>> From the Corinium Chief Analytics Officer Conference Spring San Francisco, it's The Cube >> Hey welcome back, Jeff Frick with The Cube We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event. We go to Chief Data 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. >> You've got it all. You've 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. Give us kind of your perspective as this train keeps rolling down the tracks. >> OK. 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's been sort of a consistent theme throughout my career, so the data, Fannie Mae not unlike most companies, is really the blood that keeps the entire organism functioning. So over the past few years I've actually moved into the Enterprise Data Division of the company where I have responsibility for delivery, operations, platforms, the whole 9 yards. And that's really given me the 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 is the perspective changed around the data? Before data was almost a liability because you had to store it, keep it, manage it, and take good care of it. Now it's a core asset and we see the valuations up and down. One on one probably the driver of some of the crazy valuations that you see in a lot of the companies. So how has that added to change and what have you done to take advantage of that shift in attitude? >> Sure, it's a great question. So I think the data has always been the life blood and key ingredient to success for the company, but the techniques of managing the data have changed for sure, and with that the culture has to change and how you think about the data has to change. 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 of those servers, and every time data kept getting bigger we had to buy more servers and it almost became like a bad thing. >> That's what I said, almost like a liability >> That's right And as we've certainly started adopting the cloud and technologies associated with the cloud you may step into that thinking "OK, 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?" 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 so 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. >> And that's a huge piece of the old innovation game to 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 compliances. 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 percent of data at Fannie Mae what we do is loans, we create securities from the loans. And there's liabilities. 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 of the data quality checks in place. That's the 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 can 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 it enterprise data and we've done 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 - 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. What's the increasing importance and overlay of that exterior data to your enterprise data to drive more value out of your enterprise data? >> So that enterprise data like I say may be the 85%, it's just the facts. These are the loans we brought in. Here's how we can aggregate risk or how we can aggregate what we call UPB, or 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 their special sauce. That's 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 how the big data and the analytics environment has changed from the old day where you had some PHds and some super bright guys that ran super hard algorithms and it was on Mahogany Row and you put in the request and maybe from down high someday you'll get your request versus really trying to enable a broader set of analysts to have access to that data with a much broader set of tools, enabling a bunch of tools versus picking the one or two winners that are very expensive, you got to limit the seats et cetera. How has that changed the culture of the company as well as the way that you are able to deliver products and deliver new applications if you will? >> So I think that's a work in progress. 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 want to 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 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 past 10 years have enabled that. It's not 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 access databases that were I would call them not the power users but the empowered analysts. The ones who know the data, know how to query data but they're not hard core 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 running our systems in a smoother, lower cost way. So I think the granularity, the timing, and support for that explosion of tools we'll still have the big, heavy SAS and R users that are the quants. I think that's the combination 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 will be a longer term path for larger 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 going to wait and see. >> Obviously you have a ton of legacy systems, you have all these tools. You have that core set, your enterprise data that doesn't really change that much. What's the objective down the road? Are you looking to expand on that core set? Is it such a fixture that you can't do anything with it in terms of flexibility? Where do you go from here? if we were to sit down three years from now what are we going to be talking about? >> So two things. One, I hope I'll be looking back with excitement at my huge success at 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 those things. That will be a huge success, I'll be looking back and glowing about how we did that and how 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 they can use a different sent of tools. They can move all that heavy lifting and the servers 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 the next generation. >> All right. Kevin Bates, projecting the future so we look forward to that day. Thanks for taking a few minutes out of your day. >> Thank you. >> All right, thanks. He's Kevin, I'm Jeff. You're watching The Cube from the Corinium Chief Analytics Officer Event in San Francisco. Thanks for watching. (music)