 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. We go to Chief Date Officer. This is Chief Analytics Officer. There's so much activity around big data and analytics. And this one is really focused on the practitioners. Relatively small event and we're excited to have another practitioner here today. And it's Kevin Bates. He's the VP of Enterprise Data Strategy Execution for Fannie Mae. Kevin, welcome. It's a mouthful. Thank you. Yeah, you got it all. You got strategy, which is good. And then you've got execution. And you've been at a big Fannie Mae for 15 years according to your LinkedIn. So you've seen a lot of changes. So give us kind of your perspective as this kind of trend keeps rolling down the track. Okay. Yeah, so it's been a wild ride. I've been there, like you say, for 15 years. When I started off there, I was writing code, working on their underwriting systems. And I've been in different divisions, including the credit loss division, which had a pretty exciting couple of years back around 2008. More exciting than you care to. Well, there was certainly a lot going on. Data has been sort of a consistent theme throughout my career. So the data at Fannie Mae, not unlike most companies, is really the blood that kind of keeps the entire organism functioning. So over the last few years, I've actually moved into the enterprise data division of the company where I have responsibility for delivery, operations, platforms, the whole nine yards. And that's really given me kind of a unique view of what the company does. It's given me the opportunity to touch most of the different business areas and learn a lot about what we need to do better. So how's the perspective changed around the data? Because before data was almost a liability because you had to store it and keep it and manage it and take it care of it. Now really it's a core asset. And as we see with valuations up and down 101, probably the driver of some of the crazy valuations that you see in a lot of the company. So how's that attitude changed? And what have you done to kind of take advantage of that shift in attitude? Sure, it's a great question. So I think the data has always been the lifeblood and the key ingredient to success for the company. But the techniques of managing the data have changed for sure. And with that, the culture has the change and how you think about the data has to change. So if you go back 10 years ago, all of our data was stored in our data center, which means that we had to pay for all those servers and every time data kept getting bigger, we had to buy more servers and it almost became like a bad thing, right? That's what I say, almost a liability. That's right. And as we've certainly started adopting the cloud and the technologies associated with the cloud, you may step into that thinking, okay, now I don't have to manage my own data center. I'll let Amazon or whoever do it for me. But it's much more fundamental than that because as you start embracing the cloud and now storage is no longer a limitation and compute is no longer a limitation, the numbers of tools that you use is no longer really a limitation. So as an organization, you have to change your way of thinking from I'm going to limit the number of business intelligence tools that my users can take advantage of to how can I support them to use whatever tools they want? Right, right. So the mentality around the data, I think really goes to how can I make sure the right data is available at the right time with the right quality checks that everybody can say, yep, I can hang my hat on that data, but then get out of the way and let them self serve from there. It's very challenging. There's a lot of new tools and technologies involved, but that's really the change. And that's a huge piece of the old innovation game, right? It's have the right data for the right people with the right tools and let more people play with it. But you've got this other pesky thing like governance. You've got a lot of legal restrictions and regulations and compliance issues. So how do you fold that into opening up the goodies, if you will? So I think one effort we have is we're building a platform we call the Enterprise Data Infrastructure. So for that 85% of data at Fannie Mae, what we do is loans. We create securities from the loans and then there's liabilities and there's some income and there's a few things. There's a pretty finite set of data areas that are pretty much consistent at Fannie Mae and everybody uses those data sets. So taking those and calling them enterprise data sets that will be centralized, they will be presented to our customers in a uniform way with all the data quality checks in place. That's a big effort. It means that you're standardizing your data. You're performing a consistent data quality approach on that data and then you're making it available through any number of consumption patterns. So that could be applications needed. So I'm integrating applications. It could be warehousing, analytics, but it's the same data and it comes from that promise that we've tagged at Enterprise Data and we've done all that good stuff to make sure that it's good, that it's healthy, that we know where we stand. So if it's not a good data set, we know how to tag it and make it such. For all the other data around, we have to let our business partners be accountable for how they're enriching that data and innovating and so forth. But governance is not a, it is, I think in the past, another part of your question, governance used to be more of a slow everybody down. But if we can incorporate governance and have implied governance in the platform and then allow the customers to self serve off of that platform, governance becomes really that universal good, that thing that allows you to be confident that you can take the data and innovate with that data. So I'm curious how much of the value add now comes from the not Enterprise Data, the outside, the core, which you've had forever. I mean, I don't know if you know, I have a hard number, but what's kind of the increasing importance and the increasing overlay of that exterior data until your Enterprise Data to drive more value out of the Enterprise Data? So that Enterprise Data, like I say, maybe the 85%, it's just the facts. These are the loans we brought in, here's how we can aggregate risk or we can aggregate what we call UPB or like the value of our loans. That is pretty generic and it's intended to be. The third party data sets that our business partners may bring in that they bump up against that data can give them strategic advantages. Also the data that those businesses generate, our business lines generate within their local applications, which we would not call Enterprise Data, that's very much of their special sauce and something that the broader organization doesn't need. Those things are all really what our data scientists and our business people combine to create the value added reports that they use for decisioning and so forth. And then I'm curious kind of how the big data in the analytics environment has changed from the old day where you had some PhDs and some really super bright guys that ran really super hard algorithms and it was on mahogany row and you put in a request and maybe from down high someday, you'll get your request versus really trying to enable a much broader set of analysts to have access to that data with a much broader set of tools as you said enabling a bunch of tools versus picking kind of the one or two winners that are very expensive, you got to limit the seeds, et cetera. How's that changed kind of the culture of the company as well as the way that you were able to deliver products and deliver new applications, if you will. So I think that's a work in progress. So I have to say we have, we still have all the PhDs and they still really call the shots. They're the ones that get the call from the executive vice president and they wanna see something today that tells them what decision they should make. We have to enable them. They were enabled in the past by having people basically hustle to get them what they need. The big change we're trying to make now is to present the data in a common platform where they really can take it and run with it. So there is a change in how we're delivering our systems to make sure that we have the lowest level of granularity, that we have real time data, there's no longer waiting. And the technology tools that have come out in the last 10 years have really enabled that. So it's really just about implementing that, making it available to all those PhDs. There's another population of analysts that is now empowered where they were not before. The guys that suffered just using Excel or like access databases that were the, I would call them not the power users, but kind of the empowered analysts, the ones who they know the data, they know how to query data, but they're not hardcore quants and they're not developers. Those guys have access to a plethora of tools now that were never available before that allow them to wrangle data from 20 different data sets, align it, ask questions of it. And they're really focused on operations and just running our systems in a smoother and lower cost way. So I think the granularity, the timing and support for that kind of explosion of tools while we'll still have the big, heavy SAS and R users that are the quants that, I think that's the combination. It's sort of everything has to be supported and we'll support it better with higher quality, with more recent data. But the culture change isn't going to happen even in a few years. It'll be a longer term path for large organizations to really see maybe possibilities where they can restructure themselves based on technology. Right now the technologies are early enough and young enough that I think they're gonna wait and see. Right. Well, I was gonna say, because obviously you guys have a ton of legacy systems and you've got all these tools, but you've got that core set. As you said, your enterprise data that doesn't really change that much. That's right. I mean, what's the objective kind of down the road? Are you looking to expand on that core set? Is that just such a fixture that you just can't do anything with it in terms of flexibility? I mean, where do you go from here? If we were to sit down three years from now, what are we gonna be talking about? So two things. One, I hope I'll be looking back with excitement at my huge success in transforming those legacy systems. In particular, we have what we call the legacy warehouses that have been around well over 20 years that are limited and have not been updated because we've been trying to retire them for many years. Folding all of that into my core enterprise data infrastructure that will be fully aligned on terminology, on near real time, all of those things. That will be a huge success. I'll be looking back and glowing about how we did that. Excellent. And now we've empowered the business with that core data set that is uniquely available on this platform. They don't need to go anywhere else to find it. The other thing I think we'll see is enabling analysts to utilize cloud-based assets and really be successful working both with our on-premises data center, our own data center supported applications, but also starting to move their heavy running, quantitative modeling, and all the sorts of things they do into the data lake, which will be cloud-based, and really enabling that as a true kind of empowerment for them so that they can use a different set of tools. They can move all of that kind of heavy lifting and the servers that they sometimes bring down right now move it into an environment where they can really manage their own performance. I think those are going to be the two big changes three years from now that will feel like we're in kind of the next generation. All right, Kevin Bates projecting the future. So we look forward to that day and thanks for taking a few minutes out of your day. Thank you. All right, thanks. All right, he's Kevin, I'm Jeff. You're watching theCUBE from the Carinium Chief Analytics Officer event in San Francisco. Thanks for watching.