>> Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE, covering the 12th Annual MIT Chief Data Officer and Information Quality Symposium. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host Rebecca Knight along with my cohost Peter Burris. We have two guests on this segment. We have Cortnie Abercrombie, she is the founder of the nonprofit AI Truth, and Carl Gerber, who is the managing partner at Global Data Analytics Leaders. Thanks so much for coming on theCUBE Cortnie and Carl. >> Thank you. >> Thank you. >> So I want to start by just having you introduce yourselves to our viewers, what you do. So tell us a little bit about AI Truth, Cortnie. >> So this was born out of a passion. As I, the last gig I had at IBM, everybody knows me for chief data officer and what I did with that, but the more recent role that I had was developing custom offerings for Fortune 500 in the AI solutions area, so as I would go meet and see different clients, and talk with them and start to look at different processes for how you implement AI solutions, it became very clear that not everybody is attuned, just because they're the ones funding the project or even initiating the purpose of the project, the business leaders don't necessarily know how these things work or run or what can go wrong with them. And on the flip side of that, we have very ambitious up-and-comer-type data scientists who are just trying to fulfill the mission, you know, the talent at hand, and they get really swept up in it. To the point where you can even see that data's getting bartered back and forth with any real governance over it or policies in place to say, "Hey, is that right? Should we have gotten that kind of information?" Which leads us into things like the creepy factor. Like, you know target (laughs) and some of these cases that are well-known. And so, as I saw some of these mistakes happening that were costing brand reputation, our return on investment, or possibly even creating opportunities for risk for the companies and for the business leaders, I felt like someone's got to take one for the team here and go out and start educating people on how this stuff actually works, what the issues can be and how to prevent those issues, and then also what do you do when things do go wrong, how do you fix it? So that's the mission of AI Truth and I have a book. Yes, power to the people, but you know really my main concern was concerned individuals, because I think we've all been affected when we've sent and email and all of a sudden we get a weird ad, and we're like, "Hey, what, they should not, is somebody reading my email?" You know, and we feel this, just, offense-- >> And the answer is yes. >> Yes, and they are, they are. So I mean, we, but we need to know because the only way we can empower ourselves to do something is to actually know how it works. So, that's what my missions is to try and do. So, for the concerned individuals out there, I am writing a book to kind of encapsulate all the experiences that I had so people know where to look and what they can actually do, because you'll be less fearful if you know, "Hey, I can download DuckDuckGo for my browser, or my search engine I mean, and Epic for my browser, and some private, you know, private offerings instead of the typical free offerings. There's not an answer for Facebook yet though. >> So, (laughs) we'll get there. Carl, tell us a little bit about Global Data Analytics Leaders. >> So, I launched Analytics Leaders and CDO Coach after a long career in corporate America. I started building an executive information system when I was in the military for a four-star commander, and I've really done a lot in data analytics throughout my career. Most recently, starting a CDO function at two large multinational companies in leading global transformation programs. And, what I've experienced is even though the industries may vary a little bit, the challenges are the same and the patterns of behavior are the same, both the good and bad behavior, bad habits around the data. And, through the course of my career, I've developed these frameworks and playbooks and just ways to get a repeatable outcome and bring these new technologies like machine learning to bear to really overcome the challenges that I've seen. And what I've seen is a lot of the current thinking is we're solving these data management problems manually. You know, we all hear the complaints about the people who are analysts and data scientists spending 70, 80% of their time being a data gatherer and not really generating insight from the data itself and making it actionable. Well, that's why we have computer systems, right? But that large-scale technology in automation hasn't really served us well, because we think in silos, right? We fund these projects based on departments and divisions. We acquire companies through mergers and acquisitions. And the CDO role has emerged because we need to think about this, all the data that an enterprise uses, horizontally. And with that, I bring a high degree of automation, things like machine learning, to solve those problems. So, I'm now bottling that and advising my clients. And at the same time, the CDO role is where the CIO role was 20 years ago. We're really in it's infancy, and so you see companies define it differently, have different expectations. People are filling the roles that may have not done this before, and so I provide the coaching services there. It's like a professional golfer who has a swing coach. So I come in and I help the data executives with upping their game. >> Well, it's interesting, I actually said the CIO role 40 years ago. But, here's why. If we look back in the 1970s, hardcore financial systems were made possible by the technology which allowed us to run businesses like a portfolio: Jack Welch, the GE model. That was not possible if you didn't have a common asset management system, if you didn't have a common cached management system, etc. And so, when we started creating those common systems, we needed someone that could describe how that shared asset was going to be used within the organization. And we went from the DP manager in HR, the DP manager within finance, to the CIO. And in many respects, we're doing the same thing, right? We're talking about data in a lot of different places and now the business is saying, "We can bring this data together in new and interesting ways into more a shared asset, and we need someone that can help administer that process, and you know, navigate between different groups and different needs and whatnot." Is that kind of what you guys are seeing? >> Oh yeah. >> Yeah. >> Well you know once I get to talking (laughs). For me, I can going right back to the newer technologies like AI and IOT that are coming from externally into your organization, and then also the fact that we're seeing bartering at an unprec... of data at an unprecedented level before. And yet, what the chief data officer role originally did was look at data internally, and structured data mostly. But now, we're asking them to step out of their comfort zone and start looking at all these unknown, niche data broker firms that may or may not be ethical in how they're... I mean, I... look I tell people, "If you hear the word scrape, you run." No scraping, we don't want scraped data, no, no, no (laugh). But I mean, but that's what we're talking about-- >> Well, what do you mean by scraped data, 'cause that's important? >> Well, this is a well-known data science practice. And it's not that... nobody's being malicious here, nobody's trying to have a malintent, but I think it's just data scientists are just scruffy, they roll up their sleeves and they get data however they can. And so, the practice emerged. Look, they're built off of open-source software and everything's free, right, for them, for the most part? So they just start reading in screens and things that are available that you could see, they can optical character read it in, or they can do it however without having to have a subscription to any of that data, without having to have permission to any of that data. It's, "I can see it, so it's mine." But you know, that doesn't work in candy stores. We can't just go, or jewelry stores in my case, I mean, you can't just say, "I like that diamond earring, or whatever, I'm just going to take it because I can see it." (laughs) So, I mean, yeah we got to... that's scraping though. >> And the implications of that are suddenly now you've got a great new business initiative and somebody finds out that you used their private data in that initiative, and now they've got a claim on that asset. >> Right. And this is where things start to get super hairy, and you just want to make sure that you're being on the up-and-up with your data practices and you data ethics, because, in my opinion, 90% of what's gone wrong in AI or the fear factor of AI is that your privacy's getting violated and then you're labeled with data that you may or may not know even exists half the time. I mean. >> So, what's the answer? I mean as you were talking about these data scientists are scrappy, scruffy, roll-up-your-sleeves kind of people, and they are coming up with new ideas, new innovations that sometimes are good-- >> Oh yes, they are. >> So what, so what is the answer? Is this this code of ethics? Is it a... sort of similar to a Hippocratic Oath? I mean how would you, what do you think? >> So, it's a multidimensional problem. Cortnie and I were talking earlier that you have to have more transparency into the models you're creating, and that means a significant validation process. And that's where the chief data officer partners with folks in risk and other areas and the data science team around getting more transparency and visibility into what's the data that's feeding into it? Is it really the authoritative data of the company? And as Cortnie points out, do we even have the rights to that data that's feeding our models? And so, by bringing that transparency and a little more validation before you actually start making key, bet-the-business decisions on the outcomes of these models, you need to look at how you're vetting them. >> And the vetting process is part technology, part culture, part process, it goes back to that people process technology trying. >> Yeah, absolutely, know where your data came from. Why are you doing this model? What are you doing to do with the outcomes? Are you actually going to do something with it or are you going to ignore it? Under what conditions will you empower a decision-maker to use the information that is the output of the model? A lot of these things, you have to think through when you want to operationalize it. It's not just, "I'm going to go get a bunch of data wherever I can, I put a model together. Here, don't you like the results?" >> But this is Silicon Valley way, right? An MVP for everything and you just let it run until... you can't. >> That's a great point Cortnie (laughs) I've always believed, and I want to test this with you, we talk about people process technology about information, we never talk about people process technology and information of information. There's a manner of respects what we're talking about is making explicit the information about... information, the metadata, and how we manage that and how we treat that, and how we defuse that, and how we turn that, the metadata itself, into models to try to govern and guide utilization of this. That's especially important in AI world, isn't it? >> I start with this. For me, it's simple, I mean, but everything he said was true. But, I try to keep it to this: it's about free will. If I said you can do that with my data, to me it's always my data. I don't care if it's on Facebook, I don't care where it is and I don't care if it's free or not, it's still my data. Even if it's X23andMe, or 23andMe, sorry, and they've taken the swab, or whether it's Facebook or I did a google search, I don't care, it's still my data. So if you ask me if it's okay to do a certain type of thing, then maybe I will consent to that. But I should at least be given an option. And no, be given the transparency. So it's all about free will. So in my mind, as long as you're always providing some sort of free will (laughs), the ability for me to having a decision to say, "Yes, I want to participate in that," or, "Yes, you can label me as whatever label I'm getting, Trump or a pro-Hillary or Obam-whatever, name whatever issue of the day is," then I'm okay with that as long as I get a choice. >> Let's go back to it, I want to build on that if I can, because, and then I want to ask you a question about it Carl, the issue of free will presupposes that both sides know exactly what's going into the data. So for example, if I have a medical procedure, I can sit down on that form and I can say, "Whatever happens is my responsibility." But if bad things happen because of malfeasance, guess what? That piece of paper's worthless and I can sue. Because the doctor and the medical provider is supposed to know more about what's going on than I do. >> Right. >> Does the same thing exist? You talked earlier about governance and some of the culture imperatives and transparency, doesn't that same thing exist? And I'm going to ask you a question: is that part of your nonprofit is to try to raise the bar for everybody? But doesn't that same notion exist, that at the end of the day, you don't... You do have information asymmetries, both sides don't know how the data's being used because of the nature of data? >> Right. That's why you're seeing the emergence of all these data privacy laws. And so what I'm advising executives and the board and my clients is we need to step back and think bigger about this. We need to think about as not just GDPR, the European scope, it's global data privacy. And if we look at the motivation, why are we doing this? Are we doing it just because we have to be regulatory-compliant 'cause there's a law in the books, or should we reframe it and say, "This is really about the user experience, the customer experience." This is a touchpoint that my customers have with my company. How transparent should I be with what data I have about you, how I'm using it, how I'm sharing it, and is there a way that I can turn this into a positive instead of it's just, "I'm doing this because I have to for regulatory-compliance." And so, I believe if you really examine the motivation and look at it from more of the carrot and less of the stick, you're going to find that you're more motivated to do it, you're going to be more transparent with your customers, and you're going to share, and you're ultimately going to protect that data more closely because you want to build that trust with your customers. And then lastly, let's face it, this is the data we want to analyze, right? This is the authenticated data we want to give to the data scientists, so I just flip that whole thing on its head. We do for these reasons and we increase the transparency and trust. >> So Cortnie, let me bring it back to you. >> Okay. >> That presupposes, again, an up-leveling of knowledge about data privacy not just for the executive but also for the consumer. How are you going to do that? >> Personally, I'm going to come back to free will again, and I'm also going to add: harm impacts. We need to start thinking impact assessments instead of governance, quite frankly. We need to start looking at if I, you know, start using a FICO score as a proxy for another piece of information, like a crime record in a certain district of whatever, as a way to understand how responsible you are and whether or not your car is going to get broken into, and now you have to pay more. Well, you're... if you always use a FICO score, for example, as a proxy for responsibility which, let's face it, once a data scientist latches onto something, they share it with everybody 'cause that's how they are, right? They love that and I love that about them, quite frankly. But, what I don't like is it propagates, and then before you know it, the people who are of lesser financial means, it's getting propagated because now they're going to be... Every AI pricing model is going to use FICO score as a-- >> And they're priced out of the market. >> And they're priced out of the market and how is that fair? And there's a whole group, I think you know about the Fairness Accountability Transparency group that, you know, kind of watch dogs this stuff. But I think business leaders as a whole don't really think through to that level like, "If I do this, then this this and this could incur--" >> So what would be the one thing you could say if, corporate America's listening. >> Let's do impact. Let's do impact assessments. If you're going to cost someone their livelihood, or you're going to cost them thousands of dollars, then let's put more scrutiny, let's put more government validation. To your point, let's put some... 'cause not everything needs the nth level. Like, if I present you with a blue sweater instead of a red sweater on google or whatever, (laughs) You know, that's not going to harm you. But it will harm you if I give you a teacher assessment that's based on something that you have no control over, and now you're fired because you've been laid off 'cause your rating was bad. >> This is a great conversation. Let me... Let me add something different, 'cause... Or say it a different way, and tell me if you agree. In many respects, it's: Does this practice increase inclusion or does this practice decrease inclusion? This is not some goofy, social thing, this is: Are you making your market bigger or are you making your market smaller? Because the last thing you want is that the participation by people ends with: You can't play because of some algorithmic response we had. So maybe the question of inclusion becomes a key issue. Would you agree with that? >> I do agree with it, and I still think there's levels even to inclusion. >> Of course. >> Like, you know, being a part of the blue sweater club versus the (laughs) versus, "I don't want to be a convict," you know, suddenly because of some record you found, or association with someone else. And let's just face it, a lot of these algorithmic models do do these kinds of things where they... They use n+1, you know, a lot... you know what I'm saying. And so you're associated naturally with the next person closest to you, and that's not always the right thing to do, right? So, in some ways, and so I'm positing just little bit of a new idea here, you're creating some policies, whether you're being, and we were just talking about this, but whether you're being implicit about them or explicit, more likely you're being implicit because you're just you're summarily deciding. Well, okay, I have just decided in the credit score example, that if you don't have a good credit threshold... But where in your policies and your corporate policy did it ever say that people of lesser financial means should be excluded from being able to have good car insurance for... 'cause now, the same goes with like Facebook. Some people feel like they're going to have to opt of of life, I mean, if they don't-- >> (laughs) Opt out of life. >> I mean like, seriously, when you think about grandparents who are excluded, you know, out in whatever Timbuktu place they live, and all their families are somewhere else, and the only way that they get to see is, you know, on Facebook. >> Go back to the issue you raised earlier about "Somebody read my email," I can tell you, as a person with a couple of more elderly grandparents, they inadvertently shared some information with me on Facebook about a health condition that they had. You know how grotesque the response of Facebook was to that? And, it affected me to because they had my name in it. They didn't know any better. >> Sometimes there's a stigma. Sometimes things become a stigma as well. There's an emotional response. When I put the article out about why I left IBM to start this new AI Truth nonprofit, the responses I got back that were so immediate were emotional responses about how this stuff affects people. That they're scared of what this means. Can people come after my kids or my grandkids? And if you think about how genetic information can get used, you're not just hosing yourself. I mean, breast cancer genes, I believe, aren't they, like... They run through families, so, I-- >> And they're pretty well-understood. >> If someone swabs my, and uses it and swaps it with other data, you know, people, all of a sudden, not just me is affected, but my whole entire lineage, I mean... It's hard to think of that, but... it's true (laughs). >> These are real life and death... these are-- >> Not just today, but for the future. And in many respects, it's that notion of inclusion... Going back to it, now I'm making something up, but not entirely, but going back to some of the stuff that you were talking about, Carl, the decisions we make about data today, we want to ensure that we know that there's value in the options for how we use that data in the future. So, the issue of inclusion is not just about people, but it's also about other activities, or other things that we might be able to do with data because of the nature of data. I think we always have to have an options approach to thinking about... as we make data decisions. Would you agree with that? Yes, because you know, data's not absolute. So, you can measure something and you can look at the data quality, you can look at the inputs to a model, whatever, but you still have to have that human element of, "Are you we doing the right thing?" You know, the data should guide us in our decisions, but I don't think it's ever an absolute. It's a range of options, and we chose this options for this reason. >> Right, so are we doing the right thing and do no harm too? Carl, Cortnie, we could talk all day, this has been a really fun conversation. >> Oh yeah, and we have. (laughter) >> But we're out of time. I'm Rebecca Knight for Peter Burris, we will have more from MIT CDOIQ in just a little bit. (upbeat music)

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

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

>> 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.