Informatica Inc. (INFA) Earnings Call Transcript & Summary

Hi everybody. So my name is Koji Ikeda. I am the software analyst here at Bank of America, one of the SMID Software Analysts and I cover Informatica. I am very, very thrilled to have Amit Walia, CEO; Mike McLaughlin, CFO of Informatica here for a fireside chat. And so just to kick it off, very standard question Amit. For those in the room and those on the webcast that maybe are unfamiliar with Informatica. How about a brief overview? What do you guys do? What's the opportunity you address? And what's your story? And what is your story Mike?

Well, thanks for the opportunity, Koji and very nice to meet you all. Well, I'll begin by describing Informatica is a unique company, 30 years in existence as of now. So it's a company that's a tale of 2 cities. Till 2015, it was a leader in all things on-prem ETL integration. And cut a long story short, since 2015, the company and I have been in the company 11 years. I ran products for the company, took over the role of the CEO about 4.5 years ago. And from 2015, we went through one of the most exciting and challenging also innovation-led transformations, which is all about taking the company and make it cloud-first. Also broadening the market opportunity from not just doing integration stuff but doing a much broader data management, which is integration, both data, application, data quality, master data management, governance, privacy on and on and on. Build out a company that is platform-centric all of those things on one platform, which is driven by AI. And that transformation was the transformation that has led us to also being a subscription-led company. And we took the company IPO again, it went private to do all that stuff, spent $1.5 billion in R&D to do that. And the key thing is that in that transformation, many companies which go through that are either best-of-breed in one thing or not as they become a platform company. We are the leader in Magic Quadrants for every product category we compete in, while we're the only one at that scale being a platform company. And the company says that it has grown astronomically in the world of cloud, while we have taken the old business and declined it very consciously. But our new cloud business is actually growing very handsomely, we'll get into numbers at some point. And the company is about $1.7 billion, $1.8 billion in revenue, ARR, and I'm giving directional numbers, very profitable, very cash flow centric and serves mission-critical workforce for large enterprises across the globe. So that's the new Informatica, which is a very, very different world, all consumption-based, and we'll talk more about it [indiscernible].

And maybe from there, I'll give folks a quick overview of the financial model and profile to round out the basics. Because we have been going through the transition that Amit described, we have legacy business, and we have the new business, and we have intentionally and very deliberately stopped selling the on-prem stuff maintenance and self-managed subscription, focused all of our energy on the cloud business, which has been growing at 35% plus, and we've guided to low 30s CAGR over the next 3 years. So what that has meant is that over the last few years, if you look at our total revenue and total ARR growth, it has been declining. Part of that is because we're focusing on the growth, which has been a small part of the total, and we hadn't had the benefit of the ASC 606 upfront revenue recognition of the on-prem sales either. So you've seen that total revenue growth decline. We gave midterm financial guidance in our Investor Day in December 2023. And in that guidance, we committed to this year being the inflection point for that growth. So growth in 2024 on a total basis is going to be faster than it was in 2023, and it's going to continue to grow from there because the growing cloud business is the bigger part of the mix. And the nongrowing on-prem business gets smaller and smaller. What that translates into is double-digit ARR growth in 2026, total ARR growth driven by the 31% to 33% CAGR of the cloud business over that same period of time and double-digit revenue growth in 2027. This year, we're going to have operating margins we've guided to in the low 30% range. We've also committed to mid-teens CAGR for non-GAAP Op Inc over that period of time, which means that our non-GAAP Op margin is going to also grow over that 3-year period. So we're going to exit 2016 as a company that has double-digit total top line growth and operating margins in the high 30s with all of the math suggesting that, that acceleration of growth will continue as the cloud gets to be a bigger and bigger part of our mix in '27, '28, '29.

Thanks, Mike. I was going to say some of those for a little bit later.

But we could dig into that first.

Then we'll move on to some demand questions and AI questions that kind of stuff. And so I think this is a little -- this is important. And so when we look at the financial model and you rattle a lot of metrics here. And even when I pop open the model on our computer, it's a big revenue market. There's lots of stuff going on there. And sometimes it can be overwhelming. And so maybe help us understand or distill it down to the 1, 2 or 3 metrics that we should be focused on the lowest that shows the underlying power of business.

Sure. So the first one, of course, is the cloud growth. So we are growing at what we think is a very healthy rate. We've guided to 35% for fiscal '24. We guided a 31% to 33% CAGR through fiscal '26. That's made up of 2 components. One is what I would call TAM-based growth. If you look at the IDC roll-up of the TAMs we compete in, again, the details are in our Investor Day slides from December, they roll it up to be a mid-20% lower TAM basis through 2027. So of our 31% to 33% growth over the next 3 years, think of mid-20% coming from TAM growth, we're participating in that. We're not assuming any market share gains, although we think it's very plausible, we will enjoy some. And then the rest of it is migration of our existing on-prem customers, maintenance customers from perpetual licenses we've sold in the past and subscription on-prem customers that we no longer actively sell, that's a very right base of customers to move to the cloud to ladder us up from the mid-20s percent TAM-based growth to the low 30s total cloud growth. That's the cloud. We can talk about the pace of that migration and the potential there, perhaps later. The decline businesses, though, you got to understand though, and you got to be comfortable with what is going to happen there because that's still more than 65% of our total revenue and total ARR. So maintenance is very predictable in terms of its decline, it's very seasons. We have many of those customers for 20 years. That tends to decline at a 4% to 6% annualized rate plus whatever we migrate to the cloud. The self-managed bit, the subscription bit is less seasoned, and so it has more customers that didn't fully implement and shelfware and so forth. And that's going to decline at a high single digit to low double digit, I think 9% to 11% on an annualized basis. Again, we're not selling any more into that. We've declared an end of sales. So that decline rate, we think, is not surprising, plus any migration that moves from self-managed to maintenance is important. And then the final metric that you should watch. So I understand why the cloud is going to grow, what the components are, understand how maintenance and self-manage are going to shrink is the multiple that we expect to get when we migrate folks from the self-managed on-prem or maintenance on-prem to cloud. We've historically enjoyed a 2:1 average uplift. So you're paying us $100 for power center maintenance today. You decide to move to the IDMC cloud platform, on average, folks pay us $200 for that due to the value delivered by the cloud, the more rich and flexible product, et cetera. There's a lot of variability customer by customer because we sell the value of the cloud. We don't sell a flip multiple but on average, 2:1, and we think that, that will be more or less the case going forward. So those are the key things you need to understand to model the top line.

Thank you. Let's focus on this first one. Cloud growth, 35% guide for this year at scale, big scale. I mean cloud could be -- your cloud ARR could be a stand-alone business essentially, right? It's a big company right there. And you guided to 31% to 33% through fiscal '26. So feels like high visibility, high confidence in your visibility. But what I want to bring it to is the demand environment, right? You just delivered 35% plus growth for cloud in this quarter. You guided to 35% plus, we've seen a lot of things happen in software over the past 6 months, we'll say. But you guys are operating very, very well out there. So maybe a question for you, Amit. What are you seeing out there in the macro demand environment, right now? How does it compare to maybe 6 months ago? How does it compare to a year ago? How are you feeling by now?

So a great question. I think if you put it in 3 categories. So demand overall we've said, we have seen no change in demand. We saw in Q4, we saw in Q1, what we saw in Q1, we are seeing that in Q2. But certainly, a few things have changed. And what has changed is definitely that customers are still continuing to run at an accelerated pace than digital transformations. In that, what has changed is I saw is that, obviously, the customers are playing a lot of defense last year, but the customers are definitely investing in what I call more transformational initiatives. [indiscernible]. They've done themselves out [indiscernible]. So that is definitely happening. And I'll keep coming to GenAI last because still 90% of the work that's happening in enterprise is digital transformation that's not fully done by any yardstick. Second is, customers are accelerating their modernizations. Lot of legacy stuff is still out there. We all live in Silicon Valley, we all think the world is cloud. It's not really true. There's still a lot of non-cloud sitting out there. Majority of it is still non-cloud, and that is accelerating. And what has changed definitely is GenAI. GenAI has started 18 months ago, but right now, the conversations definitely are front and center in the demand cycle. And I think what customers are evaluating, especially enterprise customers is like, look, where do I begin? What do I do? How do I do it? Do I have the skill set? What do I need to put in place? These are all many material questions. And there's a lot of implications for an enterprise than dumping a lot of open information from the Internet and OpenAI and just getting cute answer. So like if I have to run a chatbot for customer service at operational scale, may I do not get it wrong, right? That's happening. Now what it's working for us is that data management is becoming more important. Data management is always important, but it was never I would say the apps were more UX, so people see them more. But now everybody is realizing that, oh, my god, I have to put my data state in order. So things like data quality have become very conversational topics at the boardroom level. And that conversation of getting your data state in order is becoming more front-end center. And where it is actually becoming more strategic for us is that our IDMC platform on which all our products sit, can be used for non-GenAI, digital transformation. But the same platform is used for GenAI digital transformation. So customers like, Oh, great. So if I'm putting a dollar in over here, even if 90% of my workload is non-GenAI but I want to start some GenAI work. I don't have to go do what this and that. I can just do it and over the course of time, if more work goes towards GenAI because it's 100% consumption based, I can seamlessly reduce my workload over here and move over here. That's giving them a lot of comfort and that's translating into what I see the stability of the demand cycle for us. Of course, we have incrementally also shipped our copilot and GPT that makes it better for them. But these are all the things that we are seeing data management becoming more important. We have the best products on our platform can be non-GenAI, GenAI workloads. All of those things are showing up in our pipeline and relatively stable demand for us.

So still a lot of AI products. And I think when we think about data, it always feels like from the investor community, us included for sure is that it sure seems like all the data is going to eventually be in the cloud. But I think it would be helped to understand what brain or frame for us how much data is sitting in legacy? Sitting in closet, sitting at AS400s somewhere in some location. What does the typical enterprise look like today with how much data that is just old. That needs to be leveraged, monetized, governed all that kind of stuff?

A lot actually. And I would say [put] data -- data inherently is supremely fragmented. And fragmentation is people always think of, oh, there is just one -- somehow there's magically one data warehouse and everything in an enterprise, that's not true, that's just not true. Your company is a great example of that. There's so much stuff sitting if you go to an insurance company or by the way, a retail company sitting in legacy infrastructure, Sybase, Db2, the AS400s Mainframes. I'm not saying that they are primary, but they're still there, running core MIPS. Then by the way, even in the world of cloud, there's so many applications to deal with. By the way, the application landscape is supremely fragmented in the world of cloud, while there are legacy apps sitting still out there. Then people wrote custom apps also on top of the new cloud infrastructure like AWS and all. And then we come into the new packaged apps of the cloud or the new data warehouses and databases over here. So when I look at an enterprise, and I simply ask the question of a CIO and CIO tell me where does your customer data sit? They will turn around left, they will turn around right, at least 25 to 50 places. Just the customer data. So fragmentation and fragmentation from legacy to all shades of [model] is a massive issue. Not going to go away. And I think everybody has understood that, that that's not the point everybody is solving to take all the information from here and magically move to one database, nobody wants to do that. People want to manage it at certain central layer, which is where data management comes and then start doing many other things. But that issue is real and will not go away the fragmentation and the old and the new. There will be a new cloud and the old cloud also very quickly.

Let's just categorize them as data managers within organizations, just very broad. And over the past 18 months, they've been hitting in the face with generative AI, what to do with it, all knowing that data is very [indiscernible]. So these data managers that you sell to, have they figured out what they need to do. I mean, do they understand what's happening? Are they still trying to figure it out? I mean what is that data manager coming to you as their pain points today? Is it different than what it was a year ago?

All of the above. So let me kind of give 2 examples, use cases example. First, by the way, I think I talked about a lot of cerebrally about data management, right? The data management is not one thing. There are many users, data analysts, data practitioners, data governance, data stewards, data scientist. And to make it one real -- just real on a data management use cases take Unilever as a company. Unilever as a company, basically, they use us. They use us for mastering. The global supply chain runs on Informatica's MDM. What is that use case? They onboard every supplier through us in that they track what every supplier's SLA is, which products they have to bring in which countries across all 96 countries, all suppliers, all products coming from those suppliers are managed as a single payment glass so they can manage to make sure that, hey, in Indonesia, this provider has to bring these diapers on time, whereas in U.S., it's something different. So that's data management. It's not just pushing data into us [indiscernible]. I purposefully went to that example because people think of, oh, just connect some dots and push data into a data warehouse that's data management, not true. Data governance is the whole thing, and we -- again in the interest of time, I will not go there. Now the question you asked, GenAI, very early days for an [ enterprise ], but I'll give you a live example that we showed on main stage 2 weeks ago at our user conference, an actual customer. Auto insurance company. You all know that. Basically, when your car gets, let's say, rear-ended, you have to go to an auto insurance company, they will basically go through what an estimate of your claim would be so on and so forth, it takes many days. You don't get an exact amount. Today, it's being run with leveraging us by, hey, you bring data, you understand Koji, it's your car, this car, this model, this insurance blah-blah-blah. And I'll give it to you -- in two weeks I'll give you a true estimate, and I'll give you something over the course of time. The GenAI -- about the same product is GenAI. So under the covers, by the way, we support any vector database, any LLM and we also bring incremental transformations, rag, chunking and all that stuff that's needed for GenAI, where the customer basically said, I'm going to take all of these new -- and by the it's already in our product, and I'm going to run a GenAI version of this. And the GenAI version of that allowed them to take an auto claim that they were doing in two weeks to less than an hour. And in that, they were able to actually very accurately give the end customer what the claim would be worth because they could now -- they had run a model and they could choose whichever model has been trained, that's happening, right. But having said that, it's still a long way from actually taking every business process and GenAI-ing it because of issues of -- many other issues of governance, risk, compliance and all that stuff. And also operationally, it takes a long time. I think it's the best way I'll let take that example and tell you [indiscernible] top of the second of this GenAI. And it's going to be more than 14 [ innings ] for sure. So no more than top of the second, yes.

And you mentioned Informatica rule. It just happened a couple of weeks ago. We went talked a lot of customers, a lot of partners lot of positive things to say about Informatica. So maybe you could sum it up for us in the key themes, and I'm going to give you those themes maybe to hopefully talk about one thing that you mentioned on the big screen was Informatica for GenAI and GenAI for Informatica. So maybe help unpack that a little bit, what does that mean?

Yes. So it's a great [indiscernible]. Informatica for GenAI and GenAI from Informatica is a two-pronged strategy for GenAI. One is the current products we have on the platform. They are needed for GenAI and today, they are ready for GenAI, which is, as I said, the IDMC platform and its products, whether it's bringing data from any places, putting quality observability, governance and all that stuff is needed for GenAI. And that example that I gave you of auto insurance is a customer that has IDMC and has all these capabilities inherently in the platform today, and they are using it to run a GenAI app, that's Informatica for GenAI. Availability right now, nothing to do. You have it, use it, go for it. Second is, we want our users to also get the benefit of GenAI from us, which is our own co-pilot, our own GPT. So they can become more productive and more efficient. Our AI is called CLAIRE, by the way. We're not new to CLAIRE. CLAIRE was GAed in 2018. I did that when I ran products. And over the course of last many years, CLAIRE is embedded in every product, for example, we took all the thousands of machine learning algorithms that were used in the world of social media or any other place, curated to data management. Like photo tagging in Facebook is a model we curated and made a data tagging in data. Recommendations in Amazon became a recommendation for data. So CLAIRE had been doing that. Last year, we brought CLAIRE copilot available to every customer. It's there in every product, embedded what copilot does. And last year, we started the GPT [ generally ] CLAIRE GPT went to a 1-year of preview and GA now. Both of them are GenAI from Informatica, so you can make it to a chat prompt, CLAIRE can give you answers to questions. They have both strategies in the world of GenAI. You need us to get your GenAI workload going and use our GenAI to be more productive and do it a lot more easily than ever do. And both of them are going to be a tailwind for us.

Want to dig in a little bit deeper on CLAIRE GPT. We see a lot of copilots and GPT features out there from lots of software vectors. And overall it feels like a lot of them are kind of knowledge-based access, like how do we use this application better. But when I saw the demo with you guys a couple of weeks ago, it seemed different, right? You could ask -- you can use this tool to ask questions and I'm going to probably butcher this, but it's like you could put data together in real time and see things. And so it seems like it's many steps beyond just accessing a knowledge base. It's actually providing answers, putting data together. So hopefully, I did it justice, but maybe you could explain a little bit better technologically why you're able to do that?

Yes. I mean our philosophy is always that we don't build cute products. We build products that actually add material value to our customer and create economic rewards. So CLAIRE GPT, by the way, is transformation. Obviously, like anything, it's a chat interface that sits on top of the entire IDMC platform. By the way, if you guys really are looking to do some more work in Informatica, just go to our website, look at the Informatica word keynote that I gave, there are 2 demos. Both demos are 5 minute each, and you will see both of them explain very well. Both of them are live products. Just in 10 minutes, you will get a better visual of my words and eminence here. And in the CLAIRE GPT version, you can actually literally through plain English command start doing data management activities like: give me the customer churn, give me from the Western region of the U.S., give me the -- how many customers are at the risk of churn. It's a data management activity. You start looking at West region of U.S., understand what churn means, blah, blah, blah and start giving you data. And it will show you and you'll say and we'll start showing you the quality of that data, all coming from IDMC who was using it before. I got it. Then you can ask -- you start telling you which system it came from. Is it a trusted system. Then you may wanted to do something. Can you take this data and to show me whether these customers have any incremental opportunities coming in, start going to the CRMs. It will start doing data management activities for you through chat-based prompt. They want to democratize data management so we get more users. Long story short, you can keep going, and until you come to a final answer, and you say, look, I like this you can command and say, can you just do this for me and he'll start doing it for you? Or you as a user, have a great example of what you want to get done and you can give it to your power users and say, can you now make it operational for me. The old days, this could never happen or it could take you 6 months, 9 months and you will never be able to figure out, and you can't even play with data to get to what you are thinking in your mind. It's transformational.

May I add something to that. A key part of the enablement of this and what makes it, again, different than what you might see elsewhere and makes it defensible and as a real moat. Because of what the IDMC does, we have what we call the metadata system of record or the company's data. We have all the metadata on all of your sources and targets. We know where the data is, what it is, what it's quality is, what its lineage is and that's what CLAIRE GPT is working with to come up with the answers and the outcomes that Amit described. So it's not just about building an LLM that works to give the intelligence. The data the LLM has to work with is unique to us because of our position in the data stack of the company.

And I'll just want to add one more thing to it that is very true. One is we had the Switzerland of data with the Switzerland of AI also. We work with every LLM and every vector database because, by the way, we believe there will be democratization of that. Second is, one of the secret sauce of what we build this platform was my vision was Google indexed the worldwide web by creating the better data indexing that when you do a search, it knows where to go find the most relevant answer. We are the #1 at scale data management provider. The whole vision for building that platform was we want to be the Google for enterprise data through metadata. We have 50,000 connections, we collect metadata from anything in an enterprise, not Informatica workflows, cloud apps, databases, PI tools, by the way, including CLAIRE can go and if you have python code written that has no metadata, we will create metadata and then ingest it. So we are so metadata aware so that when the search is happening through AI now, that was our vision. It's helping in the world of GenAI, you know exactly where to go. I mean instead of you exactly where to go and find the most relevant information. So the best result comes through us because we are the metadata system of record. That's a very secret [ trojan ] process.

Mike, I wanted to ask you a question. One question here, then I wanted to open up to the floor to see if there's any questions from the audience. But a question for you, Mike, specifically, is when you're setting up the guidance, 2-part question on the guidance. So when you're setting up the guidance, and you did give that fiscal '26 guide for cloud. I think it's super attractive, 30% plus. What are you seeing out there, either from your pipeline perspective or demand perspective that's giving you that confidence? And kind of going back to one thing I said earlier, it's been volatile. And we're definitely in a world of just give me some software public companies where there's no surprises. And so your guidance methodology. Just can you walk us through it? So in the future, as you report, there's just -- we're just prepared to not have any surprises? How should we be thinking about your guidance?

Maybe I'll start the answer to that question by just describing our guidance philosophy. Our goal when we set guidance is to give you our honest and realistic point of view about what we really think is going to happen. We're at the high end of the range is not our real guidance. We're not trying to sandbag it so badly that we're going to be able to beat it by massive amount every quarter. That's not who we are and that's not what we're trying to achieve over the medium and long term. We're trying to consistently execute against our guidance and make it clear that the '26, '27 financial profile that we've laid out is going to happen. And when we hit that financial profile, I think you would all agree that the multiple on the earnings that we have at that period of time should be considerably higher than what we have today, simple math. We're at 5%, 6%, 7% grower now with low 30s earnings if we're a double-digit grower at that time with high 30s and 40s margins, it's just a different financial profile and what we think is a very attractive financial opportunity. So we're guiding realistically. Our goal, of course, is to do everything we can to beat it, but we're not trying to sandbag or play games with guidance. So that's part one. Giving us the confidence in that medium-term expectation starts with what we see in the near term, and that's with our pipeline. It's actual customer behavior, it's buying behavior. And that has two parts. It's the pipeline for the new workloads and the new customers and the migration pipeline. And both of those feel very steady and very consistent with what we assumed they would be when we set that guidance starting in December and affirmed it over the last 2 quarterly reports. GenAI will be a tailwind to the non-migration portion of that growth. But frankly, we're not going to see a ton of it in 2024. We have numerous customers doing pilots for GenAI workloads with the IDMC today, and many of those will go into production and consume a meaningful amount of IPUs but we're not baking any of that into 2024 at least in a material way. And then for the migration piece of it. That is -- it's going great. Again, consistent, if not a little better than our expectations driven by two things: the increasing imperative of modernizing your on-prem infrastructure, GenAI only accelerates that and makes it more imperative to make the move. And then secondly, PowerCenter Cloud Edition, which is a new set of tooling, if you will, to make the migration from on-prem PowerCenter or our other on-prem products. What used to take 2 years and was full lift and shift cut over with all the complexity and risk that, that takes. Now with PowerCenter Cloud Edition, you put IDMC on top of your existing on-premise state as more or less a control plane. You have, from day 1, the ability to leverage all the additional features of the IDMC, the IPU pricing model, get the benefit of the platform and then you can move your on-prem pipelines when you are ready, one by one without the technical risk of a full cutover. That's led to a doubling in the number of modernization deals we're signing every quarter versus the year before because of those two factors. Over the -- so that's near term. So again, we feel really good about those going the way we thought they would in 2024. And then '25 and '26, we don't have pipeline, doesn't tell you anything about '26, right? But we feel great about the tools we have today, the products we have today are more than sufficient to keep up with the TAM growth without having to buy something or fill a product hole or consolidate in addition to the migration that's going to ladder us up to that growth rate. So we feel -- we continue to feel very good about it.

Okay. Okay. Thought I was going to be able to open up to the audience. We're running out of time. So I just want to throw in one last question Amit for you. What are you most excited about for Informatica for the next 12 to 24 months, positive, right? But what are you maybe a little bit worried about? Or what keeps you up at night to over the next 12 to 24 months?

I think on the [ lighter end ], I'd say, none of us knew where the economy of the world is going to ahead. So the unpredictability is always there. I think any one of us can lose sleep over it or choose not to lose sleep over it because that's outside our control. So I don't -- that's the only one I would say that just things outside our control. But what I'm most excited about is that data management the stuff we do. It's always been behind a little bit, behind the apps. It's not the most obvious thing that is in front end, it's the most [indiscernible] that people see. But in the world of GenAI, it's becoming the most front-end center tech. People talk about data quality, people talk about governance, people talk about getting holistic data. That's becoming these topics, these lexicons are coming to the executive or the boardrooms more often than not. And people have also realized that AI alone doesn't create a vibe. By the way, the tagline we have is and we say that is "everybody is ready for AI, except your data" And to get your data house in order, you need to do some hard work. And that hard work is now getting it's fair credit and fair due it's coming to front-end center. That's something I'm super excited about. I'm somebody that's lived in this industry and living this industry, that's a terrific moment in time to not just being of value but also being front-end center, creating value in this GenAI world that we will live in.

We're out of time. Thank you so much, Amit. Mike, for doing this. This has been fun.

Thank you, Koji. Thank you so much.