Nicole McQueen, AWS & Chris Ellis, FactSet | AWS Financial Services

For all the reasons that Nicole described so well, the overarching trend we're seeing is that capital market firms are looking to forge a deeper relationship with fewer partners. I spend almost all my time at FactSet meeting with our clients, talking about their target operating model and where they're going. What I hear over and over again is this consolidation and deepening of relationships. For any critical component of the firm's target operating model, it's a critical source of data and analytics to capital markets firms worldwide. AWS becomes a more important partner for FactSet every single day. I'm thrilled to be here today and excited to talk more about why.

John, to say data is foundational almost feels trite, it almost doesn't do it justice. But let's talk about the cloud part of it. The critical benefit that the cloud has given capital markets firms that they have never experienced before is infinite elasticity of compute and storage. You're no longer constrained by how much data you need to store or how much compute you want to do. The cloud, AWS, gives you that ability to pivot quickly and react almost instantaneously. When you look at the landscape, firms are using AWS and their cloud strategy alongside traditional data warehousing solutions. The mix is a bit different from firm to firm, but everyone is challenging themselves with this question. As you think about new data coming into your workflows and new needs as an organization, you think about where that data best fits: cloud data warehouse, traditional data warehouse, or some hybrid solution. FactSet is all about feeding that hybrid solution with data that will increase the quality of the answer. We start with the problem you want to solve, then ask what data I need to solve that problem. From FactSet's perspective, it starts with our own source data. There are about 30 foundational data sets inside FactSet, like company fundamentals, estimates, ownership, and macroeconomic data. Then you move into more specialized data sets like supply chain, geographic revenue exposures, street account news, and corporate governance data like shark repellent, which are critical to solving the problems you want to solve. Beyond the data in FactSet, because FactSet is so open, there are over a thousand different data feeds from over a hundred different data partners. The challenge is to maximize the scope of the solution we can bring to our capital markets partners and help them provide the most precise answers with the absolute best data. The third leg is all around portfolio analytics: moving from companies, markets, and industries to your own funds, separately managed accounts, and portfolios. How do we bring together analytics around performance, risk characteristics, reporting, and peer comparisons into the same data set? For some, data is real-time pricing ticking in on the fly, immediate and urgent; for others, it's extensive deep history of tick data for robust and ambitious quantitative analysis. But ultimately, this all has to blend seamlessly into the client's S3 data warehouse, tailored to what the client wants and needs. That's what we think of as FactSet data as a service.

That answer starts with our clients. Our clients have to be more agile and Dynamic than ever before. That is a requirement for them to be successful, to not just survive but to thrive. If we are going to be a trusted partner for that client, we can't have a process that is arduous and littered with steps that feel like distant possibilities. We can collaborate with the client and AWS in a tri-party conversation to solve their needs much more quickly than was possible. We can get them solutions running, up and running, and evolving. More important than just up and running is evolving very quickly. As you think about the needs of the client and solution workflows, I personally don't know that we have all the answers today, but we have a great platform to evolve quickly to meet those rapidly evolving needs. Nicole, what's your reaction?

That's a great question. I wouldn't say data management was ever easy, but let's specifically hone in on why it's getting harder every day and will be harder in the future. It starts with the asset classes capital markets firms are investing in. Not that long ago, the asset owners had a simple goal. If you think about the growth of private capital, it puts the burden of data management much more on the client. There is less data, it is not naturally concorded and aligned. It isn't even about the same entities when you think about real estate versus companies. Public and private companies are different animals. How do you bring that data together? On top of that, the data you need isn't structured the way it has been in the past. There is a much greater need for unstructured data, both on private assets and for bringing in unstructured data on the public side. If your data is siloed and monolithic, and it can't be combined with other data sets, then you won't get the best answer when you ask that question, whether in GenAI or any model. It has to be perfectly blended together to maximize the quality of your analytic conclusion. That has always been true, but it is a much greater burden now than it was in the past.

Every element of your data hygiene that is sloppy or imperfect is a crack in the foundation. If you have too many cracks in the foundation, what you're trying to do will come down on you like a house of cards. Getting the data right and cleaning up those cracks to have as perfect hygiene as possible minimizes risk and fallout. On top of that, how do you bring new data sets into that environment elegantly and easily? From a FactSet and AWS perspective, the data that comes from FactSet is really standardized now. You build on top of that with analytics that will not be standardized. When we start talking about performance attribution on portfolios or stress tests and risk for portfolios against a range of events, there is a degree of customization that is not instantaneous, but can happen in hours and days as opposed to months and quarters. That's the process and workflow we've built up that allows you to transform data and derive value much faster than ever before.

John, the way we think about these questions coming in from our clients is we really listen to the question. As I'm processing it, I'm thinking, what data do I need to drive the best possible answer? Everything we are talking about today is real, in production, and ready to go. We are doing this for mutual clients of FactSet and AWS, and we want to get started.

Thank you for having us. This was great.