>> 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 Boston, it's the CUBE. Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome to the CUBE's live coverage of IBM Chief Data Officer Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight along with my co host, Paul Gillin. We're starting our coverage today. This is the very first day of the summit. We have two guests, Caitlin Halferty, she is the AI accelerator lead at IBM, and Sonia Mezzetta, the data governance technical product leader. Thank you both so much for coming on the CUBE >> Thanks for having us. >> So this is the ninth summit. Which really seems hard to belief. But we're talking about the growth of the event and just the kinds of people who come here. Just set the scene for our viewers a little bit, Caitlin. >> Sure, so when we started this event back in 2014, we really were focused on building the role of the chief data officer, and at that time, we know that there were just a handful across industries. Few in finance banking, few in health care, few in retail, that was about it. And now, you know, Gartner and Forrester, some industry analysts say there are thousands across industries. So it's not so much about demonstrating the value or the importance, now, it's about how are our Chief Data Officers going to have the most impact. The most business impact. And we're finding that they're really the decision-makers responsible for investment decisions, bringing cognition, AI to their organizations. And the role has grown and evolved. When we started the first event, we had about 20, 30 attendees. And now, we get 140, that join us in the Spring in San Francisco and 140 here today in Boston. So we've really been excited to see the growth of the community over the last four years now. >> How does that affect the relationship, IBM's relationship with the customer? Traditionally, your constituent has been the CIO perhaps the COO, but you've got this new C level executive. Now, what role do they play in the buying decision? >> There was really a lot of, I think back to, I co-authored a paper with some colleagues in 2014 on the rise of Chief Data Officer. And at that time, we interviewed 22 individuals and it was qualitative because there just weren't many to interview, I couldn't do a quantitative study. You know, I didn't have sample size. And so, it's been really exciting to see that grow and then it's not just the numbers grow, it's the impact they're having. So to you questions of what role are they playing, we are seeing that more and more their scope is increasing, their armed and equipped with teams that lead data science, machine learning, deep learning capabilities so they're differentiated from a technology perspective. And then they're really armed with the investment and budget decisions. How should we invest in technology. Use data as a strategic corporate asset to drive our progress forward in transformation. And so we've really seen a significant scope increase in terms of roles and responsibilities. And I will say though, there's still that blocking and tackling around data strategy, what makes a compelling data strategy. Is is the latest, greatest? Is it going to have an impact? So we're still working through those key items as well. >> So speaking of what makes this compelling strategy, I want to bring you into the conversation Sonia, because I now you're on the automated metadata generation initiative, which is a big push for IBM. Can you talk a little bit about what you're doing at IBM? >> Sure. So I am in charge of the data governance products internally within the company and specifically, we are talking today about the automated metadata generation tool. What we've tried to do with that particular product is to try to basically leverage automation and artificial intelligence to address metadata issues or challenges that we're facing as part of any traditional process that takes place today and trying to do curation for metadata. So specifically, what I would like to also point out is the fact that the metadata curation process in the traditional sense is something that's extremely time-consuming, very manual and actually tedious. So, one of the things that we wanted to do is to address those challenges with this solution. And to really focus in and hone in on leveraging the power of AI. And so one of the things that we did there was to basically take our traditional process, understand what were the major challenges and then focusing on how AI can address those challenges. And today at 4 p.m. I'll be giving a demo on that, so hopefully, everybody can understand the power of leveraging that. >> This may sound like a simple question, but I imagine for a lot of people outside of the CIO of the IT organization, their eyes glaze over when they hear terms like data governance. But it's really important. >> It is. >> So can you describe why it's important? >> Absolutely. >> And why metadata is important too. >> Absolutely. Well, I mean, metadata in itself is extremely critical for any data monetization position strategy, right. The other importance is in order to derive critical business insights that can lead to monetary value within a company. And the other aspect to that is data quality which Interpol talked about, right? So, in order for you to have the right data governance, you need to have right metadata in order for you to have high level of data quality can, if you don't and you're spending a lot of time cleaning dirty data and dealing with inefficiencies or perhaps making wrong business decisions based on bad data quality, it's all connected back to having the right level of data governance. >> So, I mean, I'm going to also go back to something you were talking about earlier and that's just the sheer number of CDOs that we have. We have statistic here, 90% of large global companies will have the CDO by 2019. That's really astonishing. Can you talk a little bit about what you see as sort of the top threats and opportunities that CDOs as grappling with right now. >> And let me make this tangible. I'll just describe my last two weeks, for example. I was with the CDO in person in Denver of a beer company, organization, and they were looking at some MNA opportunities and figuring out what their strategy was. I was at a bank in Chicago with the head of enterprise data government there, looking at it from a regular (mumbles) perspective. And then I was with a large multinational retail organization with their CDO and team figuring out how did they work at a sort of global scale and what did they centralize at enterprise data level. And what did they let markets and teams customize out in the field, out in the GOs. And so, that's just an example of, regardless of industry, regardless of these challenges, I'm seeing these individuals are increasingly responsible for those strategic decisions. And oftentimes, we start with the data strategy and have a good discussion about what is that organization's monetization strategy. What's the corporate business case? How are they going to make money in the future and how can we architect the data strategy that will accelerate their progress there? And again, regardless of product we're selling or retail, excuse me, our industry, those are the same types of challenges and opportunities we're grappling with. >> In the early days there was a lot of questions about the definition of the role and those CDOs set in different departments and reported to different people, are you seeing some commonality emerge now about how this role, where it sits in the organization, and what its responsibilities are? >> It's a great question, I get that all the time. And especially for organizations that recognize the need for enterprise data management. They want to invest in a senior level decision-maker. And then it's a question of where should they sit organizationally? For us internally, within IBM, we report to our Chief Financial Officer. And so, we find that to be quite a compelling fit in terms of budget. And visibility into some of those spend decisions. And we're on par in peers with our CIO, so I see that quite a bit where a Chief Data Officer is now on par and appear to the CIO. We tend to find that when it's potentially buried in the CIO's organization, you lose a little of that autonomy in terms of decision-making, so if you're able to position as partners and drive that transformation for your organization forward together, that can often work quite well. >> So that partnership, is it, I mean ideally, it is collaborative and collegial, but is it ever, are there ever tensions there and how do you recommend the companies get over, overcome those obstacles? >> Absolutely, in the fight for resources that we all have, especially talent and retaining some of our top talent, should that individual or those teams sit within a CIO's organization or a CDO's organization? How do we figure that out? I think there's always going to be the challenge of who owns what. We joke, sometimes, it feels like you own everything when you're in the data space, because you own all of the data that flows through, all your business processes, both CDO-owned and corporate HR's supply chain finance. Sometimes it feels you don't own anything. And so we joke that it's, you have to really carve that out. I think the important part is to really articulate what the data strategy is, what the CDO or enterprise data management office owns from a data perspective and then building up that platform and do it in partnership with your CIO team. And then you really start to be able to build and deploy those AI applications off that platform. That's what we've been able to see, so. >> I want to go back to something Sonia said this morning during the keynote, you talked about IBM's master metadata list catalog unifying your organization around a certain set of terms. There's 6,000 terms in that catalog. Now, how did you arrive at 6,000? And what are some rules for an organization trying to do something like that? How defined, how small should that sub-terms be? >> Sure. Well, we started off with a traditional approach which is probably something that most companies are familiar with these days. The traditional process was really just based on basically reaching out to a large number of subject matter experts across the enterprise that represent in many different data domains such as customer, offering, financial, etc. And essentially having them label this data, specifically with the business metadata that's used internally across a company. Now, another example to that is that there are different organizations across the company. We are a worldwide company. And so, what one business might call a particular piece of data, which is customer, another might call it client. Which really ended up being this very large list of 6,000 business terms which is what we're using internally. But one thing that we're trying to do to be able to kind to basically connect the different business terms is leverage knowledge management and specifically ontological relationships to be able to link the data together and make it more reasonable and provide better quality with that. >> What are the things that you were talking about, Interpol was talking about on the main stage too during the keynote, was making sure that the data is telling a story because getting by in is one of the biggest challenges. How do you recommend companies think about this and approach this very big daunting task? >> I'll start and then I'm sure you have a perspective as well. One of the things that we've seen internally and I work with my client on, is every project we initiate, we really want strong sponsorship from the business in terms of funding, making sure that the right decision-makers are involved. We've identified some projects for example, that we've been able to deploy around supply chains. So identifying the risk on our supply chain processes. Some of the risks in sites, we're going to demo a little bit later today. The AMG work that Sonia's leading. And all of those efforts are underway in partnership with the business. One of my favorite ones is around enabling our sellers to better understand information about, and data, about the customers. So like most organizations, customer data is housed in silo systems that don't necessarily talk well with each other, and so it's an effort to really pull that data together in partnership with our digital sellers and enable them to then pull up user interface, user-friendly, an app where they can identify and drill down to the types of information they need about their customers. And so our thought and recommendation based on our experience and then what I'm seeing is really having that strong partnership with the business. And the contribution funding, stakeholder involvement, engagement, and then you start to prioritize where you'll have the most impact. >> You did a program called the AI accelerator. What is that? >> We did, so when we stood up our first chief data office, it was three years ago now, we wanted to be quite transparent about the journey of driving cognition through our enterprise. And we were really targeting those CDO and processes around client master product data and then all of our enterprise processes. So that first six months was about writing the data strategy and implementing that, next we spent a year on all of our processes, really mapping out, we call it journey mapping, I think a lot of folks do that, by process. So HR, supply chain, identifying ways. How it's done today, how it will be done in a cognitive AI like future state. And then also, as we're driving out those efficiencies in automation, those reinvestment opportunities to free up that money for future initiatives. And so that was the first year, year and a half. And now, we're at the point where we've evolved far enough along that we think we're learned some lessons on the way and there's been some hurdles and stumbling blocks and obstacles. And so a year ago, we really start a cognitive enterprise blueprint and that was really intended to reflect all of our experiences, driving that transformation. A lot of customer engagements, lot of industry analysts feedback as well. And now we formalized that initiative. So now I have a really fantastic team of folks working with me. Subject matter domain expertise, really deep in different processes, solutions, folks, architects. And what we can do is pull together the right breadth and depth of IBM resources. Deploy it, customize it to customer need and really, hopefully, accelerate and apply a lot of what we've learned, lot of what the clients have learned, to accelerate their own AI transformation journey. >> But AI, IBM is the guinea pig and it showcase. And so you're learning as you go and helping customers do that too. >> Exactly and we've now built our platform, deployed that, as we mentioned, we've got about 30,000 active users, active users, using our platform. Plan to grow to 100,000. We're seeing about 600 million in business benefit internally from the work we've done. And so we want to really share that and do some good, best practice sharing and accelerate some of that process. >> IBM used the term cognitive rather than AI. What is the difference or is there one? >> I think we're starting actually to shift from cognitive to AI because of that exact perspective. AI, I think is better understood in the industry, in the market and that's what's resonating more so with clients and I think it's more reflective of what we're doing. And our particular approach is human in the loop. So we've always said rather than the black box sort of AI algorithms running behind the scenes, we want to make sure that we do that with trust and transparency, so there's a real transparency aspect to what we're doing. And the other thing I would notice, we talk about sort of your data is your data. Insights derive from that data is your insights. So we've worked quite closely with our legal teams to really articulate how your data is used. If you engage and partner with us to drive AI in your enterprise, making sure we have that trust and transparency (mumbles) clearly articulated is another important aspect for us. >> Getting right back to data governance. >> Right, right, exactly. Which is our we've come full circle. >> Well Caitlin and Sonia, thank you so much for coming on the CUBE, it was great. Great to kick off this summit together. >> Great to see you again, as always. >> I'm Rebecca Knight for Paul Gillin, stay tuned for more of the CUBE's live coverage of IBM CDO Summit here in Boston. (techno music)

>> Announcer: Live from downtown San Francisco, it's theCUBE, covering IBM Chief Data Officers Strategy Summit 2018, brought to you by IBM. >> We're back in San Francisco, everybody. This is theCUBE, the leader in live tech coverage, and we're here covering exclusive coverage of IBM's Chief Data Officer Strategy Summit. This is the summit, as I said, they book in at each coast, San Francisco and Boston. Intimate, a lot of senior practitioners, chief data officers, data folks, people who love data. Caitlyn Halferty is back. She's the Client Engagement Executive and the Chief Data Officer office at IBM. Great. And, Allen Crane, Vice President at USAA. >> Thank you. >> Good to see you. Thanks for coming on. All right. >> Thanks for having us. >> You're welcome. Well, good day today, as I said, a very intimate crowd. You're here as a sort of defacto CDO, learning, sharing, connecting with peers. Set up your role, Allen. Tell us about that. >> At USA, we've got a distributed data and analytics organization where we have centralized functions in our hub, and then each of the lines of business have their own data offices. I happen to have responsibility for all the different ways that our members interact with us, so about 100 million phone calls a year, about a couple billion internet and digital sessions a year, most of that is on mobile, and always lookin' at the ways that we can give back time to our membership, as well as our customer service reps, who we call our member service reps, so that they can serve our members better. The faster and more predictive we can be with being able to understand our members better and prompt our MSRs with the right information to serve them, then the more they can get on to the actual value of that conversation. >> A lot of data. So, one of the things that Inderpal talked about the very first time I met him, in Boston, he talked about the Five Pillars, and the first one was you have to understand as a CDO, how your organization gets value out of data. You said that could be direct monetization or, I guess, increased revenue, cut costs. That's value. >> Right. >> That's right. >> That's the starting point. >> Right. >> So, how did you start? >> Well, actually, it was the internal monetization. So, first off, I want to say USA never sells any of our member data, so we don't think of monetization in that framework, but we do think of it terms of how do we give something that's even more precious than money back to our company and to our members and the MSRs? And, that is really that gift of time. By removing friction from the system, we've been able to reduce calls per member, through digitization activities, and reduced transfers and reduced misdirects by over 10% every year. We're doing work with AI and machine learning to be able to better anticipate what the member is calling about, so that we can get them to the right place at the right time to the right set member service representatives. And, so all these things have resulted in, not just time savings but, obviously, that translates directly to bottom line savings, but at the end of the day, it's about increasing that member service level, increasing your responsiveness, increasing the speed that you're answering the phone, and ultimately increasing that member satisfaction. >> Yeah, customer satisfaction, lowers churn rates, that's a form of monetization, >> Absolutely. >> so it's hard dollars to the CFO, right? >> Absolutely, yeah. >> All right, let's talk about the role of the CDO. This is something that we touched on earlier. >> Yes. >> We're bringing it home here. >> Yes. >> Last segment. Where are we at with the role of the CDO? It was sort of isolated for years in regulated industries, >> Correct. >> permeated to mainstream organizations. >> Correct. >> Many of those mainstream organizations can move faster, 'cause their not regulated, so have we sort of reached parody between the regulated and the unregulated, and what do you discern there in terms of patterns and states of innovation? >> Sure. I think when we kicked off these summits in 2014, many of our CDOs came from CIO type organizations, defensive posture, you know, king of the data warehouse that we joke about, and now annuls reports of that time were saying maybe 20% of large organizations were investing in the CDO or similar individual responsible for enterprise data, and now we see analysts reports coming out to say upwards of 85, even 90%, of organizations are investing in someone responsible for that role of the CDO type. In my opening remarks this morning, I polled the room to say who's here for the first time. It was interesting, 69, 70% of attendees were joining us for the first time, and I went back, okay, who's been here last year, year before, and I said who was here from the beginning, 2014 with us, and Allen is one of the individuals who's been with us. And, as much as the topics have changed and the role has grown and the purview and scope of responsibilities, some topics have remained, our attendees tell us, they're still important, top-of-mind, and data monetization is one of those. So, we always have a panel on data monetization, and we've had some good discussions recently, that the idea of it's just the external resell, or something to do with selling data externally is one view, but really driving that internal value, and the ways you drive out those efficiencies is another perspective on it. So, fortunate to have Allen here. >> Well, we've been able to, for that very reason, we've been able to grow our team from about six or seven people five years ago to well over a hundred people, that's focused on how we inefficiency out of the system. That mere 10%, when your call-per-member reduction, when you're taking 30 million calls in the bank, you know, that's real dollars, three million calls out of the system that you can monetize like that. So, it's real value that the company sees in us, and I think that, in a sense, is really how you want to be growing in a data organization, because people see value in you, are willing to give you more, and then you start getting into those interesting conversations, if I gave you more people, could you get me more results? >> Let's talk about digital transformation and how it relates to all this. Presumably, you've got a top down initiative, the CEO says, he or she says, okay, this is important. We got to do it. Boom, there's the North Star. Let's go. What's the right regime that you're seeing? Obviously, you've got to have the executive buy-in, you've got the Chief Data Officer, you have the Chief Digital Officer, the Chief Operating Officer, the CFO's always going to be there, making sure things are on track. How are you seeing that whole thing shake out, at least in your organization? >> Well, one thing that we've been seeing is digital digitization or the digital transformation is not about just going only digital. It's how does all this work together. It can't just be an additive function, where you're still taking just as many calls and so forth, but it's got to be something that that experience online has got to do something that's transformative in your organization. So, we really look at the member all the way through that whole ecosystem, and not just through the digital lens. And, that's really where teams like ours have really been able to stitch together the member experience across all their channels that they're interacting with us, whether that's the marketing channels or the digital channels or the call channel, so that we can better understand that experience. But, it's certainly a complementary one. It can't just be an additive one. >> I wonder if we could talk about complacency, in terms of digital transformation. I talk to a lot of companies and there's discussion about digital, but you talk to a lot of people who say, well, we're doing fine. Maybe not in our industry. Insurance is one that hasn't been highly disruptive, financial services, things like aerospace. I'll be retired by the time this all, I mean, that's true, right? And, probably accurate. So, are you seeing a sense of complacency or are you seeing a sense of urgency, or a mix or both? What are you seeing, Caitlyn? >> Well, it's interesting, and people may not be aware, but I'm constantly polling our attendees to ask what are top-of-mind topics, what are you struggling with, where are you seeing successes, and digital was one that came up for this particular session, which is why tomorrow's keynote, we have our Chief Digital Officer giving the morning keynote, to show how our data office and digital office are partnering to drive transformation internally. So, at least for our perspective, in the internal side of it, we have a priority initiative, a cognitive sales advisor, and it's essentially intended to bring in disparate part of customer data, obtained through many different channels, all the ways that they engage with us, online and other, and then, deliver it through sales advisor app that empowers our digital sellers to better meet their revenue targets and impact, and develop more of a quality client relationship and improve that customer experience. So, internally, at least, it's been interesting to see one of our strongest partnerships, in terms of business unit, has been our data and digital office. They say, look, the quality of the data is at the core, you then enable our digital sellers, and our clients benefit, for a better client experience. >> Well, about a year ago, we absolutely changed the organization to align the data office with the digital office, so that reports to our executive counsel level, so their peers, that reporting to the same organization, to ensure that those strategies are connected. >> Yeah, so as Caitlyn was saying, this Chief Data Officer kind of emerged from a defensive posture of compliance, governance, data quality. The Chief Digital Officer, kind of new, oftentimes associated with marketing, more of an external, perhaps, facing role, not always. And then, the CIO, we'll say, well, wait a minute, data is the CIO's job, but, of course, the CIO, she's too busy trying to keep the lights on and make everything work. So, where does the technology organization fit? >> Well, all that's together, so when we brought all those things together at the organizational level, digital, data, and technology were all together, and even design. So, you guys are all peers, reporting into the executive committee, essentially, is that right? Yes, our data, technology, and design, and digital office are all peers reporting to the same executive level. And then, one of the other pillars that Inderpal talks about is the relationship with the line of business. So, how is that connective tissue created? Well, being on the side that is responsible for how all of our members interact, my organization touches every product, every line of business, every channel that our members are interacting with, so our data is actually shared across the organization, so right now, really my focus is to make sure that that data is as accessible as it can be across our enterprise partners, it's as democratized as it can be, it's as high as quality. And then, things that we're doing around machine learning and AI, can be enabled and plugged into from all those different lines of business. >> What does success look like in your organization? How do you know you're doing well? I mean, obviously, dropping money to the bottom line, but how are you guys measuring yourselves and setting objectives? What's your North Star? >> I think success, for me, is when you're doing a good job, to the point that people say that question, could you do more if I gave you more? That, to me, is the ultimate validation. It's how we grew as an organization. You know, we don't have to play that justification game When people are already coming to the table saying, You're doing great work. How can you do more great work? >> So, what's next for these summits? Are you doing Boston again in the fall? Is that right? Are you planning >> We are, we are, >> on doing that? >> and you know, fall of last year, we released the blueprint, and the intent was to say, hey, here's the reflection of our 18 months, internal journey, as well as all our client interactions and their feedback, and we said, we're coming back in the spring and we're showing you the detail of how we really built out these internal platforms. So, we released our hybrid on-prem Cloud showcase today, which was great, and to the level of specificity that shows that the product solutions, what we're using, the Flash Storage, some of the AI components of machine learning models. >> The cognitive systems component? >> Exactly. And then, our vision, to your question to the fall, is coming back with the public Cloud showcases. So, we're already internally doing work on our public Cloud, in particular respect to our backup, some of our very sensitive client data, as well as some initial deep learning models, so those are the three pieces we're doing in public Cloud internally, and just as we made the commitment to come back and unveil and show those detail, we want to come back in the fall and show a variety of public Cloud showcases where we're doing this work. And then, hopefully, we'll continue to partner and say, hey, here's how we're doing it. We'd love to see how you're doing it. Let's share some best practices, accelerate, build these capabilities. And, I'll say to your business benefit question, what we've found is once we've built that platform, we call it, internally, a one IBM architecture, out our platform, we can then drive critical initiatives for the enterprise. So, for us, GVPR, you know, we own delivery of GVPR readiness across the IBM corporation, working with senior executives in all of our lines of business, to make sure we get there. But, now we've got the responsibility to drive out initiatives like that cross business unit, to your question on the partnerships. >> The evolution of this event seems to be, well, it's got a lot of evangelism early on, and now it's really practical, sort of sharing, like you say, the blueprint, how to apply it, a lot of people asking questions, you know, there's different levels of maturity. Now, you guys back tomorrow? You got to panel, you guys are doing a panel on data monetization? >> We're doing a panel on data monetization tomorrow. >> Okay, and then, you've got Bob Lord and Inderpal talking about that, so perfect juxtaposition and teamwork of those two major roles. >> And, this is the first time we've really showcased the data/digital partnership and connection, so I'm excited, want to appeal to the developer viewpoint of this. So, I think it'll be a great conversation about data at the core, driving digital transformation. And then, as you said, our data monetization panel, both external efforts, as well as a lot of the internal value that we're all driving, so I think that'll be a great session tomorrow. >> Well, and it's important, 'cause there's a lot of confusing, and still is a lot of confusion about those roles, and you made the point early today, is look, there's a big organizational issue you have to deal with, particularly around data silos, MyData. I presume you guys are attacking that challenge? >> Absolutely. >> Still, it's still a-- >> It's an ongoing-- >> Oh, absolutely. >> I think we're getting a lot better at it, but you've got to lean in, because if it's not internal, it's some of the external challenges around. Now we're picking Cloud vendors and so forth. Ten years ago, we had our own silos and our own warehouses, if we had a warehouse, and then, we were kind of moving into our own silos in our own databases, and then as we democratized that, we solved the one problem, but now our data's so big and compute needs are so large that we have no choice but to get more external into Cloud. So, you have to lean in, because everything is changing at such a rapid rate. >> And, it requires leadership. >> Yep. >> Absolutely. >> The whole digital data really requires excellent leadership, vision. IBM's catalyzing a lot of that conversation, so congratulations on getting this going. Last thoughts. >> Oh, I would just say, we were joking that 2014, the first couple of summits, small group, maybe 20-30 participants figuring out how to best organize from a structural perspective, you set up the office, what sort of outcomes, metrics, are we going to measure against, and those things, I think, will continue to be topics of discussion, but now we see we've got about 500 data leaders that are tracking our journey and that are involved and engaged with us. We've done a lot in North America, we're starting to do more outside the geographies, as well, which is great to see. So, I just have to say I think it's interesting to see the topics that continue to be of interest, the governance, the data monetization, and then, the new areas around AI, machine learning, data science, >> data science >> the empowering developers, the DevOps delivery, how we're going to deliver that type of training. So, it's been really exciting to see the community grow and all the best practices leveraged, and look forward to continuing to do more of that this year as well. >> Well, you obviously get a lot of value out of these events. You were here at the first one, you're here today. So, 2018. Your thoughts? >> I think the first one, we were all trying to figure out who we are, what's our role, and it varied from I'm a individual contributor, data evangelist in the organization to I'm king of the warehouse thing. >> Right. >> And, largely, from that defensive standpoint. I think, today, you see a lot more people that are leaning in, leading data science teams, leading the future of where the organizations are going to be going. This is really where the center of a lot of organizations are starting to pivot and look, and see, where is the future, and how does data become the leading edge of where the organization is going, so it's pretty cool to be a part of a community like this that's evolving that way, but then also being able to have that at a local level within your own organization. >> Well, another big take-away for me is the USAA example shows that this can pay for itself when you grow your own organization from a handful of people to a hundred plus individuals, driving value, so it makes it easier to justify, when you can demonstrate a business case. Well, guys, thanks very much for helping me wrap here. >> Absolutely. >> I appreciate you having us here. >> Thank you. >> It's been a great event. Always a pleasure, hopefully, we'll see you in the fall. >> Sounds good. Thank you so much. >> All right, thanks, everybody, for watching. We're out. This is theCUBE from IBM CDO Summit. Check out theCUBE.net for all of the videos, siliconangle.com for all the news summaries of this event, and wikibon.com for all the research. We'll see you next time. (techy music)

>> Narrator: Live, from downtown San Francisco. It's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. (techno music) >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante. And we are here at the IBM CDO Strategy Summit hashtag IBMCDO. Caitlin Halferty is here. She's a client engagement executive for the chief data officer at IBM. Caitlin great to see you again. >> Great to be here, thank you. >> And she's joined by Brandon Purcell, who's principal analyst at Forrester Research. Good to have you on. >> Thanks very much, thanks for having me. >> First time on theCUBE. >> Yeah. >> You're very welcome. >> I'm a newbie. >> Caitlin... that's right, you're a newbie. You'll be a Cube alum in no time, I promise you. So Caitlin let's start with you. This is, you've done a number of these CDO events. You do some in Boston, you do some in San Francisco. And it's really great to see the practitioners here. You guys are bringing guys like Inderpal to the table. You've announced your blueprint in it. The audience seems to be lapping up the knowledge transfer. So what's the purpose of these events? How has it evolved? And just set the table for us. >> Sure, so we started back in 2014 with our first Chief Data Officer Summit and we held that here in San Francisco. Small group, probably only had about 30 or 40 attendees. And we said let's make this community focused, peer to peer networking. We're all trying to, ya know, build the role of either the Chief Data Officer or whomever is responsible for enterprise wide data strategy for their company, a variety of different titles. And we've grown that event over, since 2014. We do Spring, in San Francisco, which tends to be a bit more on the technical side, given where we are here in San Francisco in Silicon Valley. And then we do our business focused sessions in Fall in Boston. And I have to say, it's been really nice to see the community grow from a small set of attendees. And now was are at about 130 that join us on each coast. So we've built a community in total of about 500 CDOs and data executives, >> Nice. that are with us on this journey, so they're great. >> And Brandon, your focus at Forrester, part of it is AI, I know you did some other things in analytics, the ethics of AI, which we're going to talk about. I have to ask you from Forrester's perspective, we're enter... it feels like we're entering this new era of there's digital, there's data, there's AI. They seem to all overlap. What's your point of view on all this? >> So, I'm extremely optimistic about the future of AI. I realize that the term artificial intelligence is incredibly hyped right now. But I think it will ultimately fulfill it's promise. If you think about the life cycle of analytics, analytics start their lives as customer data. As customers interact and transact with you, that creates a foot print that you then have to analyze to unleash some sort of insight. This customer's likely to buy, or churn, or belongs to a specific segment. Then you have to take action. The buzzwords of the past have really focused on one piece of that life cycle. Big data, the data piece. Not much value unless you analyze that. So then predictive analytics, machine learning. What AI promises to do is to synthesize all of those pieces, from data, to insights, to action. And continuously learn and optimize. >> It's interesting you talk about that in terms of customer churn. I mean, with the internet, there was like a shift in the balance of power to the consumer. There used to be that the brand had all the knowledge about the buyer. And then with the internet, we shop around, we walk into a store and, look at them. Then we go buy it on the internet right? Now that AI maybe brings back more balance, symmetry. I mean, what are your thoughts on that? Are the clients that you work with, trying to sort of regain that advantage? So they can better understand the customer. >> Yeah, well that's a great question. I mean, if there's one kind of central ethos to Forrester's research it's that we live in the age of the customer and understanding and anticipating customer needs is paramount to be able to compete, right? And so it's the businesses in the age of AI and the age of the customer that have the data on the customer and enable the ability to distill that into insights that will ultimately succeed. And so the companies that have been able to identify the right value exchange with consumers, to give us a sense of convenience, so that we're willing to give up enough personal data to satisfy that convenience are the ones that I think are doing well. And certainly Netflix and Amazon come to mind there. >> Well for sure, and of course that gets into the privacy and the ethics of AI. I mean everyone's making a big deal out of this. You own your data. >> Yeah. >> You're not trying to monetize, ya know, figure out which ad to click on. Maybe give us your perspective, Caitlin, on IBMs point of view there? >> Sure, so we lead with this thought around trusting your data. You're data's your data. Insights derive from that data, your insights. We spend a lot of time with our Watson Legal folks. And one of the things, pieces of material we've released today is the real detail at every level how you engage the traceability of where your data is. So you have a sense of confidence that you know how it's treated, how it's curated. If it's used in some third party fashion. The ability to know that, have visibility into it. The opt-out, opt-in opt-out set of choices. Making sure that we're not exploiting the network effect, where perhaps party C benefits from data exchange between A and B. That A and B do not, or do not have an opportunity to influence. And so what we wanted to do, here at the summit over the next couple of days is really share that in detail and our thoughts around it. And it comes back to trust and being able to have that viability and traceability of your data through the value chain. >> So of course Brandon, as a customer I'm paying IBM so I would expect that IBM would look out for my privacy and make that promise. I don't really pay Facebook right? But I get some value out of it. So what are the ethics of that? Is it a pay or no pay? Or is it a value or no value? Is it everybody really needs to play by the same rules? How to you parse all that? >> Ya know, I hate to use a vague term. But it's a reasonable expectation. Like I think that when a person interacts with Facebook, there is a reasonable expectation that they're not going to take that data and sell it or monetize it to some third party, like Cambridge Analytica. And that's where they dropped the ball in that case. But, that's just in the actual data collection itself. There's also, there are also inherent ethical issues in how the data is actually transformed and analyzed. So just because you don't have like specific characteristics or attributes in data, like race and gender and age and socioeconomic status, in a multidimensional data set there are proxies for those through something called redundant encoding. So even if you don't want to use those factors to make decisions, you have to be very careful because they're probably in there anyway. And so you need to really think about what are your values as a brand? And when can you actually differentiate treatment, based on different attributes. >> Because you can make accurate inferences from that. >> Brandon: Yeah you're absolutely (mumbles). >> And is it the case of actually acting on that data? Or actually the ability to act on that data? If that makes sense to you. In other words, if an organization has that data and could, in theory, make the inference, but doesn't. Is that crossing the line? Is it the responsibility of the organization to identify those exposures and make sure that they can not be inferred? >> Yeah, I think it is. I think that that is incumbent upon our organizations today. Eventually regulators are going to get around to writing rules around this. And there's already some going into effect of course in Europe, with GDPR at the end of this month. But regulators are usually slow to catch up. So for now it's going to have to be organizations that think about this. And think about, okay, when is it okay to treat different customers differently? Because if we, if we break that promise, customers are going to ultimately leave us. >> That's a hard problem. >> Right, right. >> You guys have a lot of these discussions internally? >> We do. >> And can you share those with us? >> Yeah, absolutely, we do. And we get a lot of questions. We often engage at the data strategy perspective. And it starts with, hey we've got great activity occurring in our business units, in our functional areas, but we don't really have a handle on the enterprise wide data strategy. And at that point we start talking about trust, and privacy, and security, and what is your what does your data flows look like. So it starts at that initial data strategy discussion. And one other thing I mentioned in my opening remarks this morning is, we released this blueprint and it's intended, as you said, to put a framework in process and reflect a lot of the lessons learned that we're all going through. I know you mentioned that many companies are looking at AI adoption, perhaps more so than we realized. And so the framework was intended to help accelerate that process. And then our big announcement today has been around the showcases, in particular our platform showcase. So it's really the platform we've built, within our organization. The components, the products, the capabilities that drives for us. And then with the intent of hopefully being, illustrative and helpful to clients that are looking to build similar capabilities. >> So let's talk about adoption. >> Brandon: Yeah, sure. >> Ya know, we... you often hear this bromide that we live in a world where, that pace of change is so fast. And things are changing so quickly it's hard to deny that. But then when you look at adoption of some of the big themes in our time. Whether it's big data or AI, digital, block chains, there are some major barriers to adoption. So you see them adopted in pockets. What's your perspective, and Forrester's perspective on adoption of, let's call it machine intelligence? >> Yeah, sure, so I mean, every year Forrester does a global survey of business and technology decision leaders called Business Technographics. And we ask folks about adoptions rates of certain technologies. And so when it comes to AI, globally, 52% of companies have adopted AI in some way. And another 20% plan to in the next 12 months. What's interesting to me, actually, is when you break that down geographically, the highest adoption rate, 60 plus percent, is in APAC, followed by North America, followed by Europe. And when you think about the privacy regulations in each of those geographies, well there are far fewer in APAC than there are, and will be, in Europe. And that's, I think kind of hamstringing adoption in that geography. Now is that a problem for Europe? I don't think so actually. I think AI, the way AI is going to be adopted in Europe is going to be more refined and respectful of customers' intrinsic right to privacy. >> Dave: Ya know I want... Go ahead. >> I've got to, I have to say Dave, I have to put a plug in. I've been a huge fan of Brandon's, for a long time. I've actually, ya know, a few years now of his research. And some of the research that you're mentioning, I hope people are reading it. Because we find these reports to be really helpful to understand, as you said, the specifics of adoptions, the trends. So I've got to put a plug in there. >> Thanks Caitlin. >> Because, the quality of the work and the insights are incredible. So that is why I was quite excited when Brandon accepted our offer to join us here in this session. >> Awesome. Yeah, so, let's dig into that a little bit. >> Brandon: Sure. >> So it seems like, so 52%, I'm wondering, what the other 48 are doing? They probably are, and they just don't know it. So it's possible that the study looks at, a strategy to adopt, presumably. I mean actively adopting. But it seems, I wonder if I could run this by you, get your comment. It seems that people will, organizations will more likely be buying AI as embedded in applications or systems or just kind of invisible. Then they won't necessarily be building it. I know many are trying to probably build it today. And what's your thought on that? In terms of just AI infused everywhere? >> So the first foray for most enterprises into this world of AI is chat bots for customer service. >> Dave: Sure. >> I mean we get a ton of inquires at Forrester about that. And there are a number of solutions. Ya know, IBM certainly has one for, that fulfill that need. And that's a very narrow use case, right? And it's also a value added of use case. If you can take more of those call center agents out of the loop, or at least accelerate or make them better at their jobs, then you're going to see efficiency gains. But this isn't this company wide AI transformation. It's just one very narrow use case. And usually that's, most elements of that are pre-built. We talked this morning, or the speakers this morning talked about commoditization of certain aspects of machine learning and AI. And it's very true. I mean, machine learning algorithms, many of them have been around for a long time, and you can access them for multiple different platforms. Even natural language processing, which a few years ago was highly inaccurate, is getting really, really accurate. So when, in a world where all of these things are commoditized, it's going to end up being how you implement them that's going to drive differentiation. And so, I don't think there's any problem with buying solutions that have been pre-built. You just have to be very thoughtful about how you use them to ultimately make decisions that impact the customer experience. >> I want to, in the time we have remaining, I want to get into the tech radar, the sort of taxonomy of AI or machine intelligence. You've done some work here. How do you describe, can you paint a picture, for what that taxonomy looks like? >> So I think most people watching realize AI is not one specific thing right? It's a bunch of components, technologies that stitched together lead to something that can emulate certain things that humans do, like sense the world around us, see, read, hear, that can think or reason. That's the machine learning piece. And that can then take action. And that's the kind of automation piece. And there are different core technologies that make up each of those faculties. The kind of emerging ones are deep learning. Of course you hear about it all the time. Deep learning is inherently the use of artificial neural networks, usually to take some unstructured data, let's say pictures of cats, and identify this is actually a cat right? >> Who would have thought? That we're led to this boom right? >> Right exactly. That was something you couldn't do five or six years ago, right? You couldn't actually analyze picture data like you analyze row and column data. So that's leading to a transformation. The problem there is that not a lot of people have this massive number of pictures of cats that are consistently and accurately labeled cat, not cat, cat, not cat. And that's what you need to make that viable. So a lot of vendors, and Watson has an API for this have already trained a deep neural network to do that so the enterprises aren't starting from scratch. And I think we'll see more and more of these kind of pre-trained solutions and companies gravitating towards the pre-trained solutions. And looking for differentiation, not in the solutions themselves, but again how they actually implement it to impact the customer experience. >> Hmmm, well that's interesting, just hearing you sense, see, read, hear, reason, act. These are words that describe not the past era. This is a new era that we're entering. We're in the cloud era now. We can sort of all agree with that. But these, the cloud doesn't do these things. We are clearly entering a new wave. Maybe it's driven by Watson's Law, or whatever holds out. Caitlin I'll give you the last word. Put a bumper sticker on this event, and where we're at here in 2018? >> I'll say, it's interesting to watch the themes evolve over the last few years. Ya know, we started with sort of a defensive posture. Most of our data executives were coming perhaps from an IT type background. We see a lot more with line of business, and chief operations type role. And we've seen the, we still king of the data warehouse, that's sort of how we described at the time. And now, I see our data leaders really driving transformation. They're responsible for both the data as well as the digital transformation. On the data side, it's the AI focus. And trying to really understand the deep learning capabilities, machine learning, that they're bringing to bear. So it's been, for me, it's been really interesting to see the topics evolve, see the role in the strategic piece of it. As well as see these guys elevated, in terms of influence within their organization. And then, our big topic this year was around AI and understanding it. And so, having Brandon to share his expertise was very exciting for me because, he's our lead analyst in the AI space. And that's what our attendees are telling us. They want to better understand, and better understand how to take action to implement and see those business results. So I think we're going to continue to see more of that. And yeah, it's been great to see, great to see it evolve. >> Well congratulations on taking the lead, this is a very important space. Ya know, a lot of people didn't really believe in it early on, thought the Chief Data Officer role would just sort of disappear. But you guys, I think, made the right investment and a good call, so congratulations on that. >> I was laughed out of the room when I proposed, I said hey we're hearing of this, doing a market scan of Chief Data Officer, either by title or something similar, titled responsible for enterprise wide data. I was laughed out of the room. I said let me do a qualitative piece. Let me interview 20 and just show, and then you're right, it was the thought was, role's going to go by the wayside. And I think we've seen the opposite. >> Oh yeah, absolutely. >> Data has grown in importance. The associative capabilities have grown. And I'm seeing these individuals, their scope, their sphere of responsibility really grow quite a bit. >> Yeah Forrester's tracked this. I mean, you guys I think just a few years ago was like eh, yeah 20% of organizations have a Chief Data Officer and now it's much much higher than that. >> Yeah, yeah, it's approaching 50%. >> Yeah, so, good. Alright Brandon, Caitlin, thanks very much for coming on theCUBE. >> Thanks for having us. >> Thank you, it was great. >> Keep it right there everybody. We'll be back, at the IBM Chief Data Officer Strategy Summit. You're watching theCUBE. (techno music) (telephone tones)

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

(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)

>> 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)