>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, its theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer event in San Francisco, Spring, 2018. About 100 people, predominantly practitioners, which is a pretty unique event. Not a lot of vendors, a couple of them around, but really a lot of people that are out in the wild doing this work. We're really excited to have a return guest. We last saw him at Spark Summit East 2017. Can you believe I keep all these shows straight? I do not. Alfred Essa, he is the VP, Analytics and R&D at McGraw-Hill Education. Alfred, great to see you again. >> Great being here, thank you. >> Absolutely, so last time we were talking it was Spark Summit, it was all about data in motion and data on the fly, and real-time analytics. You talked a lot about trying to apply these types of new-edge technologies and cutting-edge things to actually education. What a concept, to use artificial intelligence, a machine learning for people learning. Give us a quick update on that journey, how's it been progressing? >> Yeah, the journey progresses. We recently have a new CEO come on board, started two weeks ago. Nana Banerjee, very interesting background. PhD in mathematics and his area of expertise is Data Analytics. It just confirms the direction of McGraw-Hill Education that our future is deeply embedded in data and analytics. >> Right. It's funny, there's a often quoted kind of fact that if somebody came from a time machine from, let's just pick 1849, here in San Francisco, everything would look different except for Market Street and the schools. The way we get around is different. >> Right. >> The things we do to earn a living are different. The way we get around is different, but the schools are just slow to change. Education, ironically, has been slow to adopt new technology. You guys are trying to really change that paradigm and bring the best and latest in cutting edge to help people learn better. Why do you think it's taken education so long and must just see nothing but opportunity ahead for you. >> Yeah, I think the... It was sort of a paradox in the 70s and 80s when it came to IT. I think we have something similar going on. Economists noticed that we were investing lots and lots of money, billions of dollars, in information technology, but there were no productivity gains. So this was somewhat of a paradox. When, and why are we not seeing productivity gains based on those investments? It turned out that the productivity gains did appear and trail, and it was because just investment in technology in itself is not sufficient. You have to also have business process transformation. >> Jeff Frick: Right. >> So I think what we're seeing is, we are at that cusp where people recognize that technology can make a difference, but it's not technology alone. Faculty have to teach differently, students have to understand what they need to do. It's a similar business transformation in education that I think we're starting to see now occur. >> Yeah it's great, 'cause I think the old way is clearly not the way for the way forward. That's, I think, pretty clear. Let's dig into some of these topics, 'cause you're a super smart guy. One thing's talk about is this algorithmic transparency. A lot of stuff in the news going on, of course we have all the stuff with self-driving cars where there's these black box machine learning algorithms, and artificial intelligence, or augmented intelligence, bunch of stuff goes in and out pops either a chihuahua or a blueberry muffin. Sometimes it's hard to tell the difference. Really, it's important to open up the black box. To open up so you can at least explain to some level of, what was the method that took these inputs and derived this outpout. People don't necessarily want to open up the black box, so kind of what is the state that you're seeing? >> Yeah, so I think this is an area where not only is it necessary that we have algorithmic transparency, but I think those companies and organizations that are transparent, I think that will become a competitive advantage. That's how we view algorithms. Specifically, I think in the world of machine learning and artificial intelligence, there's skepticism, and that skepticism is justified. What are these machines? They're making decisions, making judgments. Just because it's a machine, doesn't mean it can't be biased. We know it can be. >> Right, right. >> I think there are techniques. For example, in the case of machine learning, what the machines learns, it learns the algorithm, and those rules are embedded in parameters. I sort of think of it as gears in the black box, or in the box. >> Jeff Frick: Right. >> What we should be able to do is allow our customers, academic researchers, users, to understand at whatever level they need to understand and want to understand >> Right. >> What the gears do and how they work. >> Jeff Frick: Right. >> Fundamental, I think for us, is we believe that the smarter our customers are and the smarter our users are, and one of the ways in which they can become smarter is understanding how these algorithms work. >> Jeff Frick: Right. >> We think that that will allow us to gain a greater market share. So what we see is that our customers are becoming smarter. They're asking more questions and I think this is just the beginning. >> Jeff Frick: Right. >> We definitely see this as an area that we want to distinguish ourselves. >> So how do you draw lines, right? Because there's a lot of big science underneath those algorithms. To different degrees, some of it might be relatively easy to explain as a simple formula, other stuff maybe is going into some crazy, statistical process that most layman, or business, or stakeholders may or may not understand. Is there a way you slice it? Is there kind of wars of magnitude in how much you expose, and the way you expose within that box? >> Yeah, I think there is a tension. The tension traditionally, I think organizations think of algorithms like they think of everything else, as intellectual property. We want to lock down our intellectual property, we don't want to expose that to our competitors. I think... I think that's... We do need to have intellectual property, however, I think many organizations get locked into a mental model, which I don't think is just the right one. I think we can, and we want our customers to understand how our algorithm works. We also collaborate quite a bit with academic researchers. We want validation from the academic research community that yeah, the stuff that you're building is in fact based on learning science. That it has warrant. That when you make claims that it works, yes, we can validate that. Now, where I think... Based on the research that we do, things that we publish, our collaboration with researchers, we are exposing and letting the world know how we do things. At the same time, it's very, very difficult to build an engineer, an architect, scalable solutions that implement those algorithms for millions of users. That's not trivial. >> Right, right, right. >> Even if we give away quite a bit of our secret sauce, it's not easy to implement that. >> Jeff Frick: Right. >> At the same time, I believe and we believe, that it's good to be chased by our competition. We're just going to go faster. Being more open also creates excitement and an ecosystem around our products and solutions, and it just makes us go faster. >> Right, which gives to another transition point, which would you talk about kind of the old mental model of closed IP systems, and we're seeing that just get crushed with open source. Not only open source movements around specific applications, and like, we saw you at Spark Summit, which is an open source project. Even within what you would think for sure has got to be core IP, like Facebook opening up their hardware spec for their data centers, again. I think what's interesting, 'cause you said the mental model. I love that because the ethos of open source, by rule, is that all the smartest people are not inside your four walls. >> Exactly. >> There's more of them outside the four walls regardless of how big your four walls are, so it's more of a significant mental shift to embrace, adopt, and engage that community from a much bigger accumulative brain power than trying to just trying to hire the smartest, and keep it all inside. How is that impacting your world, how's that impacting education, how can you bring that power to bear within your products? >> Yeah, I think... You were in effect quoting, I think it was Bill Joy saying, one of the founders of Sun Microsystems, they're always, you have smart people in your organization, there are always more smarter people outside your organization, right? How can we entice, lure, and collaborate with the best and the brightest? One of the ways we're doing that is around analytics, and data, and learning science. We've put together a advisory board of learning science researchers. These are the best and brightest learning science researcher, data scientists, learning scientists, they're on our advisory board and they help and set, give us guidance on our research portfolio. That research portfolio is, it's not blue sky research, we're on Google and Facebook, but it's very much applied research. We try to take the no-knowns in learning science and we go through a very quick iterative, innovative pipeline where we do research, move a subset of those to product validation, and then another subset of that to product development. This is under the guidance, and advice, and collaboration with the academic research community. >> Right, right. You guys are at an interesting spot, because people learn one way, and you've mentioned a couple times this interview, using good learning science is the way that people learn. Machines learn a completely different way because of the way they're built and what they do well, and what they don't do so well. Again, I joked before about the chihuahua and the blueberry muffin, which is still one of my favorite pictures, if you haven't seen it, go find it on the internet. You'll laugh and smile I promise. You guys are really trying to bring together the latter to really help the former. Where do those things intersect, where do they clash, how do you meld those two methodologies together? >> Yeah, it's a very interesting question. I think where they do overlap quite a bit is... in many ways machines learn the way we learn. What do I mean by that? Machine learning and deep learning, the way machines learn is... By making errors. There's something, a technical concept in machine learning called a loss function, or a cost function. It's basically the difference between your predicted output and ground truth, and then there's some sort of optimizer that says "Okay, you didn't quite get it right. "Try again." Make this adjustment. >> Get a little closer. >> That's how machines learn, they're making lots and lots of errors, and there's something behind the scenes called the optimizer, which is giving the machine feedback. That's how humans learn. It's by making errors and getting lots and lots of feedback. That's one of the things that's been absent in traditional schooling. You have a lecture mode, and then a test. >> Jeff Frick: Right. >> So what we're trying to do is incorporate what's called formative assessment, this is just feedback. Make errors, practice. You're not going to learn something, especially something that's complicated, the first time. You need to practice, practice, practice. Need lots and lots of feedback. That's very much how we learn and how machines learn. Now, the differences are, technologically and state of knowledge, machines can now do many things really well but there's still some things and many things, that humans are really good at. What we're trying to do is not have machines replace humans, but have augmented intelligence. Unify things that machines can do really well, bring that to bear in the case of learning, also insights that we provide. Instructors, advisors. I think this is the great promise now of combining the best of machine intelligence and human intelligence. >> Right, which is great. We had Gary Kasparov on and it comes up time and time again. The machine is not better than a person, but a machine and a person together are better than a person or a machine to really add that context. >> Yeah, and that dynamics of, how do you set up the context so that both are working in tandem in the combination. >> Right, right. Alright Alfred, I think we'll leave it there 'cause I think there's not a better lesson that we could extract from our time together. I thank you for taking a few minutes out of your day, and great to catch up again. >> Thank you very much. >> Alright, he's Alfred, I'm Jeff. You're watching theCUBE from the Corinium Chief Analytics Officer event in downtown San Francisco. Thanks for watching. (energetic music)

(upbeat music) >> From the Corinium Chief Analytics Officer Conference, Spring, San Francisco. It's theCUBE! (computerized thrum) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer event in Spring 2018. Really, a ton of practitioners for such a very small event. Super, super intimate, super, super customer stories and practitioners, so we're really excited to have our next guest. She's Kirtida Parikh, she's the Head of Enterprise Business Analytics for Silicon Valley Bank. Welcome. >> Thank you. Good to be here. >> So, what do you think of the show? It's kind of an interesting little event. >> I personally do think that they do an amazing job of organizing this particular event, and out of all the events throughout the year I try to choose and come to this event. >> Right, very good. So, you were just on a panel. >> Kirtida: Yes. >> With a bunch of practitioners. For the folks that didn't attend the panel, what were some of the interesting things that came out of it? Some surprises? >> I think one of the main surprises that I had as one of the panel members is the audience, and the audience actually did say that not 99% of the people have issues working with other virtual teams within the bank, or within their own organization. And many people have tried to figure out how to work together, and that was a very pleasant surprise to me. >> And they're working better together. >> Absolutely. >> From what you said before we turned on the cameras. >> It's a higher productivity when you try to work things out together. >> What's going to happen to shadow IT if the IT department is suddenly easier to work with? >> (laughing) Well, I don't think it is either the department or a person that is difficult to work with. It's, I think, more of a clash of cultures between the two groups. And IT does need, for their own right reasons, to have a process in place and go by the rules so that they can keep the company safe from compliance and regulation perspective. >> Right. >> Whereas analytics, by nature, needs to be creative and has to focus on time to market. And they have to be agile and work really fast enough, and so they can't have the bandwidth to follow the process. So it's more of a clash of two cultures. >> Jeff: Right. >> And I think we need to open up the boundaries and think about virtual efforts to be able to get something done. >> That's interesting, because we always talk about people, process, and tech. And they're called "tech conferences," they're not called "process tech conferences." >> Yeah. >> And so there's a lot of focus on the technology and the new shiny object. >> Mm-hmm (affirmative). >> Whether it's Hadoop, or big data, or Spark, or, you know, all this fun stuff. But as you just said, really, the harder part is the people and the process. >> People. >> And as you just said, culture really is derived from the processes and the responsibilities that you have under your jurisdiction, I guess, so. >> Absolutely. And I personally feel technology is not an end by itself. It's a means to an end. >> Right, right. >> And so the success of a company is how you embrace. How people embrace technology leads to results. >> Right. >> It's neither technology nor people on their own, it's how they embrace technology is what leads to success. >> So I wonder if you can share some insight from your experience at Silicon Valley Bank? You're the head of the analytics group. You know, banks are interesting to me because banks have been data-driven forever, right? >> They have to be. >> There isn't really any money in a room somewhere. It's numbers on a page and numbers on a database. >> Kirtida: Mm-hmm (affirmative). >> And all your products are pretty digital, so, when you start to bring more advanced analytics and you try to change the culture a little bit and run it through the, overused, "digital transformation." What are some of the things you're looking at? How are they transformational? What's kind of the acceptance in the broader team, as you said, when there can be some culture clash, and you have regulation and you're a regulated industry and there's real issues and barriers that you have to overcome? >> Right. So, barriers are always there in any organization, in any industry, particularly when you are introducing a totally new way of making decisions. And when the company is very successful based on making intuition-based decisions, it's hard for you to sell the idea that, no, I can give you information, and that will expedite your decision-making process. So, I think when I joined the bank, I didn't realize, but 99% of my job was to be the change agent. (laughing) >> (laughing) Not an easy job. >> And a storyteller. >> Right, right. >> Because unless you tell the story and sell the idea, you are not able to bring the change. >> Jeff: Right. >> So, yes, there are barriers, and there are always going to be barriers. But I personally like challenges, so I embrace the challenges and try to overcome. So what I ended up doing is, I started thinking about where can I have IT add value, and where are the opportunities where I can value them? So instead of me going to the business and talking to them about what we can do together, I brought that team member along with me. So that visibility and transparency made them feel valued, and they were more than willing to partner with me, and so that changed the landscape to work with IT. But on the other hand, from the business side, I personally think that unless you have one or two examples, and one of my first examples was a business process. And it used to take a number of hours, and I reduced it to leave it only 10% of that time. And they said, oh, wow, that does make sense. What can we do more? Can we partner on this? So initially, first quarter, I had 20 questions and requests, and the second quarter... First whole year we had only twenty questions and requests, and the following quarter we had 200 of them. >> Wow. So when you're looking for an opportunity to apply your skills, your knowledge to bring some change to your organization, how much of it is you kind of searching for inefficiencies, say in the internal business process, versus maybe a business stakeholder saying, wow, you know, if we could only do X. Or I have this problem, can you help me find the root cause? Silicon Valley Bank's such a unique institution, because it's got a couple of segments that it really focuses on. >> Kirtida: Mm-hmm (affirmative). >> Obviously in tech, a little-known wine business. I think you guys do a lot of investing there. >> Yes. >> Because tech guys like to open wineries. >> Tech banking. >> (laughing) So you've got some really small specialty segments. So how did you find some of those early opportunities? >> You see, when you do something and it's successful, it's a two-edged sword. Things keep coming, and the demand grows exponentially fast, it's an exponential growth rate. So what we had to do was really focus on what matters the most, and that came only from two-way communication with the business as well as with the executive team. So if the executive team, we realize that this is the revenue-generating opportunities, here is where we can make a difference, we focus on it and show them the value. Or, if it is a process that really needed some attention, and we could benefit from cost effectiveness, so there was kind of an RY framework where we focus on it. But, to be very honest, we didn't have to look far to look for opportunities, just because revenue is the main focus for business as well as executives. >> Right, right, right. >> So it was a two-way communication that helped us really identify, but I didn't have to hunt for opportunities because, you know, that's where your experience come into play. >> Right, right. So, I'm just curious on the revenue side, the question always comes up, how do I get started, how do we get started, how do we get early wins to build momentum in my company? So was it customer retention, was it cross-selling? I mean, what were some of the things that you saw that were revenue-tied, and everybody likes being tied to revenue, where you thought you could have some success? >> So, my idea of really making a difference is very simple. What does the business focus on? How does a bank operate? They have to get new clients, and increase the size of the cake, or the size of the clientele that they have. So, acquisition is one area. >> Jeff: Okay. >> The second is, once you have them, how can you have them deepen their relationship with you so that the switching cost to another bank is higher? >> Jeff: Right. >> And the third is, once they're with you, you also want to retain them in many different ways by increasing client satisfaction. And then, of course, cost effectiveness. How do you plan your staffing needs and capacity? So, I started in each of those areas at least taking up one or two business questions and showing them the value. And now it's covering all those spectrum of businesses. >> That's great. So now you've got more inbound opportunities for places to apply your analytics than you probably have people to apply them. (laughing) >> (laughing) Yes. That's a good problem to have. >> That's a good problem to have. Well, I'd just love to get your take, too, on kind of the higher level view of the democratization of the data. Of the data itself, of the tools to operate the data, and then, of course, hopefully if you've democratized the access and the tools, hopefully when somebody finds something, they actually have the power to implement it. So how have you seen that environment change, not specifically at Silicon Valley Bank, but generically over the last couple years within your career? >> Well, I personally think that, in my career, in different organizations, democratization is a necessity. It's no longer a topic of discussion. It is something you have to do. Because analytics in general is an enabler community, and you can have as many enablers as you have the people who are users. So, how do you really create analytic center of excellence by giving them the ropes and tools to fish for themselves, or to find their own insights and create their own stories. >> Jeff: Right. >> So what I did, and this worked really well, is create a virtual team of analytic center of excellence where it's not only my team members, but it's some other pockets of analytics teams, but at the same time, the users themselves. >> Jeff: Right. >> And they become the advocates of what you do, and as far as tools are concerned, you know, we used to have an era where you have IT control tools to be able to democratize and give the insights, and now it is user-driven tools. So we did move from one end of the spectrum to the other end of the spectrum, so that it becomes easy for the user to actually grasp the insights. >> Right, right. And still maintain control and governance and all that kind of stuff, yeah. >> Oh, yeah. Security, information security control is a big one, and we can maintain that. >> Right, right. >> And as far as the governance and the data, I mean, they're not pulling their own definitions and other things. It's based off of information foundation, which is solid and scalable. >> Which is solid. Okay, so, going to give you the last word. You've said the word "story" at least four times. >> Uh-huh. (laughing) >> Maybe more since we sat down, we'll have to check the transcript. I wonder if you could expand a little bit on how valuable storytelling is in this whole process. I think it gets left off a lot, right? >> Mm-hmm (affirmative). >> People want to focus on the math and focus on the technology, and focus on the wiz-bang and the flashing lights and the datacenter, but you keep saying "story." Why do you keep saying story? Why is story so important? >> You have multiple stakeholders. First thing is the executive team, they do not have the time. I mean, they are focusing on so many different aspects that they don't have the time enough for anybody to go through the whole textbook, or whole chapter. So if you can tell them story in 30 seconds in an elevator, or three minutes in a hallway, and then request for 30 minutes, you are bound to get some time with them. And in that short time, would you rather show them the value that you can bring to the table, or would you show them how the sausage is being made? >> Jeff: Right. >> And so that's where one type of storytelling is important, to sell the idea. The second is the working team, who we are working with. And I have seen that unless you tell your story and sell the story, you can't get their buy-in, and the virtual team effort that I was talking about fails miserably. So that's another area where you need to tell the story. >> Jeff: Right. >> And the third is, once you have an analytic product, then how do you get adopters? So to tell the adopter what is in there for them is a storytelling too. >> Right, right. Small detail. >> Yeah. >> Actually getting people to use it for their benefit. >> (laughing) >> All right, well I think this is so important, because as you mentioned a number of times, it's about people, and people working together, teams working together in this collaborative effort to make it happen. As somebody else said, it's a team sport. >> And you know, the interesting that I have seen is now that I come to these conferences, there are five people, at least, in different five companies, they said they've hired a journalist on their team because they realized the storytelling is so important. >> Jeff: Really? >> Yeah, so the hybrid function analytics, we say, requires data engineers, data scientists, statisticians, communicators, storyweavers and tellers, which is a journalist, and then a change agent and project manager. >> That's why they bring theCUBE. >> (laughing) >> Trying to tell the story. So, thank you for sharing your story. >> Thank you so much. >> We really appreciate the time. All right. >> Kirtida: Take care. >> You're watching theCUBE from the Corinium Chief Analytics Officer Summit in San Francisco. Thanks for watching. (computerized music)

>> Announcer: From the Corinium Chief Analytics Officer Conference Spring, San Francisco It's theCUBE. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring Event about a hundred CAO's as opposed to CDO's talking about big data, transformation and analytics and the role of analytics and a lot of practitioners are really excited to have our next guest. He's up from Mexico City, it's Jose Murillo. He's the chief analytics officer from Banorte. Jose, great to see you. >> Thank you for having me, Jeff. >> Absolutely, so for people that aren't familiar with Banorte give us a quick overview. >> Banorte's the second largest financial group in Mexico. We, for the last, during the last three years were able to leapfrog city bank. >> Congratulations, and as we were talking before we turned the cameras on, you and your project had a big part of that. So before we get in it, you are a chief analytics officer. How did you come in, what's the reporting structure, how do you work within the broader spectrum of the bank? >> Well I moved to Banorte like about five years ago from, I was working at the central bank where I spent about 10 years in the MPC, the Monitor Policy Committee, and I was invited by initially by the president of the board and when the new chief operating officer was named he invited me to, to lead a new analytics business unit that he wanted to create. And that's the way that I arrived there. >> Okay so you report in to the COO. >> He's the COO/CFO, so he's not only a very smart guy but a very powerful guy running the organization. >> And does the CIO also report to him? >> The CIO, the CDO, the CMO report to him. >> Okay so you have a CDO as well Chief Data Officer. >> We have a CDO who I work very close with him. >> We could go for a long time I might not let you leave for lunch. So I'm just curious on the relationship between the CDO and the CAO, the data officer and the analytics officer. We often hear one or the other, it's very seldom that I've heard both. So how do you guys divide and conquer your responsibilities? How do you parse that out? >> I guess he provides the foundation that we need to find analytics projects that are going to transform the financial group and he has been a very good partner in providing the data that we need and basically what we do as the CAO we find those opportunities to improve the efficiency, to bring the customer to the center, and be able to deliver value to our stakeholders. >> Right, so he's really kind of giving you the infrastructure if you will, of making that data available, getting it to you from all various sources, et cetera, that then you can use for your analytics magic on top. >> Exactly >> Okay, so that's very good, so when we sat down you said an exciting report has come out from, I believe it was HBR, about the tremendous ROI that you guys have realized. So you tell the story better than I, what did they find in your recent article? >> Well in the recent article from the Harvard Business Review is how Banorte has made its analytics business unit pay off. And what we have found in the past two and a half years is we've been able to deliver massive value and by now we have surpassed a billion dollars in net income creation. From analytics projects made on cost saving strategies and revenue generating projects. >> So you paid for yourself just barely >> Yeah. >> No I mean that's such a great story, just barely 'cause it's so it's so important. So as you said, that billion dollars have been realized both in cost savings but more importantly on incremental revenue and that's really the most important thing. >> Exactly >> So how are you measuring that ROI? >> So basically the way we measure it is on cost saving strategies that are related to a risk operational and financial cost. It's the contemporary news effect. And that can be audited. And on the other side, on revenue generating projects, the way we do it is we estimate the customer lifetime value, which is nothing else than the net present value of the relationship with our customers, so we need to estimate survival rates plus the depth of the relationship with our customers. >> So I just love, so you're doing all kinds of projects, you're measuring the value of the projects. What are some of the projects that had a high ROI that you would've never guessed that you guys applied some analytics to and said wow, terrific value relative to what we expected. >> Let me tell you about two types of projects. The first project that we started on was on cost of risk cutting strategies. And we delivered massive value and very quickly. So that helped us gain credibility. And the way we do it, we did it, is like to analyze a dicing of the data where we had excessive cost of risk. And in the first year, actually, that was the first quarter of Operations, we yielded about a 25% incremental value to the credit card business. And after that, we start to work with them and started the discovery data process. And from there, we were able to optimize analytically the cross cell process. And that's a project that has already a three year maturity. And by this time, we are able to sell, without having any bricks or mortars, about 25% of the credit cards sold by the financial group. If we were a territory within the financial group, we would be the largest one with 400 basis points lower on cost of risk, 30% more on activation rates. And it's no surprise that the acquisition cost is 30% less, vis-a-vis our most efficient channel. >> Right, I just want to keep digging down into this, Jose, there's a lot of this stuff to go. I mean, you've been issuing cards forever. So was it just a better way to score customers, was it a better way to avoid the big fraud customers, was it a better way to steal customers maybe from a competitor with a competitive rate that you can afford, I mean, what are some of the factors that allowed you to grow this business in such a big way? >> I guess it's something that has been improving during the first three years. The first thing is that we made like, a very simple cascade on seeing why we were not that efficient cross cell process. And we kind of fixed every part of it. Like on the income estimation models that we had, and we partner with the risk department to improve them. Up to the information that we had on our customers to contact them, and we partner with data governance to improve those. And finally, on the delivery process and all the engaging process with the customers. And it seemed that we were going to find something that was going to be more costly, but it was something that we had at the center of the customers so that it was more likely for them to go and pick up the card and we deliver it to their homes. And finally, that process was much more efficient and the gains that we had, we shared them with our customers. And after three years, we've done things with artificial intelligence to have much better scripts so that we are better able to serve our customers. We do a lot of experimentation, experimentation that we didn't do before. And we use some concepts from behavioral economics to try to explain much better the value proposition to our customers. >> So I just, I love this point, is that it was a bunch of small, it was optimizing lots of little steps and little pieces of the pie that added up to such a significant thing, it wasn't like this magic AI pixie dust. >> Initially, it as a big bang, and then it has been something incremental that has since, it's a project that at the end of the day, we own, and it's something that we are tracking. We are willing to put all the effort to have all the incremental efficiency within the process. >> So people, process, and technology, we talk about, those are the three pieces always to drive organizational change. And usually, the technology is the easy part, the hard part is the people and the process. So as you and your team have started to work with the various lines of businesses for all these different pieces. Promotional piece, customary attention piece, risk and governance piece, cross sale pice, how has their attitude towards your group changed over time as you've started to deliver insight and all this incremental deltas into their business. >> I guess you are hitting just on the spot. Building the models is the easy part. The hard part is to build the consensus around, to change a process that has run for 20 years, there's a lot of inertia. >> Right, right. >> And there are a lot of silos within organizations. So initially, I guess, the credibility that we gained initially helped us move faster. And at the end of the day, I think what happens is the way that we are set up is that the incentives are very well aligned within the different units that need to interact in the sense that we are a unit that is sponsored by the, corporately sponsored, and we make it easier for our partners to attain their goals. So that's, and they don't share the cost of us, so that helps. >> And those are the goals they already had. So you're basically helping them achieve their objectives that they already had better and more efficiently. >> Yeah, and you are pointing out correctly, it's the people, and besides the math, it's a highly, you could say diplomatic or political position in the sense that you need to have all the different partners and stakeholders aligned to change something that has been running for 20 years. >> Right, right. And i just love it, it's a ton of little marginal improvements across a wide variety of tough points, it's so impactful. So as you look forward now, is there another big bang out there, or do you just see kind of this constant march of incremental improvement, and, or are you just going to start getting into more different businesses or kind of different areas in the bank to apply the same process, where do you go next? >> Well, we started with the credit card business, but we moved toward the verticals within the financial group. From mortgages, auto loans, payroll loans, to we are working with the insurance company, the long term savings company. So we've increased the scope of the group. And we moved not only from cost to revenue generating projects. And so far, it has been, we have been on an exponential increase of our impact, I guess that's the big question. The first, we were able to do 46 times our cost. The second year, we made 106 times our cost, the third year, we are close to 200 times our cost with an incremental base. And so far, we've been on this increasing slide. At some point, it's, I guess, we are going to decelerate, but so far, we haven't hit the point. >> Right, the law of big numbers, eventually, you got to, eventually, you'll slow down a little bit. All right, well Jose, I'll give you the last word before we sign off here. Kind of tips and tricks that you would share with a peer if we're sitting around on a Friday afternoon on a back porch. You know, as you've gone through this journey, three and a half years and really sold you and your vision into the company, what would you share with a peer that's kind of starting this journey or starting to run into some of the early hurdles to get past. >> I guess there are two things that I could share. And once you have built a group like this and you have already, the incentives aligned and you have support from the top in the sense that they know that there's no other way they want really to compete and be successful, and suppose that you have all these preconditions set up and suddenly, you have a bunch of really smart people that are coming to a company, so you need to focus on ROI, high ROI projects. I;s very easy to get distracted on non-impactful projects. And I guess, the most important thing is that you have to learn to say no to a lot of things. >> Speaking my language, I love it. Learn to say no, it's the most important thing you'll ever, all right, well Jose, thanks for spending a few minutes and congratulations on all your success, what a great story. >> Thank you for having me, Jeff. >> Absolutely, he's Jose, I'm Jeff, you're watching theCUBE from the Corinium Chief Analytics Officer Summit in downtown San Francisco. (electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference. Spring, San Francisco, it's theCUBE! >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event 2018. About 100 people, really intimate, a lot of practitioners sharing best practices about how they got started, how are they really leveraging data and becoming digitally transformed, analytically driven, data driven. We're excited to have Vishal Morde. He's the VP of Data Science at Barclays, welcome. >> Glad to be here, yeah. >> Absolutely. So we were just talking about Philly, you're back in Delaware, and you actually had a session yesterday talking about Barclays journey. So I was wondering if you could share some of the highlights of that story with us. >> Absolutely, so I had a talk, I opened the conference with data science journey at Barclays. And, we have been on this journey for five years now where we transform our data and analytics practices and really harness the power of Big Data, Machine Learning, and advanced analytics. And the whole idea was to use this power of, newly found power that we have, to make the customer journey better. Better through predictive models, better through deeper and richer consumer insights and better through more personalized customer experience. So that is the sole bet. >> Now it's interesting because we think of financial services as being a data driven, organization already. You guys are way ahead Obviously Wall Street's trading on microseconds. What was different about this digital transformation than what you've been doing for the past? >> I think the key was, we do have all the data in the world. If you think about it, banks know everything about you, right? We have our demographic data, behaviors data. From very granular credit card transactions data, we have your attitudal data, but what we quickly found out that we did not have a strategy to use that data well. To improve our our productivity, profitability of a business and make the customer experience better. So what we did was step one was developing a comprehensive data strategy and that was all about organizing, democratizing, and monetizing our data assets. And step towards, then we went about the monetization part in a very disciplined way. We built a data science lab where we can quickly do a lot of rapid prototyping, look at any idea in machine learning data science, incubate it, validate it, and finally, it was ready for production. >> So I'm curious on that first stage, so you've got all this data, you've been collecting it forever, suddenly now you're going to take an organized approach to it. What'd you find in that first step when you actually tried to put a little synthesis and process around what you already had? >> Well the biggest challenge was, the data came from different sources. So we do have a lot of internal data assets, but we are in the business where we do have to get a lot of external data. Think about credit bureau's, right? Also we have a co-brand business, where we work with partners like Uber, imagine the kind of data we get from them, we have data from American Airlines. So our idea was to create a data governance structure of, we formed a Chief Data Office, the officer forum, we got all the people across our organization to understand the value of data. We are a data driven company as you said but, it took us a while to take that approach and importance of data, and then, data analytics need to be embedded in the organizational DNA, and that's what we're going to focus on first. Data awareness of importance of data, importance of governance as well, and then we could think about democratizing and monetizing, organization's the key for us. >> Right, right, well so how did you organize, how has the Chief Data Officer, what did he or she, who did he or she report to, how did you organize? >> Right, so it was directly reporting to our CEO. >> Jeff: Into the CEO, not into the CIO? >> Not into the CIO. We had a technology office, we do kind of, have a line-of-sight or adopted line with technology, and we made sure that that office has a lot of high-level organization buy-in, they are given budgets to make sure the data governance was in place, key was to get data ownership going. We were using a lot of data, but there was no data ownership. And that was the key, once we know that, who actually owned this data, then you can establish a governance framework, then you can establish how you use this data, and then, how to be monetized. >> So who owned it before you went through this exercise, just kind of, it was just kind of there? >> Yeah, there wasn't a clear ownership, and that's the key for us. Once you establish ownership, then it becomes an asset, we were not treating data as an asset, so there was a change in, kind of mindset, that we had to go through, that data is an asset, and it was used as a means to an end, rather than an asset. >> Right, well what about the conflict with the governance people, I'm sure there was a lot of wait, wait, wait, we just can't open this up to anybody, I'm sure it's a pretty interesting discussion because you have to open it up to more people, but you still have to obviously follow the regs. >> Right, and that's where there are a lot of interesting advancement in data science, where, in the area of data governance, there are new tools out there which lets you track who's actually accessing your data. Once we had that infrastructure, then you can start figuring out okay, how do we allow access, how do we actually proliferate that data across different levels of the organization? Because data needs to be in the hands of decision makers, no matter who they are, could be our CEO, to somebody who's taking our phone calls. So that democratization piece became so important, then we can think about how do you-- you can't directly jump into monetization phase before you get your, all the ducks in order. >> So what was the hardest part, the biggest challenge, of that first phase in organizing the data? >> Creating that 360 degree view on our customers, we had a lot of interesting internal data assets, but we were missing big pieces of the puzzles, where we're looking at, you're trying to create a 360 degree view on a customer, it does take a while to get that right, and that's where the data, setting up the data governance piece, setting up the CDO office, those are the more painful, more difficult challenges, but they lay the foundation for all the the work that we wanted to do, and it allowed to us to kind of think through more methodically about our problems and establish a foundation that we can now, we can take any idea and use it, and monetize it for you. >> So it's interesting you, you said you've been on this journey for five years, so, from zero to a hundred, where are you on your journey do you think? >> Right, I think we're just barely scratching the surface, (both laughing) - I knew you were going to say that >> Because I do feel that, the data science field itself is evolving, I look at data science as like ever-evolving, ever-mutating kind of beast, right? And we just started our journey, I think we are off to a good start, we have really good use-cases, we have starting using the data well, we have established importance of data, and now we are operationalized on the machine learning data science projects as well. So that's been great, but I do feel there's a lot of untapped potential in this, and I think it'll only get better. >> What about on the democratization, we just, in the keynote today there was a very large retailer, I think he said he had 50 PhDs on staff and 150 data centers this is a multi-billion dollar retailer. How do you guys deal with resource constraints of your own data science team versus PhDs, and trying to democratize the decision making out to a much broader set of people? >> So I think the way we've thought about this is think big, but start small. And what we did was, created a data science lab, so what it allowed is to kind of, and it was the cross-functional team of data scientists, data engineers, software developers kind of working together, and that is a primary group. And they were equally supported by your info-sec guys, or data governance folks, so, they're a good support group as well. And with that cross-functional team, now we are able to move from generating an idea, to incubating it, making sure it has a true commercial value and once we establish that, then we'll even move forward operationalization, so it was more surgical approach rather than spending millions and millions of dollars on something that we're not really sure about. So that did help us to manage a resource constraint now, only the successful concepts were actually taken through operationalization, and we before, we truly knew the bottom line impact, we could know that, here's what it means for us, and for consumers, so that's the approach that we took. >> So, we're going to leave it there, but I want to give you the last word, what advice would give for a peer, not in the financial services industry, they're not watching this. (both laugh) But you know, in terms of doing this journey, 'cause it's obviously, it's a big investment, you've been at it for five years, you're saying you barely are getting started, you're in financial services, which is at it's base, basically an information technology industry. What advice do you give your peers, how do they get started, what do they do in the dark days, what's the biggest challenge? >> Yeah, I feel like my strong belief is, data science is a team sport, right? A lot of people come and ask me: how do we find these unicorn data scientist, and my answer always being that, they don't exist, they're figments of imagination. So it's much better to take cross-functional team, with a complimentary kind of skill set, and get them work together, how do you fit different pieces of the puzzle together, will determine the success of the program. Rather than trying to go really big into something, so that's, the team sport is the key concept here, and if I can get the word out across, that'll be really valuable. >> Alright, well thanks for sharin' that, very useful piece of insight! >> Vishal: Absolutely! >> Alright thanks Vishal, I'm Jeff Frick, you are watching theCUBE, from the Corinium Chief Analytic Officer summit, San Francisco, 2018, at the Parc 55, thanks for watching! (bubbly music plays)