 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 co-host, Peter Burris. Rejoined by Barbara Wixom, she is the principal research scientist at the MIT Sloan Center for Information Systems Research. Thanks so much for coming on theCUBE. It is a pleasure to be here. Thanks for joining, asking me to join you. So your group goes by the acronym CISR. We do. So what is CISR? So CISR is actually the oldest academic research center for tech in the world. We were established in 1974. We're a non-profit out of Sloan and our mission is to help organizations succeed in digital. Pretty exciting. And so my particular mission is to help organizations succeed in digital with their data. And so my research is really focusing on data and exploring how to create tools to help leaders in that area be successful. So I got to jump in because so I have, I've also spent a lot of time with the clients about how do you go, what is digital business? How do you do digital transformation? And my thesis is that the difference between business and digital business is the degree to which data is recognized and employed as an asset. And that you measure digital transformation by the degree to which a company is, is acculturated, re-institutionalizing work and organizing its people in engagement around the role that data can play. How can, in your mind, what is the difference between data and digital? I mean, it sounds as though you are at the vanguard of thinking about how businesses are going to do things differently. Yeah, I think that's right. I mean, data is just a foundational capability of a digital organization, exactly right. It's a core resource of a digital firm. And so as we all become digital, going through digital transformations, then any company is having to rely on data as a strategic asset of the firm. For a lot of traditional companies that will require data-driven transformation in order to get there. Unlike digital companies that are naturally, they're leveraging data as a strategic asset. So you were here presenting a case, an award-winning case that you wrote about BBVA and how in 2014 it established a center for digital, for data excellence. So what was the impetus for this center? Yeah, so I've been studying how organizations create value from data now for close to 25 years. And so not a lot surprises me at this point. And so when a best practice emerges, it's pretty easy for me to spot. I start noticing some patterns that I've never seen before, right? So back in 2015, I was talking with a guy from BBVA who was heading up this new data science center of excellence and as who was describing what he was doing, I just recognized at that point, frankly, that they were doing something really unique and I wanted to study what happened. And so I ended up following their efforts for about three years and I ended up going to Spain, to Madrid to kind of check out their headquarters and such. That must have been awful. It was wonderful. Every trip was absolutely wonderful. And it turned out to be a successful effort. And basically what happened was BBVA, getting excited about digital transformation, started innovating and initially they got a sense that they could commercialize some of their data services to create new revenue streams. And they got this idea actually by sending some of their innovators over to the MIT Media Lab. That's where it all started actually in 2011. So in 2014, they said, hey, there might be something in here, let's establish a legally separate subsidiary with a focus on data monetizing by selling new data products. Well, they established a separate entity and they brought in data scientists with a lot of talent. What ended up happening is they quickly realized that there was more value frankly in helping the bank appreciate data as an asset as opposed to selling data. And so the focus really changed to helping BBVA monetize internally. And so they did this in two ways. They started seeding these new data scientists in this new subsidiary with existing work teams across BBVA. So not just individuals, whole entire work teams. Well, they would have the data scientists seated on teams in the bank across different business units. And basically the role was to help the bank appreciate that new contemporary methods, different types of data science approaches could really get even more benefits when they were already getting using more traditional data mining methods, if you will. So for instance, every bank already is doing things like branch optimization. That's a pretty typical way to improve operations in banks. Well, by creating a new project, if you will, using contemporary methods, using one of these data scientists in 2015, they realized a $35 million lift above what they were already doing because of these new techniques. And so they realized that by using these data scientists they really would create significant value across business units in BBVA. But as they were creating value, they were also knowledge transferring and they were retooling the project teams to start using data in new ways as a more digital type of company would. And so that was happening. They also started not just using data for improving, but they started monetizing by creating some really interesting analytics features for their apps, for instance. So for instance, they used machine learning and they brought in their data scientists in order to add features to a digital app categories to help a personal finance management application communicate to a consumer what kind of spend they were engaging in as a bank customer. You could kind of apply chart of, I spend this much on utilities and this much on education and such. And so that feature was analytics powered and required data science and pretty sophisticated machine learning to create. But that was another way this data science group started again, introducing new data methods. So BBVA was thinking about, geez, what options does data create for us? What tools do we need to build to realize those options and how can we diffuse those across the bank so that we can liberate other data-oriented innovations in the bank? That sounds a lot like what, we're at the CDO conference. This sounds a lot like what a CDO should be doing in a lot of organizations. How does that line up? Yeah, absolutely. So I have a framework, a data monetization framework that I really believe helps. And it's a conceptual model that basically says you can monetize your data in three ways by using data to improve your company processes, right? Create, lift, process, lift efficiencies. You can wrap data and analytics around your existing offerings, your products in order to add value to those kind of like that categorizer I was talking about or you can sell, which is how DNA, so you can improve, you can wrap, you can sell, you can sell. Rarely do I see an organization set themselves up to be able to do all of those at the same time. Usually it takes years of getting good at each, for example. And what was really striking about BBVA is that pretty much from 2014 to 2017, so in three years, they set the bank up to make significant inroads in monetizing in all three ways, 130,000 employees. And so they were able to create significant value in all of these areas. So as a CDO, what you're trying to do is create your monetization portfolio that makes sense for your organization. You know, should you have a portfolio that's heavy and improving, or do you need to start having much more activity in wrapping, I call it, or with the features because of digital transformation, for instance, how do you do that? And once you have your strategy, then how are you going to move your organization and set up so it can be successful in driving value in those three ways? And again, I think what's really great about the case is that it shares how they were able to really move across all of these areas quickly and effectively, you know, really driving value. So what was it? What's the secret sauce? Why were they so successful? Yeah, so the good news is that all of the details of how is in the case, and it's available to the world for free on the Scissor website. So if you go to MIT Scissor and you just search my last name or BBVA, you can have the full case and give it up because there's a lot that they did. It's very hard to say, you know, they did one, two, three things, if you will. But basically, if you think about transformation, there's kind of four steps to transformation. You have to first kind of develop your target state. What is your target state going to look like for data? And BBVA did this by creating the subsidiary data science group. You know, in effect, they were creating in an incubated way where they eventually wanted to hit, right? So they say they did that. And then once you do that, you have to really promote and communicate the value of data across the organization. And they had some really interesting techniques that helped them do this. For one, they actually measured the value of the projects that they participated in, which everyone should do, but few of us actually do. So the fact that they did measure and capture in a credible way all of the value from these different projects, then that could be communicated and easily sold then for buy-in and understanding across the organization. They also actually had training for every all 130,000 employees at BBVA. They had some level of training regarding what data science and data is and why it's important. So as an example for the masses, people who just needed basic literacy regarding data, they held an event that was live-streamed in order to capture about 18,000 people at once where they communicated by executives how AI and machine learning and big data, how it was in place right now at the bank and how you could actually feel it as any employee at BBVA. And they did a number of other things in the case as well, but that was one thing. So basically they promoted and communicated value. Then you need to, if you're transforming, the third step is kind of you have to innovate new ways of work. You have to figure out what are we gonna do now that's different. And that happened at BBVA for one on the project teams. So when you have the data scientists working on projects in the business, then they are co-creating new work practices. They also took advantage of social good projects. So the BBVA data scientists worked for instance with the government of Mexico and the United Nations, and they came up with a really interesting exploration of bank card data and payment data to understand disaster response. So when Hurricane Odile hit Mexico, they could use bank transactions to understand what the economic recovery was like so they can then redirect efforts and all those kinds. So anyway, they used that social good as a way to innovate and come up with new work practices that eventually they could bring back to the bank. Which makes the employees feel good about their employer. Yes, and in fact. But also it accelerates adoption. Accelerates adoption, and actually that became one of their best channels for attracting new data scientists through those social good projects, as well as interesting new ecosystem partners like startups who might not have worked with BBVA, but through the social good projects, they were really excited to work with the organization. And then the fourth step basically of transformation is then to identify the best practices and share them. And because the BBVA had set up this subsidiary which had metrics to create long-term capabilities, they were incentivized to come up with shared platforms, shared knowledge that could spread across all of the bank. So for instance, every project that the data science worked on, they took the new algorithms, the new models, and they started creating a catalog basically of analytics tools that they then could start reusing across other projects. And so that became a really strong enterprise capability that everyone could benefit from. So these steps, these four steps, I guess the final thing is a lot of companies, I see them go through the steps sequentially where you build your, with BBVA, they did them all at once, which was really, and I think that's why also their efforts moved along so quickly and so effectively. Well, I gotta believe that they focused on the adoption of the entire set of practices, I don't want to diminish how important this is. A bank is a financial institution that already has practices and policies and procedures associated with data. So BBVA, to improve them, had to first break them. So doing this in three years is an unbelievably magnificent success, it's huge. I want to ask you one more question. So as people look at that, go read the case, but it's how do you compress the time to do it? It's very focused on getting people to adopt it and not just introducing things. I want to ask one more question to you. When you mentioned that there's the improving, the wrapping, and the monetizing. Selling, yep. Well, and selling, because I want to make it clear that all of it's monetizing, I think that's so important. Right, right, right, right, sorry. Right, so it's improving, wrapping, and selling as monetizing strategies. One of the things that I think is happening, again, in Lisa, when we think about some of the issues of digital business, digital transformation, is it's a general transformation from looking at your offering as a product, which is when you buy it, you get value, to looking at it as a service, which is as you use it, you get value. It's very difficult to turn a product in the service without data. Is that what you mean? That's wrapping. That's what you mean by wrapping. That's wrapping, exactly right. So what wrapping is doing is it's creating digital features and experiences that are fueled by analytics, that are adding value to the offering itself, and it's really converting the product to what we call a solution, right? So it's how can we, and we have all kinds of great examples of wrapping, and we also have research on that at Scissor, but it can be everything from like Pepsi, where a Pepsi realized that, okay, yeah, we sell a can of Coke, but how do you create a digital feature? No, Pepsi doesn't. Oh, I'm sorry. No, they don't, they really don't. But they're really good at selling Pepsi. They're really good at selling Pepsi. They sold a can of Cola. They sold a can of Cola. Thank you, thank you. They sold a can of Cola, but Pepsi also sells Lay's chips in different snacks and such, and so they created a party planner that is fueled by analytics that basically helps you optimize a shopping list for a party you're going to have. And so you put in some parameters around the guests that you're gonna have and such. Well, they haven't done this yet, at least not to my knowledge, but if a company can look at Gillette and say, I can sell Razors as a service, I gotta believe at some point in time, Pepsi can look at Cola and say, I can sell Cola and related stuff as an experience. Right, and we're seeing a lot of, so if you think about other types of their business, such as selling in a business to business manner, I'm not seeing this specific in Pepsi, but in other organizations with that model, a lot of the reporting and insights that you can offer regarding sales to help your business partner sell product more effectively and such, that would be considered a wrap, because you're adding value. At the end of the day, a customer is going to want to go to you because of the analytics you're associating with the offering to help you consume it better, sell more of it better, whatever it is. It's really important value add. So wrapping, honestly, is, I believe, the future. In terms of- You have incumbents, and in many respects, the wrapping is the way that the incumbents themselves are going to transform into the future. Is that right? Yeah, yeah, exactly right. And it requires a big shift. So to do wrapping all of a sudden, what it means is you need a product organization that's very analytics savvy, for instance. It changes the relationships across the organization with the data team. It's actually, and again, we have some research on that too, a lot of dynamics really get shifted. But at the end of the day, as organizations, especially as we're trying to distinguish our products in the marketplace, we're going to need to rely on analytics to help us do this. And at the end of the- And learning best practices from CISR, too. And we have a lot of them. We have a lot of them, and we hope it helps. And most practices, I'm sure, I'm sure you can also- We don't typically feature those, but we hope that if we have people to focus on the best practices, they'll avoid the ones that will. Yeah, yeah. Well, thank you so much for coming on theCUBE, Barbara. It was great having you. Fun conversation. Thank you very much. I'm Rebecca Knife of Peter Burris. We will have more from MIT CDOIQ in just a little bit.