 Okay, we're back. This is Dave Vellante, Wikibon. I'm here with Paul Gillan. This is the MIT Information Quality Symposium. We've been here for two days covering this wall-to-wall. This is theCUBE. Justin McGruder is here. He's the founder and principal owner of Noetic Partners of Financial Services, consultancy, former chief data officer at Freddie Mac right during the financial crisis. So a very interesting segment here. Justin, welcome to theCUBE. Thank you. So start by telling us a little bit about Noetic Partners. And then we want to ask you about data quality and financial services. Sure, sure. We formed Noetic about four and a half years ago as I was leaving Freddie Mac and as some of my partners were also deciding to move forward in sort of the ashes of the credit crisis, if you will. Noetic is a consultancy, but what we've done is we've really come up with an approach to managing financial information based upon the concept that every party involved in the financing world is important and the roles those parties play in transactions and the roles those parties, the relationships those parties have with other parties is fundamental to understanding financial services and the financial markets. And it's a concept that's not new, but it's a concept that I think we've taken to the next level. And we've done this work at some of the larger global investment banks and commercial banks and some smaller firms as well. And we think it's an approach that has helped our clients and our counterparts, our customers understand their risks better and frankly make more money and lose less money. Well, so straight out was what happened with the mortgage-backed security crisis and the CDOs, et cetera. Was it fundamentally a data quality problem? I think data quality and a lack of data was absolutely a fundamental component of the crisis. I think the banks that were involved in underwriting loans and requiring loans and pooling them and selling them to investors did not have information about the underlying monthly debt obligations and income and assets held by borrowers. I think it's well known. We had things we called ninja loans back then which meant no income, no job, no assets. And we had lenders, some of them no longer exist who were making ninja loans because of the froth in the market because we saw if you were calling 0607, prices were going up, people were buying and selling homes and flipping them and the transparency that the actual underwriters had to the content, the reference data about the people they were lending to. Not the collateral. I think people understand the collateral well but the reference data about borrowers and guarantors and novelers was not good and I think that was a problem. I think in my role it prevented us from truly understanding the nature of the risk of the pools that we were issuing in my time at Freddy's. Well, so a lot of people pointed the fingers at the credit rating agencies which really, given the scope of the problem, they weren't going to own that. So we've all seen that effect, the effect on the economy, the effect on Dodd-Frank now comes along. So take us back to that situation to the extent that you can and share with us. You were there, you saw firsthand what was happening. What was it like at that point, particularly from a data quality standpoint and what have we learned from that? Sure, I think most people look at the crisis and they see the securities, the pools of thousands or hundreds or thousands of mortgages and they see weighted average coupon rates and they see information about mortgages that was pooled together and aggregated and they see a price for a security and they see a rating. What they don't see is the underlying mortgages and in a pool issued by a Bear Stearns or a Lehman Brothers or a Fannie Mae or a Ginny Mae or a Freddie Mac, there might be thousands of mortgages with hundreds of data elements and those details necessarily were obscured from the investors because I don't want someone knowing about my home just because my loan is part of a pool and I think that's fair, the privacy issues are real but the lack of disclosure and the lack of transparency to investors prevented them and the ratings agencies from really understanding the risk around particular borrowers and particular markets where prices had appreciated significantly. Okay, so I mean a granular view was maybe not appropriate but at least some data and information on the quality of that granular view would have been more appropriate. Absolutely. You know I've lived through a number of financial meltdowns now the savings and loan crisis, the dot com meltdown and the last one in the late 80s, the Wall Street crash. It seems like some people say credit unions are gonna be the next great vulnerability. It seems like there's consistent thread here which is that they were all caused by people lending too much money beyond their means. People, it was too easily available credit being given to the wrong people. Is that a problem that can be addressed by data? By better quality data? Or is this fundamentally just greed? I think it's without the data we can't understand the risk that is inherent in lending money to counterparties. I think greed is always at the center of it. I think Michael Douglas was right. Greed is good. It keeps our markets moving. But I think risk controls and understanding and transparency are fundamental. And I think it's the reference data. The mundane, simple, basic reference data about who am I dealing with? What products are they using? What is the risk? What are the cash flows? What's the collateral? If people don't understand that when they're making deals and lending money, then of course you're gonna have another credit crisis. But isn't this more of a regulatory or of an issue, a tax issue? I mean, when these companies blow up spectacularly, it's almost always because the corporate organization is so complex, Enron with all of these offshore subsidiaries, nobody could figure out what Enron was or how it was organized. Enron's a great example. So, I mean, can you fix that with, is that a transparency problem? Absolutely, I think data is the root of the solution. Enron's a great example. Enron was a collection of hundreds of legal entities with complex relationships with other legal entities. During and before the Enron disaster, I worked for a large bank, an investment bank that was exposed to Enron. And we built a counterparty structure like the one I'm talking about. And it's very, the fundamentals of that model of understanding the relationships between the legal entities in Enron who had issued debt and were issuing equity securities and making loans or taking loans and buying or selling commercial paper was what allowed the firm I worked for to get out of Enron before the crash. So understanding the parties involved and the relationships between parties is the most fundamental piece, I think, which is reference data of banking and investment banking and understanding that risk and how you're exposed to all the parties involved will make and break the next financial crisis. And I think we look at, I was at the Mortgage Bankers Technology Convention in 07 just as the crisis was about to unfold. And I remember sitting with some of the older fellas in the groups and they were telling me how, well, the last crisis, the crisis in 1994 or so, 91-92, that was the big one. And this one was not gonna approach that crisis. Well, I think the 08 crisis eclipsed that early 90s crisis by an order of magnitude. And I think, and the next one will also, unless we begin to understand the parties we're working with. You know, banks are reporting earnings just this week. A number of banks reported, including Bank of America reported yesterday. I believe JPMorgan reported yesterday, record profits. I mean, they're going great guns, business is great. Are we just setting ourselves up for another disaster here? Well, I think there's always risk. I think they're talking about increasing capital requirements for banks, I think that may make sense. But I'm not a banker, I'm a technology guy, I'm a data guy. Understood. My view is the banks need to understand who they're doing business with. They need to understand the nature of their contracts with other parties. And if they understand their exposure and their agreements and their commitments and their obligations to other parties, then they can make better decisions than they've made in the past. I think structured products are interesting. We talk about CDOs and CMOs and all the crazy financial instruments that people thought up in the last decade in their 15 years. And they're interesting, but I think if we had better understandings, if AIG had a better understanding of all the counterparty exposure it had, and Hank Greenberg actually had control of that before he left, I think if those details were available to decision makers, they may not make the bad decisions they've made in the past. But it's a good question that you're asking, right? Because I mean, it was useful to go through so that the crashes lived through them as well. When the dot-com bubble crashed, there wasn't a big injection of capital to save the tech industry. That didn't happen. And then of course you had other factors, I mean 9-11 was like the stomach punch there. But one wonders, okay, because it feels like the recovery, I mean it feels like the recovery from the worst downturn in our lifetimes is actually seemingly smoother than it was coming out of the dot-com boom. Significantly, you had 9-11, you had Enron and the money got sucked in by accountants, essentially. And so now you're seeing this huge injection of capital and to your point, is this just all artificial? Yeah, you ought to back up. It seems like, not to step on you, David, it seems like people always find ways around to beat the system, right? So no matter how much disclosure you demand, someone is always going to figure out a way to manipulate it. So is data really the problem? It seems constantly to be escalating as well. I mean, you go back to, like, Lewis's books in the 80s, right? I mean, it just keeps getting bigger and bigger and bigger. So Ken, to Paul's point. I think, to your point, it is the people. Who are the parties? The parties are institutions, they're individuals. Who are they? Who are we dealing with? What's their history? Have we seen them over time with certain behaviors? Are they in a new market they've never traded in before or invested in before? What's their credit rating now? What's it been in the past? Not just right now, but five years ago, 10 years ago, 20 years ago. What's the history and what's the nature? What's the indicative information about this counterparty that I should know before I enter into a deal with them? And understanding that is, you know, people take it on face value that the big banks, that Goldman and Citi and JP Morgan are good for it. And six or eight years ago, we took it on face value that Bear Stearns and Lehman were also. Barclays, you know, or Bering's was, you know, 15 years ago was also one of the oldest names in the business. The bottom line is these things can change based upon the people inside making decisions without information that they need to have in order to understand their exposure and their risk. And I think that it comes back to data. Well, and I mean, there's hope here, right? I mean, I think of just even stock information, how difficult it was to get a real-time quote in the 80s, you had to have a big terminal, you had to be connected, it was very expensive, and that was sort of democratized. And so, well, transparency, you know, potentially, even the guys in the book, The Big Short, were concerned, and they had the data. They were math guys, they had it all figured out. Even they, for a while, were worried that they weren't going to be able to cash out. So, will that access to information sort of create a transparency? And will average people be able to analyze that data and visualize it? I don't know, what do you think? You know, it's interesting, when I joined J.B. Morgan in the mid-90s, I wrote an acceptance letter, and I said I want to help. I was joining the reference data group to lead the counterparty account information group, and I wrote an acceptance letter saying I want to improve transparency and market efficiency, and I still believe that. I think that information is what it's all about. I was reading some articles the other day about some regulators quibbling with Thompson Reuters and some analysts about investors who have a two-second advantage in getting certain kinds of data from the market about jobs and things like that. And to get to that point is just a fantastic achievement because, as you say, 15 years ago when we used Telerate and when we did things in a different world, this transparency wasn't there. And now we're talking about microseconds and even nanosecond delivery of data far too fast for you and I to do anything about it, but if we can write a program, maybe we can. I think that the speed, the delivery of data is great. I go back to my concern. What's the nature of the slowly changing reference data? Who am I dealing with? What's my contract or my relationship with this counterparty? What obligations do I have? And what are the risks I take in getting the cash flows we've agreed to getting? If I can understand that, and that's all data, that's a schedule, that's indicative data about individuals and their credit history. If I can understand that, then I think we can avert the next risk or at least minimize it. I think the problem is people are gonna decide not to follow those controls. These are controls. People don't like controls. Well, when crises happen, lawmakers react. We saw that with Sarbanes-Oxley and you can debate whether or not it was an overreaction, but it's hard to debate that it didn't affect the IPO markets, right? Mark Andreessen wrote an article recently talking about that and how it's really not attractive to be a public company anymore. So, Dodd-Frank, good idea. I've got a friend in New York who makes markets for a big bank. He's been doing the same kind of work in currencies and foreign exchange for a few decades and his business, he's the first to admit is moving offshore. Dodd-Frank is poorly conceived of. It's a lot of great ideas, but it's a bucket of bad ideas at the end of the day because they don't fit together. No one knows how to implement them. I think we're chasing capital away. I see it in some of our clients who are doing more work offshore in countries that used to be emerging and now they've emerged and they're growing like gangbusters and they're happy to take the business from the US and from the major markets. Do we have, what shortcomings, if any, do we still have in this country in terms of disclosure regulations? Are financial institutions adequately disclosing you think what they need to disclose? You know, we were talking earlier with some of our friends from the conference and some of whom have very senior roles at some of the largest banks in the world and the thing that we agreed is that we've not heard the same question from different regulators or the same regulator more than once. In other words, every time we begin to disclose one thing that begets the next question, there's not a, we don't see an end in sight in disclosure. So it's constantly drip, drip, dripping, turning. I mean, the examiners are good people and they just want to understand more and the more we give them, the more they want to know and I think that's fair, that's fine. That's why I come back to the concept of reference data. If I can understand and maintain my list of clients and products and the relationships between them, the contracts that I have, the obligations I have, then I can create any report that my counterparties or my regulators need to see. I can show them my exposure to an instrument or to some dimension of an instrument or an asset class and these are the things I can't do if I have sort of disorganized information about who I'm working with, what each desk is doing and where I want to go with each product. So bring that back to Noetic and how is Noetic contributing to moving that mission forward? Sure, I think our approach is we like to partner with our clients. When we talk about Noetic partners, Noetic means we're rational and we're not, our approach is we want to do the best thing, not necessarily the perfect thing. We want to make sure the data is fit for use and then we want to partner with our clients and we want to make sure our clients reach a level of maturity that they haven't reached before. So we begin with this, we have a model, we call it the Noetic Master Model and it's based upon our experience at the biggest banks in the world and it's a model that begins with the concept of parties. Who are the parties? And I think our clients who've decided that the model works for them include some of the largest banks and brokers and exchanges in the world, have decided to use the model in part or in whole to understand the events, the parties and the products that get transacted. Our model is technology agnostic. It will run on anything you like. If you're a Windows guy or a Unix or a Linux guy and you want to run Oracle or SQL server. But our products are designed to help our customers understand who their customers are, who their vendors are, who their counterparties are, who are their regulators, who are the owners, who are the people who need to know about things and then what are the relationships between those parties? So I think what we bring to the market and to our clients is a better understanding of who they are and where they are now so they can make decisions about where they want to go. Financial services organizations are big IT shops in a lot of ways, right? So a lot of your clients are building out data factories. They've got this data pipeline. I think of fraud detection and how that has changed. Gone from sampling, right? And then maybe getting a call from your financial store letter six months down the road. Maybe you got hacked to, now you're swiping a credit card and you get rejected in near real time. Absolutely. Is that type of analogy or example of the sampling to the, sampling's dead in that example. Can that help us with regard to this overall data transparency and data quality problem? I think it's all part of the bigger picture. I think we need many types of people involved in the process. I think the thing that most excites me is that the tide is turned in the industry, in my view, from a concern about technology, which five years ago, who had the fastest, who had the bestest new thing to the content. What is it that we're doing with this security, with this swap, with this counterparty or this group of counterparties? Who are we doing it with and why are we doing it with them? And it's allowing decision makers to make better decisions. So I think IT, and this conference is all about the new role of the chief data officer or the data czar. It's a role I've had back when it was very contentious. IT didn't want us there because that was a space they felt they owned. Now it's a space, I think, that's legitimate. And the COO and the CFO want someone focused on data quality and data architecture. So the CEO is driving this? CEO, CFO, COO, these are the people who hire us now. Well, the CFO certainly has always been involved from the standpoint of, you know, they always say the single version of the truth. Yeah. You know, post-NRAW and et cetera. But that CDO role takes us beyond the sort of financial domain. Absolutely. Into a lot of unstructured data and marketing. Absolutely. You know, the CFO I see is interested in financial risk. As a CDO, I'm interested in my clients who are interested in operational risk. Like, what's going to happen today? And business value. And transparency. Absolutely. All right, good. Well, Justin, thanks very much for coming on The Cubes. Pleasure meeting you. Thanks for the time. Nice to see you. All right, Paul Gillan and I will be right back. Thanks, Paul. We're live from MIT Information Quality Symposium. We'll be right back.