>> 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)

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

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