All right, hey, 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 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 with that, tale of two cities. Till 2015, it was a leader in all things on-prem ETL integration. And cut a long story short, since 2015, the company... Well, I've been at 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, make it cloud-first. Also broadening the market opportunity from not just doing integration stuff, but doing much broader data management, which is integration, both data, application, data quality, master data management, governance, privacy, on and on and on. Built out a company that is platform-centric, all of those things on one platform, which is driven by AI. And that transformation was a transformation that's 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 or every product category we compete in. Why? We're the only one at that scale being a platform company. And the company since then has just grown astronomically in the world of cloud. While we have taken the old business and declined it very, 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 workforces for large enterprises across the globe. So that's the new Informatica, which is in a very, very different world, all consumption-based, and we'll talk more about that as we go. Maybe from there, I'll give folks a quick overview of the financial model. Yes ... 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%+, and we've guided to low 30s CAGR over the next three 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 gonna be faster than it was in 2023, and it's gonna continue to grow from there because the growing cloud business is a bigger part of the mix, and the non-growing 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%-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 gonna also grow over that three-year period. So we're gonna exit 2016 as a company that has double-digit total top-line growth and operating margins in the high thirties, 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 2027, 2028, 2029. Thanks, Mike. I was gonna save some of those for a little bit later. But we could, we could dig into that first, actually. Okay, good. Then we'll move on to some demand questions, AI questions, you know, that kind of stuff. So I, I think this is a little... This is important. So when we look at the financial model and you rattle off a lot of metrics here, and even when I pop open the model on our computer, it's, it's a big revenue model. Yes. There's lots of stuff going on there, and sometimes it can be overwhelming. So maybe help us understand or distill it down to the one, two, or three metrics that we should be focused on the most, that shows the underlying power of the business. Sure. So the first one, of course, is the cloud growth. So we are growing at a, what we think is a very healthy rate. We've guided at 35% for fiscal 2024. We've guided at 31-33 CAGR through fiscal 2026. That's made up of two components. One is what I would call TAM-based growth. If you look at the IDC roll-up of the TAMs we compete in, and again, the details are in our Investor Day slides from December, they roll it up to be a mid-20% grower, TAM basis, through 2027. So of our 31%-33% growth over the next three years, think of mid-20% coming from TAM growth. We're participating in that, and 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 ripe base of customers to move to the cloud, to ladder us up from the mid-20s% 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 those, and you got to be comfortable with what is gonna happen there, because that's still, you know, more than 65% of our total revenue and total ARR. So maintenance is very predictable in terms of its decline. It's very seasoned. We've had many of those customers for 20 years. That tends to decline at a 4%-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 gonna decline at a high single digit to low double digit, like 9%-11% on an annualized basis. Again, we're not selling any more into that. We've declared an end of sale, so that decline rate, we think, is not surprising. Plus, any migration that moves from self-managed to maintenance. And then the final metric that you should watch, so understand why the cloud is gonna grow, what the components are, understand how maintenance and self-managed are gonna 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-to-1 average uplift. So you're paying us $100 for PowerCenter 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 to 1, and we think that that'll 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 standalone business, essentially, right? It's a big company right there. And you guided to 31%-33% through fiscal 2026. 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%+ growth for cloud in this quarter. You guided to 35%+. We've seen a lot of things happen in software over the past six months, we'll say, and 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 six months ago? How does it compare to a year ago? How are you feeling right now? So, great question. I think if you put it in three categories. So demand, overall, we've said as we have seen no change in demand. We saw in Q4, we saw in Q1, what we saw in Q1, we see like 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, their digital transformations. In that, what has changed, as I saw, is that obviously the customers are playing a lot of defense last year, but customers are definitely investing in what I call more transformational initiatives. They can't just play defense. They've done themselves out of the idea and so on. So that is definitely happening. And, you know, I'll keep coming to GenAI last, because still 90% of the work that's happening in enterprises is, is digital transformation. That's not fully done, done by any, by any yardstick. Second is, customers are accelerating their modernizations. A 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 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, are 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 very material questions, and, and there is a lot of implications for an enterprise than dumping a lot of open information from the internet on OpenAI and just getting queued answers. This is like, if I have to run a chatbot for customer service at operational scale, better 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 was always important, but was never, I would say... The apps were more UX, so people see them more. But now everybody's realizing that, oh, my God, I have to put my data estate in order. So things like data quality have become very conversational topics at the boardroom level, and that conversation of getting your data estate in order is becoming more front and center. 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 $1 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 a this and that. I can just do it, and over a 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 one platform, can be non-GenAI, GenAI workloads. All of those things are showing up in our pipeline and relatively stable demand for us. ... So So we sell 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 helpful to understand what frame for us, how much data is sitting in legacy, sitting in closets, sitting in AS/400s somewhere in some location, you know? What does the typical enterprise look like today with how much data that is just old, you know, that needs to be leveraged, monetized, governed, all that kind of stuff? A lot, actually. And I would say data inherently is supremely fragmented. And fragmentation is, you know, people always think of, oh, there is just one. Somehow there's magically one data warehouse and everything in an enterprise, and 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, DB2s, AS/400s, mainframes. I'm not saying that they are primary, but they're still there. They're running core MIPS. Then, by the way, even in the world of cloud, there's so many applications sitting there. 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 infrastructures 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'll turn around left, they turn around right, at least 25-50 places, just the customer data. So fragmentation and fragmentation from legacy to all shades of modern is a massive issue. Not gonna go away, and I think everybody's understood that, that that's not the point everybody's solving to take all of the information from here and magically move to one database. Nobody wants to do that. People want to manage it at certain central, like, 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'll be a new cloud and the old cloud also very quickly. You guys sell, let's just categorize them as data managers within organizations, you know, just very, very broadly. Over the past 18 months, they've been hit in the face with generative AI, what to do with it, all knowing that data is very key to it. These data managers that you sell to, have they figured out what they need to do? I mean, do they understand what's happening or 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 examples. First, by the way, I think I've talked about a lot of cerebrally about data management, right? So data management is not one thing. There are many users, data analysts, data practitioners, data governance, data stewards, you know, data scientists. And, 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 SLAs, 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 pane of glass, so they can manage to make sure that, hey, in Indonesia, this provider has to bring these diapers on time, whereas in the U.S., it's just something different. So that's data management. It's not just pushing data into a Snowflake warehouse. 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 a whole thing, and we'll, again, in the interest of time, I will not go there. Now to the question you asked, GenAI. Very early days for enterprise. But I'll give you a live example that we showed on main stage two weeks ago at our user conference, actual customer. An auto insurance company. We all know that. Basically, when your car gets, let's say, rear-ended, you have to go to an auto insurance company. They'll 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 the... leveraging us by, "Hey, you know, 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, in two weeks, I'll give you a true estimate, and I'll give you something over the course of time." With GenAI, but the same product is GenAI now. 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 way, 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 had been trained. That's happening right now. 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 take that example to tell you we're at the maybe top of the second of this GenAI, and it, and it's going to be more than 14 innings for sure. So no more than top of the second, yeah. And you mentioned Informatica World. It just happened a couple of weeks ago. We went, we talked to a lot of customers, a lot of partners, a lot of positive things to say about Informatica. So maybe you could sum it up for us in the key themes, and I'm gonna 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, and what does that mean? Yeah. So there's a great one. So 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 they, today, they are ready for GenAI, which is, as I said, the IDMC platform and its products, whether it's bringing data from many places, putting quality, observability, governance, and all that stuff, is needed for GenAI. And that example that I gave you, 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. Available to you 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 Copilot, 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 GA-ed 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 it data tagging in data. Recommendations on Amazon became recommendations 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 journey. CLAIRE GPT went through a one year of preview. It GA-ed now. Both of them are GenAI from Informatica, so you can make it through a chat prompt, CLAIRE can give you answers to questions. We have both strategies in the world of GenAI. You need us to get your GenAI workloads going and use our GenAI to be more productive and do it a lot more easily than ever before. And both of them are going to be a game changer. I 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 vendors, 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 last couple weeks ago, it, it seemed different, right? You could ask, you could use this tool to ask questions, and I'm going to probably butcher this, but it's like you, you could put data together in real time and see things. And so it's, 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. Yeah. I mean, our philosophy has always been, we don't build cute products. We build products that actually add material value to a customer and create economic rewards. So CLAIRE GPT, by the way, is transformational. 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 on Informatica, just go to our website, look at the Informatica World keynote that I gave. There are two demos. Both demos are five minute each, and you will see both of them explained very well, and both of them are live products. So just in 10 minutes, you will get a better visual of my words that I would use here. In the CLAIRE GPT version, you can actually literally, through plain English command, start doing data management activities like, "Give me the customer's churn, give me the, 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 western region of U.S., understand what churn means, blah, blah, blah, and start giving you data. And it'll show you, and it'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. It'll start telling you which system it came from. Is it a trusted system? Then you may want it 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'll start doing data management activities for you through chat-based prompt. We 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, you say, "Look, I like this," you can command to say, "Can you just do this for me?" 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 user and say, "Can you now make it operational for me?" The old days, this could never happen, or it could take you six months, nine months, and you will never be able to figure it out, and you cannot 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 is a real mode. Because of what the IDMC does, we have what we call the metadata system of record for 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 its 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 describes. So it's not just about building a LLM that works to give the intelligence. The data the LM, 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's very true. One is, by the way, we are the Switzerland of data. We're the Switzerland of AI also. We work with every LLM, every vector database, because, by the way, we believe there'll be democratization of that. Second is, one of the secret sauce of when we built this platform was, my vision was Google indexed the World Wide Web. By creating the metadata indexing that when you do a search, it knows where to go find the most relevant answer. We are the number one 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, BI tools. By the way, including CLAIRE can go in, 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. We know exactly where to go, and we, instead of you, we know exactly where to go and find the most relevant, relevant information. So the best result comes through us because we are the metadata system of record.... That's a very secret Trojan horse, guys. Mike, I want, 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, when you're setting up the guidance, two-part question on the guidance. So when, when you're setting up the guidance, and you did give that fiscal 2026 guide for cloud, I, I think it's super attractive, 30%+. Well, what are you seeing out there, either from your pipeline perspective or demand perspective, that's giving you that confidence? And kinda going back to one of the, one thing I said earlier, we've, it's been volatile. And, and we're definitely in a world of just give me some software public companies where there's no surprises. So your guidance methodology, can you walk us through it so, you know, in the future, as you report, 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 gonna happen. We're not, the high end of the range is not our real guidance. We're not trying to sandbag it so badly that, you know, we're gonna be able to, you know, hit, beat it, by a massive amount every quarter. That's not who we are, and that's not what we're, 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 2026, 2027 financial profile that we've laid out is gonna 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 a 5, 6, 7% grower now with low 30s margins. If we're a double-digit grower at that time with high 30s into the 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 sort of trying to sandbag or, or play games with guidance. So that's point 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, 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 two quarterly reports. Gen AI will be a tailwind to the non-migration portion of that growth, but frankly, we're not gonna see a ton of it in 2024. We have numerous customers doing pilots for Gen AI 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. Gen AI 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 two years and was a 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 one, the ability to leverage all the additional features of the IDMC, the IPU pricing model, and 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, those two factors. So that's near term. So again, we feel really good about those going the way we thought they would in 2024. And then 2025 and 2026, you know, we don't have, pipeline doesn't tell you anything about 2026, 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 gonna ladder us up to that growth rate. So we feel, we continue to feel very good about it. Okay, okay. Thought I was gonna be able to open up to the, the audience, but we're running out of time, so I just wanna throw in one last question, Amit, for you. What are you most excited about in Informatica for the next 12-24 months? Positive, right? But what are you maybe a little bit worried about, or what keeps you up at night, too, over the next 12-24 months? I think on the latter, I'm just saying none of us know where the economy of the world can head, so the unpredictability is always there, and 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. That's the only one I would say, that's just things outside our control. But what I'm most excited about is that data management, the stuff we do, you know, it's always been behind a little bit, you know, behind the apps. It's not the most obvious thing that's in front, and it's the most, quote-unquote, cool thing that people see. But in the world of GenAI, it's becoming the most front and center thing. 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. People have also realized that AI alone doesn't create value. By the way, the tagline we have is, and we say that is, "Everybody's ready for AI except your data." To get your data house in order, you need to do some hard work, and that hard work is now getting its fair credit and fair due. It's coming to front and center. That's something I'm super excited about. As somebody that's lived in this industry and living in this industry, that's a terrific moment in time to not just being of value, but also being front and center, creating value in this Gen AI world that we 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. Yep.