 Hello everyone, welcome to theCUBE's presentation of the AWS Financial Services Partner Series. I'm your host, John Furrier, here in theCUBE Studios in Palo Alto. And today we're excited to have Nicole McQueen, who is the head of Global Technology Partnerships with Capital Markets for AWS and Chris Alicenia, Director of Business Development at FACTSET here to talk about the partnership between FACTSET and AWS Financial Services Team. Folks, thanks for coming on theCUBE today. Appreciate it. Thank you for being here. Thanks for having us. You know, we've been doing a series on this first season of, you know, the financial services and data and, you know, with AI being such a hot topic, it's not new to the financial services. I mean, pioneering data, data availability for acquisition of knowledge, all kinds of high-frequency trade, we know what's going on there on the cutting edge, but a lot's going on now and the role of data is more important than ever before. So this is a great conversation, looking forward to it. Let's set the table. The industry trends right now in the capital markets of data and data management is evolving very quickly. How do you guys see this industry market changing, evolving, what's this wave look like? Sure. Yeah, I can take that one. John, thanks for that. So for us, look, over the last decade, the velocity of change impacting financial services has been truly unprecedented, right? Rapidly evolving customer expectations, new industry players, powerful emerging technologies, new regulations have all really converged to form a new landscape. In addition, the COVID-19 pandemic really accelerated digital transformation as financial institutions responded to the whole new remote everything world. What we're seeing in capital markets is that to remain competitive, firms are mining both new and existing sources of information and engaging in transformation initiatives really targeted at lowering their cost structures. At the same time, they must comply with increasingly rigorous and complex regulatory standards in all of the countries in which they operate. AWS and our community of partners are helping capital markets firms power data-driven decision-making, accelerate their time to market, lower costs, strengthen security, and comply with regulations all while really striving to enrich our customer's experience. Chris, how do you see that fact said? What's your view? Yeah, I mean, for all the reasons Nicole described so well, the overarching trend we're seeing is the 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, and this is where what I hear over and over again. As part of this consolidation and this deepening of relationships, those capital markets firms expect their partners to collaborate together on their behalf instead of dumping the burden of integration back on their firm. This is as true, if not more true, for data warehousing and data management than it is for any other critical component of the firm's target operating model. That's awesome. As 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 about more about why. I love it and appreciate that. And I think one of the things that's clear in the macro industry is speed, velocity, agility, time to insights, the competitive edge is really what people do and they want to do it fast. So I have to ask you guys, as you look at the cloud trends and some of the challenges you guys see with customers and turn them into opportunities, what are your customers focused on now? Is it integration? Is it the data management? Is it the cloud scale? How big of a role does data play in how your customers and these firms are thinking about how they're going to innovate for their customers and get that competitive advantage and even provide personalization experiences around that. So a lot to going on there with the customer as they start to operationalize and set their operating models for the future. Yeah. So John, I think to say data is foundational almost feels trite. It almost doesn't do it justice. But let's talk a little bit about the cloud part of it, right? The critical benefit of the cloud that these capital markets firms have never experienced before is the infinite elasticity of compute and storage. You're no longer constrained. If you know how much data you need to store and how much you want to compute, you want to do on it, the web, I'm sorry, the cloud, AWS gives you that ability to pivot quickly and react almost instantaneously, which was frankly never possible before. It's all about bringing them the data and the analytics into the data warehouse container or cloud solution that the client wants. This is increasingly kind of a hybrid strategy for the capital market firm as they're looking to deploy AWS and their cloud strategy more alongside traditional data warehousing solutions. So the mix is a little bit different from firm to firm, but everyone is challenging themselves with this question. And as you think about the new data that's coming into your workflows and the new needs you have as an organization, maybe around Gen AI, maybe around another product or another solution that you have to bring forth, you think about where does that data best fit? Is it in my cloud data warehouse? Is it in my traditional data warehouse? You know, what's sort of the hybrid solution there? FACSET is all about feeding than that hybrid solution with the data that's going to increase your productivity, increase your agility, and increase your efficiency at the end of the day. So when we think about sort of, what are the data that we're talking about there? Because, you know, again, you solve the problem or you think about the problem you wanna solve and then you immediately have to go back to what data do I need to solve that problem? From FACSET's perspective, it starts with our own source data. So there's about 30 foundational data sets inside of FACSET, like company fundamentals, estimates, ownership, macroeconomic data, that I would say kind of your core foundational data sets, but then very quickly, you move into more specialized data sets, like supply chain, geographic revenues, exposures, street account news, and corporate governance data like shark repellent that are critical to solving the problems you wanna solve. But then beyond the data that's in FACSET, because FACSET is so open, there are over 1,000 different third-party data partners across a really broad range of categories, whether it's indices, risk models, ratings. We think about combining FACSET data with third-party data in a complementary way to maximize the scope of the solution that we can bring to our capital markets partners and help them provide the most precise answers with the absolute best data in terms of what they're doing. The third leg of this beyond the FACSET data and the third-party data is all around portfolio analytics, right? Moving from the companies, the markets, the industries to your own funds, separately managed accounts, your portfolios, really any of your investment vehicles. How do we get the analytics that are around performance, risk, characteristics, reporting, peer comparisons? How do we bring that together into the same data set for fundamentals, for companies, for economics? It's all part of the same cloud data warehouse and available at your fingertips the same way. And then last but of course not least is the pricing data. For some users, that pricing data is real-time pricing ticking in on the fly, immediate and urgent. For others, it's extensive deep history of tick data to do some pretty robust and some pretty ambitious quantitative analysis. But ultimately this all has to blend seamlessly and it tailors into the S3 data warehouse to what the client wants to use and what the client's needs are. And that's really what we think of as FACSET data as a service. So, Nicole, you got to love the velocity of the data right now. FACSET's data rich, they got customers that have data that comes together, the data as a service. Why build something if you can just call a data API in the future? I mean, you guys have API with the cloud, Amazon's pioneered that model, now at scale comes the data. Your customers, if you're data rich right now, you got to get advantage with AI in all this future. Yeah, I know, absolutely, John. Look, I think there's a lot of different ways we can take this as far as velocity, access to data, migrations to the cloud, what are financial services firms really focusing on? Right, when there's frankly so much to do. On the topic of data specifically, the explosion of data and the ability to store and analyze it cost effectively in the cloud has had a major impact on our industry. This has led financial services companies, frankly of all sizes, to explore the unlimited possibilities for using data to innovate and improve their businesses and their business outcomes. You know, I think the thing about the cloud, going to the next gen, FACSET has all this data, clients going to want the data. This is an example of where the future is going. Data as a service combined with a hybrid, you mentioned hybrid models, also key. I got to ask how cloud enables Chris FACSET to increase the efficiency and become more agile because I mean, a couple of dots are connecting, right? You see the data growth. I mean, budgets aren't increasing as fast as data is, but new development capabilities, new data advantages, mixing data. There's a lot of alchemy involved. How are you seeing the cloud specifically enable FACSET to increase the value efficiency and become more agile for your customers? All right, John. I mean that answer really starts back with our clients. So our clients have to be more agile. They have to be more dynamic than they've ever been before in the past. That's just a requirement for them to be successful and to not to survive, but to thrive going forward. So if we're going to be a trusted partner for that client, we can have a process that's arduous and littered with bottlenecks for them. Our partnership with AWS allows us to be more responsive than we frankly never thought we could have been in the past. Even that, when I say the past, I'm even talking 18, 24 months ago as what feels like the distant past. We can collaborate with the client in AWS sort of a tri-party conversation to solve their needs so much more quickly than was possible and really get them solutions running and up and running and evolving more important than just up and running, but evolving very quickly. And as you think about sort of what are the needs of GenAI and client solution workflows, I personally don't know that we have all of the answers today but we have a great platform to evolve quickly to meet those rapidly evolving needs. Nicole, what's your reaction to that? I mean, obviously they're moving in the cloud direction to get arduous tasks. Andy Jassy says heavy lifting, more time for creativity can emerge when you can automate a way and use AI and data effectively. Absolutely, so look, an example of how we're enabling our customers to increase efficiency and agility is accelerating speed to market. Financial services institutions can go from an idea to implementation quickly using our comprehensive portfolio of cloud services and purpose-built solutions from our industry partners such as FACSET. This accelerates innovation across the front, middle and back office. AWS offers a broad and deep set of over 200 services that financial services firms can use to experiment and create new cloud-native applications from infrastructure technologies like compute storage and databases to emerging technology such as the AI and machine learning technologies that we're discussing here to data lakes and analytics. We're continually accelerating our pace of innovation to deliver new technologies to help financial services customers transform their businesses. From a partnerships perspective, many of our partners offer their software services and solutions through AWS Marketplace, for example, where it's easy to find, buy and deploy software solutions in a matter of minutes. Our customers can also access over 1300 data sets and APIs relevant to the industry through AWS Data Exchange, right? So that has everything from insurance claims to ESG data to cryptocurrency data, all easily accessible through a single cloud interface. So an example of this actually is a customer of ours, Goldman Sachs, who has publicly highlighted AWS Data Exchange and AWS Service that FACSET is leveraging for data distribution today as a key component of their financial cloud strategy as it reduces that friction in sourcing financial data from both new and existing third-party providers, enabling them to ingest and process third-party data much more efficiently, cutting down that time spent on ETL and accelerating their time to value and insights. You know, it's so exciting, Nicole and Chris is the cube when we started 13 years ago, we were really evangelizing it. Big data is going to be big, it's huge. It's the new oil, remember those days back when big data was hot. It's kind of now 10 years later, a decade later was seeing the transformation really at such a tremendous scale. Mainly because the cloud, the AI stuff is hitting at the right time, the combination of the more compute, I love that compute and example, Chris, we had someone on the cube say, compute should be oxygen, it should be free. I'm sure Amazon would be like, okay, it's somewhat free. It gets freer as it gets cheaper, but you start to see real velocity, charatical changes. I got to ask you, Chris, at FACSET, because again, you guys have been grounded in data from your entire business models based upon it. You've got great clients, they've got applications, they've got needs. How important is the data management piece of this for the capital markets? It's evolved from the old school data warehouse days to get some queries in there, slow, now you've got data in the cloud, now you have cloud next generation where you have applications that are going to be impact data as code, data marketplaces. I mean, the creativity and the development of markets is going to change, so I have to ask you, as you look at the next generation, how important is the data management today, and what is the future with Genervai? Because Genervai is going to generate more data, it's going to generate more things. Take us through your view and vision. Yeah, so, so John, that's a great question. I would, I certainly wouldn't say that data management was ever easy, but let's specifically hone in on why it's getting harder every day and it's going to be harder in the future. That starts with the asset classes that capital markets firms are investing in. Not that long ago, the asset owners of the world were in classic 60% equity, 40% fixed income allocations. Now we're seeing the growth of private capital skyrocket and it might be 40% public equity, 40% private capital and 20% fixed income, but if you think about the growth of private capital, it puts the burden of data management so much more on the client. There is less data, data is not naturally concorded and aligned and it isn't even about sort of the same entities when you sort of think about real estate versus even companies, right, public and private companies. It's a different animal. How do you bring that data together? On top of that, the data that you need isn't structured the way that it has always been structured in the past. There's a much greater need for unstructured data both on the private company, the private assets, as well as bringing in unstructured data on the public assets. It's just increasing the amount and the nuance and the complexity of the data that's coming in and then your ability and your need to concord it is more important than ever because if it's sitting in a data silo and it's monolithic and it can't be combined with other data sets, then you're not going to get the best answer when you ask that question whether it's in Gen AI or whatever model you're in. It has to be perfectly blended together to maximize the quality of your analytic conclusion. That's always been true, but that's just a much greater burden than it was in the past. Nicole, the scale what he just said is amazing because that's really the core challenge and the opportunity if you get that right, there's a lot of things going into it. It's almost a new workflow, a new methodology. And so you're going to need more resources. You can't hire more PhDs. This is where the cloud comes in. This is where you guys have a lot of focus. Take it through that piece because this is, again, game changing. This moves the needle if you get it right. Absolutely. Look, I completely agree with Chris and you, John, that data management, it can seem daunting, it's enormous, it's huge, it's exponentially challenging and growing. And it's even more so, frankly, when you're trying to kind of break down and deal with huge, complex legacy infrastructures that companies have built over decades, right? It's a very daunting task, but the growth in technical users, the need to evaluate new data sets quickly and the ever-growing data available require companies to seek out better solutions. And we are here to support our partners and our customers in achieving that. It's really about harnessing data and using analytics and machine learning to remove that friction, right? And improve the experiences for our partners and our mutual end customers. That's awesome, and this is a great conversation. And again, by the way, we're on the cutting edge of going to the next level. We're already kind of here. You guys have to address this at FactSet because you guys have a lot of data. And again, I've been saying on theCUBE, if you have data right now, if you've done some good data hygiene, you're going to be have a tailwind with AI, Genevieve AI, and others. So congratulations, FactSet, and the others on this partnership. I see great practicality to it, as well as setting the table to take advantage of the growth. Final question on this is that as people look at the data, you're starting to see a power law. Chris, you mentioned specialty models. You're going to start to see data sets. Maybe why should I reinvent the wheel? I'll just call another data set. If it's got high-quality data, it could be smaller and more acute, more better. You're starting to see kind of this mindset of data as code. What data sets, I mean data sets are valuable. Are you guys thinking about making this more available? How should, what are customers seeing today from you guys on the data sets they have and the service you have, the marketplace? Give us an order of magnitude scope of what's the data sets look like? Are they available? Can they be developed on? What's the, take us through that piece of it. It's now an important piece of it. Yeah, it's really important, John. And just to back up a second on one of the things that you said before, when you talked about data hygiene, every element of your data hygiene that is sloppy, is imperfect, think of that as a crack in the foundation. And if you have too many cracks in the foundation, what you're trying to do is going to come down on you like a house of cards. So it's getting that data right and cleaning up those cracks and having as perfect hygiene as you can have to minimize the risks and to minimize the fallout. But then on top of that, how do you bring new data sets? How do you bring any type of data really into that environment elegantly and as easily as possible? So from a fax at an AWS perspective, we put 90 of our financial and alternative data sets sort of spoon fed them for immediate, almost instantaneous access through AWS. Those are the things that are really standardized. Now, you build on top of that with the analytics that aren't standardized. So when we started talking about performance attribution on portfolios, stress tests on risk for portfolios versus a range of different stress test type events, there's a degree of customization in that. That's not instantaneous, but that can happen in hours and days as opposed to months and quarters, right? So that's really the process and the workflow that we built up that allows you to transform this data and derive value much faster than you've ever been able to before. You know, it's Chris that's interesting you mentioned on hygiene, I think it was going to be around for a while. It's going to be a conversation that's going to change. But it's interesting. If you have these data sets available, think about what you could do, right? You could start thinking about things differently. You can bring in an element and bring in other external data and actually look at the role of that data point in a product, in an analysis, in an insight. You get better visibility faster. I mean, this is a new AI kind of benefit, Nicole. This is kind of what we're seeing. No, for sure. For sure. Yeah, no, totally plus one. Yeah. You know, I think, look, from an AWS perspective, you know, our innovative data and AI and machine learning services right there, the goal of those is to allow financial services institutions really to automate and optimize processes and capture data more quickly and more efficiently to your point exactly. You know, make it more usable faster. Yeah. Well, I really appreciate this conversation. It's on the cutting edge, it's relevant, it's cool, but it's really practical. You got a great example here with fact set. Thanks, Chris, for taking the time. Nicole, this is a perfect storm of innovation and opportunity with data as the script flips, but the faster the time to value. While we wrap up here, take this last minute to share what's going on. And fact set, is everything here ready to go? Put the plug in. Yeah, John, I mean, the way that we think about these questions as they're coming in from our clients is we really look at it. I listen to the question and as I'm processing the question, I'm thinking in the back of my head, what data do I need to drive the best possible answer to this question? Everything that we're talking about today is real. It's in production, it's ready to go. We're doing this for mutual clients of fact set in AWS and we want to get started. Well, I appreciate the honor to host this conversation as we look for highlights in the industry where you see data rich, data advantage, kind of be a tailwind into the growth. And AI and good data makes things better. And then as new things emerge, net new competitive advantages, new solutions for customers emerge and new opportunities come out of it. So it's a real example, Nicole and Chris, thanks for sharing your thoughts and commentary here in theCUBE, really appreciate it. Thank you for having us. Yeah, this is great. Thanks, thanks for having us, John. This is theCUBE here in Palo Alto, bringing in the folks and talk about the financial services industry and the partner series with AWS fact set and it was together, making things a reality now and today shipping, thanks for watching and we'll be back with more after this break.