now there's this new branding of AI. AI is not what your mother might have thought AI is. It's not robots and cylons and that sort of thing that are going to be able to think intelligently. They just did intelligence tests on the different, like Siri and Alexa, quote AIs from different companies, and they scored horribly. They scored much worse than my, much worse than my very intelligent seven-year old. And that's not a comment on the deficiencies in Alexa or in Siri. It's a comment on these are not actually artificial intelligences. These are just tools that apply machine learning strategically. >> So you are all thinking about data and how it is going to change the future and one of the things you said, Tripp, is that we can now see the future. Talk to me about some of the most exciting things that you're seeing that companies do that are anticipating what customers want. >> Okay, so for example, in the customer success space, a lot of Sass businesses have a monthly subscription, so they're very worried about customer churn. So companies are now leveraging all the user behavior to understand which customers are likely to leave next month, and if they know that, they can reach out to them with maybe some retention campaigns, or even use that data to find out who's most likely to buy more from you in the next month, and then market to those in effective ways. So don't just do a blast for everybody, focus on particular customers, their needs, and try to service them or market to them in a way that resonates with them that increases retention, upselling, and recommendations. >> So they've already seen certain behaviors that show a customer is maybe not going to re-up? >> Exactly, so you just, you throw this data in a machine learning, right. You find the predictors of your outcome that interest you, and then using that information, you say oh, maybe predictors A, B, and C, are the ones that actually drive loyalty behaviors, then you can use that information to segment your customers and market to them appropriately. It's pretty cool stuff. >> February 18th, 2018. >> Okay. >> So we did a study recently just for fun of when people search for the term "Outlook, out of office." Yeah, and you really only search for that term for one reason, you're going on vacation, and you want to figure out how to turn the feature on. So we did a five-year data poll of people, of the search times for that and then inverted it, so when do people search least for that term. That's when they're in the office, and it's the week of February 18th, 2018, will be that time when people like, yep, I'm at the office, I got to work. And knowing that, prediction and data give us specificity, like yeah, we know the first quarter is busy, we know between memorial Day and Labor Day is not as busy in the B to B world. But as a marketer, we need to put specificity, data and predictive analytics gives us specificity. We know what week to send our email campaigns, what week to turn our ad budgets all the way to full, and so on and so forth. If someone's looking for The Cube, when will they be doing that, you know, going forward? That's the power of this stuff, is that specificity. >> They know what we're going to search for before we search for it. (laughter) >> I'd like to know where I'm going to be next week. Why that date? >> That's the date that people least search for the term, "Outlook, out of office." >> Okay. >> So, they're not looking for that feature, which logically means they're in the office. >> Or they're on vacation. (laughter) Right, I'm just saying. >> That brings up a good point on not just, what you're predicting for interactions right now, but also anticipating the trends. So Bob brought up a good point about figuring out when people are churning. There's a flip side to that, which is how do you get your customers to be more engaged? And now we have really an explosion in reinforcement learning in particular, which is a tool for figuring out, not just how to interact with you right now as a one off, statically. But how do I interact with you over time, this week, next week, the week after that? And using reinforcement learning, you can actually do that. This is the the sort-of technique that they used to beat Alpha-Go or to beat humans with Alpha-Go. Machine-learning algorithms, supervised learning, works well when you get that immediate feedback, but if you're playing a game, you don't get that feedback that you're going to win 300 turns from now, right now. You have to create more advanced value functions and ways of anticipating where things are going, this move, so that you see things are on track for winning in 20, 30, 40 moves, down the road. And it's the same thing when you're dealing with customer engagement. You want to, you can make a decision, I'm going to give this customer a coupon that's going to make them spend 50 cents more today, or you can make decisions algorithmically that are going to give them a 50 cent discount this week, next week, and the week after that, that are going to make them become a coffee drinker for life, or customer for life. >> It's about finding those customers for life. >> IBM uses the term cognitive business. We go to these conferences, everybody talks about digital transformation. At the end of the day it's all about how you use data. So my question is, if you think about the bell curve of organizations that you work with, how do they, what's the shape of that curve, part one. And then part two is, where do you see IBM on that curve? >> Well I think a lot of my clients make a living predicting the future, they're insurance companies and financial services. That's where the CDO currently resides and they get a lot of benefit. But one of things we're all talking about, but talking around, is that human element. So now, how do we take the human element and incorporate this into the structure of how we make our decisions? And how do we take this information, and how do we learn to trust that? The one thing I hear from most of the executives I talk to, when they talk about how data is being used in their organizations is the lack of trust. Now, when you have that, and you start to look at the trends that we're dealing with, and we call them data points verses calling them people, now you have a problem, because people become very, almost analytically challenged, right? So how do we get people to start saying, okay, let's look at this from the point of view of, it's not an either or solution in the world we live in today. Cognitive organizations are not going to happen tomorrow morning, even the most progressive organizations are probably five years away from really deploying them completely. But the organizations who take a little bit of an edge, so five, ten percent edge out of there, they now have a really, a different advantage in their markets. And that's what we're talking about, hyper-critical thinking skills. I mean, when you start to say, how do I think like Warren Buffet, how do I start to look and make these kinds of decisions analytically? How do I recreate an artificial intelligence when machine-learning practice, and program that's going to provide that solution for people. And that's where I think organizations that are forward-leaning now are looking and saying, how do I get my people to use these capabilities and ultimately trust the data that they're told. >> So I forget who said it, but it was early on in the big data movement, somebody said that we're further away from a single version of the truth than ever, and it's just going to get worse. So as a data scientist, what say you? >> I'm not familiar with the truth quote, but I think it's very relevant, well very relevant to where we are today. There's almost an arms race of, you hear all the time about automating, putting out fake news, putting out misinformation, and how that can be done using all the technology that we have at our disposal for disbursing that information. The only way that that's going to get solved is also with algorithmic solutions with creating algorithms that are going to be able to detect, is this news, is this something that is trying to attack my emotions and convince me just based on fear, or is this an article that's trying to present actual facts to me and you can do that with machine-learning algorithms. Now we have the technology to do that, algorithmically. >> Better algos than like and share. >> From a technological perspective, to your question about where IBM is, IBM has a ton of stuff that I call AI as a service, essentially where if you're a developer on Bluemix, for example, you can plug in to the different components of Watson at literally pennies per usage, to say I want to do sentiment analysis, I want to do tone analysis, I want personality insights, about this piece, who wrote this piece of content. And to Dr. Tamir's point, this is stuff that, we need these tools to do things like, fingerprint this piece of text. Did the supposed author actually write this? You can tell that, so of all the four magi, we call it, the Microsoft, Amazon, Google, IBM, getting on board, and adding that five or ten percent edge that Tripp was talking about, is easiest with IBM Bluemix. >> Great. >> Well, one of the other parts of this is you start to talk about what we're doing and you start to look at the players that are doing this. They are all organizations that I would not call classical technology organizations. They were 10 years ago, look at a Microsoft. But you look at the leadership of Microsoft today, and they're much more about figuring out what the formula is for success for business, and that's the other place I think we're seeing a transformation occurring, and the early adopters, is they have gone through the first generation, and the pain, you know, of having to have these kinds of things, and now they're moving to that second generation, where they're looking for the gain. And they're looking for people who can bring them capability and have the conversation, and discuss them in ways that they can see the landscape. I mean part of this is if you get caught in the bits and bites, you miss the landscape that you should be seeing in the market, and that's why I think there's a tremendous opportunity for us to really look at multiple markets of the same data. I mean, imagine looking and here's what I see, everyone in this group would have a different opinion in what they're seeing, but now we have the ability to see it five different ways and share that with our executive team and what we're seeing, so we can make better decisions. >> I wonder if we could have a frank conversation, an honest conversation about the data and the data ownership. You heard IBM this morning, saying hey we're going to protect your data, but I'd love you guys, as independents to weigh in. You got this data, you guys are involved with your clients, building models, the data trains the model. I got to believe that that model gets used at a lot of different places, within an industry, like insurance or across retail, whatever it is. So I'm afraid that my data is, my IP is going to seep across the industry. Should I not be worried about that? I wonder if you guys could weigh in. >> Well if you work with a particular vendor, sometimes vendors have a stipulation that we will not share your models with other clients, so you just got to stick to that. But in terms of science, I mean you build a model, right? You want to generalize that to other businesses. >> Right! >> (drowned out by others talking) So maybe if you could work somehow with your existing clients, say here, this is what we want to do, we just want to elevate the waters for everybody, right? So everybody wins when all boats rise, right? So if you can kind of convince your clients that we just want to help the world be better, and function better, make employees happier, customers happier, let's take that approach and just use models in a, that may be generalized to other situations and use them. If if you don't, then you just don't. >> Right, that's your choice. >> It's a choice, it's a choice you have to make. >> As long as you're transparent about it. >> I'm not super worried, I mean, you, Dave, Tripp, and I are all dressed similarly, right? We have the model of shirt and tie so, if I put on your clothes, we wouldn't, but if I were to put on your clothes, it would not be, even though it's the same model, it's just not going to be the same outcome. It's going to look really bad, right, so. Yes, companies can share the models and the general flows and stuff, but there's so much, if a company's doing machine learning well, there's so much feature engineering that's unique to that company that trying to apply that somewhere else, is just going to blow up. >> Yeah, but we could switch ties, like Tripp has got a really cool tie, I'd be using that tie on July 4th. >> This is turning into a different kind of panel (laughter) Chris, Tripp, Mike, and Bob, thanks so much for joining us. This has been a really fun and interesting panel. >> Thank you very much. Thank you. >> Thanks you guys. >> We will have more from the IBM Summit in Boston just after this. (techno music)

>> Announcer: Live from Boston Massachusetts. It's the cube. Covering IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to the cube's coverage of the IBM Chief Data Officer Strategy Summit. I'm your host Rebecca Knight, along with my co-host Dave Vallante. We're joined by Sanjay Saxena, He is the senior vice president, enterprise data governance at Northern trust Corporation. Thanks so much for joining us Sanjay. >> Thank you. Thank you for having me. >> So, before the cameras were rolling, we were talking about how data governance is really now seen as a business imperative. Can you talk about what's driving that? >> Initially, when we started our data governance program it was very much a regulatory program, focused on regulations, such as GDPR, anti-money laundering etc. But now, as we have evolved, most of the program in my company is focused on business and business initiatives and a lot of that is actually driven by our customers, who want to clean data. We are custodians of the data. We do asset servicing, asset management, and what the customers have, are expecting, as stable stakes, is really clean data. So, more and more, I'm seeing it as a customer driven initiative. >> Clean data. can you ... >> So, many many businesses rely on data, financial services. It's all about data and technology, but when we talk about clean data, you're talking about providing data at a certain threshold. At a certain level of expectation. You are used to data quality when it comes to cars and gadgets and things like that. But, think about data and having a certain threshold that you and your customer can agree on as the right quality of data is really important. >> Well, and that's a lot of the, sort of, governance role, some of the back-office role, but then it evolved. >> Right. >> And begin to add value, particularly in the days where IBM was talking about data warehouse was king. You know master data management and single version of the truth. Data quality became a way in which folks in your role could really add business value. >> That's right. >> How has that evolved in terms of the challenge of that with all the data explosion? You know, how to do been big data it just increased the volumes of data by massive massive amounts and then lines of business started to initiate projects. What did that do for data quality, the data quality challenge? >> So the data quality challenge has grown on two dimensions. One, is the volume of data. You simply have more data to manage, more data to govern and provide an attestation or a certification, you say "Hey, it's clean data. It's good data." The other dimension is really around discoverability of that data. We have so much of data lying in data lakes and we have so many so much of meta-data about the data, that even governing that is becoming a challenge. So, I think both those dimensions are important and are making the jobs of a CDO more complex. >> And do you feel maybe not specific to you but just as an industry that, Let's take financial services, is the industry keeping pace? Because for years very few organizations, if any have tamed the data. Just a matter of keeping up. >> Has that changed or is it sort of still that treadmill? >> It's still evolving. It's still evolving in my from my perspective. Industries, again are starting to manage their models that they have to deliver to the regulators as essential, right? Now, more and more, they're looking at customer data. their saying "Look, my email IDs have to be correct. My customer addresses have to be correct." It's really important to have an effective customer relationship. Right? So, more and more, we are seeing front-office driving data quality and data quality initiatives. But have we attained a state of perfection? No. We are getting there, in terms of more optimization, more emphasis, more money and financials being put on data quality. But still it is evolving as a >> You talk a little bit about the importance of the customer relationship and this conference is really all about sharing best practices. What you've learned along the way, even from the stakes. Can you share a little bit with our viewers about what you think are sort of the pillars of a strong customer relationship, particularly with a financial services company? >> Right. So, in the industry that we are in, we do a lot of wealth management. We have institutional customers, but let's save the example of wealth management. These are wealthy, wealthy individuals, who have assets all around the world. Right? It's a high touch customer relationship kind of a game. So, we need to not only understand them, we need to understand their other relationships, their accountants, who their doctors are etc. So, in that kind of a business, not only it is about high touch and really understanding what the customer needs are. Right? And going more towards analytics and understanding what customers want, but really having correct data about them. Right? Where they live, who are their kids etc. So, it's really data and CRM, they actually come together in that kind of environment and data plays a pivotal role, when it comes to really effective CRM. >> Sanjay, last time we talked a little bit about GDPR. Can you give us an update on where you're at? I mean, like it or not, it's coming. How does it affect your organization and where are you and being ready for the, I mean GDPR has taken effect. people don't realize that, but the penalties go into effect next May. So, where are you guys at? >> So, we are progressing well on our GDPR program and we are, as we talked before this interview, we are treating GDPR as a foundation to our data governance program and that's how I would like other companies to treat GDP our program as well. Because not only what we are doing in GDPR, which is mapping out sensitive data across hundreds of applications and creating that baseline for the whole company. So that anytime a regulator comes in and wants to know where a particular person's information is, we should be able to tell them with in no uncertain terms. So we are using that to build a foundation for our data governance program. We are progressing well, in terms of all aspects of the program. The other interesting aspect, which is really important to highlight, which I didn't last time is that, there's a huge amount of synergy between GDPR and information security. Which is a much older discipline and data protection, so all companies have to protect the data anyway, right? Think about it. So, now a regulation comes along and we are, in a systematic fashion, trying to figure out where all where all our sensitive data is and whether it is controlled protected etc. It is helping our data protection program as well. So all these things, they come together very nicely from a GDPR perspective. >> I wonder, you, you remember Federal Rules of Civil Procedure. That was a big deal back in 2006, and the courts, you know maybe weren't as advanced and understanding technology as technology wasn't as advanced. What happened back then and I wonder if we could compare it to what you think will happen or is happening with GDPRs. It was impossible to solve the problem. So, people just said "Alright, we're going to fix email archiving and plug a hole." and then it became a case where, if a company could show that it had processes these procedures in place, they were covered, and that gave them defense and litigation. Do you expect the same will happen here or is the bar much much higher with GDPR. >> I believe the bar is much much higher. Because when you look at the different provisions of the regulation, right, customers consent is a big big deal, right? No longer can you use customer data for purposes other than what the customer has given you the consent for. Nor can you collect additional data, right? Historically, companies have gone out and collected not just your basic information, but may have collected other things that are relevant to them but not relevant to you or the relationship that you have with them. So it is, the laws are becoming or the regulations are becoming more restrictive, and really it's not just a matter of checking a box. It is really actually being able to prove that you have your data under control. >> Yeah so, my follow-up there is, can you use technology to prove that? Because you can't manually figure through this stuff. Are things like machine learning and so-called AI coming in to play to help with that problem. Yes, absolutely. So one aspect that we didn't talk about is that GDPR covers not just structured data but it covers unstructured data, which is huge and it's growing by tons. So, there are two tools available in the marketplace including IBM's tools which help you map the data or what we call as the lineage for the data. There are other tools that help you develop a meta-data repository to say "Hey, if it is date of birth, where does it reside in the repository, in all the depositories, in fact?" So, there are tools around meta-data management. There are tools around lineage. There are tools around unstructured data discovery, which is an add-on to the conventional tools and software that we have. So all those are things that you have in your repository that you can use to effectively implement GDPR. >> So my next follow-up on that is, does that lead to a situation where somebody in the governance role can actually, you know going back to the data quality conversation, can actually demonstrate incremental value to the business as a result of becoming expert at using that tooling? >> Absolutely, so as I mentioned earlier on in the conversation, right? You need govern data not just for your customers, for your regulators, but for your analytics. >> Right. >> Right. Now, analytics is yet another dimension effect. So you take all this information that now you're collecting for your GDPR, right? And it's the same information that somebody would need to effectively do a marketing campaign, or effectively do insights on the customer, right? Assuming you have the consent of course, right? We talked about that, right? So, you can mine the same information. Now, you have that information tagged. It's all nicely calibrated in repositories etc. Now, you can use that for your analytics, You can use that for your top line growth or even see what your internal processes are, that can make you more effective from an operations perspective. And how you can get that. >> So you're talking about these new foundations of your data governance strategy and yet we're also talking about this at a time where there's a real shortage of people who are data experts and analytics experts. What are what is Northern Trust doing right now to make sure that you are you have enough talent to fill the pipeline? >> So, we are doing multiple things. Like most companies, we are trying a lot of different things. It's hard to recruit in these areas, especially in the data science area, where analytics. And people not only need to have a certain broad understanding of your business, but they also need to have a deep understanding of all of the statistical techniques etc., right? So, that combination is very hard to find. So, what we do is typically, we get interns, from the universities who have the technology knowledge and we couple them up with business experts. And we work in those collaborated kind of teams, right? Think about agile teams that are working with business experts and technology experts together. So that's one way to solve for that problem. >> Great, well Sanjay, thank you so much for joining us here on the cube. >> Thank you. Thank you. >> Good to see you again. >> We will have more from the IBM CDO Summit just after this.

>> Announcer: Live from Boston, Massachusetts, it's theCUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. (upbeat electronic music) >> Welcome back to theCUBE's coverage of the IBM Chief Data Officer Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Dave Vellante. We are joined by Jim Kavanaugh. He is the Senior Vice President transformation and operations at IBM. And Inderpal Bhandari he is the chief, the global chief data officer at IBM. Thanks so much for joining us. >> Thanks for having us. >> Happy to be here. >> So, you both spoke in the key note today and Jim, you were talking about how we're in a real seminal moment for businesses with this digital, this explosion in digital and data. CEOs get this obviously, but how do you think, do companies in general get it? What's the buy-in, in terms of understanding just how big a moment we're in? >> Well, as I said in the key note, to your point, I truly believe that all businesses in every industry are in a true, seminal moment. Why? Because this phenomenon, the digital disruption, is impacting everything, changing the nature of competition, altering industry structures, and forcing companies to really rethink to design a business at its core. And that's what we've been doin' here at IBM, trying to understand how we transition from an old world of going after pure efficiency just by gettin' after economies of scale, process standardization, to really know, how do you drive efficiency to enable you to get competitive advantage? And that has been the essence of what we've been trying to do at IBM to really reinvent our company from the core. >> So most people today have multiple jobs. You guys, of course, have multiple jobs. You've got an internal facing and an external facing so you come to events like this and you share knowledge. Inderpal, when we first met last year, you had a lot of knowledge up here, but you didn't have the cognitive blueprint, ya know, so you were sharing your experiences, but, year plus in now, you've developed this cognitive blueprint that you're sharing customers. So talk about that a little bit. >> Yeah so, we are internally transforming IBM to become a cognitive enterprise. And that just makes for a tremendous showcase for our enterprise customers like the large enterprises that are like IBM. They look at what we're doing internally and then they're able to understand what it means to create a cognitive enterprise. So we've now created a blueprint, a cognitive enterprise blueprint. Which really has four pillars, which we understand by now, given our own experience, that that's going to be relevant as you try to move forward and create a cognitive enterprise. They're around technology, organization considerations, and cultural considerations, data, and also business process. So we're not just documenting that. We're actually sharing not just those documents, but the architecture, the strategies, pretty much all our failures as we're learning going forward with this, in terms of, developing our own recipes as we eat our own cooking. We're sharing that with our clients and customers as a starting point. So you can imagine the acceleration that that's affording them to be able to get to process transformation which, as Jim mentioned, that's eventually where there's value to be created. >> And you talked about transparency being an important part of that. So Jim, you talked about three fundamentals shifts going on that are relevant, obviously, for IBM and your clients, data, cloud, and engagement, but you're really talking about consumerization. And then you shared with us the results of a 4,000 CXO survey where they said technology was the key to sustainable business over the next four or five years. What I want to ask you, square the circle for me, data warehouse used to be the king. I remember those days, (laughing) it was tough, and technology was very difficult, but now you're saying process is the king, but the technology is largely plentiful and not mysterious as it is anymore. The process is kind of the unknown. What do you take away from that survey? Is it the application of technology, the people and process? How does that fit into that transformation that you talked about? >> Well, the survey that you talked about came from our global businesses services organization that we went out and we interviewed 4,000 CXOs around the world and we asked one fundamental question which is, what is number one factor concerning your long term sustainability of your business? And for the first time ever, technology factors came out as the number one risk to identify. And it goes back to, what we see, as those three fundamental shifts all converging and occurring at the same time. Data, cloud, engagement. Each of those impacting how you have to rethink your design of business and drive competitive advantage going forward. So underneath that, the data architecture, we always start, as you stated, prior, this was around data warehouse technology, et cetera. You applied technology to drive efficiency and productivity back into your business. I think it's fundamentally changed now. When we look at IBM internally, I always build the blueprint that Inderpal has talked about, which everything starts with a foundation of your data architecture, strategy governance, and then business process optimization, and then determining your system's architecture. So as we're looking inside of IBM and redesigning IBM around enabling end-to-end process optimization, quote-to-cash, source to pay, hire to exit. Many different horizontal process orientation. We are first gettin' after, with Inderpal, with the cognitive enterprise data platform what is that standard data architecture, so then we can transform the business process. And just to tie this all together to your question earlier, we have not only the responsibility of transforming IBM, to improve our competitiveness and deliver value, we actually are becoming the showcase for our commercialized entities of software solutions, hardware, and services. To go sell that value back to clients over all. >> And part of that is responsibility for data ownership. Who owns the data. You talked about the West Coast, the unnamed West Coast companies which I of course tweeted out to talk about Google and Amazon. And, but I want to press on that a little bit because data scientists, you guys know a lot of them especially acquiring The Weather Company They will use data to train models. Those models, IP data seeps into those models. How do you protect your clients from that IP, ya know, seepage? Maybe you could talk about that. >> Talk about trust as a service and what it means. >> Yeah, ya know, I mentioned that in my talk at the key note, this is a critical, critical point with regard to these intelligent systems, AI systems, cognitive systems, in that, they end up capturing a lot of the intellectual capital that the company has that goes to the core of the value that the company brings to it's clients and customers. So, in our mind, we're very clear, that the client's data is their data. But not only that, but if there's insights drawn from that data, that insight too belongs to them. And so, we are very clear about that. It's architected into our setup, you know, our cloud is architected from the ground up to be able to support that. And we've thought that through very deeply. To some extent, you know, one would argue that that's taken us some time to do that, but these are very deep and fundamental issues and we had to get them right. And now, of course, we feel very confident that that's something that we are able to actually protect on the behalf of our clients, and to move forward and enable them to truly become cognitive enterprises, taking that concern off the table. >> And that is what it's all about, is helping other companies move to become cognitive enterprises as you say. >> Based on trust, at the end of the day, at the heart of our data responsibility at IBM, it's around a trusted partner, right, to protect their data, to protect their insights. And we firmly believe, companies like IBM that capture data, store data, process data, have an obligation to responsibly handle that data, and that's what Jenny Rometty has just published around data responsibility at IBM. >> Great, well thank you so much Inderpal, Jim. We really appreciate you coming on theCUBE. >> [Jim and Inderpal] Thank you. >> We will have more from the IBM Chief Data Officer Strategy Summit, just after this. (upbeat music)

>> Announcer: Live from Boston, Massachusetts, it's theCube, covering IBM Chief Data Officer's Summit, brought to you by IBM. (upbeat music) >> Welcome back to theCUBE's live coverage of the IBM CDO Strategy Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Dave Vellante. >> Hey, hey. We are joined by Gene Leganza, he is the vice president and research director at Forrester Research. Thanks so much for coming on theCUBE. >> Pleasure, thanks for having me. >> So, before the cameras were rolling, we were talking about this transformation, putting data at the front and center of an organization, and you were saying how technology is a piece of the puzzle, a very important piece of the puzzle, but so much of this transformation involves these cultural, social, organizational politics issues that can be just as big and as onerous as the technology, and maybe bigger. >> Bigger in a sense that there can be intractable without any clear path forward. I was just in a session, at a breakout session, at the conference, as I was saying before, we could have had the same discussion 15 or 20 years ago in terms of how do you get people on board for things like data governance, things that sound painful and onerous to business people, something that sound like IT should take care of that, this is not something that a business person should get involved in. But the whole notion of the value of data as an asset to drive an organization forward, to do things you couldn't do before, to be either driven by insights, and if you're even advanced, AI, and cognitive sort of things, really advancing your organization forward, data's obviously very critical. And the things that you can do should be getting business people excited, but they're still having the same complaints about 20 years ago about this is something somebody should do for me. So, clearly the message is not getting throughout the organization that data is a new and fascinating thing that they should care about. There's a disconnect for a lot of organizations, I think. >> So, from your perspective, what is the push back? I mean, as you said, the fact that data is this asset should be getting the business guys' eyes lighting up. What do you see as sort of biggest obstacle and stumbling block here? >> I think it's easy to characterize the people we talk about. I came from IT myself, so the business is always the guys that don't get it, and in this case, the people who are not on board are somehow out of it, they're really bad corporate citizens, they're just not on board in some way that characterizes them as missing something. But I think what no one ever does who's in the position of trying to sell the value of data and data processes and data capabilities, is the fact that these folks are all doing their best to do their job. I mean, nobody thinks about that, right? They just think they're intractable, they like doing things the way they've always done them, they don't like change, and they're going to resist everything I try to do. But the fact is, from their perspective, they know how to be successful, and they know when risk is going to introduce something that they don't want to go there. It's unjustifiable risk. So the missing link is that no one's made that light bulb go off, to say, there is actually a good reason to change the way you've done things, right? And it's like, maybe it's in your best interest to do things differently, and to care more about something that sounds like IT stuff, like data governance, and data quality. So, that's why I think the chief data officer role, whether it's that title or chief analytics officer, or there's actually a chief artificial intelligence officer at the conference this time around, someone has to be the evangelist who can tell really meaningful stories. I mean, you know, 20 years ago, when IT was trying to convince the business that they should care more about data, data architects and DBAs could talk till they're blue in the face about why data was important. No one wanted to hear it. People get turned off even faster now than they did before, because they have a shorter attention span now than they did before. The fact is that somebody with a lot of credibility on the business side, people who kind of really believe it's capable of driving the business forward, hasta have a very meaningful message, not a half-hour wrap on why data is good for you, but what, specifically, can change in your business that you should want to change. I mean, basically, if you can't put it in terms of what's in it for me, why should they listen to you, right? And so yeah, you know, we've got this thing goin' on, it's really important, and everybody's behind it, and I can give you a list of people whose job title begins with C who really thinks that this is a really important idea, get right down to it, if it's not going to make their area of the business work better, or more efficiently, or, especially with, you know, top line growth sort of issues, they're not going to be that interested. And so it's the job of the person who's trying to evangelize these things to put it in those terms. And it might take some research, it certainly would take some in-depth business knowledge about what happens in that area of the business, you can't give an example from another industry or even another company. You've got to go around and find out what's broken, and talk about what can be fixed, you have to have some really good ideas about what can be innovative in very material terms. One of the breakout sessions I had earlier today, well, they're all around how you define new data products, and get innovative, and very interesting to hear some of the techniques by the folks who'd been successful there, down to, you know, it was somebody's job to go around, and when I say somebody, I don't mean a flunky, I need a chief analytics officer sort of person, talking to people about, you know, what did they hate about their job. Finding, collecting all the things that are broken, and thinking about what could be my best path forward to fix something that's going to get a lot of attention, that I can actually build a marketing message here about why everybody should care about this. And so, the missing link is really not seeing the value in changing behaviors. >> So one of the things that I've always respected about George Colony is he brings people into Forrester that care about social, cultural, organizational issues, not just technology. One of your counterparts, Doug Laney, just wrote a book called Infonomics. You mighta seen it on Twitter, there's a little bit of noise going around it. Premise of the book is essentially that organization shouldn't wait for the accounting industry to tell them how to value data. They should take it upon themselves, and he went into a lot of very detailed, you know, kind of mind-numbing calculations and ways to apply it. But there's a real cultural issue there. First of all, do you buy the premise, and what are you seeing in your client base in terms of the culture of data first, data value, and understanding data value? >> Really good question, really good question. And I do follow what Doug Laney does. Actually, Peter Burris, who you folks know, a long time ago, when he was at Forrester, said, "You know what Doug Laney is doing? "We better be doing that sort of thing." So he brought my attention to it a long time ago. I'm really glad he's working on that area, and I've been in conversations with him at other conferences, where people get into those mind-numbing discussions about the details and how to measure the value of data and stuff, and it's a really good thing that that is going on, and those discussions have to happen. To link my answer to that to answer to your second part of your question about what am I seeing in our client base, is that I'm not seeing a technical answer about how to value data in the books, in a spreadsheet, in some counting rules, going to be the differentiator. The missing link has not been that we haven't had the right rules in place to take X terabytes of data and turn it into X dollars of assets on the books. To me, the problem with that point of view is just that there is data that will bring you gold, and there's data that'll sit there, and it's valuable, but it's not really all that valuable. You know, it's a matter of what do you do with it. You know, I can have a hunk of wood on this table, and it's a hunk of wood, and how much it is, you know, what kind of wood is it and how much does it cost. If I make something out of it that's really valuable to somebody else, it'll cost something completely different based on what its function is, or its value as an art piece or whatever it might be. So, it's so much the product end of it. It's like, what do you do with it, and whether there's an asset value in terms of how it supports the business, in terms of got some regular reporting, but where all the interest is at these days, and why there's a lot of interest in it is like, okay, what are we missing about our business model that can be different, because now that everything's digitized, there are products people aren't thinking of. There are, you know, things that we can sell that may be related to our business, and somehow it's not even related our business, it's just that we now have this data, and it's unique to us, and there's something we can do with it. So the value is very much in terms of who would care about this, and what can I do with it to make it into an analytics product, or, you know, at very least I've got valuable data, I think this is how people tend to think of monetizing data, I've got valuable data, maybe I can put it somewhere people will download it and pay me for it. It's more that I can take this, and then from there do something really interesting with it and create a product, or a service, it's really it's on an app, it's on a phone, or it's on a website, or it's something that you deliver in person, but is giving somebody something they didn't have before. >> So what would you say, from your perspective, what are the companies that are being the most innovative at creating new data products, monetizing, creating new analytics products? What are they doing? What are the best practices of those companies from your perspective? >> You know, I think the best practice of those companies are they've got people who are actively trying to answer the question of, what can I do with this that's new, and interesting, and innovative. I'd say, in the examples I've seen, there been more small to medium companies doing interesting things than really, really huge companies. Or if they're huge companies, they're pockets of huge companies. It's kind of very hard to kind of institutionalize at the enterprise level. It's when you have somebody who gets it about the value of data, working to understand the business at a detailed level enough to understand what might be valuable to somebody in that business if I have a product, is when the magic can potentially happen. And what I've heard people doing are things like that hackathons, where in order to kind of surface these ideas, you get a bunch of folks who kind of get technology and data together with folks who get the business. And they play around with stuff, and they're matching the data to the business problem, comin' up with really kind of cool ideas. Those kind of things tend to happen on a smaller scale. You don't have a hackathon, as far as I can tell, with a couple thousand people in a room. It's usually a smaller sort of operation, where people are digging this up. So, it's folks who kind of get it, because they've been kind of working to find the value in analytics, and it's where there's pockets of people who're kind of working together with the business to make it happen. The profile is such that it's organizations that tend to be more mature about data. They're not complaining that data is something IT should take care of for me. They've kind of been there 10 years ago, or five years ago even, and they've gotten at a point where they actually wanted to move forward from defense and do some offensive playing. They're looking for those kind of cool things to do. So, they're more mature, certainly, than folks who aren't doing it. They're more agile and nimble, I think, than your typical organization in the sense of they can build cross disciplinary teams to make this happen, and that's really where the magic happens. You don't get a genius in the room to come up with this, you get this combination of technical skills, and data knowledge, and data engineering skills, and business smarts all in the same room, and that might be four or five different people to kind of brainstorm until they kind of come up with this. And so the folks who recognize that problem, make that happen, regardless of the industry, regardless of the size of the company, are where it's actually happening. >> I know we have to go, but I wanted to ask you, what about the IBM scorecard in terms of how they're doing in that regard? >> You know, I want to talk to them more. From what they said, you know, in a day, you hear a lot of talk, it's been a long day of hearing people talk about this. It sounds pretty amazing, you know, and I think, actually, we had a half hour session with Inderpal after his keynote, I'm going to get together with him more, and hear more about what's going on under the covers, 'cause it sounds like they're being very effective in kind of making this happen at the enterprise level. And I think that's the unusual thing. I mean, IBM is a huge, huge place. So the notion that you can take these cool ideas and make them work in pockets is one thing. Trying to make it enterprise class, scalable, cognitive-driven organization, with all the right wheels in motion to the data, and analytics, and process, and business change, and operating model change, is kind of amazing. From what I've heard so far, they're actually making it happen. And if it's really, really true, it's really amazing. So it makes me want to hear more, certainly, I have no reason to doubt that what they're saying is happening is happening, I just would love to hear just some more of the story. >> Yeah, you're making us all want to hear more. Well, thanks so much, Gene. It's been a pleasure-- >> Not a problem. >> having you on the show. >> A pleasure. >> Thanks. >> Thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from the CDO Summit just after this. (upbeat music)

(dramatic music) >> Narrator: 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 Frank here with theCube. We are wrapping up day one at the IBM CEO Strategy Summit Spring 2017 here at the Fisherman's Wharf Hyatt. A new venue for us, never been here. It's kind of a cool venue. Joined by Peter Burris, Chief Research Officer from Wikibon, and we're excited to have practitioners. We love getting practitioners on. So we're joined by this segment by Bruce Tyler. He's a VP Data Analytics for IBM Global Business Services. Bruce, nice to see you. >> Thank you. >> And he's brought along Fawad Butt, the Chief Data Governance Officer for Kaiser Permanente. Welcome. >> Thank you, thank you. >> So Kaiser Permanente. Regulated industry, health care, a lot of complex medical issues, medical devices, electronic health records, insurance. You are in a data cornucopia, I guess. >> It's data heaven all the way. So as you mentioned, Kaiser is a vertically integrated organization, Kaiser Permanente is. And as such the opportunity for us is the fact that we have access to a tremendous amount of data. So we sell insurance, we run hospitals, medical practices, pharmacies, research labs, you name it. So it's an end to end healthcare system that generates a tremendous amount of dataset. And for us the real opportunity is to be able to figure out all the data we have and the best uses for it. >> I guess I never really thought of it from the vertical stack perspective. I used to think it was just the hospital, but the fact that you have all those layers of the cake, if you will, and can operate within them, trade data within them, and it gives you a lot of kind of classic vertical stack integration. That fits. >> Very much so. And I didn't give you the whole stack. I mean, we're actually building a medical school in Southern California. We have a residency program in addition to everything else we've talked about. But yeah, the vertical stack does provide us access to data and assets related to data that are quite unique. On the one side, it's a great opportunity. On the other side, it has to be all managed and protected and served in the best interest of our patrons and members. >> Jeff: Right, right. And just the whole electronic health records by themselves that people want access to that, they want to take them with. But then there's all kinds of scary regulations around access to that data. >> So the portability, I think what you're talking about is the medical record portability, which is becoming a really new construct in the industry because people want to be able to move from practitioner to practitioner and have that access to records. There are some regulation that provide cover at a national scale but a lot of this also is impacted by the states that you're operating in. So there's a lot of opportunities where I can tell some of the regulation in this space over time and I think that will, then we'll see a lot more adoption in terms of these portability standards which tend to be a little one off right now. >> Right, right. So I guess the obvious question is how the heck do you prioritize? (laughter) You got a lot of things going on. >> You know, I think it's really the standard blocking/tackling sort of situation, right? So one of the things that we've done is taken a look at our holistic dataset end to end and broken it down into pieces. How do you solve this big problem? You solve it by piecing it out a little bit. So what we've done is that we've put our critical dataset into a set of what we call data domains. Patient, member, providers, workers, HR, finance, you name it. And then that gives us the opportunity to not only just say how good is our data holistically but we can also go and say how good is our patient data versus member data versus provider data versus HR data. And then not only just know how good it is but it also gives us the opportunity to sort of say, "Hey, there's no conceivable way we can invest "in all 20 of these areas at any given point." So what's the priority that aligns with business objectives and goals? If you think about corporate strategy in general, it's based on customers and demand and availability and opportunities but now we're adding one more tool set and giving that to our executives. As they're making decisions on investments in longer term, and this isn't just KP, it's happening across industries, is that the data folks are bringing another lens to the table, which is to say what dataset do we want to invest in over the course of the next five years? If you had to choose between 20, what are the three that you prioritize first versus the other. So I think it's another lever, it's another mechanism to prioritize your strategy and your investments associated with that. >> But you're specifically focused on governance. >> Fawad: I am. >> In the health care industry, software for example is governed by a different set of rules as softwares in other areas. Data is governed by a different set of rules than data is governed in most other industries. >> Fawad: Correct. >> Finance has its own set of things and then some others. What does data governance mean at KP? Which is a great company by the way. A Bay Area company. >> Absolutely. >> What does it mean to KP? >> It's a great question, first of all. Every data governance program has to be independent and unique because it should be trying to solve for a set of things that are relevant in that context. For us at KP, there are a few drivers. So first is, as you mentioned, regulation. There's increased regulation. There's increased regulatory scrutiny in pressure. Some things that have happened in financial services over the last eight or ten years are starting to come and trickle in to the healthcare space. So there's that. There's also a changing environment in terms of how, at least from an insurance standpoint, how people acquire health insurance. It used to be that your employer provided a lot of that, those services and those insurances. Now you have private marketplaces where a lot of people are buying their own insurance. And you're going from a B2B construct to a B2C construct in certain ways. And these folks are walking around with their Android phones or their iPhones and they're used to accessing all sorts of information. So that's the customer experience that you to to deliver to them. So there's this digital transformation that's happening that's driving some of the need around governance. The other areas that I think are front and center for us are obviously privacy and security. So we're custodians of a lot of datasets that relate to patients' health information and their personal information. And that's a great responsibility and I think from a governance standpoint that's one of the key drivers that define our focus areas in the governance space. There are other things that are happening. There's obviously our mission within the organization which is to deliver the highest coverage and care at the lowest cost. So there's the ability for us to leverage our data and govern our data in a way which supports those two mission statements, but the bigger challenge in nuts and bolts terms for organizations like ours, which are vertically integrated, is around understanding and taking stock of the entire dataset first. Two, protecting it and making sure that all the defenses are in place. But then three, figuring out the right purposes to use this, to use the data. So data production is great but data consumption is where a lot of the value gets captured. So for us some of the things that data governance facilitates above all is what data gets shared for what purposes and how. Those are things that an organization of our size deliver a tremendous amount of value both on the offensive and the defensive side. >> So in our research we've discovered that there are a lot of big data functions or analytic functions that fail because they started with the idea of setting up the infrastructure, creating a place to put the data. Then they never actually got to the use case or when they did get to the use case they didn't know what to do next. And what a surprise. No returns, lot of costs, boom. >> Yep. >> The companies that tend to start with the use case independently individual technologies actually have a clear path and then the challenge is to accrete knowledge, >> Yes. >> accrete experience and turn it into knowledge. So from a governance standpoint, what role do you play at KP to make sure that people stay focused in use cases, that the lessons you learn about pursuing those use cases then turn to a general business capability in KP. >> I mean, again, I think you hit it right on the head. Data governance, data quality, data management, they're all great words, right? But what do they support in terms of the outcomes? So from our standpoint, we have a tremendous amount of use cases that if we weren't careful, we would sort of be scatterbrained around. You can't solve for everything all at once. So you have to find the first set of key use cases that you were trying to solve for. For us, privacy and security is a big part of that. To be able to, there's a regulatory pressure there so in some cases if you lose a patient record, it may end up costing you $250,000 for a record. So I think it's clear and critical for us to be able to continue to support that function in an outstanding way. The second thing is agility. So for us one of the things that we're trying to do with governance and data management in general, is to increase our agility. If you think about it, a lot of companies go on these transformation journeys. Whether it's transforming HR or trying to transform their finance functions or their business in general, and that requires transforming their systems. A lot of that work, people don't realize, is supported and around data. It's about integrating your old data with the new business processes that you're putting out. And if you don't have that governance or that data management function in place to be able to support that from the beginning or have some maturity in place, a lot of those activities end up costing you a lot more, taking a lot longer, having a lower success rate. So for us delivering value by creating additional agility for a set of activities that as an organization, we have committed to, is one for of core use cases. So we're doing a transformation. We're doing some transformation around HR. That's an area where we're making a lot of investments from a data governance standpoint to be able to support that as well as inpatient care and membership management. >> Great, great lessons. Really good feedback for fellow practitioners. Bruce, I want to get your perspective. You're kind of sitting on the other side of the table. As you look at the experience at Kaiser Permanente, how does this equate with what you're seeing with some of your other customers, is this leading edge or? >> Clearly on point. In fact, we were talking about this before we came up and I'm not saying that you guys led, we led the witness here but really how do you master around the foundational aspects around the data, because at the end of the day it's always about the data. But then how do you start to drive the value out of that and go down that cognitive journey that's going to either increase value onto your insights or improve your business optimization? We've done a healthy business within IBM helping customers go through those transformation processes. I would say five years ago or even three years ago we would start big. Let's solve the data aspect of it. Let's build the foundational management processes around there so that it ensures that level of integrity and trusted data source that you need across an organization like KP because they're massive because of all the different types of business entities that they have. So those transformation initiatives, they delivered but it was more from an IT perspective so the business partners that really need to adopt and are going to get the value out of that were kind of in a waiting game until that came about. So what we're seeing now is looking at things around from a use case-driven approach. Let's start small. So whether you're looking at trying to do something within your call center and looking at how to improve automation and insights in that spec, build a proof of value point around a subset of the data, prove that value, and those things can typically go from 10 to 12 weeks, and once you've demonstrated that, now how do can you scale? But you're doing it under your core foundational aspects around the architecture, how you're going to be able to sustain and maintain and govern the data that you have out there. >> It's a really important lesson all three of you have mentioned now. That old method of let's just get all the infrastructure in place is really not a path to success. You getting hung up, spend a lot of money, people get pissed off and oh by the way, today your competitors are transforming right around you while you're >> Unless they're also putting >> tying your shoes. >> infrastructure. >> Unless they're also >> That's right. (laughter) >> tying their shoes too. >> Build it and they will come sounds great, but in the data space, it's a change management function. One of my favorite lines that I use these days is data management is a team sport. So this isn't about IT, or this isn't just about business, and can you can't call business one monolith. So it's about the various stakeholders and their needs and your ability to satisfy them to the changes you're about to implement. And I think that gets lost a lot of times. It turns into a technical conversation around just capability development versus actually solving and solutioning for that business problem set that are at hand. >> Jeff: Yeah. >> Peter: But you got to do both, right? >> You have to. >> Bruce: Absolutely, yeah. >> Can I ask you, do we have time for another couple of questions? >> Absolutely. >> So really quickly, Fawad, do you have staff? >> Fawad: I do. >> Tell us about the people on your staff, where they came from, what you're looking for. >> So one of the core components of data governance program are stewards, data stewards. So to me, there are multiple dimensions to what stewards, what skills they should have. So for stewards, I'm looking for somebody that has some sort of data background. They would come from design, they would come from architecture, they would come from development. It doesn't really matter as long as they have some understanding. >> As long as you know what a data structure is and how you do data monitoring. >> Absolutely. The second aspect is that they have to have an understanding of what influence means. Be able to influence outcomes, to be able to influence conversations and discussions way above their pay grade, so to be able to punch above your weight so to speak in the influence game. And that's a science. That's a very, very definitive science. >> Yeah, we've heard many times today that politics is an absolute crucial game you have to play. >> It is part of the game and if you're not accounting for it, it's going to hit you in the face when you least expect it. >> Right. >> And the third thing is, I look for people that have some sort of an execution background. So ability to execute. It's great to be able to know data and understand data and go out and influence people and get them to agree with you, but then you have to deliver. So you have to be able to deliver against that. So those are the dimensions I look at typically when I'm looking at talent as it relates particularly to stewardship talent. In terms of where I find it, I try to find it within the organization because if I do find it within the organization, it gives me that organizational understanding and those relationship portfolios that people bring to the table which tend to be part of that influence-building process. I can teach people data, I can teach them some execution, I can't teach them how to do influence management. That just has to-- >> You can't teach them to social network. >> Fawad: (laughing) That's exactly right. >> Are they like are the frustrated individuals that have been seen the data that they're like (screams) this is-- >> They come from a lot of different backgrounds. So I have a steward that is an attorney, is a lawyer. She comes from that background. I have a steward that used to be a data modeler. I have a steward that used to run compliance function within HR. I have a steward that comes from a strong IT background. So it's not one formula. It's a combination of skills and everybody's going to have a different set of strengths and weaknesses and as long as you can balance those out. >> So people who had an operational role, but now are more in an execution setup role. >> Fawad: Yeah, very much so. >> They probably have a common theme, though, across them that they understand the data, they understand the value of it, and they're able to build consensus to make an action. >> Fawad: That's correct. >> That's great. That's perfect close. They understand it and they can influence, and they can get to action. Pretty much sums it up, I think so. All right. >> Bruce: All right thank you. >> Well, thanks a lot, Bruce and Fawad for stopping by. Great story. Love all the commercials on the Warriors, I'm a big fan and watch KNBR. (laughter) But really a cool story and thanks for sharing it and continued success. >> Thank you for the opportunity. >> Absolutely. All right, with Peter Burris, I'm Jeff Frank. You're watching theCube from the IBM Chief Data Officer Strategy Summit Spring 2017 from Fisherman's Wharf, San Francisco. We'll be right back after this short break. Thanks for watching. (electronic music)

>> Announcer: 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 with theCUBE, along with Peter Burris from Wikibon. We are in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. Coming to the end of a busy day, running out of steam. Blah, blah, blah. I need more water. But Joe's going to take us home. We're joined by Joe Selle. He is the global operations analytic solution lead for IBM. Joe, welcome. >> Thank you, thank you very much. It's great to be here. >> So you've been in sessions all day. I'm just curious to get kind of your general impressions of the event and any surprises or kind of validations that are coming out of these sessions. >> Well, general impression is that everybody is thrilled to be here and the participants, the speakers, the audience members all know that they're at the cusp of a moment in business history of great change. And that is as we graduate from regular analytics which are descriptive and dashboarding into the world of cognitive which is taking the capabilities to a whole other level. Many levels actually advanced from the basic things. >> And you're in a really interesting position because IBM has accepted the charter of basically consuming your own champagne, drinking your own champagne, whatever expression you want to use. >> I'm so glad you said that cause most people say eating your dog food. >> Well, if we were in Germany we'd talk about beer, but you know, we'll stick with the champagne analogy. But really, trying to build, not only to build and demonstrate the values that you're trying to sell to your customers within IBM but then actually documenting it and delivering it basically, it's called the blueprint, in October. We've already been told it's coming in October. So what a great opportunity. >> Part of that is the fact that Ginni Rometty, our CEO, had her start in IBM in the consulting part of IBM, GBS, Global Business Services. She was all about consulting to clients and creating big change in other organizations. Then she went through a series of job roles and now she's CEO and she's driving two things. One is the internal transformation of IBM, which is where I am, part of my role is, I should say. Reporting to the chief data officer and the chief analytics officer and their jobs are to accelerate the transformation of big blue into the cognitive era. And Ginni also talks about showcasing what we're doing internally for the rest of the world and the rest of the economy to see because parts of this other companies can do. They can emulate our road map, the blueprint rather, sorry, that Inderpal introduced, is going to be presented in the fall. That's our own blueprint for how we've been transforming ourselves so, some part of that blueprint is going to be valid and relevant for other companies. >> So you have a dual reporting relationship, you said. The chief data officer, which is this group, but also the chief analytics officer. What's the difference between the Chief data officer, the chief data analytics officer and how does that combination drive your mission? >> Well, the difference really is the chief data officer is in charge of making some very long-term investments, including short-term investments, but let me talk about the long-term investment. Anything around an enterprise data lake would be considered a long-term investment. This is where you're creating an environment where users can go in, these would be internal to IBM or whatever client company we're talking about, where they can use some themes around self-service, get out this information, create analysis, everything's available to them. They can grab external data. They can grab internal data. They can observe Twitter feeds. They can look at weather company information. In our case we get that because we're partnered with the weather company. That's the long-term vision of the chief data officer is to create a data lake environment that serves to democratize all of this for users within a company, within IBM. The chief analytics officer has the responsibility to deliver projects that are sort of the leading projects that prove out the value of analytics. So on that side of my dual relationship, we're forming projects that can deliver a result literally in a 10 or a 12 week time period. Or a half a year. Not a year and a half but short term and we're sprinting to the finish, we're delivering something. It's quite minimally scaled. The first project is always a minimally viable product or project. It's using as few data sources as we can and still getting a notable result. >> The chief analytics officer is at the vanguard of helping the business think about use cases, going after those use cases, asking problems the right way, finding data with effectiveness as well as efficiency and leading the charge. And then the Chief data officer is helping to accrete that experience and institutionalize it in the technology, the practices, the people, et cetera. So the business builds a capability over time. >> Yes, scalable. It's sort of an issue of it can scale. Once Inderpal and the Chief data officer come to the equation, we're going to scale this thing massively. So, high volume, high speed, that's all coming from a data lake and the early wins and the medium term wins maybe will be more in the realm of the chief analytics officer. So on your first summary a second ago, you're right in that the chief analytics officer is going around, and the team that I'm working with is doing this, to each functional group of IBM. HR, Legal, Supply Chain, Finance, you name it, and we're engaging in cognitive discovery sessions with them. You know, what is your roadmap? You're doing some dashboarding now, you're doing some first generation analytics or something but, what is your roadmap for getting cognitive? So we're helping to burst the boundaries of what their roadmap is, really build it out into something that was bigger then they had been conceiving of it. Adding the cognitive projects and then, program managing this giant portfolio so that we're making some progress and milestones that we can report to various stake holders like Ginni Rometty or Jim Kavanaugh who are driving this from a senior senior executive standpoint. We need to be able to tell them, in one case, every couple of weeks, what have you gotten done. Which is a terrible cadence, by the way, it's too fast. >> So in many Respects-- >> But we have to get there every couple of weeks we've got to deliver another few nuggets. >> So in many respects, analytics becomes the capability and data becomes the asset. >> Yes, that's true. Analytics has assets as well though. >> Paul: Sure, of course. >> Because we have models and we have techniques and we bake the models into a business process to make it real so people actually use it. It doesn't just sit over there as this really nifty science experiment. >> Right but kind of where are we on the journey? It's real still early days, right? Because, you know, we hear all the time about machine learning and deep learning and AI and VR and AI and all this stuff. >> We're patchy, every organization is patchy even IBM, but I'm learning from being here, so this is end of day one, I'm learning. I'm getting a little more perspective on the fact that we at IBM are actually, 'cause we've been investing in this heavily for a number of years. I came through the ranks and supply chain. We've been investing in these capabilities for six or seven years. We were some of the early adopters within IBM. But, I would say that maybe 10% of the people at this conference are sort of in the category of I'm running fast and I'm doing things. So that's 10%. Then there's maybe another 30% that are jogging or fast walking. And then there's the rest of them, so maybe 50%, if my math is right, it's been a long day. Are kind of looking and saying, yeah, I got to get that going at some point and I have two or three initiatives but I'm really looking forward to scaling it at some point. >> Right. >> I've just painted a picture to you of the fact that the industry in general is just starting this whole journey and the big potential is still in front of us. >> And then on the Champagne. So you've got the cognitive, you've got the brute and then you've got the Watson. And you know, there's a lot of, from the outside looking in at IBM, there's a lot of messaging about Watson and a lot of messaging about cognitive. How the two mesh and do they mesh within some of the projects that you're working on? Or how should people think of the two of them? >> Well, people should know that Watson is a brand and there are many specific technologies under the Watson brand. So, and then, think of it more as capabilities instead of technologies. Things like being able to absorb unstructured information. So you've heard, if you've been to any conferences, whether they're analytics or data, any company, any industry, 80% of your data is unstructured and invisible and you're probably working with 20% of your data on an active basis. So, do you want to go the 80%-- >> With 40% shrinking. >> As a percentage. >> That's true. >> As a percentage. >> Yeah because the volumes are growing. >> Tripling in size but shrinking as a percentage. >> Right, right. So, just, you know, think about that. >> Is Watson really then kind of the packaging of cognitive, more specific application? Because we're walking for health or. >> I'll tell you, Watson is a mechanism and a tool to achieve the outcome of cognitive business. That's a good way to think of it. And Watson capabilities that I was just about to get to are things like reading, if you will. In Watson Health, he reads oncology articles and they know, once one of them has been read, it's never forgotten. And by the way, you can read 200 a week and you can create the smartest doctor that there is on oncology. So, a Watson capability is absorbing information, reading. It's in an automated fashion, improving its abilities. So these are concepts around deep learning and machine learning. So the algorithms are either self correcting or people are providing feedback to correct them. So there's two forms of learning in there. >> Right, right. >> But these are kind of capabilities all around Watson. I mean, there are so many more. Optical, character recognition. >> Right. >> Retrieve and rank. >> Right. >> So giving me a strategy and telling me there's an 85% chance, Joe, that you're best move right now, given all these factors is to do x. And then I can say, well, x wouldn't work because of this other constraint which maybe the system didn't know about. >> Jeff: Right. >> Then the system will tell me, in that case, you should consider y and it's still an 81% chance of success verses the first which was at 85. >> Jeff: Right. >> So retrieving and ranking, these are capabilities that we call Watson. >> Jeff: Okay. >> And we try to work those in to all the job roles. >> Jeff: Okay. >> So again, whether you're in HR, legal, intellectual property management, environmental compliance. You know, regulations around the globe are changing all the time. Trade compliance. And if you violate some of these rules and regs, then you're prohibited from doing business in a certain geography. >> Jeff: Right. >> It's devastating. The stakes are really high. So these are the kind of tools we want. >> So I'm just curious, from your perspective, you've got a corporate edict behind you at the highest level, and your customers, your internal customers, have that same edict to go execute quickly. So given that you're not in that kind of slow moving or walking or observing half, what are the biggest challenges that you have to overcome even given the fact that you've got the highest level most senior edict both behind you as well as your internal customers. >> Yeah, well it, guess what, it comes down to data. Often, a lot of times, it comes to data. We can put together an example of a solution that is a minimally viable solution which might have only three or four or five different pieces of data and that's pretty neat and we can deliver a good result. But if we want to scale it and really move the needle so that it's something that Ginni Rometty sees and cares about, or a shareholder, then we have to scale. Then we need a lot of data, so then we come back to Inderpal, and the chief data officer role. So the constraint is on many of the programs and projects is if you want to get beyond the initial proof of concept, >> Jeff: Right. >> You need to access and be able to manipulate the big data and then you need to train these cognitive systems. This is the other area that's taking a lot of time. And I think we're going to have some technology and innovation here, but you have to train a cognitive system. You don't program it. You do some painstaking back and forth. You take a room full of your best experts in whatever the process is and they interact with the system. They provide input, yes, no. They rank the efficacy of the recommendations coming out of the system and the system improves. But it takes months. >> That's even the starting point. >> Joe: That's a problem. >> And then you trade it over often, an extended period of time. >> Joe: A lot of it gets better over time. >> Exactly. >> As long as you use this thing, like a corpus of information is built and then you can mine the corpus. >> But a lot of people seem to believe that you roll all this data, you run a bunch of algorithms and suddenly, boom, you've got this new way of doing things. And it is a very very deep set of relationships between people who are being given recommendations as you said, weighing them, voting them, voting on them, et cetera. This is a highly interactive process. >> Yeah, it is. If you're expecting lightning fast results, you're really talking about a more deterministic kind of solution. You know, if/then. If this is, then that's the answer. But we're talking about systems that understand and they reason and they tap you on the shoulder with a recommendation and tell you that there's an 85% chance that this is what you should do. And you can talk back to the system, like my story a minute ago, and you can say, well it makes sense, but, or great, thanks very much Watson, and then go ahead and do it. Those systems that are expert systems that have expertise just woven through them, you cannot just turn those on. But, as I was saying, one of the things we talked about on some of the panels today, was there's new techniques around training. There's new techniques around working with these corpuses of information. Actually, I'm not sure what the plural of corpus. Corpi? It's not Corpi. >> Jeff: I can look that up. >> Yeah, somebody look that up. >> It's not corpi. >> So anyway, I want to give you the last word, Jeff. So you've been doing this for a while, what advice would you give to someone kind of in your role at another company who's trying to be the catalyst to get these things moving. What kind of tips and tricks would you share, you know, having gone through it and working on this for a while? >> Sure. I would, the first thing I would do is, in your first move, keep the projects tightly defined and small with a minimum of input and keep, contain your risk and your risk of failure, and make sure that if you do three projects, at least one of them is going to be a hands down winner. And then once you have a winner, tout it through your organization. A lot of folks get so enamored with the technology that they start talking more about the technology than the business impact. And what you should be touting and bragging about is not the fact that I was able to simultaneously read 5,000 procurement contracts with this tool, you should be saying, it used to take us three weeks in a conference room with a team of one dozen lawyers and now we can do that whole thing in one week with six lawyers. That's what you should talk about, not the technology piece of it. >> Great, great. Well thank you very much for sharing and I'm glad to hear the conference is going so well. Thank you. >> And it's Corpa. >> Corpa? >> The answer to the question? Corpa. >> Peter: Not corpuses. >> With Joe, Peter, and Jeff, you're watching theCUBE. We'll be right back from the IBM chief data operator's strategy summit. Thanks for watching.

(orchestral music) (energetic music) >> Narrator: Live from Fisherman's Wharf in San Francisco. It's the Cube! Covering IBM Chief Data Officer Strategy Summit, Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody! Jeff Frick here with the Cube. I am joined by Peter Burris, the Chief Research Officer at Wikibon. We are in downtown San Francisco at the IBM Chief Data Officer Strategy Summit 2017. It's a lot of practitioners. It's almost 200 CDOs here sharing best practices, learning from the IBM team and we're excited to be here and cover it. It's an ongoing series and this is just one of many of these summits. So, if you are a CDO get involved. But, the most important thing is to not just talk to the IBM folks but to talk to the practitioners. And, we are really excited for our next segment to be joined by Allen Crane. He is the assistant VP from USAA. Welcome! >> Thank you. >> Jeff: And also Glenn Finch. He is the Global Managing Partner Cognitive and Analytics at IBM. Welcome! >> Thank you, thank you both. >> It's kind of like the Serengeti of CDOs here, isn't it? >> It is. It's unbelievable! >> So, the overview Allen to just kind of, you know, this opportunity to come together with a bunch of your peers. What's kind of the vibe? What are you taking away? I know it's still pretty early on but it's a cool little event. It's not a big giant event in Vegas. You know, it's a smaller of an affair. >> That's right. I've been coming to this event for the last three years since they had it and started it when Glenn started this event. And, truly it's probably the best conference I come to every year because it's practitioners. You don't have a lot of different tracks to get lost in. This is really about understanding from your own peers what they are going through. Everything from how are you organizing the organization? What are you focused on? Where are you going? And all the way through talent discussions and where do you source these jobs? >> What is always a big discussion is organizational structure which on one hand side is kind of, you know, who really cares? But is vitally important as to how it is executed, how the strategy gets implemented in the business groups. I wonder if you can tell us a little bit about how it works at USAA, your role specifically and how does a Chief Data Officer eat it, work his way into the business bugs trying to make better decisions. >> Absolutely, we are a 27 billion dollar 95 year old company that focuses on the military and their members and their families. And our members, we offer a full range of financial services. So, you can imagine we've got lots of data offices for all of our different lines of business. Because of that, we have elected to go with what we call a hub and spoke model where we centralize certain functions around governance, standards, core data assets, and we subscribe to those things from a standard standpoint so that we're in the spokes like I am. I run all of the data analytics for all of our channels and how our members interact with USAA. So, we can actually have standards that we can apply in our own area as does the bank, as does the insurance company, as does the investments company. And so, it enables the flexibility of business close to the business data and analytics while you also sort of maintain the governance layer on top of that. >> Well, USAA has been at the vanguard of customer experience for many years now. >> Yes >> And the channel world is now starting to apply some of the lessons learned elsewhere. Are you finding that USAA is teaching channels how to think about customer experience? And if so, what is your job as an individual who's, I presume, expected to get data about customer experience out to channel companies. How is that working? >> Well, it's almost like when you borrow a page back from history and in 1922 when we were founded the organization said service is the foundation of our industry. And, it's the foundation of what we do and how we message to our membership. So, take that forward 95 years and we are finding that with the explosion in digital, in mobile, and how does that interact with the phone call. And, when you get a document in the mail is it clear? Or do you have to call us, because of that? We find that there's a lot of interplay between our channels, that our channels had tended to be owned by different silo leaders that weren't really thinking laterally or horizontally across the experience that the member was facing. Now, the member is already multichannel. We all know this. We are all customers in our own right, getting things in the mail. It's not clear. Or getting things in an e-mail. >> Absolutely. >> Or a mobile notice or SMS text message. And, this is confusing. I need to talk to somebody about this. That type of thing. So, we're here to really make sure that we're providing as direct interaction and direct answers and direct access with our membership to make those as compelling experiences as we possibly can. >> So, how is data making that easier? >> We're bringing the data altogether is the first thing. We've got to be able to make sure that our phone data is in the same place as our digital data, is in the same place as our document data, is in the same place as our mobile data because when you are not able to see that path of how the member got here, you're kind of at a loss of what to fix. And so, what we're finding is the more data that we're stitching together, these are really just an extension of a conversation with the membership. If someone is calling you after being online within just a few minutes you kind of know that that's an extension of the same intent that they had before. >> Right. >> So, what was it upfront and upstream that caused them to call. What couldn't you answer for the member upstream that now required a phone call and possibly a couple of transfers to be able to answer that phone interaction. So, that's how we start with bringing all the data together. >> So, how are you working with other functions within USAA to ensure that the data that the channel organizations to ensure those conversations can persist over time with products and underwriters and others that are actually responsible for putting forward the commitments that are being made. >> Yeah. >> How is that coming together? >> I think, simply put it, it's a pull versus push. So, showing the value that we are providing back to our lines of business. So, for example, the bank line of business president looks to us to help them reduce the number of calls which affects their bottom line. And so, when we can do that and show that we are being more efficient with our member, getting them the right place to the right MSR the first time, that is a very material impact in their bottom line. So, connecting into the things that they care about is the pull factor that we often called, that gets us that seat at the table that says we need this channel analyst to come to me and be my advisor as I'm making these decisions. >> You know what, I was just going to say what Allen is describing is probably what I think is the most complicated piece of data analytics, cognitive, all that stuff. That last mile of getting someone whether it's a push or pull. >> Right. >> Fundamentally, you want somebody to do something different whether it's an end consumer, whether it's a research analyst, whether it's a COO or a CFO, you need to do something that causes them to make a different decision. You know, ten years ago as we were just at the dawn of a lot of this new analytical techniques, everybody was focused on amassing data and new machine learning and all that stuff. Now, quite honestly, a lot of that stuff is present and it's about how do we get someone who adapts something that feels completely wrong. That's probably the hardest. I mean, and I joke with people, but you know that thing when your spouse finds something in you and says something immediately about it. >> No, no. >> That's right. (laughs) That's the first thing and you guys are probably better men than I am. The first I want to do is say "prove them wrong". Right? That's the same thing when an artificial intelligence asset tries to tell a knowledge worker what to do. >> Right, right. >> Right? That's what I think the hardest thing is right now. >> So, is it an accumulative kind of knock down or eventually they kind of get it. Alright, I'll stop resisting. Or, is it a AHA moment where people come at 'cause usually for changing behavior, usually there's a carrot or a stick. Either you got to do it. >> Push or pull. >> And the analogy, right. Or save money versus now really trying to transform and reorganize things in new, innovative ways that A. Change the customer experience, but B. Add new revenue streams and unveil a new business opportunity. >> I think it's finding what's important to that business user and sometimes it's an insight that saves them money. In other cases, it's no one can explain to me what's happening. So, in the case of Call Centers for example, we do a lot of forecasting and routing work, getting the call to the right place at the right time. But often, a business leader may say " I want to change the routing rules". But, the contact center, think of it as a closed environment, and something that changes over here, actually ultimately has an effect over here. And, they may not understand the interplay between if I move more calls this way, well those calls that were going there have to go some place else now, right? So, they may not understand the interplay of these things. So, sometimes the analyst comes in in a time of crisis and sometimes it's that crisis, that sort of shared enemy if you will, the enemy of the situation, that is, not your customer. But, the enemy of the shared situation that sort of bonds people together and you sort of have that brothers in arms kind of moment and you build trust that way. It comes down to trust and it comes down to " you have my best interest in mind". And, sometimes it's repeating the message over and over again. Sometimes, it's story telling. Sometimes, it's having that seat at the table during those times of crisis, but we use all of those tools to help us earn that seat at the table with our business customer. >> So, let me build on something that you said (mumbles) 'Cause it's the trying to get many people in the service experience to change. Not just one. So, the end goal is to have the customer to have a great experience. >> Exactly. >> But, the business executive has to be part of that change. >> Exactly. >> The call center individual has to be part of that change. And, ultimately it's the data that ensures that that process of change or those changes are in fact equally manifest. >> Right. >> You need to be across the entire community that's responsible for making something happen. >> Right. >> Is that kind of where your job comes in. That you are making sure that that experience that's impacted by multiple things, that everybody gets a single version of the truth of the data necessary to act as a unit? >> Yeah, I think data, bringing it all together is the first thing so that people can understand where it's all coming from. We brought together dozens of systems that are the systems of record into a new system of record that we can all share and use as a collective resource. That is a great place to start when everyone is operating of the same fact base, if you will. Other disciplines like process disciplines, things that we call designed for measurability so that we're not just building things and seeing how it works when we roll it out as a release on mobile or a release on .com but truly making sure that we are instrumenting these new processes along the way. So, that we can develop these correlations and causal models for what's helping, what's working and what's not working. >> That's an interesting concept. So, you design the measurability in at the beginning. >> I have to. >> As opposed to kind of after the fact. Obviously, you need to measure-- >> Are you participating in that process? >> Absolutely. We have and my role is mainly more from and educational standpoint of knowing why it's important to do this. But, certainly everyone of our analysts is deeply engaged in project work, more upstream than ever. And now, we're doing more work with our design teams so that data is part of the design process. >> You know, this measurability concept, incredibly important in the consultancy as well. You know, for the longest time all the procurement officers said the best thing you can do to hold consults accountable is a fixed priced, milestone based thing, that program number 32 was it red or green? And if it's green, you'll get paid. If not, I am not paying you. You know, we in the cognitive analytics business have tried to move away from that because if we, if our work is not instrumented the same way as Allen's, if I am not looking at that same KPI, first of all I might have project 32 greener than grass, but that KPI isn't moving, right? Secondly, if I don't know that KPI then I am not going to be able to work across multiple levels in an organization, starting often times at the sea suite to make sure that there is a right sponsorship because often times somebody want to change routing and it seems like a great idea two or three levels below. But, when it gets out of whack when it feels uncomfortable and the sea suite needs to step in, that's when everybody's staring at the same set of KPIs and the same metrics. So, you say "No, no. We are going to go after this". We are willing to take these trade offs to go after this because everybody looks at the KPI and says " Wow. I want that KPI". Everybody always forgets that "Oh wait. To get this I got to give these two things up". And, nobody wants to give anything up to get it, right? It is probably the hardest thing that I work on in big transformational things. >> As a consultant? >> Yeah, as a consultant it's to get everybody aligned around. This is what needle we want to move, not what program we want to deliver. Very hard to get the line of business to define it. It's a great challenge. >> It's interesting because in the keynote they laid out exactly what is cognitive. And the 4 E's, I thought they were interesting. Expert. Expression. It's got to be a white box. It's got to be known. Education and Evolution. Those are not kind of traditional consulting benchmarks. You don't want them to evolve, right? >> Right. >> You want to deliver on what you wrote down in the SOW. >> Exactly. >> It doesn't necessarily have a white box element to it because sometimes a little hocus pocus, so just by its very definition, in cognitive and its evolutionary nature and its learning nature, it's this ongoing evolution of it or the processes. It's not a lock it down. You know, this is what I said I'd deliver. This is what we delivered 'cause you might find new things along the path. >> I think this concept of evolution and one of the things we try to be very careful with when you have a brand and a reputation, like USAA, right? It's impeccable, it's flawless, right? You want to make sure that a cognitive asset is trained appropriately and then allowed to learn appropriate things so it doesn't erode the brand. And, that can happen so quickly. So, if you train a cognitive asset with euphemisms, right? Often times the way we speak. And then, you let it surf the internet to get better at using euphemisms, pretty soon you've got a cognitive asset that's going to start to use slang, use racial slurs, all of those things (laughs) because-- No, I am serious. >> Hell you are. >> That's not good. >> Right, that's not bad so, you know, that's one of the things that Ginni has been really, really careful with us about is to make sure that we have a cognitive manifesto that says we'll start here, we'll stop here. We are not going to go in the Ex Machina territory where full cognition and humans are gone, right? That's not what we're going to do because we need to make sure that IBM is protecting the brand reputation of USAA. >> Human discretion still matters. >> Absolutely. >> It has to. >> Alright. Well, we are out of time. Allen, I wanted to give you the last word kind of what you look forward to 2017. We're already, I can't believe we're all the way through. What are some of your top priorities that you are working on? Some new exciting things that you can share. >> I think one of the things that we are very proud of is our work in the text analytics space and what I mean by that is we're ingesting about two years of speech data from our call center every day. And, we are mining that data for emergent trends. Sometimes you don't know what you don't know and it's those unknown unknowns that gets you. They are the things that creep up in your data and you don't really realize it until they are a big enough issue. And so, this really is helping us understand emerging trends, the emerging trend of millennials, the emerging trend of things like Apple Pay, and it also gives us insight as to how our own MSRs are interacting with our members in a very personal level. So, beyond words and language we're also getting into things like recognizing things like babies crying in the background, to be able to detect things like life events because a lot of your financial needs center around life events. >> Right, right. >> You know, getting a new home, having another child, getting a new car, those types of things. And so, that's really where we're trying to bring the computer more as an assistant to the human, as opposed to trying to replace the human. >> Right. >> But, it is a very exciting space for us and areas that we are actually able to scale about 100 times faster than we were fast before. >> Wow. That's awesome. We look forward to hearing more about that and thanks for taking a few minutes to stop by. Appreciated. >> Peter: Thanks, guys. >> Allen: Thank you. >> Alright. Thank you both. With Peter Burris, I'm Jeff Frick. You're watching the Cube from the IBM Chief Data Officer Strategy Summit, Spring 2017. Thanks for watching. We'll be back after the short break. (upbeat music)

(inspiring piano and string music) >> Announcer: 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 in downtown San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. That's a mouthful, but it's important because there's a series of these strategy summits that are happening not only in the United States, but they're expanding it all over the world, and it's really a chance for practitioners to come together, the chief data officers, to share best practices, really learn from the best, and as we love to do on theCUBE, we get the smartest people we can find, and we have them here. So first off, let me introduce Peter Burris, Chief Research Officer from Wikibon, but from IBM coming right off the keynote-- >> The smart people. >> The smart people, Inderpal Bhandari, he is the IBM Global Chief Data Officer, which is a short title and a big job, and Jesus Mantas, he's the General Manager, Cognitive Transformation, IBM Global Business Services. First off, gentlemen, welcome. >> Thank you. >> Thank you. >> It's really interesting how this chief data officer space has evolved. We've been watching it for years, back to some of the MIT CDOIQ, I think like three or four years ago nobody knew who they were, who were they going to report to, what are they going to do, what's the scope of the job. That's changed dramatically, and it really says something to IBM's credit that they just went out and got one to help really to refine and define for your customers where this is going. So first off, welcome, and let's get into it. How is the role starting to solidify as to what do chief data officers do? >> So, I'll take that. In terms of chief data officers, if you think in terms of the advent of the position, when it started out, I was one of the earliest in 2006, and I've done the job four times, and it has been continuously evolving ever since. When the job was first, in my very first job, I actually had to create the job because there was a company very interested in recruiting me, and they said they sensed that data was critical. It was a company in pharmaceutical insurance, so really very data based, right, everything is driven through data. And so, they had a sense that data was going to be extremely important, extremely relevant, but they didn't really have the position, or they didn't coin the phrase. And I suggested that there were three other chief data officers at that time in the U.S., and so, I became the fourth. At that time, it had to do with, essentially aligning data with strategy, with the strategy of the company, which means how is the company actually planning to monetize itself? Not its data, but itself. And then, essentially make sure that the data is now fit for purpose, to help them with that monetization. And so, that's all about aligning with the corporate strategy, and you have to have an officer who's capable of doing that and has that focus and is able to push that because then, once you start with that strategy, and then, there are plenty of different branches that shoot off, like governance, centralization of data, analytics, data science, and so on and so forth, and then, you have to manage that process. >> And data used to be kind of a liability, hard to think today looking back, 'cause you had to buy servers and storage, and it was expensive, and what do you do with it all? You can't analyze it. Boy, how the world has flipped. Now, data is probably one of your most important assets, but then, the big question, right, what do you do with it to really make it an asset? >> It is, it is, and it's actually fascinating to see here in the summit how even the role that was created in a few years, chief data officer, is coupled with this change in the nature of the value of that role has changed. To your point, I remember meeting some CIO friends 10 years ago that they were telling me how they were deleting data because it was too costly to have it. Now, those same CIOs would give whatever they could have to get that data back and have that history and be able to monetize the data. Because of the evolution of computing, and because now, not only the portion of the physical world that we've been able to represent with data for the last 50 years with information technology, but we're adding to that space all of this 80% of the data that even if digitized we were unable to use in processes, in decision making, in manufacturing. Now we have cognitive technology that can actually use that data, the role of the chief data officer is actually expanding significantly from what used to be the element of data science, of data governance, of data sovereignty, of data security, to now this idea of value creation with basically five times more categories of data, and it actually is a dialogue that we're having here at the summit that is the fascinating from the people who are doing this job every day. >> If you think about the challenges associated with the chief data officer, it's a job that's evolving, but partly one of the reasons why the chief data officer job is evolving is the very concept of the role that data plays in business is evolving, and that's forcing every job in business to evolve. So, the CMO's job's evolving, the CEO's job's evolving, and the CIO's job is evolving. How are you navigating this interesting combination of forces on the role of the CDO as you stake out, this is the value I'm going to bring to the business, even as other jobs start to themselves change and respond to this concept of the value of data? >> People ask me to describe my job, and there are just two words that I use to describe it. It's change agent, and that's exactly how a CDO needs to be, needs to look at their job, and also, actually act on that. Because to your point, it's not just the CDO job is evolving, it's all these other jobs are all evolving simultaneously, and there are times when I'm sitting at the table, it appears that, well, you don't really own anything because everybody else owns all the processes in the business. On the other hand, sometimes you're sitting there, and you're thinking, no, you actually own everything because the data that feeds those processes or comes out of those process is not coming back to you. I think the best way to think about the CDO job is that of a change agent. You are essentially entrusted with creating value from the data, as Jesus said, and then, enabling all the other jobs to change, to take advantage of this. >> 'Cause it's the enablement that that's where you bring the multiplier effect, it's the democratization of the data across the organization, across business roles, across departments is where you're going to get this huge multiplier. >> Yeah, and I think the role of one of the things that we're seeing and the partnership that Inderpal and I have in the way that we do this within IBM, but also, we do it for the rest of our clients is that change agency element of it is the constant infusion of design. Chief data officers were very well-known for the data science elements of it, but part of the constraint is actually no longer the computing capability or the algorithms themselves or the access to the data, which solved those constraints, is now actually preparing the business leaders to consume that and to actually create value, which changes the nature of their job as well, and that's the resistance point where embedding these technologies in the workflows, in a way that they create value in the natural flow of what these jobs actually do is extremely important. Otherwise, I mean, we were having a fascinating discussion before this, even if the data is correct, many business leaders will say, "Well, I don't believe it." And then, if you don't adopt it, you don't get the value. >> You guys are putting together this wonderful community of CDOs, chief data officers, trying to diffuse what the job is, how you go about doing the job. If you're giving advice and counsel to a CEO or board of directors who are interested in trying to apply this role in their business, what should they be looking for? What type of person, what type of background, what type of skills? >> I'll take it, and then, you can. I think it's almost what I would call a new Da Vinci. >> Peter: A new Da Vinci? >> A new Da Vinci is the Renaissance of someone that is, he's got a technology background, because you need to actually understand the mathematical and the data and the technology co-engineering aspect. >> So, if not an IT background, at least a STEM background. >> Exactly, it's a STEM background, but combined with enough knowledge of business architecture. So I call it Da Vinci because if you see the most remarkable paintings and products of Da Vinci was the fusion of mathematics and arts in a way that hadn't been done before. I think the new role of a data science is someone that can be in the boardroom elegantly describing a very sophisticated problem in a very easy to understand manner, but still having the depth of really understanding what's behind it and drawing the line versus what's possible and what's likely to happen. >> I think that's right on. I think the biggest hurdle for a chief data officer is the culture change, and to do that, you actually have to be a Da Vinci, otherwise, you really can't pull that off. >> Peter: You have to be a Da Vinci? >> You have to be a Da Vinci to pull that off. It's not just, you have to appreciate not just the technology, but also the business architecture as well as the fact that people are used to working in certain ways which are now changing on them, and then, there is an aspect of anxiety that goes with it, so you have to be able to understand that, and actually, perhaps even harness that to your advantage as you move forward as opposed to letting that become some kind of a threat or counterproductive mechanism as you move forward. >> I've done a fair amount of research over the years on the relationship between business model, business model design profitability, and this is, there's a lot of different ways of attacking this problem, I'm not going to tell you I have the right answer yet, but one of the things that I discovered when talking to businesses about this is that often it fails when the business fails to, I'm going to use the word secure, but it may not be the right word, secure the ongoing rents or value streams from the intellectual property that they create as part of the strategy. Companies with great business model design also find ways to appropriate that value from what they're doing over an extended period of time, and in digital business, increasingly that's data. That raises this interesting question, what is the relationship between data, value streams over time, ownership, intellectual property? Do you have any insight into that? It's a big question. >> Yeah, no no, I mean, I think we touched on it also in the discussion, both Jesus and I touched on that. We've staked out a very clear ground on that, and when I say we, I mean IBM, the way we are defining that is we are pretty clear that for all the reasons you just outlined, the client's data has to be their data. >> Peter: Has to be? >> Has to be their data. It has to be their insight because otherwise, you run into this notion of, well, whose intellectual property is it, whose expertise is it? Because these systems learn as they go. And so, we're architecting towards offerings that are very clear on that, that we're going to make it possible for a client that, for instance, just wants to keep their data and derive whatever insight they can from that data and not let anybody else derive that insight, and it'll be possible for them to do that. As well as clients where they're actually comfortable setting up a community, and perhaps within an industry-specific setup, they will allow insights that are then shared across that. We think that's extremely important to be really clear about that up front and to be able to architect to support that, in a way that that is going to be welcomed by the business. >> Is that part of the CDO's remit within business to work with legal and work with others to ensure that the rules and mechanisms to sustain management of intellectual property and retain rents out of intellectual property, some call it the monetization process, are in place, are enforced, are sustained? >> That's always been part of the CDO remit, right. I mean, in the sense that even before cognition that was always part of it, that if we were bringing in data or if data was leaving the company that we wanted to make sure that it was being done in the right way. And so, that partnership not just with legal but also with IT, also with the business areas, that we had to put in place, and that's the essence of governance. In the broadest sense, you could think of governance as doing that, as protecting the data asset that the company has. >> They have the derivatives now, though. You're getting stacked derivatives. >> Inderpal: It's much more complicated. >> Of data, and then insight combined, so it's not just that core baseline data anymore. >> And I like to make it an element. You've heard us say for the last five years we believe that data has become the new natural resource for the business. And when you go back to other natural resources, and you see what happened with people that were in charge of them, you can kind of predict a little bit that evolution on the chief data officer role. If you were a landowner in Texas when there was no ability to basically either extract or decline petroleum, you were not preoccupied with how would you protect land rights under the line that you can see. So, as a landowner you have a job, but you were basically focused on what's over the surface. Once actually was known that below the surface there was massive amount of value that could be obtained, suddenly that land ownership expanded in responsibility. You then have to be preoccupied, "Okay, wait a minute, who owns those land rights "to actually get that oil, and who's going to do that?" I think you can project that to the role of the chief data officer. If you don't have a business model that monetizes data, you are not preoccupied to actually figure out how to govern it or how to monetize it or how to put royalties on it, you are just preoccupied with just making sure that the data you have, it was well-maintained and it could be usable. The role's massively expanding to this whole below the line where not only the data is being used for internal purposes, but it's becoming a potential element of a strategy that is new. >> The value proposition, simply stated. >> Jesus: Value proposition, exactly. >> But you're right, so I agree with that, but data as an asset has different characteristics than oil as an asset, or people as an asset. People can effectively be applied to one thing at a time. I mean, we can multitask, but right now, you're having a conversation with us, and so, IBM is not seeing you talk to customers here at the show, for example. Data does not follow the economics of scarcity. >> Jesus: Right. >> It follows a new economics, it's easy to copy, it's easy to share. If it's done right, it's easy to integrate. You can do an enormous number of things with data that you've never been able to do with any other asset ever, and that's one of the reasons why this digital transformation is so interesting and challenging, and fraught with risk, but also potentially rewarding. So, as you think about the CDO role and being the executive in the business that is looking at taking care of an asset, but a special type of asset, how that does change the idea of taking care of the energy or the oil to now doing it a little bit differently because it can be shared, because it can be combined. >> I mean, I think in the way as technology has moved from being a mechanism to provide efficiency to the business to actually being core to defining what the business is, I think every role related to technology is following that theme, so I would say, for example, Inderpal and I, when we're working with clients or on our models, he's not just focused on the data, he's actually forming what is possible for the business to do. What should be the components of the new business architecture? It's this homogenized role, and that's why I kept saying it's like, you could have been one of those Da Vincis. I mean, you get to do it every day, but I don't know if you want to comment on that. >> I think that's exactly right. You are right in the sense that it is a different kind of asset, it has certain characteristics which are different from what you'd find in, say, land or oil or something like a natural resource, but in terms of, and you can create a lot of value at times by holding onto it, or you could create a lot of value by sharing it, and we've seen examples of both metaphors. I think as part of being the CDO, it's being cognizant that there is going to be a lot of change in this role as data is changing, not just in its nature in the sense that now you have a lot more unstructured data, many different forms of data, but also in terms of that's application within the business, and this expansion to changing processes and transforming processes, which was never the case when I first did the job in 2006. It was not about process transformation. It was about a much more classic view of an asset where it's, we create this data warehouse, that becomes the corporate asset, and now, you generate some insights from it, disseminate the insights. Now it's all about actually transforming the business by changing the processes, reimagining what they could be, because the nature of data has changed. >> I have one quick question. >> Last one. >> Very quickly, well, maybe it's not a quick question, so if you could just give me a quick answer. A couple times you both have mentioned the relationship between the CDO and business architecture. Currently, there's a relationship between the CIO and IT architecture, even the CIO and data architecture at a technical level. At IBM, do you actually have staff that does business architecture work? Is there someone, is that a formal, defined set of resources that you have, or should CDOs have access to a group of people who do business architecture? What do you think? >> We've traditionally had business architects at IBM, I think for a long time, that predates me. But again, as Jesus said, their role is also evolving. As it becomes much more about process transformation, it's different than it was before. I mean, this is much more now about a collaborative effort where you essentially sit down in a squad in an agile setting, and you're working together to redesign and reinvent the process that's there. And then, there's business value. It's less about creating large monolithic architectures that span an entire enterprise. It's all about being agile, data-driven, and reacting to the changes that are happening. >> So, turning strategy into action. >> Yes. >> And I think, again, in IBM, one of the things that we have done, our CIO, that is the organization that actually is the custodian of this cognitive enterprise architecture of which Inderpal actually is part of. So, we are actually putting it all together. It used to be an organization. Most COOs have evolved from running operations to defining shared services to now have to figure out what is the digital services version of the enterprise they need to implement, and they can't do that without a CDO in place, they just can't. >> Alright, gentlemen. Unfortunately, we'll have to leave it there. For viewers at home, tune into season two with Inderpal and Jesus. Really a great topic. Congratulations on the event, and we look to forward to the next time. >> Thank you. >> Thank you very much. >> Absolutely. With Peter Burris, I'm Jeff Frick. You're watching theCUBE from the IBM Chief Data Officer Strategy Summit Spring 2017. Be right back with more after this short break. Thanks for watching. (electronic keyboard music)

(orchestra music) >> Man: Live from Fisherman's Wharf in San Francisco, it's the Cube, covering IBM Chief Data Officer Strategy Summit, Spring 2017, brought to you by IBM. >> Welcome back everybody, Jeff Rick here at the Cube. We're in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, but it's an ongoing series you know, it's not just one show. They're doing them on the east coast, west coast, and starting to take it all over the world. Really, a community of chief data officers coming together with the likes of their own, talking about common issues, best practices. And of course, IBM's got something to offer as well. So, we're excited to have our next guest, Ken Jacquier here. He's the Information Governance Practice from IBM. Welcome. >> Thank you. >> So, what have you been hearing in the hallways outside of the sessions? What's kind of the hot buzz topic? >> Well, actually everybody's pretty much talking about what came up in the sessions, it's all about the talent. How do these Chief Data Officers get the talent that they need to meet the mandate they've been given? >> It's not just automatically just like connect the data, via some APIs and the magic happens (laughs). >> Sometimes the people part is the hardest part. The technology's important, the machine learning is great, the algorithms are amazing, but it does come down to people. And there's some new skill sets that these chief data >> officers need in their people, so that's what they're talking about. >> So when you think about the talent, what kinds of jobs are talking about? We know the CDO job. >> Ken: Yeah. >> What kind of jobs are now underneath the CDO that are going to help the CDO get their job done? >> Yeah, absolutely. You've got the classic data scientist role that we are all talking about, we're all excited about because that can monetize the data. That's what gets the board's attention. So there's a lot of focus there. But a term that came up in the last session that I was in that I really liked was the data translator. And the point there was data scientists can be schooled in certain things, understand their algorithms, understand machine learning, but this really important skill set they're looking for is the data translator. >> So the business is looking to drive outcomes. The chief marking officer may have an objective. >> The vice president of sales has an objective. Supply chain needs to optimize. Who is the data translator that can get from this deep, difficult, often dirty data and translate it into what the business is trying to accomplish? It's a really cool role. >> Yeah, we've actually heard about this role pretty frequently, this concept very frequently when you come right down to it. And a lot of it pertains to who is in a position to understand data quality, how data transformation works, so that the outcome in fact is what's expected as opposed to just a consequence data wrong. >> Exactly. Two examples of that that I've heard today in the initial keynote session, it came up, that in this renaissance of data, we're going to look for people to bring the left side of their brain together and the right side of their brain together. In the last session, of the ladies at a large international bank, the chief data officer there, she said, "for me, honestly, even though this is difficult, "it's not about IQ, it's about EQ." I've got to have the people that can collaborate. I've got to have the people that can communicate both with the business and with the IT side. I mean, we all know that story, right. Such a challenge to pull IT and business together, >> but data is really forcing individually talented people to actually do that wherever they reside in the org chart. >> If you're the embed, you're the embed person from the CEO office working with that business unit, you've got to listen, you've got to convince them that you can help them, so it is really a softer skill. You know, the Da Vinci word has come up a couple of times. And what made Da Vinci so amazing is he had the science, but he also had the art, and the two are very, very connected. >> Exactly what we were talking about, exactly. And the listening skill is incredibly important as well. I mean a lot of times, there's so much emphasis in communication on getting your perspective out there. A lot of times in these situations, you're trying to express your view. Way underestimated skill, listening, how important that is for this stuff to work. >> So, your formal title is Information Governance Practice? >> Ken: Yes. >> Now, governance means a lot of things to a lot of people, and I don't want to put words in your mouth, but from my >> perspective, it means how are you going to ensure, put in place rules and mechanisms and methods to ensure that works get done around a particular set of issues. So, when we talk about talent, we talk about creativity, we also can talk about governance so that we in fact get the right set of practices put in place, so not that it >> runs by itself, but it runs at a high quality. >> So one of the things that you're doing with clients, to try to take talent and rules and turn it into an actual function that does (mumbles) business values. >> Yeah, it's a great question. So again, and if anybody's listening to this and they're talking about careers, or they're thinking about work coming up, or you're coming out of college, and you're like what would I want to do, think about this conversation we're having and the opportunity here. So, you just described I've got to drive business agility, and I've got to mitigate risk. Those sound like conflicting objectives. They can't be anymore. The talent has to come in. And what we're trying to help companies with is how do you build both a culture, but then also how do you bring in talent that can be excited, and creative, and innovative to drive that business agility, but respects the fact that if we don't take care of this data, important people can get in trouble. If we don't take care of this data, our clients can be in trouble, and our credibility can be damaged. But that has to be handled in tandem. It can't be two separate functions. In the past, a lot of times, we did have maybe an EIM organization that does the institutional, keep the data quality clean, and then there were innovation teams over here playing around building the new business model acquiring companies. In this new world, all this data's coming together, and you've got to be able to develop. So the word we like to use nowadays with our clients is the appropriate governments. With your financial data, you're still going to have that locked down. You're still going to have all those policies, all those business rules. That's got to be in place. But then, there's certain data that we can maybe not manage quite as tightly. We can create a landing zone where we brought in external data or third party data, and we can let marketing have a little more freedom with that. And we can be a little more creative and innovative and I don't think they have to be opposite perspectives. If they have the right architecture and the right processes, and the right governance, you can do both. >> Is it easy for someone who's had the lockdown governance for so long to start to open up their mind and think about ways that they can open it? Or does it have to come from an external point of view that looks at it from a different lens and isn't kind of locked down by the old paradigm? >> Yeah, that's a great question. And there were three R's that came up in the meeting today in terms of talent. It was recruit. So to your point, to some degree, we're going to have to recruit new folks with new paradigms. A lot of conversation in there about what an incredible opportunity for the millennials and the newer folks in the workforce if they don't have those paradigms. On the other hand, we have to still >> retain deep institutional knowledge of our data. So that might mean retraining existing skill sets, people that really know our databases, that really know where the most important data lives, but retrain them a little bit for this new environment. And then the third R was retain. So as we build these hybrid skill sets, people that are good on the business side, good on the IT side, we make that investment. How does an organization, how does a company retrain them? And for the HR professionals out there, for the senior VPs of HR, that's where you come in. You need to help these companies write job descriptions, build career paths, show people that they can work in these environments and still grow, both financially, professional, and career wise. Does that make sense? >> That makes a ton of sense, interesting challenge. I just interviewed a millennial speaker at the Professional Businesswoman's Conference, and he just flat out said, the new paradigm from his point of view as a 26 year old, is most people aren't staying on the job for more than six years. It's almost kind of built in life sabbatical every couple three or four years. So, the retention challenge is very difficult and for that generation, so much it's kind of the purposefulness. And if you can get the purposefulness in, big motivator behavior. >> Purposefulness, being a part of something bigger. >> So that's where this balance can come in. If I'm working to appropriately govern my financial data, but I'm also given an opportunity to work with the acquisitions team that's bringing an international flavor into my company, that can give that younger person a little bit of both, and help with that retention. >> One of the challenges though when we think about governance is to ensure as you said, that the rules were appropriate. >> Ken: Yes. >> One of the other things we've heard here and we certainly know about is data as an asset is different than other assets, in that it's not following the economic scarcity because it's so easy to copy, share, combine, recombine, everything else. >> Ken: Very good point. >> As you think about combining those two things, that appropriateness of data governance for financial data is different from the appropriateness of data governance for marketing data, when you combine them, which appropriateness wins? >> (laughing) >> That's a good question. So, ultimately-- >> Do we have an answer? Is that something we're discovering, is that one of the things that we need to better understand over time? What do you think? >> Yeah I do. And you used the keyword, understand. >> So, a very old terminology in our space is data profiling, of truly understanding your data and understanding where everything lives. That's never been more important than it is today. The right amount of tagging in your data links. So to do what you just described. The answer lies within truly understanding and inventorying what you have, and then you have at least an opportunity to strike that balance. But a lot of folks are skipping that step. So just moving data, they're replicating data, >> they're populating their data links in the Hadoop systems. You've got to have governance even that environment. >> Oh absolutely. And we're seeing that being one of the greatest challenges as people try to put together these analytic pipelines. Is to ensure that there's appropriate governance at each stage in the pipeline to ensure that the outcomes are both what they expected. They can be surprised, but at least it's relevant. And that they themselves are not breaking any laws or rules, or ethical or otherwise, associated with how the data gets used. >> I'd like your economic analogy, because I think that's what customers need to do, and that's what I try to help them with. >> Depending on what their business model is, they're going to understand some concept of a supply chain. But likely they don't understand what you just said, the concept of an information supply chain. So rather than try to explain it in geek speak, with IBM tooling, or all the things we typically do, I encourage customers to think about their perception of a supply chain. How does something move from a raw material to a sold product in their industry, whether it's finance, or whether they're building airplanes or whatever >> they're doing? And then, the customer can start to relate. Okay, my data's doing the same thing isn't it? And oh, I need to start thinking, I get that, my engineering brain and my process, and I have roles in the company. I have (mumbles) that their job is to work on my supply chain out in the factory, you're saying apply those types of approaches to a supply chain for data, what you just described. And once that light bulb starts to go off, there's an opportunity to do what you just said. >> Absolutely, in fact, we specifically talk to our clients about the notion first of, the role of data, first of all, data as an asset. In other words, something that has a consequential impact on a set of activities so you can put it into with other things in supply chain. But we also talk about the value chain. The role the data plays in the value chain. Whatever metaphor, both of those concepts are not broadly understood. Because data is so sharable, is so easily copied, too frequently, people say uh, it's really not an asset. Until they start making the wrong decision widely and repeatedly. So they have to think about it as an asset, they have to think about it as a value chain, and that's where the governance becomes so crucial. It's because if you're not putting in place good governance for your value chains, then you're not creating any value pretty quickly. >> And it's interesting if we think about it. So, data's an asset. Marketing people, software companies have been using that term for a long time. But now that we're at this stage and we have chief data officers, at the C-level folks reporting into the board that have this responsibility. So now the concept's a little better understood. So now the next step is what does that mean? What do I do with my typical assets? What do I do with my human resources assets? If I manage a fleet, what do I do with that fleet? So if something's truly an asset, what do I do? What do I do with it on the general ledger? What do I do from a staffing perspective? Where does it fit into to my overall operating model? And that's kind of what we're seeing unfold here. At an event like this, that's the level of conversation that's starting to happen. Not that it's a marketing buzzword anymore, but if it's true, organizationally, what have I done with other assets? Does that apply to my data as well if I'm using that statement? >> Alright, Ken, we're going to have to leave it there. I know you've got to run off to a session, but thanks for taking a few minutes out of your day. >> Thanks gentlemen. >> Alright, he's Ken. Peter, Jeff, you're watching the Cube at the IBM Chief Data Officer Strategy Summit 2017. Thanks for watching. (easy listening music) (percussive music)

(lively music) >> To the world. Over 31 million people have viewed theCUBE and that is the result of great content, great conversations and I'm so proud to be part of theCUBE, of a great team. Hi, I'm John Furrier. Thanks for watching theCUBE. For more information, click here. >> Narrator: 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 at theCUBE. It is lunchtime at the IBM CDO Summit. Packed house, you can see them back there getting their nutrition. But we're going to give you some mental nutrition. We're excited to be joined by a repeat performance of Cortnie Abercrombie. Coming on back with Vijay Vijayasankar. He's the GM Cognitive, IOT, and Analytics for IBM, welcome. >> Thanks for having me. >> So first off, did you eat before you came on? >> I did thank you. >> I want to make sure you don't pass out or anything. (group laughing) Cortnie and I both managed to grab a quick bite. >> Excellent. So let's jump into it. Cognitive, lot of buzz, IoT, lot of buzz. How do they fit? Where do they mesh? Why is it, why are they so important to one another? >> Excellent question. >> IoT has been around for a long time even though we never called it IoT. My favorite example is smart meters that utility companies use. So these things have been here for more than a decade. And if you think about IoT, there are two aspects to it. There's the instrumentation by putting the sensors in and getting the data. And the insides aspect where there's making sense of what the sensor is trying to tell us. Combining these two, is where the value is for the client. Just by putting outwardly sensors, it doesn't make much sense. So, look at the world around us now, right? The traditional utility, I will stick with the utilities to complete the story. Utilities all get dissected from both sides. On one hand you have your electric vehicles plugging into the grid to draw power. On the other hand, you have supply coming from solar roofs and so on. So optimizing this is where the cognitive and analytics kicks in. So that's the beauty of this world. All these things come together, that convergence is where the big value is. >> Right because the third element that you didn't have in your original one was what's going on, what should we do, and then actually doing something. >> Vijay: Exactly. >> You got to have the action to pull it all together. >> Yes, and learning as we go. The one thing that is available today with cognitive systems that we did not have in the past was this ability to learn as you go. So you don't need human intervention to keep changing the optimization algorithms. These things can learn by itself and improve over time which is huge. >> But do you still need a person to help kind of figure out what you're optimizing for? That's where, can you have a pure, machine-driven algorithm without knowing exactly what are you optimizing for? >> We are no where close to that today. Generally, where the system is super smart by itself is a far away concept. But there are lots of aspects of specific AI optimizing a given process that can still go into this unsupervised learning aspects. But it needs boundaries. The system can get smart within boundaries, the system cannot just replace human thought. Just augmenting our intelligence. >> Jeff: Cortnie, you're shaking you head over there. >> I'm completely in agreement. We are no where near, and my husband's actually looking forward to the robotic apocalypse by the way, so. (group laughing) >> He must be an Arnold Schwarzenegger fan. >> He's the opposite of me. I love people, he's like looking forward to that. He's like, the less people, the better. >> Jeff: He must have his Zoomba, or whatever those little vacuum cleaner things are called. >> Yeah, no. (group laughing) >> Peter: Tell him it's the fewer the people, the better. >> The fewer the people the better for him. He's a finance guy, he'd rather just sit with the money all day. What does that say about me? Anyway, (laughing) no, less with the gross. Yeah no, I think we're never going to really get to that point. Because we always as people always have to be training these systems to think like us. So we're never going to have systems that are just autonomically out there without having an intervention here and there to learn the next steps. That's just how it works. >> I always thought the autonomous vehicle, just example, cause it's just so clean. You know, if somebody jumps in front of the car, does the car hit the person, or run into the ditch? >> Where today a person can't make that judgment very fast. They're just going to react. But in computer time, that's like forever. So you can actually make rules. And then people go bananas, well what if it's a grandma on one side and kids on the other? Which do you go? Or what if it's a criminal that just robbed a bank? Do you take him out on purpose? >> Trade off. >> So, you get into a lot of, interesting parameters that have nothing to do necessarily with the mechanics of making that decision. >> And this changes the fundamentals of computing big time too, right? Because a car cannot wait to ping the Cloud to find out, you know, should I break, or should I just run over this person in front of me. So it needs to make that determination right away. And hopefully the right decision which is to break. But on the other hand, all the cars that have this algorithm, together have collective learning, which needs some kind of Cloud computing. So this whole idea of Edge computing will come and replace a lot of what exists today. So see this disruption even behind the scenes on how we architect these systems, it's a fascinating time. >> And then how much of the compute, the store is at the Edge? How much of the computed to store in the Cloud and then depending on the decision, how do you say it, can you do it locally or do you have to send it upstream or break it in pieces. >> I mean if you look at a car of the future, forget car of the future, car of the present like Tesla, that has more compute power than a small data center, at multiple CPU's, lots of RAM, a lot of hard disk. It's a little Cloud that runs on wheels. >> Well it's a little data center that runs on wheels. But, let me ask you a question. And here's the question, we talk about systems that learn, cognitive systems that are constantly learning, and we're training them. How do we ensure that Watson, for example is constantly operating in the interest of the customer, and not the interest of IBM? Now there's a reason I'm asking this question, because at some point in time, I can perceive some other company offering up a similar set of services. I can see those services competing for attention. As we move forward with increasingly complex decisions, with increasingly complex sources of information, what does that say about how these systems are going to interact with each other? >> He always with the loaded questions today. (group laughing) >> It's an excellent question, it's something that I worry about all the time as well. >> Something we worry about with our clients too. >> So, couple of approaches by which this will exist. And to begin with, while we have the big lead in cognitive computing now, there is no hesitation on my part to admit that the ecosystem around us is also fast developing and there will be hefty competition going forward, which is a good thing. 'Cause if you look at how this world is developing, it is developing as API. APIs will fight on their own merits. So it's a very pluggable architecture. If my API is not very good, then it will get replaced by somebody else's API. So that's one aspect. The second aspect is, there is a difference between the provider and the client in terms of who owns the data. We strongly believe from IBM that client owns the data. So we will not go in and do anything crazy with it. We won't even touch it. So we will provide a framework and a cartridge that is very industry specific. Like for example, if Watson has to act as a call center agent for a Telco, we will provide a set of instructions that are applicable to Telco. But, all the learning that Watson does is on top of that clients data. We are not going to take it from one Telco and put it in another Telco. That will stay very local to that Telco. And hopefully that is the way the rest of the industry develops too. That they don't take information from one and provide to another. Even on an anonymous basis, it's a really bad idea to take a clients data and then feed it elsewhere. It has all kinds of ethical and moral consequences, even if it's legal. >> Absolutely. >> And we would encourage clients to take a look at some of the others out there and make sure that that's the arrangement that they have. >> Absolutely, what a great job for an analyst firm, right? But I want to build upon this point, because I heard something very interesting in the keynote, the CDO of IBM, in the keynote this morning. >> He used a term that I've thought about, but never heard before, trust as a service. Are you guys familiar with his use of that term? >> Vijay: Yep. >> Okay, what does trust as a service mean, and how does it play out so that as a consumer of IMB cognitive services, I have a measurable difference in how I trust IBM's cognitive services versus somebody else? >> Some would call that Blockchain. In fact Blockchain has often been called trust as a service. >> Okay, and Blockchain is probably the most physical form of it that we can find at the moment, right? At the (mumbles) where it's open to everybody but then no one brand section can be tabbed by somebody else. But if we extend that concept philosophically, it also includes a lot of the concept about identity. Identity. I as a user today don't have an easy way to identify myself across systems. Like, if I'm behind the firewall I have one identity, if I am outside the firewall I have another identity. But, if you look at the world tomorrow where I have to deal with a zillion APIs, this concept of a consistent identity needs to pass through all of them. It's a very complicated a difficult concept to implement. So that trust as a service, essentially, the light blocking that needs to be an identity service that follows me around that is not restrictive to an IBM system, or a Nautical system or something. >> But at the end of the day, Blockchain's a mechanism. >> Yes. >> Trust in the service sounds like a-- >> It's a transparency is what it is, the more transparency, the more trust. >> It's a way of doing business. >> Yes. >> Sure. >> So is IBM going to be a leader in defining what that means? >> Well look, in all cases, IBM has, we have always strove, what's the right word? Striven, strove, whatever it. >> Strove. >> Strove (laughing)? >> I'll take that anyway. >> Strove, thank you. To be a leader in how we approach everything ethically. I mean, this is truly in our blood, I mean, we are here for our clients. And we aren't trying to just get them to give us all of their data and then go off and use it anywhere. You have to pay attention sometimes, that what you're paying for is exactly what you're getting, because people will try to do those things, and you just need to have a partner that you trust in this. And, I know it's self-serving to say, but we think about data ethics, we think about these things when we talk to our clients, and that's one of the things that we try to bring to the table is that moral, ethical, should you. Just because you can, and we have, just so you know walked away from deals that were very lucrative before, because we didn't feel it was the right thing to do. And we will always, I mean, I know it sounds self-serving, I don't know how to, you won't know until you deal with us, but pay attention, buyer beware. >> You're just Cortnie from IBM, we know what side you're on. (group laughing) It's not a mystery. >> Believe me, if I'm associated with it, it's yeah. >> But you know, it's a great point, because the other kind of ethical thing that comes up a lot with data, is do you have the ethical conversation before you collect that data, and how you're going to be using it. >> Exactly. >> But that's just today. You don't necessarily know what's going to, what and how that might be used tomorrow. >> Well, in other countries. >> That's what gets really tricky. >> Future-proofing is a very interesting concept. For example, vast majority of our analytics conversation today is around structure and security, those kinds of terms. But, where is the vast majority of data sitting today? It is in video and sound files, which okay. >> Cortnie: That's even more scary. >> It is significantly scary because the technology to get insights out of this is still developing. So all these things like cluster and identity and security and so on, and quantum computing for that matter. All these things need to think about the future. But some arbitrary form of data can come hit you and all these principles of ethics and legality and all should apply. It's a very non-trivial challenge. >> But I do see that some countries are starting to develop their own protections like the General Data Protection Regulation is going to be a huge driver of forced ethics. >> And some countries are not. >> And some countries are not. I mean, it's just like, cognitive is just like anything else. When the car was developed, I'm sure people said, hey everybody's going to go out killing people with their cars now, you know? But it's the same thing, you can use it as a mode of transportation, or you can do something evil with it. It really is going to be governed by the societal norms that you live in, as to how much you're going to get away with. And transparency is our friend, so the more transparent we can be, things like Blockchain, other enablers like that that allow you to see what's going on, and have multiple copies, the better. >> All right, well Cortnie, Vijay, great topics. And that's why gatherings like this are so important to be with your peer group, you know, to talk about these much deeper issues that are really kind of tangental to technology but really to the bigger picture. So, keep getting out on the fringe to help us figure this stuff out. >> I appreciate it, thanks for having us. >> Thanks. >> Pleasure. All right, I'm Jeff Frick with Peter Burris. We're at the Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit 2017. Thanks for watching. (upbeat music) (dramatic music)