 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. We are wrapping up a day of coverage here at theCUBE for MIT CDOIQ here in Cambridge, Massachusetts. I'm Rebecca Knight along with Peter Burris. We've been here all day, folks. We've learned a lot. We've had a lot of great conversations here. A lot of lively debate and interest. So, Peter, this morning, you were talking about this fundamental idea, that data needs to be viewed as an asset within an organization. Obviously, we're here with a bunch of people who are drinking that Kool-Aid, but- Living that Kool-Aid. Living that Kool-Aid, embodying that Kool-Aid. So, how, but based on what we've heard today, do you think that business has caught up? Well, I would say two things. First of all, this has been, as you said, it's been an absolutely marvelous series of conversations. In other respects, this is what theCUBE was built for, right? Smart people in conversation on camera. And we've had some smart people here today. What I got out of it on that particular issue is that there is general agreement among CDOs that they have to start introducing this notion of asset and what that means in their business. There's not general agreement, or there's a general, I guess not agreement, but there's general concern that we still aren't there yet. I think that everybody that we talk to, I think would come back and say, yes, we agree those practices, but the conventions are not as established and mature as they need to be for everybody in our business so that we, to agree so that we can acculturate. Now, we did hear some examples of folks that have done it, so that great BBVA case we talked about was an example. There's a company that is actually becoming, is really truly institutionalizing, acculturating that notion of data as an asset that performs work. But I think we got general agreement that that's the right way of thinking about it, but also a recognition that more work needs to be done, and that's why conferences like this are so important. Well, one of the things that really struck me about what BBVA did was this education campaign of it's 130,000 employees. And as you said, really starting from the ground and saying, this is how we're going to do things. This is who we are as an organization. Yeah, and it was a great conversation because one of the points I made was specifically that BBVA is a bank, it is an information-based business that has very deep practices and principles associated with information. And when they decided that they need to move beyond that, they were able to get the entire bank to adopt a set of practices that are leading to new types of engagement models, product orientation, service capabilities. That's a pretty phenomenal feat. So it's happening and it can get done and there are examples of it happening. Another thing we talked about was the fact that over the course of the next few years, one of the most exciting things about digital business is not just digital business and digital, what people call digital natives, but that transformation practices that way forward. We talked about the idea of how you wrapper existing goods and services and offerings with data to turn them into something else and the incumbents are going to find ways of doing that so that they can reestablish themselves as leaders in a lot of different markets. And that's what will separate the people who really get this from the people who or from the organizations that are going to lag. Yeah, we're starting to hear that a lot more from clients is that the idea increasingly is, okay, I've already got customers. I've already got offers. How do I wrapper them using the term that we heard from the professor of MIT? How do I wrapper them to improve them utilizing data? And that's a big challenge, but it's happening. One of the other fun interviews we had was all about clinical trials and in the use of data in these clinical trials, there are so many challenges with clinical trials because of the time it takes to conduct one of these, the cost that it takes. And then at the end, you are dealing with patients who just say, oh, I think I'm not going to take that drug today or other factors that take place here. I mean, what do you see? I know your dad is a physician. What do you see as the most exciting thing about the use of data in clinical trials but also just in the healthcare industry in general? So what we heard, and it was a great combination of interviews, but what we heard is that to bring a new drug to market can cost $4 billion and take 15 years. And the question is, can data, first off, reduce the cost of bringing a new drug to market? And we heard numbers like, yeah, buy a billion dollars or even more. So imagine having the cost of bringing a new drug to market but also reducing the time by as much as two thirds. That's very, very powerful stuff when we come down to it. And as you said, the way you do that is you have to protect your data and make sure that you're complying with various regulations. But as you said, for example, sustaining someone in the trial, even though they're starting to feel better because the drug's working, well, people opt out. They abandon the trial. Well, can you use data to keep them tied in to provide new types of benefits and new types of capabilities so they want to sustain their participation in the trial? Or at least alert the pharma company, hey, this person's dropping out. You need to explain that to the FDA and that's going to become a point. Exactly, or you need to provide an incentive to keep them in. Or another example that was used was if we can compress the amount of time but then recognize that we can sustain an engagement with a patient and collect data longer that even though we can satisfy the specific regulatory mandates of a trial, shorter, we can still be collecting data because we have a digital engagement model as part of this whole process. Subject to keeping privacy in place and ownership notions in place and everything else, regulatory complying with regulatory notions. So that is, I think, a very powerful example. And again, Dr. Santy was talking specifically about how ERT is helping to accelerate this whole process because over the course of the next dozen years, we're going to learn more about people. The genome's going to become better understood. Genomics is going to continue to evolve. Data's going to become increasingly sensual to how we think about defining disease and disease processes. And one of the key responses is to learn from that and apply data so that we can more rapidly build the new procedures, devices, and drugs that are capable of responding. When we're thinking about what keeps the chief data officers up at night, and we know that data security, data fidelity, privacy, the other thing we've really heard about from Elana Gulbin at PWC Accelerator is the idea about bias. And that is a real concern. And it sounds, from the way she was talking about it, it sounded as though companies are more aware of this. It really is an organizational challenge that they recognize that's not just matters for social reasons, but really for business reasons, too, frankly. It affects your bottom line. Where do you come out on that? Do you think we're moving in the right direction? First of all, it was a great interview, and a lot of what Elana said was illuminating to me, and I agree with virtually everything she said. We're doing a piece of research on that right now. I would say that, in fact, most companies are not fully factoring the role that bias plays in a lot of different ways. And it's one of the things that absolutely must happen as part of the acculturation process. What's known as evidence-based management starts to take grip more within businesses is to understand not only what bias is introduced into data now, but as you create derivatives on that data, how that bias changes, the lineage of that data. And that is a relatively poorly understood problem. But it's a big problem. And it's going to be even bigger because we're going to utilize AI that's actually going to limit the range of options that people consider as they make a decision, or make the decisions directly for the individual on behalf of the brand, what we call agency, or a system of agency, and not understanding that lineage, not having it be auditable, not understanding what the inherent bias is, can very quickly send a business off the rails in unexpected ways. So we're devoting a lot of time and energy into understanding that right now. But here's the challenge that we've got business decision makers who are very familiar with certain kinds of information. There's nobody gets to be the CEO or the CEO or a senior person within business if they don't have a pretty decent understanding of finance. So financial information is absolutely adopted within the boardroom and the senior ranks of management and virtually all businesses of any consequential size today. What we're asking them to do is to learn about all the new classes of data, new data conventions, what it means, how to apply it, how you should factor it, how to converge agreement around things that allows them to be as mature in their use of customer data or production data or partner data or any number of other metrics as they are with financial data. That's a real tall order. It's one of the significant challenges that a lot of businesses face today. So it's not that they don't get data or they don't understand data, it's that the sources of data and therefore the range of options that are going to be shaped by data are becoming that much more significant than business. And it's how they need to think about data too. I mean, I was really struck by Tom Sosala at the very beginning saying, one of the reasons the intelligence community didn't predict 9-11 is that we didn't have people who were thinking like Hollywood people, thinking audaciously enough about what could happen and that similarly, we need to have business leaders and executives who may be very good at crunching numbers really think much more broadly about the kinds of... And Tom is absolutely right. We also, because I was very close to the DOD at the time, there was some serious confirmation by us. It was going on at that time too. But clearly he's right, that the objective is for executives to as a group acknowledge the powerful role that data can play, have a data-first mentality as opposed to a bias or experience-first mentality because my experience is very private relative to your experience. And it takes a lot of time for us to negotiate that before we can make a very, very consequential move. That's not going to go away. We're human beings. But we increasingly need to look at data which can provide a common foundation for us to build our biases upon so that we can be more specific and more transparent about articulating my interpretations. You can't start doing that until you are better or more willing to utilize data as a potentially unifying tool and mechanism for thinking about how we move forward with something. That's great. It's a great way to end our day of coverage here at MIT CDOIQ. Thank you so much. It's been a pleasure posting with you. And thanks to the crew and everyone here. It's been really a lot of fun. I'm Rebecca Knight for Peter Burris. We will see you next time on theCUBE.