 From the campus of MIT in Cambridge, Massachusetts, it's theCUBE, covering the MIT Chief Data Officer and the Information Quality Symposium. Now, here's your host, Stu Miniman. Welcome back to theCUBE, SiliconANGLE Media's flagship program. We go out to all the technology shows, help extract the signal from the noise, and here at the MIT CDOIQ Chief Data Officer and Information Quality, it's actually pretty easy for us to separate the signal from the noise, because the quality of guest here is really good. You've got lots of people that have been involved in the industry, both the Chief Data Officers, people looking at that trend. Happy to bring on the program for the first time. David Blaskowski, who's with the Financial Semantic Collaborative. David, welcome to the program. Wonderful, Stu. Thank you so much for the invitation to participate. Yeah, I'm glad to, you know, we have some of these events, we can just kind of pull somebody off the show floor here, as it were, and bring them there. Man on the street kinds of interviews. So, you actually have, it's our fourth year that we've had theCUBE here. It's actually my first time here. You've been coming for a number of years. Give our audience a little bit of just your background, what you're doing, and what led you to this event. Sure, well, I mean, by training, I'm a business guy, economist, MBA type, who somewhere about 20 years ago got waylaid and found himself going deeper and deeper into this realm of data. Data, which, you know, we all love analytics, we all love, we all love analytics, we all love the things we can do with data, but we keep forgetting that in the beginning, it's the data, and our results are only as good as our data. So, in financial services, I've been involved with data, then I was sucked up just before the financial crisis into starting the data programs at the SEC for public company reporting, and then starting up the, helping start up the Office of Financial Research at the Treasury Department, all in the interest of improving financial stability by having the kind of data that you really could analyze and understand reliably, then I did a couple of years stint heading up data governance at State Street, so I see data from, you know, the analytical side, data from the government and regulatory compliance side, and then from how do you really make the data within a major financial institution fit for purpose? Excellent, and you've been coming for a number of years, you usually speak at the event, I think you're just an attendee this year, but few responsibilities. Give us your kind of longitudinal view as to this group here, how you've seen some of the challenges changing the participants and the discussion changing. Well, the topics really have evolved significantly, I mean, from the days when there weren't many people here and questions were around, how do we find our data, how do we even know what we have to a point now where we have use cases, we have tremendous experience. Now, I'm not aware of very many, maybe not even any organizations that have absolute mastery of their data, but the sharing environment here between those coming from the public sector side, from the private sector side and the academic side, is just phenomenal in terms of getting that knowledge out there. We know a lot more about how to make data better and how to make chief data officers more effective at carrying out their job, providing value and providing reliability. Okay. And you've been on kind of the regulation side, I'm curious, you're a lot to talk here about open data mandates and there's open source, so kind of regulation, governance, open source, how do those all fit together in your mind? Very loosely, there are a lot of interesting concepts there. In fact, I just came from a wonderful session on open data with the deputy CIO of the federal government and the chief data officer for the Commonwealth of Massachusetts, talking about the tremendous progress made in making data more available, open data, more available to those who want to make government more accountable or those who want to innovate with the data to make better tools, do more for the community. But there still remain significant issues, how to make that data through standards and interoperability approaches to make it more usable, more able to be cultivated by those who want to do cool and interesting things with it. Okay, I'm curious, from kind of the analytics standpoint, how can we use that from a prediction standpoint? I think your background, how do we prevent the future financial disasters? Oh gosh, you know, it's unlikely that you can prevent a financial crisis through data. If you could predict the future of financial markets, well frankly, you'd go and you'd invest on your own to make money from it, that's out there. You really can't predict markets. What you can do is improve your ability to identify risks that are emerging and then if something happens to understand where you are and recover from it and you're through the work of one of my former agencies, the Office of Financial Research at the United States Treasury, they've really made tremendous steps as have their international partners around the world in terms of identifying the kinds of analytics that would make it possible to identify risks and to then standardize the data that would be required in order to be able to populate those analytics. So it'll never predict, but you can identify that there are problems emerging and if something happens, whether expected or unexpected, that you can do what we couldn't do in 2009, which was to clean up effectively from it. What about the sources of data? A lot of time we talk about, you know, in time to company, you know, you've got all the silos with the data, but many companies we talk to are leveraging internal data, external data, you know, how do you see kind of sources of data changing? You know, they'll always be internal and external sources of data. I think the real frontier around it is what data that's out there would benefit from standardization, from collaboration among participants in an industry or even across industries. For example, I've been privileged to work on the financial industry business ontology, FIBO, which was really about it related to how would you represent financial contracts? Interest rate swaps, derivatives of all kinds. And if you can, that data might exist within a firm, but frankly, you don't do deals with yourself. If you're trading a security, a derivative, it's with another firm. So it really helps if the two kinds of information that are coming together are identical. And one, I as the receiver know what you, my counterparty are sending me. So it's not yet a full-fledged standard. It's still being worked on by the Enterprise Data Management Council. But this is illustrative of the kind of work that's going on around the country and around the world and saying, look, we're not making, no company is making its money on having its data exist in its own unique form. We all make money by having, by being able to carry out economic transactions. And if we are able to use data that facilitates that, all the better. Yeah, data quality is something we talk a lot about at this show. Any guidance on kind of techniques or things that you see on how people can improve data quality in general? It's a bit of a truism, but really it does come down to wanting to build a data culture. I mean, you can instill all kinds of rules on folks, but in the end, you have to build within your organization the understanding that data actually matters. Whether you're a bank or whether you're a credit card company or whether you're selling widgets. At the end of the day, whether you're a public company or a private company, you have customers. And you have other people, management committees and others who need to know investors, who need to know how you're doing. And it's all so much easier if from the beginning you're thinking about what that data ought to be, what it ought to mean, how you want to use it and what the implications are to how you should collect it and maintain it. Those are the big lessons. I'd like to kind of bring that all together into it's Monday morning, do you know where your data is? And that opens a whole lot of other questions around, do you know where your data lives? Do you know what it means? Do you know how it's being managed? These are issues that apply to every organization, not just to data intensive organizations. And there's a lot of work to be done. But it's ultimately within the grasp of any leader. Yeah, so you've been on both kind of the government side and the enterprise side. Technology, people, processes, can you pick what's the hardest? I'll go back to what I said before. I mean, there are a lot of technical challenges in everything, but the hardest part is it could be the easiest part in the hands of the right leader, but is building the support that data actually really matters. And it matters enough that you should take some time and some effort to understand what it is you have rather than moving on to the next pressing business problem. You have to solve some of these problems in real time and recognize that as you do it, it will further facilitate your ability to add value to your business. All right, just curious we're running low on time, but have you looked at blockchain at all and kind of the impact there? Yes, I absolutely have. You know, on the one hand, blockchain is way up on the hype curve. On the other hand, you know, ledger technologies such as blockchain offer a lot of promise to being able to facilitate exchanges, not just a value, but of information from organization to organization. You know, are they right for every purpose? No, no cool neat technology has turned out to be right for everything, but it has a role to play in organizations not just banks, but all kinds of organizations. And it's a tool, it's a weapon really in the Chief Data Officer's arsenal. All right, well, David Buskowski, really appreciate you taking some time out of the experience here at the conference. We'll be back with lots more coverage here from the CDO IQ here at MIT. You've been watching theCUBE.