 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. I'm joined by Peter Burris, the Chief Research Officer at Wikibon. We're in downtown San Francisco at the IBM Chief Data Officer Strategy Summit 2017. It's a lot of practitioners. 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're 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're really excited for our next segment to be joined by Alan Crane. He's the Assistant VP from USAA. Welcome. Thank you. 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 over with you Alan, 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 small intimate affair. That's right. I've been coming to this event for the last three years since they had it and started it with, 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're going through, everything from how are you organized in the organization? What are you focused on? Where are you going? And all the way through talent discussions and how do you source these jobs? And it's always a big discussion as to, you know, organizational structure, which on one hand, saying it's kind of, you know, who really cares. But it's vitally important as the out is executed. How the strategy gets implemented in the business group. So I wonder if you can tell us a little bit about how it works at USA, your role specifically and how does a chief data officer edict, you know, work its way into, you know, the business folks trying to make better decisions? Absolutely. We're a $27 billion 95 year old company that focuses on the military and their member 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 a, what we call a hub and a 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 and analytics for all of our channels and how our members interact with USA. 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 that the governance layer on top of that. Well, USA has been at the vanguard of customer experience for many years now. And the channel world is now starting to apply some of the lessons learned elsewhere. Are you finding that USA 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 a 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 and mobile and how does that interact with a 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 siloed leaders that weren't really thinking laterally or horizontally across the experience that the member was facing. Now the member is already multi-channel. We all know this. We're all customers in our own right, getting things in the mail, it's not clear or getting things on an email or a mobile notice or SMS text message. And hey, 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 all together, 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. It's in the same place as our document data. It's in the same place as our mobile data. Because when you're not able to see that path of how the member got here, you're kind of at a loss for 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. 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 that data together. So how are you working with other functions within USAA to ensure that the data, that the channel organization needs 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? How is that coming together? I think simply put it's pull versus push. So showing the value that we're 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're being more efficient with our member getting in the right place to the right MSR the first time, that has a very material impact on their bottom line. So connecting into the things that they care about is the pull factor that we often call 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 Alan's 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, 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, 10 years ago as we were just at the dawn of a lot of these 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 to adopt 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? No, I don't. That's right. That's the first thing, and you guys are probably better than men than I am, their first thing I'm going to do is say, prove them wrong, right? That's a same thing when an artificially intelligent asset tries to tell a knowledge worker what to do. You know, you, pow, right? That's what I think the hardest thing is right now. So is it an accumulative kind of knock down or eventually, you know, they kind of get it, they're like, all right, I'll stop resisting? Or is it a aha moment where people come back? Because usually for changing behavior, right, it's usually their character stick. Either you got to do it in 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 really new business opportunity. I think it's finding what's important to that business user, and sometimes it's an insight that saves the money. In other cases, it's how, 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, is 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 we're going there have to go someplace else now. And so they may not understand the interplay of these things. So sometimes the analyst comes in at 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 interests in mind and sometimes it's repeating the message over and over again, sometimes it's storytelling, 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 customers. Let me build on that, build on something that you said Glenn, because it's trying to get many people in the service experience to change, not just one. So the end goal is to have the customer have a great experience, but the business executive has to be part of that change, 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 whether you need to be across the entire community and it's responsible for making something happen. Is that kind of where your job comes in, that you're making sure that that experience that's impacted by multiple things, that everybody gets a single version of the truth or 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've 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 off the same fact base, if you will. Other disciplines like process disciplines, things that we call design 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 release on dot com, but truly making sure that we're 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. So that's an interesting concept. So you design the measurability in at the beginning as opposed to, you know, kind of after the fact because obviously you need a measure. Are you participating in that process? Absolutely, we have, and my role is mainly more from an education standpoint of knowing why it's important to do this, but certainly every one 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 price milestone based thing. Did program number 32, was it red or green? And if it's green, you'll get paid. If not, I'm not paying it. 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 Alan's, if I'm 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'm not going to be able to work across multiple levels in an organization. Starting off in time at the C-suite to make sure that there's the right sponsorship. Because, you know, often time, somebody want to change routing and it seems like a great idea, two or three levels below, but when it gets out of whack and it feels uncomfortable and the C-suite needs to step in, that's when you need everybody staring at the same set of KPIs and same metrics saying, no, no, we're going to go after this. We're 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, it's probably the hardest thing that I work on in big transformational things. As a consultant. Yeah, as a consultant is to get everybody aligned around, yeah, 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. Well, it's interesting because in the keynote, they lay out exactly what is cognitive in the four E's, which I thought were interesting, expert, you know, 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 that you don't want them to evolve, right? You want to deliver to what you wrote down in the SOW. Exactly. It doesn't necessarily have a white box element to it because, you know, sometimes a little focus, focus. So just by its very definition in cognitive and its evolutionary nature and its learning nature, it's this ongoing evolution of iterative processes. It's not a lock it down, you know, this is what I said I delivered, this is what we delivered, because you might find new things along the path. I think this concept of evolution and, you know, one of the things we try and be very, very careful with, when you have a brand and a reputation, like you say, 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? Oftentimes the way we speak and then you let it serve the internet to get better at using euphemisms, pretty soon you have a cognitive assets that kind of start to use slang, make racial slurs, all those things because it, no, I'm serious. I know. That's not bad. So, you know, that's one of the things that Ginny's been really, really careful with us about is to make sure that, you know, we have a cognitive manifesto that says, we'll start here, we'll stop here. We're not going to go into ex machina territory where full cognition and humans are gone, right? That's not what we want to, because we need to make sure that IBM is protecting the brand reputation of USA. Your discretion still matters. Absolutely, absolutely. It has to. All right, well, at the time, I don't want to give you the last word kind of, as you look forward to 2017, we're already, I can't believe we're a quarter of the way through, what are some of your top priorities that you're working on, some new exciting things that you can share? I think one of the things that we're very proud of is our work in the text analytic 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're mining that data for emerging trends. Sometimes you don't know what you don't know and it's the, those unknown unknowns that get you. They're the things that creep up in your data and you don't really realize it until they're at a big enough issue. And so this really is helping us and 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 on 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. 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. But it's a very exciting space for us and areas that we're actually able to scale about 100 times faster than we were past people. So that's awesome. We look forward to hearing more about that and thanks for taking a few minutes to stop by. I appreciate it. Thank you. All right, thank you both. With Peter Burris, I'm Jeff Frick. You're watching theCUBE from the IBM Chief Data Officer Strategy Summit, Spring 2017. Thanks for watching. We'll be back after the short break.