>> live from San Francisco, California. It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> Welcome back to Fisherman's Fisherman's Wharf in San Francisco. Everybody, my name is David wanted. You're watching the Cube, the leader in live tech coverage, you ought to events. We extract the signal from the noise. We're here. The IBM CDO event. This is the 10th anniversary of this event. Caitlin Hallford is here. She's the director of a I Accelerator and client success at IBM. Caitlin, great to see you again. Wow. 10 years. Amazing. They and Carlo Apple Apple Glace e is here. Who is the program director for data and a I at IBM. Because you again, my friend. Thanks for coming on to Cuba. Lums. Wow, this is 10 years, and I think the Cube is covered. Probably eight of these now. Yeah, kind of. We bounce between San Francisco and Boston to great places for CEOs. Good places to have intimate events, but and you're taking it global. I understand. Congratulations. Congratulations on the promotion. Thank you. Going. Thank you so much. >> So we, as you know well are well, no. We started our chief date officer summits in San Francisco here, and it's gone 2014. So this is our 10th 1 We do two a year. We found we really have a unique cohort of clients. The join us about 100 40 in San Francisco on the spring 140 in Boston in the fall, and we're here celebrating the 10th 10 Summit. >> So, Carlo, talk about your role and then let's get into how you guys, you know, work together. How you hand the baton way we'll get to the client piece. >> So I lead the Data Center League team, which is a group within our product development, working side by side with clients really to understand their needs as well developed, use cases on our platform and tools and make sure we are able to deliver on those. And then we work closely with the CDO team, the global CEO team on best practices, what patterns they're seeing from an architecture perspective. Make sure that our platforms really incorporating that stuff. >> And if I recall the data science that lead team is its presales correct and could >> be posted that it could, it really depends on the client, so it could be prior to them buying software or after they bought the software. If they need the help, we can also come in. >> Okay, so? So it can be a for pay service. Is that correct or Yeah, we can >> before pay. Or sometimes we do it based on just our relation with >> It's kind of a mixed then. Right? Okay, so you're learning the client's learning, so they're obviously good, good customers. And so you want to treat him right >> now? How do you guys work >> together? Maybe Caitlin, you can explain. The two organizations >> were often the early testers, early adopters of some of the capabilities. And so what we'll do is we'll test will literally will prove it out of skill internally using IBM itself as an example. And then, as we build out the capability, work with Carlo and his team to really drive that in a product and drive that into market, and we share a lot of client relationships where CEOs come to us, they're want advice and counsel on best practices across the organization. And they're looking for latest applications to deploy deploy known environments and so we can capture a lot of that feedback in some of the market user testing proved that out. Using IBM is an example and then work with you to really commercialized and bring it to market in the most efficient manner. >> You were talking this morning. You had a picture up of the first CDO event. No Internet, no wife in the basement. I love it. So how is this evolved from a theme standpoint? What do you What are the patterns? Sure. So when >> we started this, it was really a response. Thio primarily financial service is sector regulatory requirements, trying to get data right to meet those regulatory compliance initiatives. Defensive posture certainly weren't driving transformation within their enterprises. And what I've seen is a couple of those core elements are still key for us or data governance and data management. And some of those security access controls are always going to be important. But we're finding his videos more and more, have expanded scope of responsibilities with the enterprise they're looked at as a leader. They're no longer sitting within a c i o function there either appear or, you know, working in partnership with, and they're driving enterprise wide, you know, initiatives for the for their enterprises and organizations, which has been great to see. >> So we all remember when you know how very and declared data science was gonna be the number one job, and it actually kind of has become. I think I saw somewhere, maybe in Glass door was anointed that the top job, which is >> kind of cool to see. So what are you seeing >> with customers, Carlo? You guys, you have these these blueprints, you're now applying them, accelerating different industries. You mentioned health care this morning. >> What are some >> of those industry accelerators And how is that actually coming to fruition? Yes. >> So some of the things we're seeing is speaking of financial clients way go into a lot of them. We do these one on one engagements, we build them from custom. We co create these engineering solutions, our platform, and we're seeing patterns, patterns around different use cases that are coming up over and over again. And the one thing about data science Aye, aye. It's difficult to develop a solution because everybody's date is different. Everybody's business is different. So what we're trying to do is build these. We can't just build a widget that's going to solve the problem, because then you have to force your data into that, and we're seeing that that doesn't really work. So building a platform for these clients. But these accelerators, which are a set of core code source code notebooks, industry models in terms a CZ wells dashboards that allow them to quickly build out these use cases around a turn or segmentation on dhe. You know some other models we can grab the box provide the models, provide the know how with the source code, as well as a way for them to train them, deploy them and operationalize them in an organization. That's kind of what we're doing. >> You prime the pump >> prime minute pump, we call them there right now, we're doing client in eights for wealth management, and we're doing that, ref SS. And they come right on the box of our cloudpack for data platform. You could quickly click and install button, and in there you'll get the sample data files. You get no books. You get industry terms, your governance capability, as well as deployed dashboards and models. >> So talk more about >> cloudpack for data. What's inside of that brought back the >> data is a collection of micro Service's Andi. It includes a lot of things that we bring to market to help customers with their journey things from like data ingestion collection to all the way Thio, eh? I model development from building your models to deploying them to actually infusing them in your business process with bias detection or integration way have a lot of capability. Part >> of it's actually tooling. It's not just sort of so how to Pdf >> dualism entire platform eso. So the platform itself has everything you need an organization to kind of go from an idea to data ingestion and governance and management all the way to model training, development, deployment into integration into your business process. >> Now Caitlin, in the early days of the CDO, saw CDO emerging in healthcare, financialservices and government. And now it's kind of gone mainstream to the point where we had Mark Clare on who's the head of data neighborhood AstraZeneca. And he said, I'm not taking the CDO title, you know, because I'm all about data enablement and CDO. You know, title has sort of evolved. What have you seen? It's got clearly gone mainstream Yep. What are you seeing? In terms of adoption of that, that role and its impact on organizations, >> So couple of transit has been interesting both domestically and internationally as well. So we're seeing a lot of growth outside of the U. S. So we did our first inaugural summit in Tokyo. In Japan, there's a number of day leaders in Japan that are really eager to jump start their transformation initiatives. Also did our first Dubai summit. Middle East and Africa will be in South Africa next month at another studio summit. And what I'm seeing is outside of North America a lot of activity and interest in creating an enabling studio light capability. Data Leader, Like, um, and some of these guys, I think we're gonna leapfrog ahead. I think they're going to just absolutely jump jump ahead and in parallel, those traditional industries, you know, there's a new federal legislation coming down by year end for most federal agencies to appoint a chief data officer. So, you know, Washington, D. C. Is is hopping right now, we're getting a number of agencies requesting advice and counsel on how to set up the office how to be successful I think there's some great opportunity in those traditional industries and also seeing it, you know, outside the U. S. And cross nontraditional, >> you say >> Jump ahead. You mean jump ahead of where maybe some of the U. S. >> Absolute best? Absolutely. And I'm >> seeing a trend where you know, a lot of CEOs they're moving. They're really closer to the line of business, right? They're moving outside of technology, but they have to be technology savvy. They have a team of engineers and data scientists. So there is really an important role in every organization that I'm seeing for every client I go to. It's a little different, but you're right, it's it's definitely up and coming. Role is very important for especially for digital transformation. >> This is so good. I was gonna say one of the ways they are teens really, partner Well, together, I think is weaken source some of these in terms of enabling that you know, acceleration and leap frog. What are those pain points or use cases in traditional data management space? You know, the metadata. So I think you talk with Steven earlier about how we're doing some automated meditate a generation and really using a i t. O instead of manually having to label and tag that we're able to generate about 85% of our labels internally and drive that into existing product. Carlos using. And our clients are saying, Hey, we're spending, you know, hundreds of millions of dollars and we've got teams of massive teams of people manual work. And so we're able to recognize it, adopts something like that, press internally and then work with you guys >> actually think of every detail developer out there that has to go figure out what this date is. If you have a tool which we're trying to cooperate the platform based on the guidance from the CDO Global CEO team, we can automatically create that metadata are likely ingested and provide into platform so that data scientists can start to get value out >> of it quickly. So we heard Martin Schroeder talked about digital trade and public policy, and he said there were three things free flow of data. Unless it doesn't make sense like personal information prevent data localization mandates, yeah, and then protect algorithms and source code, which is an I P protection thing. So I'm interested in how your customers air Reacting to that framework, I presume the protect the algorithms and source code I p. That's near and dear right? They want to make sure that you're not taking models and then giving it to their competitors. >> Absolutely. And we talk about that every time we go in there and we work on projects. What's the I p? You know, how do we manage this? And you know, what we bring to the table with the accelerators is to help them jump start them right, even though that it's kind of our a p we created, but we give it to them and then what they derive from that when they incorporate their data, which is their i p, and create new models, that is then their i. P. So those air complicated questions and every company is a little different on what they're worried about with that, so but many banks, we give them all the I P to make sure that they're comfortable and especially in financial service is but some other spaces. It's very competitive. And then I was worried about it because it's, ah, known space. A lot of the algorithm for youse are all open source. They're known algorithms, so there's not a lot of problem there. >> It's how you apply them. That's >> exactly right how you apply them in that boundary of what >> is P, What's not. It's kind of >> fuzzy, >> and we encourage our clients a lot of times to drive that for >> the >> organisation, for us, internally, GDP, our readiness, it was occurring to the business unit level functional area. So it was, you know, we weren't where we needed to be in terms of achieving compliance. And we have the CEO office took ownership of that across the business and got it where we needed to be. And so we often encourage our clients to take ownership of something like that and use it as an opportunity to differentiate. >> And I talked about the whole time of clients. Their data is impor onto them. Them training models with that data for some new making new decisions is their unique value. Prop In there, I'd be so so we encourage them to make sure they're aware that don't just tore their data in any can, um, service out there model because they could be giving away their intellectual property, and it's important. Didn't understand that. >> So that's a complicated one. Write the piece and the other two seem to be even tougher. And some regards, like the free flow of data. I could see a lot of governments not wanting the free flow of data, but and the client is in the middle. OK, d'oh. Government is gonna adjudicate. What's that conversation like? The example that he gave was, maybe was interpolate. If it's if it's information about baggage claims, you can you can use the Blockchain and crypt it and then only see the data at the other end. So that was actually, I thought, a good example. Why do you want to restrict that flow of data? But if it's personal information, keep it in country. But how is that conversation going with clients? >> Leo. Those can involve depending on the country, right and where you're at in the industry. >> But some Western countries are strict about that. >> Absolutely. And this is why we've created a platform that allows for data virtualization. We use Cooper nannies and technologies under the covers so that you can manage that in different locations. You could manage it across. Ah, hybrid of data centers or hybrid of public cloud vendors. And it allows you to still have one business application, and you can kind of do some of the separation and even separation of data. So there's there's, there's, there's an approach there, you know. But you gotta do a balance. Balance it. You gotta balance between innovation, digital transformation and how much you wanna, you know, govern so governs important. And then, you know. But for some projects, we may want to just quickly prototype. So there's a balance there, too. >> Well, that data virtualization tech is interesting because it gets the other piece, which was prevent data localization mandates. But if there is a mandate and we know that some countries aren't going to relax that mandate, you have, ah, a technical solution for that >> architecture that will support that. And that's a big investment for us right now. And where we're doing a lot of work in that space. Obviously, with red hat, you saw partnership or acquisition. So that's been >> really Yeah, I heard something about that's important. That's that's that's a big part of Chapter two. Yeah, all right. We'll give you the final world Caitlyn on the spring. I guess it's not spring it. Secondly, this summer, right? CDO event? >> No, it's been agreed. First day. So we kicked off. Today. We've got a full set of client panel's tomorrow. We've got some announcements around our meta data that I mentioned. Risk insights is a really cool offering. We'll be talking more about. We also have cognitive support. This is another one. Our clients that I really wanted to help with some of their support back in systems. So a lot of exciting announcements, new thought leadership coming out. It's been a great event and looking forward to the next next day. >> Well, I love the fact >> that you guys have have tied data science into the sea. Sweet roll. You guys have done a great job, I think, better than anybody in terms of of, of really advocating for the chief data officer. And this is a great event because it's piers talking. Appears a lot of private conversations going on. So congratulations on all the success and continued success worldwide. >> Thank you so much. Thank you, Dave. >> You welcome. Keep it right there, everybody. We'll be back with our next guest. Ready for this short break. We have a panel coming up. This is David. Dante. You're >> watching the Cube from IBM CDO right back.

>> Live from San Francisco, California, it's theCUBE covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> Hi everybody, welcome back to theCUBE's coverage of the IBM Chief Data Officer Event. We're here at Fisherman's Wharf in San Francisco at the Centric Hyatt Hotel. This is the 10th anniversary of IBM's Chief Data Officer Summits. In the recent years, anyway, they do one in San Francisco and one in Boston each year, and theCUBE has covered a number of them. I think this is our eighth CDO conference. I'm Dave Vellante, and theCUBE, we like to go out, especially to events like this that are intimate, there's about 140 chief data officers here. We've had the chief data officer from AstraZeneca on, even though he doesn't take that title. We've got a panel coming up later on in the day. And I want to talk about the evolution of that role. The chief data officer emerged out of kind of a wonky, back-office role. It was all about 10, 12 years ago, data quality, master data management, governance, compliance. And as the whole big data meme came into focus and people were realizing that data is the new source of competitive advantage, that data was going to be a source of innovation, what happened was that role emerged, that CDO, chief data officer role, emerged out of the back office and came right to the front and center. And the chief data officer really started to better understand and help companies understand how to monetize the data. Now monetization of data could mean more revenue. It could mean cutting costs. It could mean lowering risk. It could mean, in a hospital situation, saving lives, sort of broad definition of monetization. But it was really understanding how data contributed to value, and then finding ways to operationalize that to speed up time to value, to lower cost, to lower risk. And that required a lot of things. It required new skill sets, new training. It required a partnership with the lines of business. It required new technologies like artificial intelligence, which have just only recently come into a point where it's gone mainstream. Of course, when I started in the business several years ago, AI was the hot topic, but you didn't have the compute power. You didn't have the data, you didn't have the cloud. So we see the new innovation engine, not as Moore's Law, the doubling of transistors every 18 months, doubling of performance. Really no, we see the new innovation cocktail as data as the substrate, applying machine intelligence to that data, and then scaling it with the cloud. And through that cloud model, being able to attract startups and innovation. I come back to the chief data officer here, and IBM Chief Data Officer Summit, that's really where the chief data officer comes in. Now, the role in the organization is fuzzy. If you ask people what's a chief data officer, you'll get 20 different answers. Many answers are focused on compliance, particularly in what emerged, again, in those regulated industries: financial service, healthcare, and government. Those are the first to have chief data officers. But now CDOs have gone mainstream. So what we're seeing here from IBM is the broadening of that role and that definition and those responsibilities. Confusing things is the chief digital officer or the chief analytics officer. Those are roles that have also emerged, so there's a lot of overlap and a lot of fuzziness. To whom should the chief data officer report? Many say it should not be the CIO. Many say they should be peers. Many say the CIO's responsibility is similar to the chief data officer, getting value out of data, although I would argue that's never really been the case. The role of the CIO has largely been to make sure that the technology infrastructure works and that applications are delivered with high availability, with great performance, and are able to be developed in an agile manner. That's sort of a more recent sort of phenomenon that's come forth. And the chief digital officer is really around the company's face. What does that company's brand look like? What does that company's go-to-market look like? What does the customer see? Whereas the chief data officer's really been around the data strategy, what the sort of framework should be around compliance and governance, and, again, monetization. Not that they're responsible for the monetization, but they responsible for setting that framework and then communicating it across the company, accelerating the skill sets and the training of existing staff and complementing with new staff and really driving that framework throughout the organization in partnership with the chief digital officer, the chief analytics officer, and the chief information officer. That's how I see it anyway. Martin Schroeder, the senior vice president of IBM, came on today with Inderpal Bhandari, who is the chief data officer of IBM, the global chief data officer. Martin Schroeder used to be the CFO at IBM. He talked a lot, kind of borrowing from Ginni Rometty's themes in previous conferences, chapter one of digital which he called random acts of digital, and chapter two is how to take this mainstream. IBM makes a big deal out of the fact that it doesn't appropriate your data, particularly your personal data, to sell ads. IBM's obviously in the B2B business, so that's IBM's little back-ended shot at Google and Facebook and Amazon who obviously appropriate our data to sell ads or sell goods. IBM doesn't do that. I'm interested in IBM's opinion on big tech. There's a lot of conversations now. Elizabeth Warren wants to break up big tech. IBM was under the watchful eye of the DOJ 25 years ago, 30 years ago. IBM essentially had a monopoly in the business, and the DOJ wanted to make sure that IBM wasn't using that monopoly to hurt consumers and competitors. Now what IBM did, the DOJ ruled that IBM had to separate its applications business, actually couldn't be in the applications business. Another ruling was that they had to publish the interfaces to IBM mainframes so that competitors could actually build plug-compatible products. That was the world back then. It was all about peripherals plugging into mainframes and sort of applications being developed. So the DOJ took away IBM's power. Fast forward 30 years, now we're hearing Google, Amazon, and Facebook coming under fire from politicians. Should they break up those companies? Now those companies are probably the three leaders in AI. IBM might debate that. I think generally, at theCUBE and SiliconANGLE, we believe that those three companies are leading the charge in AI, along with China Inc: Alibaba, Tencent, Baidu, et cetera, and the Chinese government. So here's the question. What would happen if you broke up big tech? I would surmise that if you break up big tech, those little techs that you break up, Amazon Web Services, WhatsApp, Instagram, those little techs would get bigger. Now, however, the government is implying that it wants to break those up because those entities have access to our data. Google's got access to all the search data. If you start splitting them up, that'll make it harder for them to leverage that data. I would argue those small techs would get bigger, number one. Number two, I would argue if you're worried about China, which clearly you're seeing President Trump is worried about China, placing tariffs on China, playing hardball with China, which is not necessarily a bad thing. In fact, I think it's a good thing because China has been accused, and we all know, of taking IP, stealing IP essentially, and really not putting in those IP protections. So, okay, playing hardball to try to get a quid pro quo on IP protections is a good thing. Not good for trade long term. I'd like to see those trade barriers go away, but if it's a negotiation tactic, okay. I can live with it. However, going after the three AI leaders, Amazon, Facebook, and Google, and trying to take them down or break them up, actually, if you're a nationalist, could be a bad thing. Why would you want to handcuff the AI leaders? Third point is unless they're breaking the law. So I think that should be the decision point. Are those three companies, and others, using monopoly power to thwart competition? I would argue that Microsoft actually did use its monopoly power back in the '80s and '90s, in particular in the '90s, when it put Netscape out of business, it put Lotus out of business, it put WordPerfect out of business, it put Novell out of the business. Now, maybe those are strong words, but in fact, Microsoft's bundling, its pricing practices, caught those companies off guard. Remember, Jim Barksdale, the CEO of Netscape, said we don't need the browser. He was wrong. Microsoft killed Netscape by bundling Internet Explorer into its operating system. So the DOJ stepped in, some would argue too late, and put handcuffs on Microsoft so they couldn't use that monopoly power. And I would argue that you saw from that two things. One, granted, Microsoft was overly focused on Windows. That was kind of their raison d'etre, and they missed a lot of other opportunities. But the DOJ definitely slowed them down, and I think appropriately. And if out of that myopic focus on Windows, and to a certain extent, the Department of Justice and the government, the FTC as well, you saw the emergence of internet companies. Now, Microsoft did a major pivot to the internet. They didn't do a major pivot to the cloud until Satya Nadella came in, and now Microsoft is one of those other big tech companies that is under the watchful eye. But I think Microsoft went through that and perhaps learned its lesson. We'll see what happens with Facebook, Google, and Amazon. Facebook, in particular, seems to be conflicted right now. Should we take down a video that has somewhat fake news implications or is a deep hack? Or should we just dial down? We saw this recently with Facebook. They dialed down the promotion. So you almost see Facebook trying to have its cake and eat it too, which personally, I don't think that's the right approach. I think Facebook either has to say damn the torpedoes. It's open content, we're going to promote it. Or do the right thing and take those videos down, those fake news videos. It can't have it both ways. So Facebook seems to be somewhat conflicted. They are probably under the most scrutiny now, as well as Google, who's being accused, anyway, certainly we've seen this in the EU, of promoting its own ads over its competitors' ads. So people are going to be watching that. And, of course, Amazon just having too much power. Having too much power is not necessarily an indication of abusing monopoly power, but you know the government is watching. So that bears watching. theCUBE is going to be covering that. We'll be here all day, covering the IBM CDO event. I'm Dave Vallente, you're watching theCUBE. #IBMCDO, DM us or Tweet us @theCUBE. I'm @Dvallente, keep it right there. We'll be right back right after this short break. (upbeat music)

(electronica) >> Live, from San Francisco, California it's theCube. Covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at Fisherman's Wharf covering the IBM Chief Data Officer event, the 10th anniversary. You're watching theCube, the leader in live tech coverage. Just off the keynotes, Martin Schroeter is here as the Senior Vice President of IBM Global Markets responsible for revenue, profit, IBM's brand, just a few important things. Martin, welcome to theCube. >> They're important, they're important. >> Inderpal Bhandari, Cube alum, Global Chief Data Officer at IBM. Good to see you again. >> Good to see you Dave, >> So you guys, just off the keynotes, Martin, you talked a lot about disruption, things like digital trade that we're going to get into, digital transformation. What are you hearing when you talk to clients? You spent a lot of time as the CFO. >> I did. >> Now you're spending a lot of time with clients. What are they telling you about disruption and digital transformation? >> Yeah, you know the interesting thing Dave, is the first thing every CEO starts with now is that "I run a technology company." And it doesn't matter if they're writing code or manufacturing corrugated cardboard boxes, every CEO believes they are running a technology company. Now interestingly, maybe we could've predicted this already five or six years ago because we run a CEO survey, we run a CFO, we run surveys of the C-suite. And already about five years ago, technology was number one on the CEO's list of what's going to change their company in the next 3-5 years. It led. The CFO lagged, the CMO lagged, everyone else. Like, CEO saw it first. So CEOs now believe they are running technology businesses, and when you run a technology business, that means you have to fundamentally change the way you work, how you work, who does the work, and how you're finding and reaching and engaging with your clients. So when we talk, we shorthand of digitizing the enterprise. Or, what does it mean to become a digitally enable enterprise? It really is about how to use today's technology embedded into your workflows to make sure you don't get disintermediated from your clients? And you're bringing them value at every step, every touchpoint of their journey. >> So that brings up a point. Every CEO I talk to is trying to get "digital right." And that comes back to the data. Now you're of course, biased on that. But what are your thoughts on a digital business? Is digital businesses all about how they use data and leverage data? What does it mean to get "digital right" in your view? >> So data has to be the starting point. You actually do see examples of companies that'll start out on a digital transformation, or a technology transformation, and then eventually back into the data transformation. So in a sense, you've got to have the digital piece of it, which is really the experience that users have of the products of the company, as well as the technology, which is kind of the backend engines that are running. But also the workflow, and being able to infuse AI into workflows. And then data, because everything really rides on the data being in good enough shape to be able to pull all this off. So eventually people realize that really it's not just a digital transformation or technology transformation, but it is a data transformation to begin with. >> And you guys have talked a lot at this event, at least this pre-event, I've talked to people about operationalizing AI, that's a big part of your responsibilities. How do you feel about where you're at? I mean, it's a journey I know. You're never done. But feel like you're making some good progress there? Internally at IBM specifically. >> Yes, internally at IBM. Very good progress. Because our whole goal is to infuse AI into every major business process, and touch every IBM. So that's the whole goal of what we've been doing for the last few years. And we're already at the stage where our central AI and data platform for this year, over 100,000 active users will be making use of it on a regular basis. So we think we're pretty far along in terms of our transformation. And the whole goal behind this summit and the previous summits as you know, Dave, has been to use that as a showcase for our clients and customers so that they can replicate that journey as well. >> So we heard Ginni Rometty two IBM thinks ago talk about incumbent disruptors, which resonates, 'cause IBM's an incumbent disruptor. You talked about Chapter One being random acts of digital. and then Chapter Two is sort of how to take that mainstream. So what do you see as the next wave, Martin? >> Well as Inderpal said, and if I use us as an example. Now, we are using AI heavily. We have an advantage, right? We have this thing called IBM Research, one of the most prolific Inventors of Things still leads the world. You know we still lead the world in patents so have the benefit. For our our clients, however, we have to help them down that journey. And the clients today are on a journey of finding the right hybrid cloud solution that gives them bridges sort of "I have this data. "The incumbency advantage of having data," along with "Where are the tools and "where is the compute power that I need to take advantage of the data." So they're on that journey at the same time they're on the journey as Inderpal said, of embedding it into their workflows. So for IBM, the company that's always lived sort of at the intersection of technology and business, that's what we're helping our clients to do today. Helping them take their incumbent advantage of data, having data, helping them co-create. We're working with them to co-create solutions that they can deploy and then helping them to put that into work, into production, if you will, in their environments and in their workflows. >> So one of the things you stressed today, two of the things. You've talked about transparency, and open digital trade. I want to get into the latter, but talk about what's important in Chapter Two. Just, what are those ingredients of success? You've talked about things like free flow of data, prevent data localization, mandates, and protect algorithms and source codes. You also made another statement which is very powerful "IBM is never giving up its source code to our government, and we'd leave the country first." >> We wouldn't give up our source code. >> So what are some of those success factors that we need to be thinking about in that context? >> If we look at IBM. IBM today runs, you know 87% of the world's credit card transactions, right? IBM today runs the world's banking systems, we run the airline reservation systems, we run the supply chains of the world. Hearts and lungs, right? If I just shorthand all of that, hearts and lungs. The reason our clients allow us to do that is because they trust us at the very core. If they didn't trust us with our data they wouldn't give it to us. If they didn't trust us to run the process correctly, they wouldn't give it to us. So when we say trust, it happens at a very base level of "who do you really trust to run you're data?" And importantly, who is someone else going to trust with your data, with your systems? Any bank can maybe figure out, you know, how to run a little bit of a process. But you need scale, that's where we come in. So big banks need us. And secondly, you need someone you can trust that can get into the global banking system, because the system has to trust you as well. So they trust us at a very base level. That's why we still run the hearts and lungs of the enterprise world. >> Yeah, and you also made the point, you're not talking about necessarily personal data, that's not your business. But when you talked about the free flow of data, there are governments of many, western governments who are sort of putting in this mandate of not being able to persist data out of the country. But then you gave an example of "If you're trying to track a bag at baggage claim, you actually want that free flow of data." So what are those conversations like? >> So first I do think we have to distinguish between the kinds of data that should frow freely and the kinds of data that should absolutely, personal information is not what we're talking about, right? But the supply chains of the world work on data, the banking system works on data, right? So when we talk about the data that has to flow freely, it's all the data that doesn't have a good reason for it to stay local. Citizen's data, healthcare data, might have to stay, because they're protecting their citizen's privacy. That's the issue I think, that most governments are on. So we have disaggregate the data discussion, the free flow of data from the privacy issues, which are very important. >> Is there a gray area there between the personal information and the type of data that Martin's talking about? Or is it pretty clear cut in your view? >> No, I think this is obviously got to play itself out. But I'll give you one example. So, the whole use of a blockchain potentially helps you address and find the right balance between privacy of sensitive data, versus actually the free flow of data. >> Right. >> Right? So for instance, you could have an encryption or a hashtag. Or hash, sorry. Not a hashtag. A hash, say, off the person's name whose luggage is lost. And you could pass that information through, and then on the other side, it's decrypted, and then you're able to make sure that, you know, essentially you're able to satisfy the client, the customer. And so there's flow of data, there's no issue with regard to exposure. Because only the rightful parties are able to use it. So these things are, in a sense, the technologies that we're talking about, that Martin talked about with the blockchain, and so forth. They are in place to be able to really revolutionize and transform digital trade. But there are other factors as well. Martin touched on a bunch of those in the keynote with regard to, you know, the imbalances, some of the protectionism that comes in, and so on and so forth. Which all that stuff has to be played through. >> So much to talk about, so little time. So digital trade, let's get into that a little bit. What is that and why is it so important? >> So if you look at the economic throughput in the digital economy, the size of the GDP if you will, of what travels around the world in the way data flows, it's greater than the traded goods flow. So this is a very important discussion. Over the last 10 years, you know, out of the 100% of jobs that were created, 80% or so had a digital component to it. Which means that the next set of jobs that we're creating, they require digital skills. So we need a set of skills that will enable a workforce. And we need a regulatory environment that's cooperative, that's supportive. So in the regulatory environment, as we said before, we think data should flow freely unless there's a reason for it not to flow. And I think there will be some really good reasons why certain data should not flow.. But data should flow freely, except for certain reasons that are important. We need to make sure we don't create a series of mandates that force someone to store data here. If you want to be in business in a country, the country shouldn't say "Well if you want to business here "you have to store all your data here." It tends to be done on the auspice of a security concern, but we know enough about security that doesn't help. It's a false sense of security. So data has to flow freely. Don't make someone store it there just because it may be moving through or it's being processed in your country. And then thirdly, we have to protect the source code that companies are using. We cannot force, no country should force, a company to give up their source code. People will leave, they just won't do business there. >> That's just not about intellectual property issue there, right? >> It's huge intellectual property issue, that's exactly right. >> So the public policy framework then, is really free flow of data where it makes sense. No mandates unless it makes sense, and- >> And protection of IP. >> Protection of IP. >> That's right. >> Okay, good. >> It's a pretty simple structure. And based on my discussions I think most sort of aligned with that. And we're encouraged. I'm encouraged by what I see in TPP, it has that. What I see in Europe, it has that. What I see in USMCA it has that. So all three of those very good, but they're three separate things. We need to bring it all together to have one. >> So it was a good example. GDDPR maybe as a framework that seems to be seeping its way into other areas. >> So GDPR is an important discussion, but that's the privacy discussion wrapped around a broader trade issue. But privacy is important. GDPR does a good job on it, but we have a broader trade issue of data. >> Inderpal give me the final word, it's kind of your show. >> Well, you know. So I was just going to say Dave, I think one way to think about it is you have to have the free flow of data. And maybe the way to think about it is certain data you do need controls on. And it's more of the form in which the data flows that you restrict. As opposed to letting the data flow at all. >> What do you mean? >> So the hash example that I gave you. It's okay for the hash to go across, that way you're not exposing the data itself. So those technologies are all there. It's much more the regulatory frameworks that Martin's talking about, that they've got to be there in place so that we are not impeding the progress. That's going to be inevitable when you do have the free flow of data. >> So in that instance, the hash example that you gave. It's the parties that are adjudicating, the machines are adjudicating. Unless the parties want to expose that data it won't be exposed. >> It won't happen, they won't be exposed. >> All right. Inderpal, Martin, I know you got to run. Thanks so much for coming out. >> Thank you. Thanks for the talk. >> Thank you >> You're welcome. All right. Keep it right there everybody, we'll be back with our next guest from IBMCDO Summit in San Francisco. You're watching theCube. (electronica)

>> Live from San Francisco, California. It's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at Fisherman's Wharf at the IBM CDO conference. You're watching theCUBE, the leader in live tech coverage. My name is Dave Volante, Joe Selle is here. He's the Global Advanced Analytics and Cognitive Lead at IBM, Boston base. Joe, good to see you again. >> You to Dave. >> And Jerry Gupta, the Senior Vice President and Digital Catalyst at Swiss Re Institute at Swiss Re, great to see you. Thanks for coming on. >> Thank you for having me Dave. >> You're very welcome. So Jerry, you've been at this event now a couple of years, we've been here I think the last four or five years and in the early, now this goes back 10 years this event, now 10 years ago, it was kind of before the whole big data meme took off. It was a lot of focus I'm sure on data quality and data compliance and all of a sudden data became the new source of value. And then we rolled into digital transformation. But how from your perspective, how have things changed? Maybe the themes over the last couple of years, how have they changed? >> I think, from a theme perspective, I would frame the question a little bit differently, right? For me, this conference is a must have on my calendar, because it's very relevant. The topics are very current. So two years ago, when I first attended this conference, it was about cyber and when we went out in the market, they were not too many companies talking about cyber. And so you come to a place like this and you're not and you're sort of blown away by the depth of knowledge that IBM has, the statistics that you guys did a great job presenting. And that really helped us inform ourselves about the cyber risk that we're going on in cyber and so evolve a little bit the consistent theme is it's relevant, it's topical. The other thing that's very consistent is that you always learn something new. The struggle with large conferences like this is sometimes it becomes a lot of me too environment. But in conference that IBM organizes the CDO, in particular, I always learn something new because the practitioners, they do a really good job curating the practitioners. >> And Joe, this has always been an intimate event. You do 'em in San Francisco and Boston, it's, a couple hundred people, kind of belly to belly interactions. So that's kind of nice. But how do you scale this globally? >> Well, I would say that is the key question 'cause I think the AI algorithms and the machine learning has been proven to work. And we've infiltrated that into all of the business processes at IBM, and in many of our client companies. But we've been doing proof of concepts and small applications, and maybe there's a dozen or 50 people using it. But the the themes now are around scale AI at scale. How do you do that? Like we have a remit at IBM to get 100,000 IBMers that's the real number. On our Cognitive Enterprise Data Platform by the end of this calendar year, and we're making great progress there. But that's the key question, how do you do that? and it involves cultural issues of teams and business process owners being willing to share the data, which is really key. And it also involves technical issues around cloud computing models, hybrid public and private clouds, multi cloud environments where we know we're not the only game in town. So there's a Microsoft Cloud, there's an IBM Cloud, there's another cloud. And all of those clouds have to be woven together in some sort of a multi-cloud management model. So that's the techie geek part. But the cultural change part is equally as challenging and important and you need both to get to 100,000 users at IBM. >> You know guys what this conversation brings into focus for me is that for decades, we've marched to the cadence of Moore's laws, as the innovation engine for our industry, that feels like just so yesterday. Today, it's like you've got this data bedrock that we built up over the last decade. You've got machine intelligence or AI, that you now can apply to that data. And then for scale, you've got cloud. And there's all kinds of innovation coming in. Does that sort of innovation cocktail or sandwich makes sense in your business? >> So there's the innovation piece of it, which is new and exciting, the shiny, new toy. And that's definitely exciting and we definitely tried that. But from my perspective and the perspective of my company, it's not the shiny, new toy that's attractive, or that really moves the needle for us. It is the underlying risk. So if you have the shiny new toy of an autonomous vehicle, what mayhem is it going to cause?, right? What are the underlying risks that's what we are focused on. And Joe alluded to, to AI and algorithms and stuff. And it clearly is a very, it's starting to become a very big topic globally. Even people are starting to talk about the risks and dangers inherent in algorithms and AI. And for us, that's an opportunity that we need to study more, look into deeply to see if this is something that we can help address and solve. >> So you're looking for blind spots, essentially. And then and one of them is this sort of algorithmic risk. Is that the right way to look at it? I mean, how do you think about risk of algorithms? >> So yeah, so algorithmic risk would be I would call blind spot I think that's really good way of saying it. We look at not just blind spots, so risks that we don't even know about that we are facing. We also look at known risks, right? >> So we are one of the largest reinsurers in the world. And we insure just you name a risk, we reinsure it, right? so your auto risk, your catastrophe risk, you name it, we probably have some exposure to it. The blind spot as you call it are, anytime you create something new, there are pros and cons. The shiny, new toy is the pro. What risks, what damage, what liability can result there in that's the piece that we're starting to look at. >> So you got the potentially Joe these unintended consequences of algorithms. So how do you address that? Is there a way in which you've thought through, some kind of oversight of the algorithms? Maybe you could talk about IBM's point of view there. >> Well we have >> Yeah and that's a fantastic and interesting conversation that Jerry and I are having together on behalf of our organizations. IBM knowing in great detail about how these AI algorithms work and are built and are deployed, Jerry and his organization, knowing the bigger risk picture and how you understand, predict, remediate and protect against the risk so that companies can happily adopt these new technologies and put them everywhere in their business. So the name of the game is really understanding how as we all move towards a digital enterprise with big data streaming in, in every format, so we use AI to modify the data to a train the models and then we set some of the models up as self training. So they're learning on their own. They're enhancing data sets. And once we turn them on, we can go to sleep, so they do their own thing, then what? We need a way to understand how these models are producing results. Are they results that we agree with? Are these self training algorithms making these, like railroad trains going off the track? Or are they still on the track? So we want to monitor understand and remediate, but it's at scale again, my earlier comments. So you might be an organization, you might have 10,000 not models at work. You can't watch those. >> So you're looking at the intersection of risk and machine intelligence and then you're, if I understand it correctly applying AI, what I call machine intelligence to oversee the algorithms, is that correct? >> Well yes and you could think of it as an AI, watching over the other AI. That's really what we have 'cause we're using AI in as we envision what might or might not be the future. It's an AI and it's watching other AI. >> That's kind of mind blowing. Jerry, you mentioned autonomous vehicles before that's obviously a potential disruptor to your business. What can you share about how you guys are thinking about that? I mean, a lot of people are skeptical. Like there's not enough data, every time there's a another accident, they'll point to that. What's your point of view on that? From your corporation standpoint are you guys thinking is near term, mid term, very long term or it's sort of this journey, that there's quasi-autonomous that sort of gets us there. >> So on autonomous vehicles or algorithmic risk? >> On autonomous vehicles. >> So, the journey towards full automation is a series of continuous steps, right? So it's a continuum and to a certain extent, we are in a space now, where even though we may not have full autonomy while we're driving, there is significant feedback and signals that a car provides and acts or not in an automated manner that eventually move us towards full autonomy, right? So for example, the anti-lock braking system. That's a component of that, right? which is it prevents the car from skidding out of control. So if you're asking for a time horizon when it might have happened, yeah, at our previous firm, we had done some analysis and the horizons were as sort of aggressive as 15 years to as conservative as 50 years. But the component that we all agreed to where there was not such a wide range was that the cars are becoming more sophisticated because the cars are not just cars, any automobile or truck vehicles, they're becoming more automated. Where does risk lie at each piece? Or each piece of the value chain, right? And the answer is different. If you look at commercial versus personal. If you look at commercial space, autonomous fleets are already on the road. >> Right >> Right? And so the question then becomes where does liability lie? Owner, manufacturer, driver >> Shared model >> Shared, manual versus automated mode, conditions of driving, what decisions algorithm is making, which is when you know, the physics don't allow you to avoid an accident? Who do you end up hitting? (crosstalk) >> Again, not just the technology problem. Now, last thing is you guys are doing a panel, on wowing customers making customers the king, I think, is what the title of it is. What's that all about? And get into that a little bit? >> Sure. Well, we focus as IBM mostly on a B2B framework. So the example that I that I'll share to you is, somewhere between like making a customer or making a client the king, the example is that we're using some of our AI to create an alert system that we call Operations Risks Insights. And so the example that I wanted to share was that, we've been giving this away to nonprofit relief agencies who can deploy it around a geo-fenced area like say, North Carolina and South Carolina. And if you're a relief agency providing flood relief or services to people affected by floods, you can use our solution to understand the magnitude and the potential damage impact from a storm. We can layer up a map with not only normal geospatial information, but socio-economic data. So I can say find the relief agency and I've got a huge storm coming in and I can't cover the entire two-state area. I can say okay, well show me the area where there's greater population density than 1000 per square kilometer and the socio-economic level is, lower than a certain point and those are the people that don't have a lot of resources can't move, are going to shelter in place. So I want to know that because they need my help. >> That's where the risk is. Yeah, right they can't get out >> And we use AI to do to use that those are happy customers, and I've delivered wow to them. >> That's pretty wow, that's right. Jerry, anything you would add to that sort of wow customer experience? Yeah, absolutely, So we are a B2B company as well. >> Yeah. >> And so the span of interaction is dictated by that piece of our business. And so we tried to create wow, by either making our customers' life easier, providing tools and technologies that make them do their jobs better, cheaper, faster, more efficiently, or by helping create, goal create, modify products, such that, it accomplishes the former, right? So, Joe mentioned about the product that you launched. So we have what we call parametric insurance and we are one of the pioneers in the field. And so we've launched three products in that area. For earthquake, for hurricanes and for flight delay. And so, for example, our flight delay product is really unique in the market, where we are able to insure a traveler for flight delays. And then if there is a flight delay event that exceeds a pre established threshold, the customer gets paid without even having to file a claim. >> I love that product, I want to learn more about that. You can say (mumbles) but then it's like then it's not a wow experience for the customer, nobody's happy. So that's for Jerry. Guys, we're out of time. We're going to leave it there but Jerry, Joe, thanks so much for. >> We could go on Dave but thank you Let's do that down the road. Maybe have you guys in Boston in the fall? it'll be great. Thanks again for coming on. >> Thanks Dave. >> All right, keep it right there everybody. We'll back with our next guest. You're watching theCUBE live from IBM CDO in San Francisco. We'll be right back. (upbeat music)

>> Live from San Francisco, California, it's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at San Francisco. We're here at Fisherman's Wharf covering the IBM Chief Data Officer event #IBMCDO. This is the tenth year of this event. They tend to bookend them both in San Francisco and in Boston, and you're watching theCUBE, the leader in live tech coverage. My name is Dave Valante. John Thomas is here, Cube alum and distinguished engineer, Director of Analytics at IBM, and somebody who provides technical direction to the data science elite team. John, good to see you again. Steve Aliouk is back. He is the Vice President of Deep Learning in the Global Chief Data Office, thanks for comin' on again. >> No problem. >> Let's get into it. So John, you and I have talked over the years at this event. What's new these days, what are you working on? >> So Dave, still working with clients on implementing data science and AI data use cases, mostly enterprise clients, and seeing a variety of different things developing in that space. Things have moved into broader discussions around AI and how to actually get value out of that. >> Okay, so I know one of the things that you've talked about is operationalizing machine intelligence and AI and cognitive and that's always a challenge, right. Sounds good, we see this potential but unless you change the operating model, you're not going to get the type of business value, so how do you operationalize AI? >> Yeah, this is a good question Dave. So, enterprises, many of them, are beginning to realize that it is not enough to focus on just the coding and development of the models, right. So they can hire super-talented Python TensorFlow programmers and get the model building done, but there's no value in it until these models actually are operationalized in the context of the business. So one aspect of this is, actually we know, we are thinking of this in a very systematic way and talking about this in a prescriptive way. So, you've got to scope your use cases out. You got to understand what is involved in implementing the use case. Then the steps are build, run, manage, and each of these have technical aspects and business aspects around, right. So most people jump right into the build aspect, which is writing the code. Yeah, that's great, but once you build the code, build the models by writing code, how do you actually deploy these models? Whether that is for online invocation or back storing or whatever, how do you manage the performance of these models over time, how do you retrain these models, and most importantly, when these models are in production, how do I actually understand the business metrics around them? 'Cause this goes back to that first step of scoping. What are the business KPI's that the line of business cares about? The data scientist talks about data science metrics, position and recall and Area Under the ROC Curve and accuracy and so on. But how do these relate to business KPI's. >> All right, so we're going to get into each of those steps in a moment, but Steve I want to ask you, so part of your charter, Inderpal, Global Chief Data Officer, you guys have to do this for IBM, right, drink your own champagne, dog footing, whatever you call it. But there's real business reasons for you to do that. So how is IBM operationalizing AI? What kind of learnings can you share? >> Well, the beauty is I got a wide portfolio of products that I can pull from, so that's nice. Like things like AI open to Watson, some of the hardware components, all that stuffs kind of being baked in. But part of the reason that John and I want to do this interview together, is because what he's producing, what his thoughts are kind of resonates very well for our own practices internally. We've got so many enterprise use cases, how are we deciding, you know, which ones to work on, which ones have the data, potentially which ones have the biggest business impact, all those KPI's etcetera, also, in addition to, for the practitioners, once we decide on a specific enterprise use case to work on, when have they reached the level where the enterprise is having a return on investment? They don't need to keep refining and refining and refining, or maybe they do, but they don't know these practitioners. So we have to clearly justify it, and scope it accordingly, or these practitioners are left in this kind of limbo, where they're producing things, but not able to iterate effectively for the business, right? So that process is a big problem I'm facing internally. We got hundreds of internal use cases, and we're trying to iterate through them. There's an immense amount of scoping, understanding, etcetera, but at the same time, we're building more and more technical debt, as the process evolves, being able to move from project to project, my team is ballooning, we can't do this, we can't keep growing, they're not going to give me another hundred head count, another hundred head count, so we're definitely need to manage it more appropriately. And that's where this mentality comes in there's-- >> All right, so I got a lot of questions. I want to start unpacking this stuff. So the scope piece, that's we're setting goals, identifying the metrics, success metrics, KPI's, and the like, okay, reasonable starting point. But then you go into this, I think you call it, the explore or understanding phase. What's that all about, is that where governance comes in? >> That's exactly where governance comes in. Right, so because it is, you know, we all know the expression, garbage in, garbage out, if you don't know what data you're working with for your machine learning and deep learning enterprise projects, you will not have the resource that you want. And you might think this is obvious, but in an enterprise setting, understanding where the data comes from, who owns the data, who work on the data, the lineage of that data, who is allowed access to the data, policies and rules around that, it's all important. Because without all of these things in place, the models will be questioned later on, and the value of the models will not realized, right? So that part of exploration or understanding, whatever you want to call it, is about understanding data that has to be used by the ML process, but then at a point in time, the models themselves need to be cataloged, need to be published, because the business as a whole needs to understand what models have been produced out of this data. So who built these models? Just as you have lineage of data, you need lineage of models. You need to understand what API's are associated with the models that are being produced. What are the business KPI's that are linked to model metrics? So all of that is part of this understand and explore path. >> Okay, and then you go to build. I think people understand that, everybody wants to start there, just start the dessert, and then you get into the sort of run and manage piece. Run, you want a time to value, and then when you get to the management phase, you really want to be efficient, cost-effective, and then iterative. Okay, so here's the hard question here is. What you just described, some of the folks, particularly the builders are going to say, "Aw, such a waterfall approach. Just start coding." Remember 15 years ago, it was like, "Okay, how do we "write better software, just start building! "Forget about the requirements, "Just start writing code." Okay, but then what happens, is you have to bolt on governance and security and everything else so, talk about how you are able to maintain agility in this model. >> Yeah, I was going to use the word agile, right? So even in each of these phases, it is an agile approach. So the mindset is about agile sprints and our two week long sprints, with very specific metrics at the end of each sprint that is validated against the line of business requirements. So although it might sound waterfall, you're actually taking an agile approach to each of these steps. And if you are going through this, you have also the option to course correct as it goes along, because think of this, the first step was scoping. The line of business gave you a bunch of business metrics or business KPI's they care about, but somewhere in the build phase, past sprint one or sprint 2, you realize, oh well, you know what, that business KPI is not directly achievable or it needs to be refined or tweaked. And there is that circle back with the line of business and a course correction as it was. So it's a very agile approach that you have to take. >> Are they, are they, That's I think right on, because again, if you go and bolt on compliance and governance and security after the fact, we know from years of experience, that it really doesn't work well. You build up technical debt faster. But are these quasi-parallel? I mean there's somethings that you can do in build as the scoping is going on. Is there collaboration so you can describe, can you describe that a little bit? >> Absolutely, so for example, if I know the domain of the problem, I can actually get started with templates that help me accelerate the build process. So I think in your group, for example, IBM internally, there are many, many templates these guys are using. Want to talk a little bit about that? >> Well, we can't just start building up every single time. You know, that's again, I'm going to use this word and really resonate it, you know it's not extensible. Each project, we have to get to the point of using templates, so we had to look at those initiatives and invest in those initiatives, 'cause initially it's harder. But at least once we have some of those cookie-cutter templates and some of them, they might have to have abstractions around certain parts of them, but that's the only way we're ever able to kind of tackle so many problems. So no, without a doubt, it's an important consideration, but at the same time, you have to appreciate there's a lot of projects that are fundamentally different. And that's when you have to have very senior people kind of looking at how to abstract those templates to make them reusable and consumable by others. >> But the team structure, it's not a single amoeba going through all these steps right? These are smaller teams that are, and then there's some threading between each step? >> This is important. >> Yeah, that's tough. We were just talking about that concept. >> Just talking about skills and >> The bind between those groups is something that we're trying to figure out how to break down. 'Cause that's something he recognizes, I recognize internally, but understanding that those peoples tasks, they're never going to be able to iterate through different enterprise problems, unless they break down those borders and really invest in the communication and building those tools. >> Exactly, you talk about full stack teams. So you, it is not enough to have coding skills obviously. >> Right. What is the skill needed to get this into a run environment, right? What is the skill needed to take metrics like not metrics, but explainability, fairness in the moderates, and map that to business metrics. That's a very different skill from Python coding skills. So full stack teams are important, and at the beginning of this process where someone, line of business throws 100 different ideas at you, and you have to go through the scoping exercise, that is a very specific skill that is needed, working together with your coders and runtime administrators. Because how do you define the business KPI's and how do you refine them later on in the life cycle? And how do you translate between line of business lingo and what the coders are going to call it? So it's a full stack team concept. It may not necessarily all be in one group, it may be, but they have to work together across these different side loads to make it successful. >> All right guys, we got to leave it there, the trains are backing up here at IBM CDO conference. Thanks so much for sharing the perspectives on this. All right, keep it right there everybody. You're watchin' "theCUBE" from San Francisco, we're here at Fisherman's Wharf. The IBM Chief Data Officer event. Right back. (bubbly electronic music)

>> Live from San Francisco, California, it's the Cube, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Hello, everyone. Welcome to historic Fisherman's Wharf in San Francisco. We're covering the IBM Chief Data Officer event, #IBMCDO. This is the Cube's, I think, eighth time covering this event. This is the tenth year anniversary of the IBM CDO event, and it's a little different format today. We're here at day one. It's like a half day. They start at noon, and then the keynotes. We're starting a little bit early. We're going to go all day today. My name is Dave Volante. Steve Eliuk is here. He's a Cube alum and Vice President of Deep Learning and the Global Chief Data Officer at IBM. And Tim Humphrey, the VP at the Chief Data Office at IBM. Gents, welcome to the Cube. >> Welcome, glad to be here. >> So, couple years ago, Ginni Rometty, at a big conference, talked about incumbent disruptors, and the whole notion was that you've got established businesses that need to transform into data businesses. Well, that struck me, that well, if IBM's going to sell that to its customers, it has to go through its own transformation, Steve. So let's start there. What is IBM doing to transform into a data company? >> Well, I've been at IBM for, you know, two years now, and luckily I'm benefiting from a lot of that transformation that's taken place over the past three or four years. So, internally, getting (mumbling) in order, understanding it, going through various different foundation stones, building those building blocks so that we can gather new insights and traverse through the cognitive journey. One of the nice things though, is that we have such a wide, diverse set of data within the company. So for different types of enterprise use cases that have benefits from AI, we have a lot of data assets that we can pull from. Now, keeping those data assets in good order is a challenging task in itself. And I'm able to pull from a lot of different tools that IBM's building for our customers. I get to use them internally, look at them, evaluate them, give them real practitioner's point of view to ultimately get insight for our internal business practices, but also for our customers in turn. >> Okay, so, when you think about a data business, they've got data at the core. I'm going to draw a, like, simple conceptual picture, and you've got people around it, maybe you've got processes around it. IBM, hundred-plus-year-old company, you've got different things at the core. It's products. It's people. It's business process. So maybe you could talk, Tim, about how you guys have gone about putting data at the center of the universe. Is that the right way to think about it? >> It is the right way to think about it, and I like how you were describing it. Because when you think about IBM, we've been around over a hundred years, and we do business in roughly over 170 countries. And we have businesses that span hardware, software, services, financing. And along the way, we've also acquired and divested a lot of companies and a lot of businesses. So what that leaves you with is a very fragmented data landscape, right? You know, to support regulations in this country, taxes, tax rules in another country, and having all these different types of businesses. Some you inherit. Some are born from within your company. It just leaves a lot of data silos. And as we see transformations being so important, and data is at the heart of that transformation, it was important for us to really be able to organize ourselves such that access to data is not a problem. Such that being able to combine data across disciplines from finance to HR to sales to marketing to procurement. That was the big challenge, right? And to do this in a way that really unlocks the value of the data, right? It's very easy to use somebody like one of my good, smart friends here, Steven Eliuk to develop models within a domain. But when you talk about cross-functional, complex data coming together to enable models, that's like the Holy Grail of transformation. Then we can deliver real business value. Then you're not waiting to make decisions. Then you can actually be ahead of trends. And so that's what we've been trying to do And the thought and the journey that we have been on is build a enterprise data platform. So, take the concept of a data lake. Bring in all your data sources into one place, but on top of that, make it more than just a data lake. Bring the services and capabilities that allow you to deliver insights from data together with the data so we have a data platform. And our Cognitive Enterprise data platform sort of enables that transformation, and it makes people like my good friend here much more productive and much more valuable to the business. >> This sounds like just a massive challenge. It's not just a technology challenge, obviously. You've got cultural. I mean, people, "This is my data." >> Yes. >> (laughs) And I'm referring, Tim, you're talking like you're largely through this process, right? So it first of all is... Can you talk about-- >> Basically, I will say this. This is a journey. You're never done, right? And one of the reasons why it is a journey is, if you're going to have a successful business, your business is going to keep transforming. Things are going to keep changing. And even in our landscape today, regulations are going to come. So there's always going to be some type of challenge. So I like to say, we're in a journey. We're not finished. (laughing) We're well down the path, and we've learned a lot. And one of the things we have learned, you hit on it, is culture, right? And it's a little hard to say, okay, I'm opening things up. I don't own the data. The company owns the data. There is that sort of cultural change that has to go along with this transformation. >> And there are technology challenges. I mean, when I first started in this business, AI was a hot concept, but you needed, like, massive supercomputers to actually make them work. Today, you now see their sort of rebirth. You know, (mumbling) talks about the AI winter, and now it's like the AI spring. >> Yeah. >> So how are you guys applying machine intelligence to make IBM a better business? >> Well, ultimately, the technology is really, basically transitioned us from the Dark Ages forward. Previously in the supercomputer mentality, didn't fit well for a lot of AI tasks. Now with GPUs and accelerators and FBGAs and things like that, we're definitely able, along with the data and the curated data that we need, to just fast-track. You know, the practitioners would spend an amazing amount of time gathering, crowdsourcing data, getting it in good order, and then the computational challenges were tough. Now, IBM came to the market with a very interesting computer. The POWER8 and POWER9 architecture has NVLink, which is a proprietary Nvidia, interconnect directly to the CPU. So we can feed GPUs a lot quicker for certain types of tasks. And for certain types of tasks that could mean, you know, you get to market quicker, or we get insights for enterprise problems quicker. So technology's a big deal, but it doesn't just center around GPUs. If you're slow to get access to the data, then that's a big problem. So the governance (mumbling) aspects are just as important, in addition to that, security, privacy, et cetera, also important. The quality of the data, where the data is. So it's and end-to-end system, and if there's any sort of impedance on any of it, it slows down the entire process. But then you have very expensive practitioners who are trying to do their job that are waiting on data or waiting on results. So it's really an end-to-end process. >> Okay, so let's assume for a second the technology box is checked. And again, as you say, Tim, it's a journey, and technology's going to continue to evolve. But we're at a point in technology now where this stuff actually can work. But what about data quality? What about compliance and governance? How are you dealing with the natural data quality problem? Because I'm a PNL manager. I'm saying, well, we're making data decisions, but if I don't like the decision, I'm going to attack the quality of the data. (laughing) So who adjudicates all that, and how have you resolved those challenges? >> Well, I like to think of... I'm an engineer by study, and I just like to think of simple formulas. Garbage in, garbage out. It applies to everything, and it definitely applies to data. >> (laughs) >> Your insights, the models, anything that you build is only going to be as good as the data foundation you have. So one of the key things that we've embarked on a journey on is, how do we standardize all aspects of data across the company? Now, you might say, hey, that's not a hard challenge, but it's really easy to do standards in a silo. For this organization, this is how we're going to call terms like geography, and this is how we'll represent these other terms. But when you do that across functions, it becomes conflict, right? Because people want to do it their own way. So we're on the path of standardizing data across the enterprise. That's going to allow us to have good definitions. And then, as you mentioned earlier, we are trying to use AI to be able to improve our data quality. One of the most important things about data is the metadata, the data that describes the data. >> Mm-hm. >> And we're trying to use AI to enhance our metadata. I'd love for Steven to talk a little bit about this, 'cause this is sort of his brainchild. But it's fascinating to me that we can be on a AI transformation, data can be at the heart of it, and we can use AI (laughs) to help improve the quality of our data. >> Right. >> It's fascinating. >> So the metadata problem is (mumbling) because you've talked about data length before. Then in this day and age, you're talking schema lists. Throw it into a data lake and figure out because you have to be agile for your business. So you can't do that with just human categorization, and you know, it's got to-- >> It could take hours, maybe years. >> For a company the size of IBM, the market would shift so fast, right? So how do you deal with that problem? >> That's exactly it. We're not patient enough to do the normative kind of mentality where you just throw a whole bunch of bodies at it. We're definitely moving from that non-extensible man count, full-time-employee type situation, to looking for ways that we can utilize automation. So around the metadata, quality and understanding of that data was incredibly problematic, and we were just hiring people left, right, and center. And then it's a really tough job that they have dealing with so many different business islands, et cetera. So looking for ways that we could automate that process, we finally found away to do it. So there's a lot of curated data. Now we're looking at data quality in addition to looking at regulatory and governance issues, in addition to automating the labeling of business metadata. And the business metadata is the taxonomy that everything is linked together. We understand it under the same normative umbrella. So then when one of the enterprise use cases says, "Hey, we're looking for additional data assets," oh, it's (snaps) in the cloud here, or it's in a private instance here. But we know it's there, and you can grab it, right? So we're definitely at probably the tail end of that curve now, and it started off really hard, but it's getting easier. So that's-- >> Guys, we got to leave it there. Awesome discussion. I hope we can pick it up in the future when maybe we have more metadata than data. >> (laughs) >> And metadata's going to become more and more valuable. But thank you so much for sharing a little bit about IBM's transformation. It was great having you guys on. >> Thank you. >> Alright, keep it right there, everybody. We'll be back with our next guest right after this short break. You're watching the Cube at IBM CDO in San Francisco. Right back. (electronic music) >> Alright, long clear. Alright, thank you guys. Appreciate it, I wish we had more time.

>> Announcer: From San Francisco, it's theCUBE covering OSIsoft PI World 2018, brought to you by OSIsoft. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at OSIsoft's PI World. It's been going on for 28 year, I think. I saw some 15 year pins. It's my first year pin, but I just heard that 28 years, 68 people. This year 3,000 people talking about the industrial internet, the internet of things, and it's happening here. A lot of places talk about it's coming, it's happening here. We're really excited to have two guests on from OSIsoft Richard Beeson. He's a CTO. Richard, great to see you. >> Yeah, thank you. >> And Michael Van Der Veeken, he's a senior developer. Welcome. So first off, impressions on this year's PI World compared to when you started out 28 years ago. >> Yeah, you said it. We started in San Francisco in 1990 at a small hotel down by Fisherman's Wharf, and we had 68 of our closest friends. And it's just been an amazing journey, an amazing journey to see the customer base just continue to appreciate the message, appreciate the value and the consistency that we've bene bring, and most recently just seeing this incredible explosion around the value of information in operations, in IoT and the time-space. >> It's funny because we usually cover it from the IT side and a lot of the IT players are excited now to be bringing IT and connecting it with OT and, in fact, I can show you very formal handshakes and exchanges of pleasantries around that. But you guys have been coming at it from the OT side for a very long time, before there was IP sensors on all these machines, before there was 5G, before there was saduke, before there was all these kind of enabling technologies for what people are talking about now for the industrial internet, but you guys have been doing it for a very long time with the existing infrastructure that was already in place at these places >> Yeah, it is kind of funny. Sometimes we'll say, hey, we've been doing this IoT or industrial IoT for the last 30 years. It's what process control engineers have been doing. You need to get the data from the sensors, from the operation to be able to control it. So the act of control, the act of optimization, the act of running a plant, of running any kind of operation requires that. >> Jeff: Right. >> The big shift has just been fundamentally in the scale, the cost point and just the general availability of that kind of information. It's really changing the game. >> Right. >> And a lot of the same principles still apply. And we've had experience here for 30 years now. And with the whole IoT boom, a lot of the same principles still apply to streaming data, to real-time data, and the PI system is able to support that. >> Right, but it's interesting because now you have a whole new level of computer horsepower that you did have many years ago. You've have a whole new level of networking speed which is even going to go up again with 5G on the mobile side shortly which is going to give massive amounts of more data, and the, of course, to store and everything else just gets cheaper, cheaper and cheaper so you're kind of enabling technologies under the cover or probably just allowing you to explore and expand dramatically the value that you guys are able to generate. >> Yeah, on one had it changes how we do what we do, but, fundamentally, you go back to the original proposition. For our customers, it's all about getting all of the information into the system, no matter where it's coming from, traditionally DCSs, now IoT devices and beyond. And it then becomes all about making that data available in the way, in the place, in the form that they will value it, and there's a myriad. One of the beautiful things about this conference is we see our partners, we see our customers. We see hundreds and thousands of different technologies and applications built around this information. That hasn't changed. It think that's one of the things Michael was eluding to. >> Yeah and you mentioned more available computing power and things like that, but what we see is that using that, people can get much more actionable information out of their data, things or types of analyses that were previously, we were unable to do that because we didn't have the right technology or the right computing power. >> Jeff: Right. >> But now we do. And especially if you can combine different sources of data and people are starting to share that data, you can get way more value out of that raw data that comes from those sensors. >> Right, but now we're going to talk about kind of the next thing, one of the next things. There's always the next thing. And that's blockchain. A lot of talk about blockchains. There's talk about bitcoin and cryptocurrencies. We're going to just put that on the side for now, and really talk about the fundamental technology under the covers which is this blockchain. We see IBM making big investments in it. We hear about it all the time. What are you guys doing in blockchain? And what do you kind of see as an opportunity that you hope that you eventually you'll be able to execute on using blockchain technology? >> Right so we have been researching blockchain for a little while now, and we're still kind of in exploration phase. We first wanted to really get a good understanding of the technology. Mainly to be able to separate the hype from the hope. There is a big hype around everything that is blockchain. But we really want to start looking at where does it actually make sense. Where does it actually add value? Are there situations where a centralized system might actually make much more sense? Or are there actually situations where this decentralized shared ecosystem makes more sense. So I think we have a decent understanding of the technology now, and we're starting to have those conversations with customers. Where should this make sense to you? So this week at PI World, we had our first conversations about that. We had our first session The session was very well attended. There was very good feedback. We'll have a more of a deep dive session this Thursday. And, yeah, we're really looking for those different use cases and to identify patterns within those different use cases across our different industries basically. >> And are you getting pull from the industries. Are they asking you for you guys to do this? Do they see either the curiosity or the opportunity or, I don't want to say hope, that's not a good word, to use blockchain in this distributed, trusted, non-centralized transaction engine to take care of some big issues that are out there right now. >> When I get out and I talk to executives around our customer base, I'm hearing at least three things, multiple times. It's a bit of a pattern. One is how could we use or would it be possible to use blockchain or some other technology in protecting or verifying the consumption or the use or the sharing of data, so kind of the outbound field. Another thing that I'm hearing frequently is most of our customers have very complex supply chains, very complex distribution chains, and as materials that they either depend on or create flow through these supply chains, there's often data around the conditions or the volumes or the paths that they take. And as that information transitions across various ownerships, various boundaries, how do they guarantee the authenticity, the availability and where that information can go in conjunction with that product. And then another one I've been hearing recently which was, I guess, not surprising, but it was novel when I first heard it is one of the activities in operations that every operator goes through is they send instructions or commands or settings or operational conditions down into their factory. How do you know if you can trust the instruction that has been delegated down? How do you know who did it? How do you know how long that instruction is valid for? All different aspects around that. So those are just three very, very significant challenges that our customers are surfacing for which this may be a solution. >> Right. >> And that's some of the fun, I think in going to this research path that we're going down. >> And I want to add to that the whole concept of the exchange of value within a blockchain network also makes the monetization of data very possible. People are starting to realize that the data they're collecting or the information they collected out of that data actually has value to other people. So can we find an easy way for them to monetize on that so see the data as an asset. And that's something that, you know, there are a number of startup projects that focus around that, and they're really looking into that, okay, would that make sense for our customers and how could we potentially tie into that or make that available to our customers. >> Right, the balance sheet value of data is an interesting topic because, you know, before data was just expensive because we had to store it and we had to keep it and we threw most of it away because we had to buy servers and machines to store it. Now, obviously, on the consumer side, you see the valuation of the data with companies like Google and Facebook whose valuation is a function of the value of that data even though its not reflected on their balance sheet and it's an interesting concept. How do you not only monetize it, but eventually get it on the balance sheet so that there is all the benefits that come by having that on the balance sheet with the value of that data. And that's the first time I've ever heard of using blockchain potentially as a way to capture, track and extract that value from that data. >> Exactly, and there are many different applications. It could be, for instance, a renewable company that has a wind farm that is monitoring the environment or monitoring the weather. That data is something that they use. But that data could potentially be very interesting to other companies or maybe to local governments as well. So is that data that they can monetize on? Another aspect could be, for instance, in autonomous vehicles where you're driving past somewhere and you want to get information about what are the gas prices or where can I get something to eat or things like that. So those could be really quick even microsecond transactions >> Jeff: Right. or interactions between a vehicle and whatever is in its environment. But maybe there are some way to do some quick micropayments of that data because that is valuable to that vehicle, and, in turn, that vehicle could also sell some of the data that it is collecting about the weather, about the road conditions, about traffic. So, in general, potentially we could see this whole economy around data arising. >> Right. >> And there's also a lot of cost in validating the trust now. We talked to some of the shipping lines and like 50% of the cost of shipping is the processing of the paperwork that basically does the validation that you just kind of outlined. Is it what it's supposed to be? Did it come from where it's supposed to be coming from? It is going to where it's supposed to be going to? And literally it's like 50% of the cost of shipments is processing this paper. So not only does it provide value, but it unlocks another whole set of value that currently is just getting eating up by super inefficient, still paper-based not even Excel, right. They probably still have copy machines. >> Transportation is one of the worse. (Jeff laughs) >> But you look at that scenario and a number of these others, immediately you go to this notion of data ownership. You eluded to it. Philosophically and practically, OSI is firmly committed to all of the information that we manage for our customers is our customer's data. They own that. But even as they get into these complex landscapes, then there really is that question. As materials flow through these supply chains, who owns the data associated with that. So this is going to be an interesting frontier >> Right. where these things have to get resolved and understood. And most of our customers consider the 10, 20, 30 years of operational data that they've preserved one of their more valuable IP assets. It's both an amazing frontier and amazing opportunity and something that's going to stir up some emotions as well. >> Right. And then you got the geopolitics of it as well because of the disparate laws all over the place about data, data treatment and exactly where was the data generated. That's always one of my favorite things when you really dig down as to where was that data actually generated. And it's not necessarily an easy thing to determine. So here we are 2018, what are you guys working on this year? If we come back a year from now, what are we going to be talking about? >> So right now, we are starting the conversation. We are starting to have this discussion. We have some assumptions where blockchain might make sense to us as a company especially to our customers. So this year, we really want to use this year to validate some of those assumptions, to really work with our customers but also with academia to find out where does this actually make sense. How can we get the most value out of this amazing new technology that has a lot of promise. And maybe we'll see us starting prototyping some of these solutions together with our customers. >> You going with that? >> Yeah, I'm going with that. >> All right, Richard's going with Michael, all right. So we're going to leave it there. And thanks for taking a few minutes and congratulations. I don't know if you've been here for all 28 years, Michael. >> Seven years. >> Seven years, pretty good. But what a great story, what a great success and really happy to come here and learn some of the story. >> Yeah, I'm honored every year. It just blows me away what I get to see and listen to and the people I get to meet so thank you. >> Thank you. All right, and he's Richard. >> Thank you. >> And he's Michael, I'm Jeff. You're watching theCUBE from OSIsoft PI World 2018 in downtown San Francisco. Thanks for watching. (upbeat music)

(hip-hop music) (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. (crowd) >> Hey welcome back everybody, Jeff Fricke here with Peter Burris. We're wrapping up a very full day here at the IBM Chief Data Officer Strategy Summit Spring 2017, Fisherman's Wharf, San Francisco. An all-day affair, really an intimate affair, 170 people, but Chief Data Officers with their peers, sharing information, getting good information from IBM. And it's an interesting event. They're doing a lot of them around the country, and eventually around the world. And we're excited to have kind of the power behind the whole thing. (laughing) Caitlin Lepech, she's the one who's driving the train. Don't believe the guys in the front. She's the one behind the curtain that's pulling all the levers. So we wanted to wrap the day. It's been a really good day, some fantastic conversations, great practitioners. >> Right. >> Want to get your impression of the day? Right, it's been great. The thing I love about this event the most is this is all client-led discussion, client-led conversation. And we're quite fortunate in that we get a lot leading CDOs to come join us. I've seen quite a number this time. We tried something new. We expanded to this 170 attendees, by far the largest group that we've ever had, so we ran these four breakout session tracks. And I am hearing some good feedback about some of the discussions. So I think it's been a good and full day (laughing). >> Yes, it has been. Any surprises? Anything that kind of jumped out to you that you didn't expect? >> Yeah, a couple of things. So we structure these breakout sessions... Pointed feedback from last session was, Hey, we want the opportunity to network with peers, share use cases, learn from each other, so I've got my notes here, and that we did a function builder. So these are all our CDOs that are starting to build the CDO office. They're new in the journey, right. We've got our data integrators, so they're really our data management, data wranglers, the business optimizers, thinking about how do I make sure I've got the impact throughout the business, and then market innovators. And one of the surprises is how many people are doing really innovative things, and they don't realize it. They tell me-- >> Jeff: Oh, really. >> Ahhh, I'm just in the early stages of setting up the office. I don't have the good use cases to share. And they absolutely do! They absolutely do! So that's always the surprise, is how many are actually quite more innovative than I think they give themselves credit. >> Well, that was a pretty consistent theme that came out today, is that you can't do all the foundational work, and then wait to get that finished before you start actually innovating delivering value. >> If you want to be successful. >> (laughing) Right, and keep your job (laughing) If you're one of the 41%. So you have to be parallel tracking, that first process'll never finish, but you've got to find some short-term wins that you can execute on right away. >> And that was one of our major objectives and sort of convening this event, and continuing to invest in the CDO community, is how do I improve the failure rate? We all agree, growth in the role, okay. But over half are going to fail. >> Right. >> And we start to see some of these folks now that they're four, six years in having some challenges. And so, what we're trying to do is reduce that failure rate. >> Jeff: Yeah, hopefully they-- >> But still four to six years in is still not a bad start. >> Caitlin: Yeah, yeah. >> There's most functions that fail quick... That fail tend to fail pretty quickly. >> Yeah. >> So one of the things that I was struck by, and I want to get your feedback on this, is that 170 people, sounds like a lot. >> Caitlin: Yeah, yeah. >> But it's not so much if there is a unity of purpose. >> Caitlin: Correct, correct! >> If there's pretty clear understanding of what it is they do and how they do it, and I think the CDO's role is still evolving very rapidly. So everybody's coming at this from a different perspective. And you mentioned the four tracks. But they seem to be honing in on the same end-state. >> Absolutely. >> So talk about what you think that end-state is. Where is the CDO in five years? >> Absolutely, so I did some live polling, as we kicked off the morning, and asked a couple of questions along those lines. Where do folks report? I think we mentioned this-- >> Right. >> When we kicked off. >> Right. >> A third to the CEO, a third to CIO, and a third to a CXO-type role, functional role. And reflected in the room was about that split. I saw about a third, third, third. And, yet, regardless of where in the organization, it's how do we get data governance, right? How do we get data management, right? And then there's this, I think, reflection around, okay, machine learning, deep learning, some of these new opportunities, new technologies. What sort of skills do we need to deliver? I had an interesting conversation with a CDO that said, We make a call across the board. We're not investing to build these technical skills in-house because we know in two years the guys I had doing Python and all that stuff, it's on to the next thing. And now I've got to get machine learning, deep learning, two years I need to move to the next. So it's more identifying technologies in partnership bringing those and bringing us through, and driving the business results. >> And we heard also very frequently the role the politics played. >> Caitlin: Oh, absolutely. >> And, in fact, Fow-wad Boot from-- >> Kaiser. >> Kaiser Permanente, yeah. >> Specifically talked about this... He's looking in the stewards that he's hiring in his function. He's looking for people that have learned the fine art of influencing others. >> And I think it's a stretch for a lot of these folks. Another poll we did is, who comes from an engineering, technical background. A lot of hands in the room. And we're seeing more and more come from line of business, and more and more emphasize the relationship component of it, relationship skills, which is I think is very interesting. We also see a high number of women in CDO roles, as compared to other C-suite roles. And I like to think, perhaps, it has to do-- >> Jeff: Right, right. >> With the relationship component of it as well because it is... >> Jeff: Yeah, well-- >> Peter: That's interesting. I'm not going to touch it, but it's interesting (laughing). >> Well, no, we were-- >> (laughing) I threw it out there. >> We were at the Stanford-- >> No, no, we-- >> Women in Data Science event, which is a phenomenal event. We've covered it for a couple years, and Jayna George from Western Digital, phenomenal, super smart lady, so it is an opportunity, and I don't think it's got so much of the legacy stuff that maybe some of the other things had that people can jump in. Diane Green kicked it off-- >> Yeah. >> So I think there is a lot of examples women doing their own thing in data science. >> Yeah, I agree, and I'll give you another context. In another CUBE, another event, I actually raised that issue, relationships, because men walk into a room, they get very competitive very quickly, who's the smartest guy in the room. And on what days is blah, blah, blah. And we're talking about the need to forge relationships that facilitate influence. >> Absolutely. >> And sharing of insight and sharing of knowledge. And it was a woman guest, and she... And I said, Do you see that women are better at this than others? And she looked at me, she said, Well, that's sexist. (laughing). And it was! I guess it kind of was. >> Right, right. >> But do you... You're saying that it's a place where, perhaps, women can actually take a step into senior roles in a technology-oriented space. >> Yeah. >> And have enormous success because of some of the things that they bring to the table. >> Yeah, one quote stuck with me is, when someone comes in with great experience, really smart, Are they here to hurt me or help me? And the trust component of it and building the trust, And I think there is one event we do here, the second day of all of our CDO summits, so women in breakfast, the data divas' breakfast. And we explore some opportunities for women leaders, and it was well-attended by men and women. And I think there really is when you're establishing a data strategy for your entire organization, and you need lines of business to contribute money and funding and resources, and sign off, there is I feel sometimes like we're on the Hill. I'm back in D.C., working on Capitol Hill (laughing), and we're shopping around to deliver, so absolutely. Another tying back to what you mentioned about something that was surprising today, we started building out this trust as a service idea. And a couple people on panels mentioned thinking about the value of trust and how you instill trust. I'm hearing more and more about that, so that was interesting. >> We actually brought that up. >> Caitlin: Oh, did you! >> Yeah, we actually brought it up here in theCUBE. And it was specifically and I made an observation that when you start thinking about Watson and you start thinking about potentially-competitive offerings at some point in time they're going to offer alternative opinions-- >> Absolutely. >> And find ways to learn to offer their opinions better than their's just for competitive purposes. >> Absolutely. >> And so, this notion of trust becomes essential to the brand. >> Absolutely. >> My system is working in your best interest. >> Absolutely. >> Not my best interest. And that's not something that people have spent a lot of time thinking about. >> Exactly, and what it means when we say, when we work with clients and say, It's your data, your insight. So we certainly tap that information-- >> Sure. >> And that data to train Watson, but it's not... We don't to keep that, right. It's back to you, but how do you design that engagement model to fulfill the privacy concerns, the ethical use of data, establish that trust. >> Right. >> I think it's something we're just starting to really dig into. >> But also if you think about something like... I don't know if you ever heard of this, but this notion of principal agent theory. >> Umm-hmm. >> Where the principal being the owner, in typical-- >> Right. >> Economic terms. The agent being the manager that's working on behalf of the owner. >> Right. >> And how do their agendas align or misalign. >> Right. >> The same thing is just here. We're not talking about systems that have... Are able to undertake very, very complex problems. >> Right. >> Sometimes will do so, and people will sit back and say, I'm not sure how it actually worked. >> Yeah. >> So they have to be a good agent for the business. >> Absolutely, absolutely, definitely. >> And this notion of trust is essential to that. >> Absolutely, and it's both... It originated internally, right, trying to trust the answers you're getting-- >> Sure! >> On a client. Who's our largest... Where's our largest client opportunity, you get multiple answers, so it's kind of trusting the voracity of the data, but now it's also a competitive differentiator. As a brand you can offer that to your client. >> Right, the other big thing that came up is you guys doing it internally, and trying to drive your own internal transformation at IBM, which is interesting in of itself, but more interesting is the fact that (laughing) you actually want to publish what you're doing and how you did it-- >> Yeah. >> As a road map. I think you guys are calling it the Blueprint-- >> Yes. >> For your customers. And talk about publishing that actually in October, so I wonder if you can share a little bit more color around what exactly is this Blueprint-- >> Sure. >> How's it's going to be exposed? >> What should people look forward to? >> Sure, I'm very fortunate in that Inderpal Bhandari when he came on board as IBM's First Chief Data Officer, said, I want to be completely transparent with clients on what we're doing. And it started with the data strategy, here's how we arrived at the data strategy, here's how we're setting up our organization internally, here's how we're prioritizing selecting use cases, so client prefixes is important to us, here's why. Down at every level we've been very transparent about what we're doing internally. Here's the skill sets I'm bringing on board and why. One thing we've talked a lot about is the Business Unit Data Officer, so having someone that sits in the business unit responsible for requirements from the unit, but also ensuring that there's some level of consistency at the enterprise level. >> Right. >> So, we've had some Business Unit Data Officers that we've plucked (laughing) from other organizations that have come and joined IBM last year, which is great. And so, what we wanted to do is follow that up with an actual Blueprint, so I own the Blueprint for Inderpal, and what we want to do is deliver it along three components, so one, the technology component, what technology can you leverage. Two, the business processes both the CDO processes and the enterprise, like HR, finance, supply chain, procurement, et cetera. And then finally the organizational considerations, so what sort of strategy, culture, what talent do you need to recruit, how do you retain your existing workforce to meet some of these new technology needs. And then all the sort of relationship piece we were talking about earlier, the culture changes required. >> Right. >> How do you go out and solicit that buy-in. And so, our intent is to come back around in October and deliver that Blueprint in a way that can be implemented within organization. And, oh, one thing we were saying is the homework assignment from this event (laughing), we're going to send out the template. >> Right. And our version of it, and be very transparent, here's how we're doing it internally. And inviting clients to come back to say-- >> Right. >> You need to dig in deeper here, this part's relevant to me, along the information governance, the master data management, et cetera. And then hopefully come back in October and deliver something that's really of value and usable for our clients across the industry. >> So for folks who didn't make it today, too bad for them. >> Exactly, we missed them, (laughing) but... >> So what's the next summit? Where's it's going to be, how do people get involved? Give us a kind of a plug for the other people that wished they were here, but weren't able to make it today. >> Sure, so we will come back around in the fall, September, October timeframe, in Boston, and do our east coast version of this summit. So I hope to see you guys there. >> Jeff: Sure, we'll be there. >> It should be a lot of fun. And at that point we'll deliver the Blueprint, and I think that will be a fantastic event. We committed to 170 data executives here, which fortunately we were able to get to that point, and are targeting a little over 200 for the fall, so looking to, again, expand, continue to expand and invite folks to join us. >> Be careful, you're going to be interconnected before you know. >> (laughing) No, no, no, I want it small! >> (laughing) Okay. >> And then also as I mentioned earlier, we're starting to see more industry-specific financial services, government. We have a government CDO summit coming up, June six, seven, in Washington D.C. So I think that'll be another great event. And then we're starting to see outside of the U.S., outside of North America, more of the GO summits as well, so... >> Very exciting times. Well, thanks for inviting us along. >> Sure, it's been a great day! It's been a lot of fun. Thank you so much! >> (laughing) Alright, thank you, Caitlin. I'm Jeff Fricke with Peter Burris. You're watching theCUBE. We've been here all day at the IBM Chief Data Officer Strategy Summit, that's right the Spring version, 2017, in Fisherman's Wharf, San Francisco. Thanks for watching. We'll see you next time. (electronic music) (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.

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

(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)

>> 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 at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, it's 170 people here, all high-level CXOs learning about data, and it's part of an ongoing series that IBM is doing around chief data officers and data, part of a big initiative with Cognitive and Watson, I'm sure you've heard all about it, Watson TV if nothing else, if not going to the shows, and we're really excited to have the drivers behind this activity with us today, also Peter Burris from Wikibon, chief strategy officer, but we've got Caitlin Lepech who's really driving this whole show. She is the Communications and Client Engagement Executive, IBM Global Chief Data Office. That's a mouthful, she's got a really big card. And Cortnie Abercrombie, who I'm thrilled to see you, seen her many, many times, I'm sure, at the MIT CDOIQ, so she's been playing in this space for a long time. She is a Cognitive and Analytics Offerings leader, IBM Global Business. So first off, welcome. >> Thank you, great to be here. >> Thanks, always a pleasure on theCUBE. It's so comfortable, I forget you guys aren't just buddies hanging out. >> Before we jump into it, let's talk about kind of what is this series? Because it's not World of Watson, it's not InterConnect, it's a much smaller, more intimate event, but you're having a series of them, and in the keynote is a lot of talk about what's coming next and what's coming in October, so I don't know. >> Let me let you start, because this was originally Cortnie's program. >> This was a long time ago. >> 2014. >> Yeah, 2014, the role was just starting, and I was tasked with can we identify and start to build relationships with this new line of business role that's cropping up everywhere. And at that time there were only 50 chief data officers worldwide. And so I-- >> Jeff: 50? In 2014. >> 50, and I can tell you that earnestly because I knew every single of them. >> More than that here today. >> I made it a point of my career over the last three years to get to know every single chief data officer as they took their jobs. I would literally, well, hopefully I'm not a chief data officer stalker, but I basically was calling them once I'd see them on LinkedIn, or if I saw a press announcement, I would call them up and say, "You've got a tough job. "Let me help connect you with each other "and share best practices." And before we knew, it became a whole summit. It became, there were so many always asking to be connected to each other, and how do we share best practices, and what do you guys know as IBM because you're always working with different clients on this stuff? >> And Cortnie and I first started working in 2014, we wrote IBM's first paper on chief data officers, and at the time, there was a lot of skepticism within our organization, why spend the time with data officers? There's other C-suite roles you may want to focus on instead. But we were saying just the rise of data, external data, unstructured data, lot of opportunity to rise in the role, and so, I think we're seeing it reflected in the numbers. Again, first summit three years ago, 30 participants. We have 170 data executives, clients joining us today and tomorrow. >> And six papers later, and we're goin' strong still. >> And six papers later. >> Exactly, exactly. >> Before we jump into the details, some of the really top-level stuff that, again, you talked about with John and David, MIT CDOIQ, in terms of reporting structure. Where do CDOs report? What exactly are they responsible for? You covered some of that earlier in the keynote, I wonder if you can review some of those findings. >> Yeah, that was amazing >> Sure, I can share that, and then, have Cortnie add. So, we find about a third report directly to the CEO, a third report through the CIO's office, sort of the traditional relationship with CIOs, and then, a third, and what we see growing quite a bit, are CXOs, so functional or business line function. Originally, traditionally it was really a spin-off of CIO, a lot of technical folks coming up, and we're seeing more and more the shift to business expertise, and the focus on making sure we're demonstrating the business impact these data programs are driving for our organization. >> Yeah, it kind of started more as a data governance type of role, and so, it was born out of IT to some degree because, but IT was having problems with getting the line of business leaders to come to the table, and we knew that there had to be a shift over to the business leaders to get them to come and share their domain expertise because as every chief data officer will tell you, you can't have lineage or know anything about all of this great data unless you have the experts who have been sitting there creating all of that data through their processes. And so, that's kind of how we came to have this line of business type of function. >> And Inderpal really talked about, in terms of the strategy, if you don't start from the business strategy-- >> Inderpal? >> Yeah, on the keynote. >> Peter: Yeah, yeah, yeah, yeah. >> You are really in big risk of the boiling the ocean problem. I mean, you can't just come at it from the data first. You really have to come at it from the business problem first. >> It was interesting, so Inderpal was one of our clients as a CEO three times prior to rejoining IBM a year ago, and so, Cortnie and I have known him-- >> Express Scripts, Cambia. >> Exactly, we've interviewed him, featured him in our research prior, too, so when he joined IBM in December a year ago, his first task was data strategy. And where we see a lot of our clients struggle is they make data strategy an 18-month, 24-month process, getting the strategy mapped out and implemented. And we say, "You don't have the time for it." You don't have 18 months to come to data, to come to a data strategy and get by and get it implemented. >> Nail something right away. >> Exactly. >> Get it in the door, start showing some results right away. You cannot wait, or your line of business people will just, you know. >> What is a data strategy? >> Sure, so I can say what we've done internally, and then, I know you've worked with a lot of clients on what they're building. For us internally, it started with the value proposition of the data office, and so, we got very clear on what that was, and it was the ability to take internal, external data, structured, unstructured, and pull that together. If I can summarize it, it's drive to cognitive business, and it's infusing cognition across all of our business processes internally. And then, we identified all of these use cases that'll help accelerate, and the catalyst that will get us there faster. And so, Client 360, product catalog, et cetera. We took data strategy, got buy-in at the highest levels at our organization, senior vice president level, and then, once we had that support and mandate from the top, went to the implementation piece. It was moving very quickly to specify, for us, it's about transforming to cognitive business. That then guides what's critical data and critical use cases for us. >> Before you answer, before you get into it, so is a data strategy a means to cognitive, or is it an end in itself? >> I would say it, to be most effective, it's a succinct, one-page description of how you're going to get to that end. And so, we always say-- >> Peter: Of cognitive? >> Exactly, for us, it's cognitive. So, we always ask very simple question, how is your company going to make money? Not today, what's its monetization strategy for the future? For us, it's coming to cognitive business. I have a lot of clients that say, "We're product-centric. "We want to become customer, client-centric. "That's our key piece there." So, it's that key at the highest level for us becoming a cognitive business. >> Well, and data strategies are as big or as small as you want them to be, quite frankly. They're better when they have a larger vision, but let's just face it, some companies have a crisis going on, and they need to know, what's my data strategy to get myself through this crisis and into the next step so that I don't become the person whose cheese moved overnight. Am I giving myself away? Do you all know the cheese, you know, Who Moved My Cheese? >> Every time the new iOS comes up, my wife's like-- >> I don't know if the younger people don't know that term, I don't think. >> Ah, but who cares about them? >> Who cares about the millenials? I do, I love the millenials. But yes, cheese, you don't want your cheese to move overnight. >> But the reason I ask the question, and the reason why I think it's important is because strategy is many things to many people, but anybody who has a view on strategy ultimately concludes that the strategic process is what's important. It's the process of creating consensus amongst planners, executives, financial people about what we're going to do. And so, the concept of a data strategy has to be, I presume, as crucial to getting the organization to build a consensus about the role the data's going to play in business. >> Absolutely. >> And that is the hardest. That is the hardest job. Everybody thinks of a data officer as being a technical, highly technical person, when in fact, the best thing you can be as a chief data officer is political, very, very adept at politics and understanding what drives the business forward and how to bring results that the CEO will get behind and that the C-suite table will get behind. >> And by politics here you mean influencing others to get on board and participate in this process? >> Even just understanding, sometimes leaders of business don't articulate very well in terms of data and analytics, what is it that they actually need to accomplish to get to their end goal, and you find them kind of stammering when it comes to, "Well, I don't really know "how you as Inderpal Bhandari can help me, "but here's what I've got to do." And it's a crisis usually. "I've got to get this done, "and I've got to make these numbers by this date. "How can you help me do that?" And that's when the chief data officer kicks into gear and is very creative and actually brings a whole new mindset to the person to understand their business and really dive in and understand, "Okay, this is how "we're going to help you meet that sales number," or, "This is how we're going to help you "get the new revenue growth." >> In certain respects, there's a business strategy, and then, you have to resource the business strategy. And the data strategy then is how are we going to use data as a resource to achieve our business strategy? >> Cortnie: Yes. >> So, let me test something. The way that we at SiliconANGLE, Wikibon have defined digital business is that a business, a digital business uses data as an asset to differentially create and keep customers. >> Caitlin: Right. >> Does that work for you guys? >> Cortnie: Yeah, sure. >> It's focused on, and therefore, you can look at a business and say is it more or less digital based on how, whether it's more or less focused on data as an asset and as a resource that's going to differentiate how it's business behaves and what it does for customers. >> Cortnie: And it goes from the front office all the way to the back. >> Yes, because it's not just, but that's what, create and keep, I'm borrowing from Peter Drucker, right. Peter Drucker said the goal of business is to create and keep customers. >> Yeah, that's right. Absolutely, at the end of the day-- >> He included front end and back end. >> You got to make money and you got to have customers. >> Exactly. >> You got to have customers to make the money. >> So data becomes a de-differentiating asset in the digital business, and increasingly, digital is becoming the differentiating approach in all business. >> I would argue it's not the data, because everybody's drowning in data, it's how you use the data and how creative you can be to come up with the methods that you're going to employ. And I'll give you an example. Here's just an example that I've been using with retailers lately. I can look at all kinds of digital exhaust, that's what we call it these days. Let's say you have a personal digital shopping experience that you're creating for these new millenials, we'll go with that example, because shoppers, 'cause retailers really do need to get more millenials in the door. They're used to their Amazon.coms and their online shopping, so they're trying to get more of them in the door. When you start to combine all of that data that's underlying all of these cool things that you're doing, so personal shopping, thumbs up, thumb down, you like this dress, you like that cut, you like these heels? Yeah, yes, yes or no, yes or no. I'm getting all this rich data that I'm building with my app, 'cause you got to be opted in, no violating privacy here, but you're opting in all the way along, and we're building and building, and so, we even have, for us, we have this Metro Pulse retail asset that we use that actually has hyperlocal information. So, you could, knowing that millenials like, for example, food trucks, we all like food trucks, let's just face it, but millenials really love food trucks. You could even, if you are a retailer, you could even provide a fashion truck directly to their location outside their office equipped with things that you know they like because you've mined that digital exhaust that's coming off the personal digital shopping experience, and you've understood how they like to pair up what they've got, so you're doing a next best action type of thing where you're cross-selling, up-selling. And now, you bring it into the actual real world for them, and you take it straight to them. That's a new experience, that's a new millennial experience for retail. But it's how creative you are with all that data, 'cause you could have just sat there before and done nothing about that. You could have just looked at it and said, "Well, let's run some reports, "let's look at a dashboard." But unless you actually have someone creative enough, and usually it's a pairing of data scientist, chief data officers, digital officers all working together who come up with these great ideas, and it's all based, if you go back to what my example was, that example is how do I create a new experience that will get millenials through my doors, or at least get them buying from me in a different way. If you think about that was the goal, but how I combined it was data, a digital process, and then, I put it together in a brand new way to take action on it. That's how you get somewhere. >> Let me see if I can summarize very quickly. And again, just as an also test, 'cause this is the way we're looking at it as well, that there's human beings operate and businesses operate in an analog world, so the first test is to take analog data and turn it into digital data. IOT does that. >> Cortnie: Otherwise, there's not digital exhaust. >> Otherwise, there's no digital anything. >> Cortnie: That's right. >> And we call it IOT and P, Internet of Things and People, because of the people element is so crucial in this process. Then we have analytics, big data, that's taking those data streams and turning them into models that have suggestions and predictions about what might be the right way to go about doing things, and then there's these systems of action, or what we've been calling systems of enactment, but we're going to lose that battle, it's probably going to be called systems of action that then take and transduce the output of the model back into the real world, and that's going to be a combination of digital and physical. >> And robotic process automation. We won't even introduce that yet. >> Which is all great. >> But that's fun. >> That's going to be in October. >> But I really like the example that you gave of the fashion truck because people don't look at a truck and say, "Oh, that's digital business." >> Cortnie: Right, but it manifested in that. >> But it absolutely is digital business because the data allows you to bring a more personal experience >> Understand it, that's right. >> right there at that moment, and it's virtually impossible to even conceive of how you can make money doing that unless you're able to intercept that person with that ensemble in a way that makes both parties happy. >> And wouldn't that be cheaper than having big, huge retail stores? Someone's going to take me up on that. Retailers are going to take me up on this, I'm telling you. >> But I think the other part is-- >> Right next to the taco truck. >> There could be other trucks in that, a much cleaner truck, and this and that. But one thing, Cortnie, you talk about and you got to still have a hypothesis, I think of the early false promises of big data and Hadoop, just that you throw all this stuff in, and the answer just comes out. That just isn't the way. You've got to be creative, and you have to have a hypothesis to test, and I'm just curious from your experience, how ready are people to take in the external data sources and the unstructured data sources and start to incorporate that in with the proprietary data, 'cause that's a really important piece of the puzzle? It's very different now. >> I think they're ready to do it, it depends on who in the business you are working with. Digital offices, marketing offices, merchandising offices, medical offices, they're very interested in how can we do this, but they don't know what they need. They need guidance from a data officer or a data science head, or something like this, because it's all about the creativity of what can I bring together to actually reach that patient diagnostic, that whatever the case may be, the right fashion truck mix, or whatever. Taco Tuesday. >> So, does somebody from the chief data office, if you will, you know, get assigned to, you're assigned to marketing and you're assigned to finance, and you're assigned to sales. >> I have somebody assigned to us. >> To put this in-- >> Caitlin: Exactly, exactly. >> To put this in kind of a common or more modern parlance, there's a design element. You have to have use case design, and what are we going, how are we going to get better at designing use cases so we can go off and explore the role that data is going to play, how we're going to combine it with other things, and to your point, and it's a great point, how that turns into a new business activity. >> And if I can connect two points there, the single biggest question I get from clients is how do you prioritize your use cases. >> Oh, gosh, yeah. >> How can you help me select where I'm going to have the biggest impact? And it goes, I think my thing's falling again. (laughing) >> Jeff: It's nice and quiet in here. >> Okay, good. It goes back to what you were saying about data strategy. We say what's your data strategy? What's your overarching mission of the organization? For us, it's becoming cognitive business, so for us, it's selecting projects where we can infuse cognition the quickest way, so Client 360, for example. We'll often say what's your strategy, and that guides your prioritization. That's the question we get the most, what use case do I select? Where am I going to have the most impact for the business, and that's where you have to work with close partnership with the business. >> But is it the most impact, which just sounds scary, and you could get in analysis paralysis, or where can I show some impact the easiest or the fastest? >> You're going to delineate both, right? >> Exactly. >> Inderpal's got his shortlist, and he's got his long list. Here's the long term that we need to be focused on to make sure that we are becoming holistically a cognitive company so that we can be flexible and agile in this marketplace and respond to all kinds of different situations, whether they're HR and we need more skills and talent, 'cause let's face it, we're a technology company who's rapidly evolving to fit with the marketplace, or whether it's just good old-fashioned we need more consultants. Whatever the case may be. >> Always, always. >> Yes! >> I worked my business in. >> More consultants! >> Alright, we could go, we could go and go and go, but we're running out of time, we had a full slate. >> Caitlin: We just started. >> I know. >> I agree, we're just starting this convers, I started a whole other conversation to him. We haven't even hit the robotics yet. >> We need to keep going, guys. >> Get control. >> Cortnie: Less coffee for us. >> What do people think about when they think about this series? What should they look forward to, what's the next one for the people that didn't make it here today, where should they go on the calendar and book in their calendars? >> So, I'll speak to the summits first. It's great, we do Spring in San Francisco. We'll come back, reconvene in Boston in fall, so that'll be September, October frame. I'm seeing two other trends, which I'm quite excited about, we're also looking at more industry-specific CDO summits. So, for those of our friends that are in government sectors, we'll be in June 6th and 7th at a government CDO summit in D.C., so we're starting to see more of the industry-specific, as well as global, so we just ran our first in Rio, Brazil for that area. We're working on a South Africa summit. >> Cortnie: I know, right. >> We actually have a CDO here with us that traveled from South Africa from a bank to see our summit here and hoping to take some of that back. >> We have several from Peru and Mexico and Chile, so yeah. >> We'll continue to do our two flagship North America-based summits, but I'm seeing a lot of growth out in our geographies, which is fantastic. >> And it was interesting, too, in your keynote talking about people's request for more networking time. You know, it is really a sharing of best practices amongst peers, and that cannot be overstated. >> Well, it's community. A community is building. >> It really is. >> It's a family, it really is. >> We joke, this is a reunion. >> We all come in and hug, I don't know if you noticed, but we're all hugging each other. >> Everybody likes to hug their own team. It's a CUBE thing, too. >> It's like therapy. It's like data therapy, that's what it is. >> Alright, well, Caitlin, Cortnie, again, thanks for having us, congratulations on a great event, and I'm sure it's going to be a super productive day. >> Thank you so much. Pleasure. >> Thanks. >> Jeff Frick with Peter Burris, you're watchin' theCUBE from the IBM Chief Data Officer Summit Spring 2017 San Francisco, thanks for watching. (electronic keyboard music)

>> (lively music) (lively music) >> [Narrator] Live, from Fisherman's Wharf in San Francisco, it's theCUBE. Covering IBM Chief Data Officers Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. >> Jeff Flick here with theCUBE alongside Peter Burris, our chief research officer from Wikibon. We're at the IBM Chief Data Officers Strategy Summit Sprint 2017. It's a mouthful but it's an important event. There's 170 plus CDO's here sharing information, really binding their community, sharing best practices and of course, IBM is sharing their journey which is pretty interesting cause they're taking their own transformational journey, writing up a blue print and going to deliver it in October. Drinking their own champagne as they like to say. We're really excited to have CUBE alumni, many time visitor Seth Dobrin. He is the chief data officer of IBM Analytics. Seth welcome. >> Yeah, thanks for having me again. >> Absolutely, so again, these events are interesting. There's a series of them. They're in multiple cities. They're, now, going to go to multiple countries. And it's really intended, I believe, or tell me, it's a learning experience in this great, little, tight community for this, very specific, role. >> Yeah, so these events are, actually, really good. I've been participating in these since the second one. >> So, since the first one in Boston about 2 1/2 years ago. They're really great events because it's an opportunity for CDO's or de facto CDO's in organizations to have in depth conversations with their peers about struggles, challenges, successes. >> It really helps to, kind of, one piece says you can benchmark yourself, how are we doing as an organization and how am I doing as a CDO and where do I fit within the bigger community or within your industry? >> How have you seen it evolve? Not just the role, per say, but some of the specific challenges or implementation issues that these people have had in trying to deliver a value inside their company. >> Yeah, so when they started, three years ago, there, really, were not a whole lot of tools that CDO's could use to solve your data science problems, to solve your cloud problems, to solve your governance problem. We're starting to get to a place in the world where there are actual tools out there that help you do these things. So you don't struggle to figure out how do I find talent that can build the tools internally and deploy em. It's now getting the talent to, actually, start implementing things that already exist. >> Is the CDO job well enough defined at this point in time? Do you think that you can, actually, start thinking about tools as opposed to the challenges of the business? In other words, is every CDO different or are the practices, now, becoming a little bit more and the conventions becoming a little bit better understood and stable so you >> can outdo a better job of practicing the CDO role? >> Yeah, I think today, the CDO role is still very ill defined. It's, really, industry by industry and company by company even, CDO's play different roles within each of those. I've only been with IBM for the last four months. I've been spending a lot of that time talking to our clients. Financial services, manufacturing, all over the board and really, the CDO's in those people are all industry specific, they're in different places and even company by company, they're in different places. It really depends on where the company's are on their data and digital journey what role the CDO has. Is it really a defensive play to make sure we're not going to violate any regulations or is it an offensive play and how do we disrupt our industry instead of being disrupted because, really, every industry is in a place where you're either going to be the disruptor or you're going to be the distruptee. And so, that's the scope, the breadth of, I think, the role the CDO plays. >> Do you see it all eventually converging to a common point? Cause, obviously, the CFO and the CMO, those are pretty good at standardized functions over time that wasn't always that way. >> Well, I sure hope it does. I think CDO's are becoming pretty pervasive. I think you're starting to see, when this started, the first one I went to, there were, literally, 35 people >> and only 1/2 of then were called CDO's. We've progressed now where we've got 100 people over 170 some odd people that are here that are CDO's. Most of them have the CDO title even. >> The fact that that title is much more pervasive says that we're heading that way. I think industry by industry you'll start seeing similar responsibilities for CDO's but I don't think you'll start seeing it across the board like a CFO where a CFO does the same thing regardless of the industry. I don't think you'll see that in a CDO for quite some time. >> Well one of the things, certainly, we find interesting is that the role the data's playing in business involvement. And it, partly, the CDO's job is to explain to his or her peers, at that chief level, how using data is going to change the way that they do things from the way that they're function works. And that's part of the reason, I think, why you're suggesting that on a vertical basis that the CDO's job is different. Cause different industries are being impacted themselves by data differently. So as you think about the job that you're performing and the job the CDO's are performing, what part is technical? What part is organizational? What part is political? Et cetera. >> I think a lot of the role of a CDO is political. Most of the CDO's that I know have built their careers on stomping on people's toes. How do I drive change by infringing on other people's turf effectively? >> Peter: In a nice way. >> Well, it depends. In the appropriate way, right? >> Peter: In a productive way. >> In the appropriate way. It could be nice, it could not be nice >> depending on the politics and the culture of the organization. I think a lot of the role of a CDO, it's, almost, like chief disruption officer as much as it is data officer. I think it's a lot about using data >> but, I think, more importantly, it's about using analytics. >> So how do you use analytics to, actually, drive insights and next best action from the data? I think just looking at data and still using gut based on data is not good enough. For chief data officers to really have an impact and really be successful, it's how do you use analytics on that data whether it's machine learning, deep learning, operations research, to really change how the business operates? Because as chief data officers, you need to justify your existence a lot. The way you do that is you tie real value to decisions that your company is making. The data and the analytics that are needed for those decisions. That's, really, the role of a CDO in my mind is, how do I tie value of data based on decisions and how do I use analytics to make those decisions more effective? >> Were the early days more defensive and now, shifting to offensive? It sounds like it. That's a typical case where you use technology, initially, often to save money before you start to use it to create new value, new revenue streams. Is that consistent here? By answering that, you say they have to defend themselves sometimes when you would think it'd be patently obvious >> that if you're not getting on a data software defined train, you're going to be left behind. >> I think there's two types. There's CDO's that are there to protect freedom to operate and that's what I call, think of, as defensive. And then, there's offensive CDO's and that's really bringing more value out of existing processes. In my mind, every company is on this digital transformation journey and there's two steps to it. >> One is this data science transformation which is where you use data and analytics to accelerate your businesses current goals. How do I use data analytics to accelerate my businesses march towards it's current goals? Then there's the second stage which is the true digital transformation which is how do I use data and analytics to, fundamentally, change how my industry and my company operates? So, actually, changing the goals of the industry. For example, moving from selling physical products to selling outcomes. You can't do that until you've done this data transformation till you've started operating on data, till you've started operating on analytics. You can't sell outcomes until you've done that. It's this two step journey. >> You said this a couple of times and I want to test an idea on you and see what you think. Industry classifications are tied back to assets. So, you look at industries and they have common organization of assets, right? >> Seth: Yep. Data, as an asset, has very, very, different attributes because it can be shared. It's not scarce, it's something that can be shared. As we become more digital and as this notion of data science or analytics, the world of data places in asset and analytics plays as assets becomes more pervasive, does that start to change the notion of industry because, now, by using data differently, you can use other assets and deploy other assets differently? >> Yeah, I think it, fundamentally, changes how business operates and even how businesses are measured because you hit on this point pretty well which is data is reusable. And so as I build these data or digital assets, the quality of a company's margins should change. For every dollar of revenue I generate. Maybe today I generate 15% profit. As you start moving to a digital being a more digital company built on data and analytics, that percent of profit based on revenue should go up. Because these assets that you're building to reuse them is extremely cheap. I don't have to build another factory to scale up, I buy a little bit more compute time. Or I develop a new machine learning model. And so it's very scalable unlike building physical products. I think you will see a fundamental shift in how businesses are measured. What standards that investors hold businesses to. I think, another good point is, a mind set shift that needs to happen for companies is that companies need to stop thinking of data as a digital dropping of applications and start thinking of it as an asset. Cause data has value. It's no longer just something that's dropped on the table from applications that I built. It's we are building to, fundamentally, create data to drive analytics, to generate value, to build new revenue for a company that didn't exist today. >> Well the thing that changes the least, ultimately, is the customer. And so it suggests that companies that have customers can use data to get in a new product, or new service domains faster than companies who don't think about data as an asset and are locked into how can I take my core set up, my organization, >> my plant, my machinery and keep stamping out something that's common to it or similar to it. So this notion of customer becomes the driver, increasingly, of what industry you're in or what activities you perform. Does that make sense? >> I think everything needs to be driven from the prospective of the customer. As you become a data driven or a digital company, everything needs to be shifted in that organization from the perspective of the customer. Even companies that are B to B. B to B companies need to start thinking about what is the ultimate end user. How are they going to use what I'm building, for my business partner, my B to B partner, >> what is their, actual, human being that's sitting down using it, how are they going to use it? How are they going to interact with it? It really, fundamentally, changes how businesses approach B to B relationships. It, fundamentally, changes the type of information that, if I'm a B to B company, how do I get more information about the end users and how do I connect? Even if I don't come in direct contact with them, how do I understand how they're using my product better. That's a fundamental just like you need to stop thinking of data as a digital dropping. Every question needs to come from how is the end user, ultimately, going to use this? How do I better deploy that? >> So the utility that the customer gets capturing data about the use of that, the generation of that utility and drive it all the way back. Does the CDO have to take a more explicit role in getting people to see that? >> Yes, absolutely. I think that's part of the cultural shift that needs to happen. >> Peter: So how does the CDO do that? >> I think every question needs to start with what impact does this have on the end user? >> What is the customer perspective on this? Really starting to think about. >> I'm sorry for interrupting. I'd turn that around. I would say it's what impact does the customer have on us? Because you don't know unless you capture data. That notion of the customer impact measurement >> which we heard last time, the measureability and then drive that all the way back. That seems like it's going to become an increasingly, a central design point. >> Yeah, it's a loop and you got to start using these new methodologies that are out there. These design thinking methodologies. It's not just about building an Uber app. It's not just about building an app. It's about how do I, fundamentally, shift my business to this design thinking methodology to start thinking cause that's what design thinking is all about. It's all about how is this going to be used? And every aspect of your business you need to approach that way. >> Seth, I'm afraid they're going to put us in the chaffing dish here if we don't get off soon. >> Seth: I think so too, yeah. >> So we're going to leave it there. It's great to see you again and we look forward to seeing you at the next one of these things. >> Yeah, thanks so much. >> He's Seth, he's Peter, I'm Jeff. You're watching theCUBE from the IBM Chief Data Officers Strategy Summit Spring 2017, I got it all in in a mouthful. We'll be back after lunch which they're >> setting up right now. (laughs) (lively music) (drum beats)