Snowflake Inc. (SNOW) Earnings Call Transcript & Summary

Hello, everyone, and thank you for joining today. My name is Tom Gray. I'm a principal focused on Snowflake marketplace and data and app strategy for financial services. Before Snowflake, I had a long background in leading data and implementation teams in the fintech sector. So excited to chat with you guys today. Today we'll be talking about data to dealmaking and how Snowflake and Equilar are transforming the industry as it pertains to private equity and customer banking. We're going to go through some introductions. I'd like to introduce David and Peter. David is the CEO of Equilar, and Peter is the Director of Business Technology at Warburg Pincus. First, I'm going to go into some context about Snowflake. I'll provide a little overview of how organizations are thinking about data collaboration. We'll talk particularly around native apps, which we're super excited about here. And then I'm going to pass the mic to David. David is going to talk about how Equilar has partnered with Snowflake to build RainMaker. And then lastly, we will round things out with an executive panel, during which we will be joined by Peter, who will share some insights in terms of how to access data integrations with different workflows and solutions. We'll talk about enterprise data strategy. We'll talk about AI. Of course, we have to. And we'll also discuss specifics around deal analytics in his experience at Equilar. So looking forward to the session. So I want to start and talk a little bit about some trends and considerations that we're seeing in the PE space. One of the big things we're seeing is that volumes continue to improve, but there are some key areas that remain critical for execution. And one of those areas that we'll focus on today is how do you access more third-party and alternative data. There's also the rise of alternative assets in the industry, and really this continued pivot to digital integration tools and move to things like cloud technology to really transform the industry. On the Snowflake side, we're super excited about the opportunity in the space. One of the areas that we focus on is the distribution of data and apps, and we'll get into how specifically that ties into the Equilar app today. But with Snowflake, for those who aren't familiar, we are a cloud data platform, where folks are able to run a bunch of different workloads, including data warehousing, data engineering and data science. One of the things that the platform does quite well is this ability to allow folks to distribute across cloud. And with Snowflake, we sit on top of all 3 of the major cloud vendors. So AWS, Azure and GCP. We allow folks to essentially get live access to content and apps where there's really no ETL required. And so the beauty of this model of being able to apply both data and applications is one can get instant access, right, to the insights. And there's really a lot of less work from a data pipelining and application management perspective. And just continuing with that line of thought, one of the areas that folks are presented with these apps is through the Snowflake Marketplace. So with the Snowflake Marketplace, we have, at this point, well over 2,000 data, apps and services, including all the great applications and content from our friends at Equilar. And as I mentioned, right, this data is available for instant access. There's no movement of applications or data in the ecosystem. And then below, you can see some stats on some of the traffic we're seeing. So today, obviously, we're very focused on talking about private equity and what we're doing there. But we have a whole variety of customers who are gaining benefit to this across many different industries. You can see close to 40,000 people are visiting the marketplace every month. But with that, I'm going to pass it over to David, who's going to talk specifically around their RainMaker application and really go into how they are transforming the space.

Great. Thanks so much, Tom. Well, let's go to the next slide and high-level overview. So we're a unique company where we both have data and also a native app, which we'll get to in a second. So I'll focus on the data side first. We'd like to say we've built the world's most connected network. We have over 2.5 million profiles, detailed work histories, career time lines and whatnot. But the other unique piece that we bring is we have also identified which executives have overlapped with each other during their careers. And what we're helping is we like to say solve that last mile. We all -- this is a business challenge, not only in private equity, but frankly in any B2B business. It's like you've identified a company you want to get to, but more importantly, how do you get to the right people within that company. And rather than trying to cold call your way in, is there somebody within your network, people that you work with that can help get you there, and that's what we're solving. So going to the next slide. The question is, okay, well, how do you get this data? Are you pulling this from LinkedIn? And I want to be very clear. This is our data set that we've curated coming from 3 primary sources. One, as you can see on the left, there are press releases. So literally as press releases are being filed every day, we're ingesting those and tracking those changes and updating people's profiles. SEC filings. We've got 20-plus years of history where we've tracked which executives have served together as a senior executive within a corporation or on the Board. And then mining corporate websites. And we're up to about 700,000 companies in our data set, and we're monitoring corporate websites roughly about once a week to detect changes on when executives or Board members are being added or taken off. So that's -- so if you think about those 3 sources, we've basically have built engines to adjust all of that. And then on the back end, frankly, the challenge here is how to clean all this up, make sure you have a golden record for an executive that goes across the various companies. So that's really the uniqueness of what we're doing. So if you go to the next slide. So now that you have this data set, we can then identify which executives -- when they show up in a filing together, when they show up on a corporate website together, we can then start to infer those relationships. So as you can start to see here, on the left, you can see the Excel file showing like the raw detail of individual connections. But then right -- to the right, we're visualizing how you may be connected to -- this example is Lionel Nowell. And so that's essentially what we're able to do, where it gets back to that solving that last mile. So we're trying to get to Lionel, who in our ecosystem can help us get there. So if you go to the next slide. So if you think about private equity firm, there are many different teams within PE, and Peter will talk more about this later. And we like to see ourselves as that connective tissue where, hey, on the talent side, some -- they're recruiting executives. And oh, by the way, a deal team is pursuing an opportunity where one of those executives that they're talking to may be connected. And today, many PE firms and other businesses, the left and the right hand, may not even realize they're talking the same individual. And by unifying all of this with our RainMaker app within Snowflake, we're allowing the different teams to be able to share this knowledge and this information to, frankly, be much more effective and to ultimately win. So it gives you a sense of if you think about private equity -- but this can also be any, frankly, B2B business, an investment bank and whatnot, that there are these relationships that go across the various departments. And the beauty with Snowflake is we're able to pull all this together. Going to the next slide. So talk about RainMaker, what is that? So it's a native app that we've built on Snowflake. And on the left-hand side, we're pulling in different signals that are sitting in different data silos. And with the power of Snowflake, we're able to unify all that and bring that into a single data warehouse. And then with our native app, enrich it to be able to show how your network -- who they may be connected to. So you may know who you know but you don't know who they know, and that's what RainMaker is all about, is bringing that extra hop of all these great relationships you have and their network. And the most basic one, you take at the bottom there, e-mail. Your e-mail server is such a gold mine of relationships and recognizing who is Peter e-mailing regularly, who does Tom e-mail regularly. Well, now if you start to layer on the networks of who they're connected to, now you're able to take that gold mine and start to find those diamonds in the rough to say, oh, my, I didn't realize that the person I've been trying to e-mail is actually connected to the CFO at this opportunity that we're trying to pursue. And so that's what the Rainmaker native app is, to be able to pull in the disparate signals, unify those and then enrich those. And so what we then have available within the native app is you can go in and test this out. We can -- you can pull in your LinkedIn connections, and this is available to anybody. You can go right after this call, download your LinkedIn connections, upload them, run the native app. And then it will then show you in the state of Massachusetts, based on who you know, here are companies in the state of Massachusetts, how you can get to key decision makers off of that. So that's all available for free. And we made it super easy. So just go download your LinkedIn connections and take it from there. So that's a very high level what the RainMaker native app is all about. We can talk more about that later, but I'll turn this back over to Tom.

Thank you, David. And now I'd like to turn it to our executive panel. I'd like to first give Peter the opportunity to introduce himself and tell the world what he's doing at Warburg Pincus.

Sure. Thanks, Tom. Peter Connelly, I'm a director within our business technology team at Warburg Pincus. I spent my career in financial services. I've been at Warburg Pincus for 14 years now. Hard to believe but true. Various roles. But my primary focus today is to ensure our dealmakers and our fundraisers have the technology, the processes and the workflows in place to enable their desired income. So that's really what I'm thinking about and focused on these days.

Thank you. And I began the webinar with a couple of trends and considerations that PE and banking executives need to watch out for in 2024. Are there any other priorities that stand out to you? Or are there others that will be top of mind for the industry this year?

Yes. What we're seeing is a tremendous amount of interest from business leaders, like Peter, recognizing that. We're seeing the rise of Chief Data Officers, Chief Digital Officers and businesses are -- PE firms are recognizing, hey, we've got all these assets, we need to unify those. And we want to build a strategic asset. Everybody has got capital. That is -- I wouldn't quite call it a commodity, but it's very difficult to differentiate off of capital alone, so they are looking to build out strategic assets. And if you take a great firm like Warburg Pincus, I mean, they have an amazing network of executives that they've worked with over the years and to be able to harness those relationships and to be able to use those to touch on the various teams within Warburg Pincus. So that's exciting to see, that firms are recognizing that opportunity and looking to build those out.

Yes. And I'll add on. I mean I think the obvious ones always top of mind are things like data security, gen AI, unless you've been under a rock. But what we're really focused on is kind of the increasing complexity and competitiveness of the industry overall. So the need for data solutions, not just for operational efficiencies, but also to enable intelligent decision-making. And so that's where we spend a lot of time thinking about that and how to help there.

That's great. Peter, I'd love to hear from you, what are some of the key challenges or PE workflows that you're looking to solve today?

Sure. I think the industry, probably like all industries, continues to shift. What defined an interesting deal opportunity in 2022 is not necessarily the same things that define an interesting deal opportunity today. And that truism exists across all of our business units. So that's challenging for us. It really creates a matrix of like deal attributes that are interesting to various teams. So thinking about that, thinking about flexibility and optionality, that's -- those are challenges for us to kind of manage all of that across an ever more complex and competitive industry.

And what are some key considerations when you're building out a technology and enterprise data strategy like you've done at Warburg Pincus?

Certainly, flexibility is a primary consideration. We need to be able to adapt to this changing environment, whether that's like a new asset class or just a general change in priority. Time to market, of course. We don't have the luxury of taking a year to implement technology solutions, I don't think anybody does, or even data solutions. And third-party data and the ability to easily and quickly ingest that data, share it, break down those data silos, combine that data with our data. It's one of our key considerations when we looked at tools like RainMaker.

Awesome. Third-party data is obviously near and dear to our heart in Snowflake Marketplace. And David, on that topic, obviously, building RainMaker as a foundation, why do you think it's critical for organizations to think about data strategies and how tools like Snowflake native apps and RainMaker can fit into that strategy?

Yes. I mean at the end of the day, why do you need data? It's to be able to make decisions. And you want to make sure you're making informed decisions and making accurate decisions. And as Peter pointed out, the world is -- we're in a sea of data, right? And that's sea just -- we're just being flooded with it. So it's not a quantity issue, it's a quality issue. And the key here is recognize there are disparate data silos, and you can dump all that together. But as Peter pointed out, you want to -- are there opportunities to enrich what you have. And then by -- it's a classic 1 plus 1 equals 3, where we're able to take what's sitting in for -- I mentioned earlier like Warburg Pincus' e-mail server and then layering on our data to say, okay, look, these executives that you've been working with or e-mailing, look how they're connected to all these other individuals. Now all of a sudden, I've got this incredible asset that you didn't have before. In our discussions with numerous PE firms as we've worked on this, it's like you don't understand how many times we found out after the fact that we're in another Board meeting. Peter is smiling here that one of the other Board members, when he talked about this deal we just lost and like, oh, I know so and so with that company. And so that's really what we're helping, is like you just don't want to be in that situation where after fact that you missed out on that opportunity.

Right. And it's pretty fascinating. Instead of word of mouth or the old school who do you know, to actually look at the data, right, and pinpoint connections you may not have even known you've had. It's fascinating.

Yes. And sorry to jump in here. Because we're working with one major investment bank, and they said this has been a game changer for them, and they -- something that they talked about that they've been trying to build for a while. And we look at Q4 league table results. That had an immediate impact on their business to -- because like I said, this is the question. The Monday meeting. The teams are reviewing deals and like who does anybody know there, anybody is trying to go on LinkedIn and trying to dig through this stuff. And LinkedIn is great. I love it. I use it every day. But let's face it, right? We look at -- we pull somebody up, we have 80 people in common, right? It's like, okay, which of these 80 do I even start? And yes, I looked at my LinkedIn connections the other day, 3,000. At least 1,000 I cannot identify how I know this person, right? And that's really what the challenge. It's not the -- having a lot of connections but having the more meaningful ones that you're like, oh, that's a valuable one there.

Yes. And having the richness of the associated facts, right, of -- it's not just some random person who sent you an invite and you connected, it's the real -- what's the actual relationship there.

Yes.

And what are some of the technology and business use cases that can be addressed with cloud-enabled data access and collaboration?

Well, specific to the topic we're talking about today, how to -- David hit the nail on the head, it's how to find that relationship, that meaningful relationship. How do you connect to it and engage with the C-suite decision-makers at a particular company? We talked -- David talked about it earlier. Like at this point, we all know data is king and we all know it's important, whether you're using that data to generate like algorithms or applying AI to identify like an interesting perspective, limited partner or deal opportunity. But the keyword for us is the collaboration that's formed here. That's what's paramount for us. Equilar, Snowflake, RainMaker takes their robust data sets, combines it with our robust data sets and presents this holistic picture of our relationship intelligence. And so that's -- in this specific case, that's really what we're thinking about here.

We just started with e-mail. But at some point, we'll be able to pull in -- we were able to do it today, but we're not doing this yet with Peter. But we'll pull in, for example, DocuSign e-signatures. I mean the list goes on of others -- proxies of relationships that we can enrich, that the more advanced firms will certainly take advantage of that.

It's fascinating. And it wouldn't be a webinar if I didn't bring up the 2 letters, AI. So 2023 can only be described as a year of generative AI. And Peter, I'd love to hear from you. Can you speak about how financial services should be thinking about AI initiatives? And any thoughts in that area?

Yes. Gosh, I guess I hesitate to tell all the financial services how to think about it, but I'll tell you how I about it. I think the most important focus is outcome. Like what problem statement are you trying to solve with the AI? And I think that that's challenging in the current environment where AI is like this open spigot where everybody thinks sort of everything can be solved with AI. So that leads to the second thing that's top of mind for me, which is education, and how do you -- what does it take to implement an AI solution and how do you get your users to understand what the actual art of the possible is. And then the third kind of top of mind for me is that partnership and alignment, right, bringing the business and technology very close together in a very meaningful way so that the solutions are accounting for workflow and process. Too often, these solutions are created in a vacuum, and I think that risk goes up substantially in this kind of like energetic and excited atmosphere we're in related to AI. So it's a little bit of a caution statement. But getting back to some of the basics and remembering you're trying to solve a problem and what is that problem, prioritizing those problems and getting focused on which one you're going to do first, second and third.

Yes. I can't agree with Peter more. I mean just to build on that, I was watching CNBC right before the holidays. And Julie Sweet, CEO of Accenture, was on CNBC and they're talking about AI. And I think her quote was somewhere along the lines of like less than 10% of companies actually have the data -- the foundation data layer in place to really effectively take advantage of AI. And as Peter pointed out, there's this rush to move fast. But it's so critical to get the house in order, right? Because if you've got data models that are flawed, you can throw the best AI at it, but you're going to get flawed results. You're going to get hallucinations and whatnot. So I think as Peter pointed out, I mean, I think there's a series of steps that need to be done upfront, understanding what you're trying to solve, making sure the data -- you have the best data assets there so that when you're trying to solve the problem, you're getting the results that you expect to get.

Right. And at Snowflake, our strategy, we think the data assets are obviously highly critical. It's one thing. You can have the best model in the world, but to your point, garbage in, garbage out, right, you need to have high-quality content. And I think it's -- I liked what you said, Peter, around being able to solve a problem. I think there's so many exciting toys and the growth of ChatGPT, and every way people are looking at it. That it's easy to spin up a bunch of stuff and activity because, hey, it's what we need to do and it's what we need to talk about. But getting back to what are the actual results you're looking to achieve is important. And so I'd love to hear, why did Warburg Pincus select Equilar and Snowflake to support your strategic growth initiatives?

Sure. I'd start by saying I think the way Equilar thinks about their solution, right, their platform surfaces information in a very easy-to-consume format. You saw that in the previous slide. Our dealmakers don't have time to like interpret what they're seeing. They need to look at that at a glance and then get really to the action. I think the way Equilar, their thoughtfulness about how to integrate into our CRM, that was set up very quickly as a window pane into this data. Our users are doing their prospecting work inside of our CRM. So surfacing relationship intelligence right at the point where they're working is very important to us, kind of get to fewer platforms with more meaningful and interesting data within them. That's a big strategy for us. And then not only are they thinking about how to kind of bring this information into the CRM, but they're also thinking about how to help you maintain the cleanliness of your CRM. That's a pretty unique delivery point, and it's strategically very important to us, not to just, to your point, Tom, dump a bunch of garbage in there and then walk away. Contacts are constantly moving, shifting, changing, new titles, new roles, new companies. You have to have a handle on that or you get bad data results pretty quickly. And then finally, this kind of Equilar-Snowflake relationship, the RainMaker tool, it really enables us to visualize our own relationships across the firm and our portfolio companies. So combining all this great information within Equilar and combining our information, get that holistic picture of what's the strength of this relationship, how meaningful is it. And then dealmaker A has a good sense of whether to reach out to dealmaker B to say, hey, can you help me with this relationship? They have a good kind of gut instinct of whether that's worth their time. So it's really a starting point. It's a visual starting point for these teams to kind of take action. That's pretty impressive.

Yes. And I think you hit an interesting point that we've certainly seen a collaboration at Snowflake. We often think of it from the outside perspective of taking third-party data in and how easy it is to get that to the organization. But there's also, to your point, right, the internal perspective of how do you organize your own CRM data, and we think about all the PortCos and being able to bring that all together, how powerful that could be. So that's fascinating. I'm going to ask a difficult forecasting question, and feel free to use AI to predict this. But I'd love your just perspective in terms of, by the year 2030, if you have predictions around how will top financial services organizations be doing in terms of investment data and AI and use of cloud collaboration. Anything you want to put your neck out and say, hey, this is what the world will look like.

Yes, Tom. No, I think you touched on it a second ago, is the collaboration aspect of things. And what -- we feel very fortunate to be part of the Snowflake ecosystem because our ability to quickly get RainMaker up and running for Peter, but not only get it up and running, but maintaining it. But then also, once the data is there, to be able to [ push ] that out and share that with the teams and systems that would -- that benefit from that. But a great example would be a company like Crossbeam, where you have businesses who are able to share information and a data clean room. And both sides benefit, but they're able to also keep their cards close to the chest, right? And I think as you touched on, I think there will be opportunities down the road where Warburg Pincus, it's -- what's your AUM, Peter, right now? $100 billion, give or take, right?

Yes. $80 billion to $100 billion, yes.

Yes. $80 billion to $100 billion, right. And you think of the number of companies, number of employees at all the PortCos, you think of all the different advisers that you work with, law firms, investment banks and whatnot. So it's a powerful ecosystem just within Warburg Pincus. And I see down the road, there will be information that Warburg Pincus can potentially share with PortCos and vice versa where both sides would benefit. So I think it's amazing to think about what the potential could be down the road as you see more sharing of data across the different affiliates.

Yes. I think that's really well said, David. And I think as I think about -- like it's very hard to forecast out to 2030. I sometimes don't know what I'm doing tomorrow. But when I think about -- like this is happening now, right? It's actually happening now. And we've talked about kind of all the pieces of this, right? The way you get to a great AI outcome and how you think about that and how you approach that, but that data being key. And so for me, these partnerships become ever more important, right? So that's where -- a lens for me that I'm really thinking about, like which third-party providers are thinking this way and kind of on the leading edge of this, right? Think about their data, not as silos, but as a collaborative tool to really share with clients. I think no data provider is ever going to get the solution right in a vacuum, in a silo. And every firm, like Warburg and others, they're going to want to take that data. They're going to want to ingest it. They're going to want to apply their own kind of thinking, logic and AI against that. And they're going to want to do that very quickly, and they're going to want to be able to be very flexible and change. And so providers that are thinking about that, like how can I start to really collaborate and share this data out with my clients, that's going to be important. So fundamentally getting these solutions in place that kind of support that framework, so the Snowflake, the RainMaker, like this is very important to us because it kind of lets us ingest this data very quickly and get to those outcomes very quickly. So that's how we're thinking. And I can't see that changing anytime soon. I think that's just going to compound and become even more complex and challenging going forward. But also super exciting, like it's a really exciting time to be working in this space, I think.

I agree. And look, any final thoughts, maybe any takeaways you guys want to leave the audience? Why not to throw a sales plug out there, maybe how they can get started with Equilar. But yes, any final thoughts, guys?

Yes. Thanks, Tom. I would -- along those lines, just a point I mentioned earlier but just to stress that, is that the native app. It's very easy to go in and start with your LinkedIn connections, and you can quickly see some results. And I think it's -- when people see this, the reaction is typically, oh, that's pretty amazing. I had no idea that [ someone's so and so ] to that person. And we're excited to be able to share that with everybody.

Yes. I would say if you're interested in this, like dive in. David and the team have been really great to work with. And just jump in, dive in. This is a pretty good firm to work with. And I think as David pointed, you get to see the outcomes very quickly. You can really ingest them very quickly, and that's meaningful.

Thanks, Peter.

Well, thank you, both. And as I mentioned at the start, you can find the native application in the Snowflake Marketplace amongst a lot of other data, apps and services. And one of the things we pride ourselves on is there's a lot of actionable content in there, so you'll see things like samples and so forth as well. So diving in is certainly what we would suggest as well. Well, I want to thank David and Peter for joining us today. This has been an awesome discussion. I'm going to move to the next slide in -- at Snowflake, the learning doesn't stop. If you enjoyed this webinar, please be sure to register for Snowflake financial services' virtual event this year called Accelerate: Financial Services. We'll be hosting that on March 14, and we'll explore how industry-leading financial services organizations are building their data, app and AI strategies. It's sponsored by Microsoft. We're going to have a lot of great speakers, including Franklin Templeton, Goldman Sachs, Bloomberg, State Street Alpha and many more. So thank you all for joining us today. We look forward to hearing from you soon.