 My purpose here today, what I was asked to do was to describe briefly the proof of concept using live in the wild data at Street Street Corporation over recent months. As David Newman said, I had the privilege of starting and leading this project with my good friend and former colleague, David Saul, who asked me to step in when he couldn't participate due to a conflict. Let me make clear we didn't aim to push the state of the art in the project, but what we wanted to show was whether it actually works now for business processes with real business people. Unlike David Saul, I'm not a technologist, I'm just a financial services business guy with some experience as a financial markets and data regulator. So my discussion will be very different from the one that he would have given, but in common, we share the conclusion from the work that was done, the five vote as a semantic data standard worked for our purposes and appeared and appears fit to, in fact, make important financial data smarter and more governable. This is important for individual large banks like State Street, but its importance and urgency extend to the broader financial system and its own stability. You should be seeing page two. Yes? Yes. Great. We'll solve this somehow some day, but not now. Let me make clear, if it wasn't already, that I am no longer affiliated with State Street and cannot and do not speak for it. My observations on this work are solely my own. I'm gonna talk a little bit about some background and why this was important to us, what we did and what we learned from it. So let's harken back to an older, better time, the 60s and the 70s when there was a popular TV at a public service announcement with a sonorous base voice and toning late at night, quote, it's 10 o'clock. Do you know where your children are? Well, the situation, as we saw it, was the situation in most financial institutions of any size, it was analogous. Lots of data dear to us in our job responsibilities, at least as our children in our private lives. And yet we often don't know what it's up to, we're hanging out how to get it back when we need it. And that was, and to some extent still is, the current state. Why is this so important? There was a financial economy, right? Let's remind ourselves from the, there was a financial economy that had booms and busts made up of separate but interconnected sub-segments, banks, and asset managers, markets of various times that did their thing. But from the 30s all the way through the great moderation, so-called in the 90s and the early noughts, they basically funded but did not disturb the real economy, the one that most of us live our lives in, even if we may happen to work for a bank during the day. But the interconnectedness of the wildly growing financial economy was the big story and meant more interconnected markets. The crazy ball of yarn that you see is really interconnections between the largest banks, insurers, and sovereigns in the world, only a small piece of interconnectivity. More interconnected institutions meant more institutions subject to stress events and resultant runs and contagions, which in 2008 at least meant that the financial economy infected and devastated the real economy. It leaked out of the financial economy, so to speak, and caused the problems that we all lived our lives in. Data did not cause this, but it's hard to understand securities. Let's call them financial contracts that did contribute and in particular impeded the work of understanding and cleaning up the mess that resulted. So, you know, the central problem was that the terms, conditions, and contingencies of financial contracts, especially swaps and derivatives, like anti-fact securities, CDOs, and so on, were dumb and opaque to analysts and examiners alike. The then instilled current state was like the wonderful picture that David had found of my office, no, it was taken offline, but the current state of financial contracts is that they are opaque, they are complex, inconsistent. Ultimately, if you go to that last bullet point, there is no or very little meaning associated with most of the meaningful terms and conditions of contracts. And there's the, we see the move now, the urgency to move towards contracts that are smart to err with greater transparency, ability to be functionally decomposed into components, well-defined, trusted, and so on, ultimately, semantically meaningful in their hole. Getting to in the blue picker box at the bottom, an evolving definition of a smart contract as standardized executable code that identifies all the components of a legal instrument, defining its economics, its legal characteristics, its logic and contingencies, of valid financial transactions and exchanges. How do we represent all of that in a way that, in other words, computers can consume them and do useful things with them and help their masters of us extract information, knowledge, wisdom, and make better decisions? But what about all that stuff in the contracts? It really got us back to that question or a set of questions based on the, do you know where your data is? Question, do you know where your data is? Do you know where it is? Yes, but also where it is, from who owns it? Do you know what your data means? Do you know how it relates to other data you have? In the end of that first core question, can you take advantage of your data in new and important ways? A second major category that we identified was, do you know which regulations apply to you, especially with respect to data? Can you track them? Ultimately, can you meet your regulators' expectations with respect to data, especially in times of stress, in times of significant need? A third important set of questions was around what your solution providers are doing for you. Do you have the solution set that will enable you to reach best data practices? And finally, but by no means least, do you understand how standards can help you? In particular, can you engage with those who are setting standards and bring those standards to ways that will benefit you and your colleagues in the financial system, and valuable, important, and business-friendly ways? By business-friendly, I don't mean you like them, but ways that will actually help you run your business much more effectively and easily. This is all non-trivial, because banks, like some other organizations, but maybe more so than most, take in terabytes of data daily and transactional and operational data and split it among data warehouses, risk repositories, and other locations locally on enterprise systems, and in the cloud and have to rely, in the end, on reconciliations to make things work. And this is hard, this is expensive, it takes armies of people in organizations, and it's often unreliable or at least non-definitive, and non-authoritative with the kinds of current data practices we have in place. Especially when we're pressed to meet regulatory mandates and get our data and risk analytics in order, notably but not exclusively things like the Basel Committee for Banking Supervisors 239, otherwise known as BCBS 239, we'll get into that here, and to be more aggressive and successful at deriving greater business value from that same data. So, does there have to be a conflict between the left hand and the right hand here, between the regulatory mandates and the desire to do better and more interesting and more commercially valuable things with the data we have? How do we connect these? How do we turn from conflict to opportunity? We felt and still believe that semantic data standards provide the right and best path to make this conflict go away and become synergetic, become a real opportunity. Look, you are a sophisticated crowd, most of you know more about semantics than I could ever dream to if I spent 10 years, but let's all remember some basics here. The semantics are about, at least semantic data, semantic technologies are about giving meaning to data so you can do important things with them and describing that meaning in terms of structured knowledge, whether it's lists or dictionaries or next level of hierarchical taxonomies or more sophisticated still related or interconnected sets of knowledge meaning, meaning ontologies, that financial contracts are complex and we can make them smart or, and maybe truly smart, get rid of the suffix there, by using ontologies effectively, using semantic data. What makes them smart, truly smart, not just smarter is when we're able to agree that there's a standard, a standard that applies to that meaning of the data and when that standard of meaning is held in common cross institutions, processes and jurisdictions and when to use the word that John Logan used a few moments ago, one of my favorite phrases, it's also on the FIBO hack that I imagine is circulating around there, unambiguous business meaning held in common across institutions, processes and jurisdictions and why can't we do that in this space in the financial contract space and that's where we arrived at FIBO. I'm sure this has been dealt with already, that's probably some of the prior content has, but these things don't emerge from just one place. It takes a village, it used to be said, but here it takes a collaboration to build industry standards and common share meaning. The industry, rate regulators and standard setters have all been deeply engaged, probably are represented in the room there in order to make FIBO possible as they have in the past with other important standards and that needs to be maintained as we move forward if this is going to be a useful endeavor and our messages is meant for them for all the participants. I imagine many versions of this page have been seen already, but FIBO is a broad and deep ontology to describe financial contracts and all the supporting knowledge of processes developed by such a public-private relationship led the EDM Council to make the investment that it did in starting and in driving forward this effort. It's still a work in process, we knew that as we got involved, but we thought it was time to try out some of the many parts that were in fact ready. So as a version of what you've all probably seen and I do hope that this will turn out well when I move to the next page, but standards as governance. We decided to look at interest rate swaps as our use case. A typical swap in human language is when one party exchanges a fixed flow of payments for another party's floating as judged by some particular measure and some particular time frames of payments. Each of these payment sets are called LEX. Rest of the chart disappeared and I don't know how to, no, I can get back here. But what if we defined going past those human definitions and move to a definition for every fact type that could be participating in an actual swap, meaning every possible data element precisely in a way that any market participant or regulator could understand at any point in time. That means real transparency. That means a real common language across the financial system and it means a real way around the kind of opacity that was such a barrier in 2008. So what did we do? In this proof of concept, we wanted to develop to demonstrate the practicality of using FIBO to harmonized diverse derivatives and entity data. We want to do that in a using the real people that are available. We weren't planning to bring in or build new teams. We wanted to then apply the data that's been harmonized to carry out a meaningful and comprehensive recording and analytics. Some traditional to check the data and also some of the innovative kinds of work that can and should be done with link to data. We chose to in the actual approach, apply FIBO to real and unmodified operational data, what it called before in the wild, which was done using data from the asset management unit of State Street SSGA. And to use you to do this using an existing commercially available at State of the Arts Semantics platform. There was, let me be clear to say this, there was no intent on our part to do any actual development. We didn't have the folks. We didn't have the people available to do it. We wanted to keep this simple and built a team where State Street was providing the business requirements and operational data. EDM Council provided the FIBO itself. Cambridge Semantics provided its ANZO platform, that particular State of the Arts Semantics platform and services to help implement it within State Street. Dunn and Brad Street came in to provide a business entity and corporate hierarchy data and many things to Wells Fargo, particularly David Newman, or his expertise in making it possible to get through the process successfully and directly. So what was the architecture for this? It was fairly straightforward. The data came from the most part a State Street platform called Front Arena that contained all of the SWAP data. That SWAP data was extracted. And external data, while State Street had originally planned to use its own store of entity data and entity hierarchies, was a very fortunate opportunity to engage with D&B to be able to apply this to an additional and even more external data set provided the entity and corporate hierarchy data. All this data was brought together into FIBO and into the ANZO platform, where it was possible to be to carry out the mapping and other important processing, the mapping and the loading of the data that enables linking and querying the generation of reports and analytics. Sadly, I can't go deeper because of the way this is working out can go deeper into some of the charts, although they wouldn't even be that readable at full size. But a more logical approach is time was taken to load an operationalized FIBO in the ANZO platform. Let me be clear in saying that it turned out that there were challenges that we had to engage in there but it was either than expected. What really blew us away was that the actual mapping. So the second step mapping data sources into FIBO, which maybe was out of fear of the ontology, but it turned out to be a near trivial effort. SMEs and a couple of ANZO experts that we were able to in basically in our map data stores. Real surprise, a real thrill. Some time was taken to load and harmonize and conduct QA on the data and then to classify to make sure that we had it exactly right that data from our sources were going to be meaningful and connected across. And then building analytic dashboards, which you see here and going beyond the conventional analytic dashboards to the analysis that I'd love to show you in a larger size at the top, which I can I guess since I have control of that. Not too useful, unfortunately. So I'll leave it there, but which is basically a follow the money chart, a connection of all of the cash, all of the legs to all of the relevant institutions that were counterparties of State Street all the way up through their hierarchies to the ultimate parent. Some of the banks of some of you in the room that were there. And it was magnificent to see that actually work. Our findings were fairly exciting. Bottom line, we're starting at the top is that the FIBO model, at least for interest rate swaps, we can't speak beyond that appears fit for purpose and able to map easily and readily to harmonize with data sources that weren't developed with FIBO in mind to provision important and innovative analytics both the traditional and the forward-looking and innovative. And that it was very simple to add new sources, whether they came from inside or outside and create new analytics. There were some challenges we had. There are ways in which, in particularly the data set is the ontology is not truly intuitive. Everything was there. It just wasn't always obvious for a newbie to be able to find and map, but with help of David and others, we were able to solve these problems readily. And frankly, this is a learning curve concern in many cases, increased years by folks like State Street and others will drive improvements and vendors will come up with the tools to make these things work even better. The platform that we used and so worked phenomenally well and delivered the kind of value that really was exciting. You know, because we were using open standards, it meant that the model and the tool worked together seamlessly. There was no need to build connectivity or do custom work. Probably the most exciting one, I wish this were in color and bold and italicized was no coding was required. We didn't have to have an IT to make this work. We mapped, harmonized, analyzed and visualized using the standard and how it was able to be expressed in filters and other simple tools. There were delays involving the platform, but those were in many cases because of the availability of people access to data and IT resources mostly set up not for operating the system. And there were some updates to FIBO that were required along the way. In the end, and again, this should be colored and shaded and so on, it really facilitated the construction of simplified operational ontologies for state streets use. Everything we did was standards-based. There were some one-time adaptations because of where we were on the learning curve and where FIBO is on the learning curve, but it worked. It worked and it worked well and our looking to the kicker was as I said at the beginning, FIBO is fit for purpose. It is ready to be part of a comprehensive data governance program within a financial institution, at least. Just a final comment here. I won't go into this in detail, but when we look at the different folks who are involved in bringing about trust in financial system standards is really at the foundation as the little Greek temple so shows. We are financial services organizations. You have product and service providers. You have regulators, standards organizations. When we all work together on an endeavor such as FIBO exciting things happen and if you look to the green box down in the southeast, there are the benefits of competence, growth, lower risk, heightened transparency, efficiency, effectiveness, simplicity, precision. These are all qualities. They're Boy Scout oath words, but they're also qualities that we need to see in the financial system and that seem to be quite possible with a public private collaboration going further with the FIBO standard. There are a number of folks who had comments on where this is going or we extracted, but I think it helps to end with a look at the first two coming from regulators. One, the Office of Financial Research at the US Treasury and on the moderate of mine. The second, the Central Bank of Ireland, which has been very exciting and interesting things in this direction. Some ways to overcome the challenge of being a smaller market. If I can go to Gareth Murphy's comments, it's not as efficient to collect data on the same sector from different sources, different formats. It will lend itself to the use of data and building a picture of financial entities and other intro linkages. What that is supposed to imply is there is need for the mechanisms to make these linkages come to life, to make them automatic, to make them real or an operational, whether you're a big bank, a small bank, a large regulator, a small regulator, wherever you are in the system. Final slide, last reference to the, do you know where your data is concept? So if you decide that you don't and you need more, the question is what do you do that next morning when you come on in? And I'd like to close that, the suggestions we had were, you have now the opportunity to develop the right definitions in discovery process. Know your data, know its meaning, know where it is. Catalog and monitor those regulatory requirements that are increasingly data. Develop the heat map of how your solutions carry out the job and what you need to fill the gaps in your infrastructure. And then find an influence relevant semantic data standards in your business and with final endorsement here that 505C is able to carry out the job. Sorry for the challenges in making this go forward. Thank you very much. And let me stop now for questions or comments from the other folks on the desk. But first I wanna kick off with one question for a low preside from Cambridge semantics and a low, please come up here. You're hooked up. So, okay. So could you tell us a bit about the operational side of this, the technology, the semantic infrastructure that you applied for this proof of concept as well as what other use cases in the financial industry would this pattern apply to? I'm hoping everyone can hear me. So just a background where a smart data platform company using semantic technology and our platform is used both in data management and in analytics. And for the piece which we did the POC with State Street and the Denver Brad Street out here and with David Newman's help, that was much more our analytics platform where the data was brought in harmonized and then used to create dashboards. What we are seeing in the last one year, we've seen a lot more adoption in both semantic technology in financial services industry. We had seen before that, we had seen quite a bit of adoption, especially outside of financial services industry for using semantic standards, especially in pharma, which is a big vertical for us. In the financial services side, there are really two things which we are seeing. One is using ontologies and semantics for not only harmonizing data and using it for data management. And there on that side is really for providing business understanding of what data lies in which systems and then using our tools for creating data marks or data extracts on the fly. And there's a demo at our booth which you can see on that side on our smart data manager we're calling. We can see some of the examples. The other thing which people are doing is really doing analytics at large scales and giving users self-serve tools for discovering and analyzing diverse data from different sources, structured, unstructured, internal and external data. And while IT can govern and manage that data, so they can get some of the data quality elements into it, but also define who sees what and have some policies there and do it at large scale. And based on that platform, people are doing a variety in financial services, doing a variety of solutions and applications. So they're doing solutions like voice of the customer, they're doing solutions like knowledge management solution, understanding what's available, what data is available across the enterprise. They're doing compliance solutions. There is a white paper which we have on our website on PwC using it for insider trading and compliance investigation. So there's a whole solution piece which people are doing.