Good morning and welcome to Informatica and the Modern Cloud Data Architecture webinar. We are coming to you live from headquarters in beautiful Redwood City, California. I'm Victoria Hyde-Dunn. I lead Informatica's investor relations efforts. Before we get started, a friendly reminder that we will be discussing our technology capabilities. Please see our most recent SEC filings for more information. This webcast will be made available for replay, and a copy of the presentation deck will be posted subsequently on the investor relations website. We have a full agenda, and we'll be discussing a wide array of topics. We will also have time at the end to take live Q&A. Please send an email to investors@informatica.com. To get things started, it is my pleasure to welcome Informatica's Chief Financial Officer, Mike McLaughlin. Thank you, Victoria, and welcome to everyone joining us live online and here in our Redwood City office. We're going to do something a little bit different today than what we did a year ago at our investor day. Instead of a 30,000-foot comprehensive view of our business, including product, go-to-market, and financials, we're just going to focus on tech and product and Informatica's place in the modern cloud data architecture. Amit, Victoria, and I have had hundreds of conversations over the last year with investors and analysts, and one thing has become really clear to us: it's not easy to understand exactly what our software does, and therefore, where do we compete, where do we fit in the stack, and why do we win? So we're going to tackle that head-on today, and most of the discussion is going to be led by my partners, Pratik and Brett. One quick programming note before we get started: we are not going to give any forward-looking financial information in this presentation, and you should not infer anything about our Q4 financial performance from what we discuss today. So let me get started with a brief history lesson. Informatica is synonymous with data management. We've been at it for 30 years, and we've been a leader that whole time. So where did data management come from, and how did we fit in that pattern back in the on-prem era? To answer that question, I ask you to cast your mind back to 1993, the year Informatica was founded. Bill Clinton was being inaugurated as our 42nd president. Ford Taurus was the best-selling car in the United States, and agents Mulder and Scully were introducing us to the paranormal with the X-Files. In IT, we were in the midst of a really important architectural revolution. Distributed computing was displacing mainframes. Disk storage was becoming faster, cheaper, and more reliable, and enterprise software was becoming much more powerful, solving more use cases and generating massive amounts of data, so what did that translate into in terms of a data architecture in the on-prem world? It started, of course, with the data sources, the applications that were generating data: ERP, CRM, et cetera, and you had business users seeking to get value from that data for reporting, analytics, and business intelligence. The problem was it wasn't easy for those business users to get at the data. The connections themselves were hard. The data in the applications was messy, and you needed data from multiple sources, so you needed to do merges, joins, deduplications, and format fixes. That's where the data warehouse came in. The data warehouse is fundamentally an optimized storage layer combined with an optimized query engine together in one package that allows enterprise users to query vast amounts of data quickly and efficiently. We'll talk a lot more about that as we get into the technicals later on. But to utilize that data warehouse, you had to get the data into it. You had to extract it from the sources. You had to transform it as necessary, and you had to load it reliably into the on-prem data warehouse. That's where Informatica fit in, and that's where we built our business, and that's the business we led for 20 years. One aspect of this architecture that I want to point out, and I will get back to when we talk about the cloud architecture, is that in this on-prem environment, the customer paid for and provided the storage and the compute. It was their storage arrays and their servers. Informatica provided the on-prem software that enabled the data management. This was a great business for Informatica. We grew it from a standing start to over $1 billion in 2015, and we did so amidst strong competition. We weren't the only ones that could do data management, but our innovation and the strength of our product enabled us to be a leader for decades, but by 2015, the world had changed a lot. The Eurozone was in resurgence after their debt crisis. The Toyota Camry was the best-selling car, and Hamilton debuted on Broadway, and in the world of IT, the world was going to the cloud. SaaS applications enabled by cloud service providers were revolutionizing the way we do business, and they were generating exponentially greater amounts of data. Unfortunately, at that time, Informatica was still primarily an on-prem company. What did we do? We took the company private in 2015, and we began building a cloud-native true platform for cloud data management using a clean sheet of paper, cutting-edge architecture, and building blocks born in the cloud, as they say. We spent over $1 billion in R&D in the process. The result was Informatica's Intelligent Data Management Cloud, the IDMC. The IDMC, as we sell it today, consists of the best data management products category by category delivered on the industry's only cloud data management platform, serving the multi-vendor, multi-cloud, and hybrid needs of the modern enterprise. Don't just take our word for that. Ask Gartner, Forrester, IDC, and other analysts who consistently, year after year, rank us as the leader in cloud data management. Let's go back to our simplistic data architecture diagram and compare the cloud data world to the on-prem data world. These days, most of the data is being generated by cloud data sources, and you still have business users that want value from that data, but they're using cloud tools for the same reporting, analytics, and business intelligence themes than increasingly for data science and AI. And in the middle, it's the cloud data warehouse, data lake, or data lakehouse that makes it all possible. But you still have to extract the data, transform it as needed, and load it reliably into the cloud data warehouse to make it useful for business users. And like in the on-prem world, that is the value that Informatica provides today. And also, like in the on-prem world, it is the customer that provides and pays for the compute and storage for cloud data management, for cloud data workloads. Informatica provides the cloud software that enables the data management. Now, I want to make one more key point before I turn it over to my technical colleagues. In technology, you will, from time to time, hear convergence narratives. Analysts or vendors will forecast that a new way of doing things, whether it's a new standard or a new architecture, is going to drive a simplification of the complexity that exists today, a convergence on a single vendor or a single way of doing things. And in fact, you're hearing that narrative in some parts of the data world today. In our view, that is not the way enterprise data works in the cloud. It is, in fact, not converging. It is becoming more complex, and enterprise customers are using more clouds, not fewer. We're not the only people who think that. Just yesterday, IDC released their 2024 Office of the CDO survey. And in that survey, among other things, those CDOs reported that they had an average of 18 data repositories in their environment. That's multiple data warehouses, multiple data lakes, multiple operational reporting stores. Forrester, in September, released their Global Enterprise Cloud Decision Maker survey. They asked 700 of these folks, "What impact do you expect an increased public cloud budget to have on the number of cloud providers that your organization uses?" 64% of the responders said it's going to increase the number of cloud providers. Only 6%, one in 20, said that it's going to decrease the number of providers. This complexity and this growing multi-cloudedness of the cloud data environment directly drives the demand for and the need for Informatica's IDMC, which is a collection of the best products on the industry's only cloud data management platform acting as the Switzerland of data. And it is the key part of what has enabled us to grow our cloud subscription ARR to over $748 million as of Q3 2024. So enough of the layperson's history lesson. I'm going to now turn it over to my colleague, Pratik, who's going to talk about data integration and engineering in the cloud. Pratik? Thanks, Mike. There you go. Thanks, Mike. Hello, everyone. I'm Pratik Parekh, Senior Vice President and General Manager for Integration Business at Informatica. I've been with the company for 12 years. I'm excited to be here. Today, I'm going to cover the technologies fueling the modern data architecture and role Informatica data integration and engineering plays. We'll also look at some of the emerging trends that are and will play a significant role in this architecture. Let's go back in time and understand why a new software stack was needed for analytics in the first place. Transaction systems are highly optimized for transactional workloads. It involves complex schema with data distributed across a number of tables, thousands in an enterprise system such as ERP. The slide shows a representative data model, also called an entity relationship model, for orders and deliveries in an ERP system. Here is what a supply chain executive at this manufacturing organization would like to see. He wants to know how they are doing in vehicle shipments, how many vehicles were delivered on time, by type, and by region. The top table here shows the raw data as it is in the transactional system. As you can see, it doesn't do much. It has codes and values that do not mean much unless you know what that stands for. The table below is a fully formed report and visualization. This is the data ready for analytics. The raw data needs to go through preparation to produce the data ready for this analytics. This is called transformation of data. Example transformation includes joins, cleans, conversion, standardization, application of business rules, and formulas. Once transformed, data then mapped to the analytical model. This is just one example. Real-world situations are significantly more complex, requiring specialized skills and technology. Now, this use case led to a special class of database that we call data warehouse. This is fine-tuned for analytical workloads, also called OLAP, or online analytical processing engines. Data warehouse fundamentally has three components: a query interface layer, a data processing layer that executes that query, and a data storage layer that organizes the data against the analytical model defined and is highly optimized. Data warehouse differentiated on query processing, amount of data it can store, and performance and scale. Typically, it existed as one logical unit. Vendors built highly optimized physical appliance, building the required compute and storage. Here are some of the vendors who specialize in the market that you will recognize. Analytics vendors overlapped in some functionality, especially the query layer and included processing and storage, but its primary role was to build and serve the reports and visualization to the large user base in the organization. Now, the problem that was not solved was data. How do you bring all the changing transactional data under all these locked systems in different formats and different latencies on time for analytics? That gave rise to an integration pattern called ETL, which Informatica pioneered. ETL later expanded to data integration that addressed a broad range of data ingestion and processing patterns. ETL involves extraction of data, transformation of data, and loading it into the data warehouse. Once loaded, which is typically considered a Bronze zone, it goes through further refinement and ultimately is ready for analytics. This gold data is supposed to be gold for the enterprise. Enterprises relied on this for critical decisions, and Informatica established itself as the leader providing this capability. Note, Informatica's value was data pipeline management and did not offer any compute or storage. The software ran on commodity hardware and operating systems. We further expanded our offering beyond data integration to do comprehensive data management. Data quality, data catalog, and master data management were added to the stack. Now, however, we were not alone. It was a competitive landscape. Enterprise data warehouse, database providers, platform providers, and analytics vendors all offered some form of data integration. For example, IBM had DataStage then. Oracle had Oracle Data Integrator. SAP had their own version. Even likes of Teradata, who specialize on the data warehouse appliance, had sophisticated SQL-based interface to do data transformation. Informatica was appreciated and acknowledged for the best-of-breed data integration and value it offered. Informatica's value proposition was end-to-end data pipeline management. That included these five key capabilities that did better than anybody else, provided a high productivity environment, had the best workload optimization, provided connectivity to all critical data systems, and was portable across platform, future-proofing of the business logic, and finally, provided the enterprise-grade operationalization. We'll look into each of these later in this presentation. From the economic standpoint, Informatica was not competing for the spend on data warehouse or analytics layer. Customers spent roughly 15%-20% over and above the analytics infrastructure for data integration. The market was also defined that way, where data integration was a separate market segment and a category and growing rapidly. This led to Informatica's growth to a billion-dollar on-premises by 2015 that Mike talked about earlier. So now, let's fast forward to the modern era. We are now looking at the years 2010 and beyond. Emergence of cloud was a defining milestone in computing. Conceptually, it enabled infinite compute and infinite storage at a fraction of the cost of the on-premises infrastructure. This led to acceleration of digital transformation. Digital transformation involved optimizing business processes, new digital initiatives, new and modernizing applications, many of which were software as a service, but also new application development. New data stores and data structures emerged to support the different types of applications, cloud and mobile. Data was generated in more latencies than needed to be handled for integration and analytics. The end result was organizations were able to bring agility to their business, modernizing business processes and digitizing their assets, but it also created a huge problem of data fragmentation and explosion. This became a massive problem as enterprises looked at providing analytics and insights to their users on all these newly generated business processes and applications. Reports suggest that enterprises today have over 1,000-plus applications. It is estimated that over 90% of the data is created in the last two years and is growing at over 40% year over year. With the economics of cloud, analytics stack also went through big change. Biggest was compute and storage were decoupled. Both can now scale horizontally, independently of the workloads. Optimized storage was split further into what we now call data lakes. Data lake promised to be a single destination for all enterprise data built upon cheap cloud storage. Data processing layers were optimized to take advantage of these changes. On the use case, enterprise now not only wanted to see the historical data, they also wanted to model this data to see the future. That gave rise to data science as a discipline. Again, like in the case of the previous era, ingesting all this data that is under the transactional system and on-premises was a challenge. The more things change, the more they stay the same. Informatica saw this trend early. We introduced cloud data integration, a cloud-native offering optimized for cloud workloads. We have continued to evolve that rapidly as use cases and architectures have evolved. Gartner believes, and we agree, that data integration is fundamental for modern data architecture, such as data fabric and data mesh. Organizations need comprehensive capabilities for operational, analytical, and AI use cases. Here are all the capabilities Gartner identifies as critical capabilities for data integration, supporting the core use cases identified here in the blue boxes. Today, Informatica is the most comprehensive and complete data integration solution supporting all data integration and engineering patterns, including operational analytics, real-time analytics, data science, and AI. The solution is designed for power users as well as line of business, and it is best in class with modern cloud-native, cloud-first microservices-based architecture. Now, just like in the on-premises world, we have heavy competition. Only difference is some names are different. Hyperscalers, cloud data warehouse vendors, and point vendors make up the competitive landscape for data integration and data engineering. Informatica has continued to retain the leadership for 19 years running. While we are now on cloud-native modern stack and handle the requirements of modern architectures and use cases, the core value proposition that we saw earlier has not changed. Our unique differentiation is still end-to-end data pipeline management that we saw earlier. Let's look at these core capabilities in more detail. High productivity development. Informatica, right from on-premises day, has supported a model-driven, metadata-aware development paradigm. This involves no-code design experience with out-of-box pre-built connectors and integration processes. It offers a 10-20x advantage over hand-coding solutions. Not to mention, hand-coding is difficult and error-prone and requires a high degree of skills. Hand-coding also binds the code to the underlying platform, to the language, to the engine, to the data warehouse. So it provides low portability, if any. Next core technology differentiator is the workload optimizer. The graph, also known as the mapping, which was created in that designer, is translated and optimized for the runtime and for the workload. This execution can happen anywhere per customer's choice: on-premises, in elastic runtime, in serverless environment, or in a data warehouse system in itself. Workload optimizer offers unparalleled flexibility as every customer and every project has certain criteria of security, cost, performance, and scale. We recently published an IEEE paper that demonstrated 7.5x price performance with the Informatica optimizer, helping customers to select the best deployment configuration to run their workloads. Next, let's look at data and platform portability. This is enabled by out-of-box connectors and platform-agnostic design time and runtime. Informatica offers over 50,000-plus metadata-aware connections. This gave us the status of Switzerland of data. Cloud data integration can connect to 100-year-old systems such as mainframe and most latest data structure and formats such as Iceberg, Delta, vector databases, and large language models. Connectivity handles all latencies: batch, real-time, and streaming, and all data formats: structured, semi-structured, and unstructured. Next, I'll cover the key differentiation of future-proofing of business logic. Integrations that we saw earlier have critical business logic embedded, and often this routine remains the same for decades with minor incremental changes. Future-proofing this means if a customer moves from one cloud platform to another, they do not have to rewrite the integrations. If they move from one data warehouse to another, they do not have to rewrite. If they change one source or target system, for example, move from Oracle to Salesforce, they do not have to rewrite their business logic. If they change from one technology stack to the other, they can carry forward the business logic. A great example was the big data world and Hadoop. The Hadoop world brought a completely new stack with different runtime and technologies such as MapReduce and Spark. This stack evolved every six months, making it extremely difficult for customers to standardize on. Informatica was able to help customers deploy their business logic on Hadoop platforms without rewriting. And when technology died, well, almost, we were able to bring that business logic to the new paradigms such as cloud data warehouse, and now lakehouses. And finally, the enterprise-grade optimization, operationalization. This involves scale, availability, reliability, management, and security needed for mission-critical use cases in the most demanding enterprise workloads. Today, we process over 101 trillion transactions a month. The underlying architecture is horizontally scalable. Service is available globally and is most secure, providing the highest level of SLA. CDI supports full data lifecycle management with built-in CI/CD, also known as continuous integration and continuous deployment, or data operations, financial operations, and workload management. This core capability is truly differentiated Informatica data integration offering and why we have maintained our leadership for this long. Let's look at a customer story on what that means. Paycor is one of the hundreds of customers who leverage Informatica cloud data integration. But they did not start with Informatica for their cloud journey. They used a competing solution from one of the hyperscalers. Not only were they successful with the use case, in this case, empowering organizations for faster and better decision-making, they realized all the benefits that I talked about. They were able to achieve that with reduced cost and better performance. We are talking about 5x faster performance at 50% of the cost. Now, our internal benchmarks also show similar results. We observed 4.5x performance at 42% less cost compared to one of our top competitors. The benchmark was done against all the modern data integration patterns shown on the right of the screen. On the economic front, as we saw earlier, cloud reduced the unit cost of compute and storage. In fact, it continues to fall. That has had an impact on the optimized storage component of the data warehouse and also showing up on the compute tier. However, Informatica is able to maintain our share because of the value we are offering. Also, as we saw earlier, Informatica is not dependent on compute and storage. The slight dips for workloads do not factor the growth of data and new use cases, which counters the decline of the price in compute and storage. That benefits all parties: hyperscalers, CDW vendors, and Informatica. Now, let's look at some of the emerging technology trends and how it impacts the world of analytics and Informatica. We'll look at the three key trends: open table formats, zero copy, Zero-ETL, and finally, AI. Let's start with the open table formats. Technology is continuously evolving. Right from the early days of analytics, the computing world is trying to identify ways to standardize data such that it enables access, reach, and improved usage. It got better with every attempt. XML and JSON were largely used for transactional data. There were some analytical use cases there, but it did not become mainstream. Avro and Parquet were a big leap forward on transactional systems and for analytical systems. It provided ways to describe data, included the metadata that made it easy to understand. Iceberg and Delta are built on top of this that now allow ACID operations on data. Basically, in short, it allows data to be retrieved and written like one would do in a database. It is also called ACID properties, where transaction controls are maintained. This is great for the industry as it offers a common format to store and manage data that can be understood by different processing engines. However, note that there are multiple of these standards and more evolving, which is given. Let's go back to our modern data warehousing view. We talked about decoupling of compute and storage. What Open Table does to this view is now you can query directly on top of these open table formats, minimizing or eliminating the need for the optimized storage, further reducing the cost of the architecture. Given Open Table is an extension to data lake, you can directly operate on very large data sets. Provided query engines can be optimized to take advantage of it. Hence, we see a big push by hyperscaler and pure-play CDW vendors to support this new format. While the storage layer is commoditized further, they believe the technology will enable new analytical use cases. In the case of Informatica, this is net positive. As customers look to store data in Open Table, Informatica will continue to provide the value of pipeline management we discussed earlier. Data still needs to be extracted and transformed to new data formats. We support all the formats that I showed in the previous slide: XML, JSON, Avro, Parquet, ORC, Iceberg, Delta, and also hundreds of industry-specific formats like EDIFACT, EDI, HL7 that have evolved in the last two to three decades. From an economic point of view, more formats and more data is good for Informatica. Now, let's look at the zero copy, Zero-ETL. Again, back to the baseline of modern data warehouse. Basically, what zero copy or Zero-ETL suggests is analytics directly on transactional data, and in theory, you should be able to do analytics directly on the transactional system. As the visual shows, you are querying the data directly on the transactional system. Many vendors have claimed to support this pattern. Underlying architectures are different, and in most cases, there is a copy involved, which is a paradox to the claim. Basically, what is involved is data is made available in open table formats we saw earlier. And because data processing engines can now work with this format, you should be able to directly query it. For simple data sets, like an example here, it does a fairly good job. We call that ad hoc data lookup or query. In this example, what you are seeing is a marketing analyst collecting leads from a conference that she just attended. The analyst wants to know how many people attended the conference, how many customers visited the booth, how many opt-ins happened. And those are easy questions that that particular data set can easily provide. But what happens when the data involves how does that conference participation impact the pipeline in an organization? How many opportunities are created? What was the campaign performance? What is the eventual impact on the sales forecast? As you can see, these questions are much harder. Remember, transactional data is never ready for analytics. It needs to go through the same transformation we saw earlier in the presentation. That results in that deeper analytical queries that I just posed. Data integration and engineering is required, handled by the data center of excellence in the organization. In summary, zero copy, Zero-ETL is good for ad hoc queries, but it does not impact Informatica, which is used for enterprise analytics workloads. Now, let's look at the mega trend that is changing and affecting everything: AI and generative AI. This topic is much broader, needing a separate deep dive. I'll try to provide a quick overview. At Informatica, we have two initiatives: AI for data management and data management for AI. I'll cover the topic in the context of data integration. Let's start with AI for data integration. This is built on years of AI and ML innovation we have done at the metadata intelligence layer of our stack. As you are aware, we call these capabilities CLAIRE. CLAIRE enables us to bring best-in-class generative AI-enabled NLP-based services. For data integration, we have two primary offerings: DI Copilot, or Data Integration Copilot, that is releasing in Q1 of 2025, and CLAIRE GPT, that has been available for over a year. Both allow users to explore the data in the organization, prepare the data, build data integration pipelines, and deploy that just with natural language prompts. We believe this will foster democratization of data and thereby improve decision-making in the organization. For data engineers, it means more productivity and solving more advanced use cases. Underneath this, Informatica has built fit-for-purpose AI and ML models. These are fine-tuned and trained for data integration and data management. Next, we will look into data integration for AI and GenAI. This topic covers how Informatica helps organizations to leverage GenAI and build AI applications. After all, AI is as good as data there is. Data integration for AI enables enterprises to implement their AI pipelines, also known as AI engineering. AI engineering involves two critical steps. One, extracting and loading large volumes of data, structured data as well as unstructured data, into a special type of data store, such as vector or graph databases. In the process, you apply special transformations called chunking and embedding that basically generate the numerical tokens or references called vectors that describe that chunk of data. When an AI query prompt is issued directly or via the AI application, it goes through another pipeline that is called RAG, or Retrieval, Augmentation, and Generation. Simply put, this generates the AI response by interacting with large or small language models and blending that data with the enterprise data or vector tokens that were created in the first step. While there are more steps involved in any AI implementation, these two are fundamental to any AI implementation in an enterprise. Informatica brings the same value proposition we discussed earlier, enabling data engineers to now do AI engineering. It is no surprise that data leaders consider supporting AI initiatives in the organization as the top strategic priority, and next on the list is good data for analytics. This is from just-published CDO survey results from IDC. We are adding new capabilities rapidly across all of IDMC to handle unstructured data, support the major vector databases and large language models, and enable integration of GenAI applications, GenAI capabilities into the applications. The investment customers have made to build modern data architecture can seamlessly be extended to support AI initiatives. Today, I only covered cloud data integration and how it supports the modern data architecture. Modern data architecture also needs to have a strong governance foundation and support broader data management use cases, including master data management, data marketplaces, application integration, and data and process orchestration. Informatica offers a full integrated platform with all of these capabilities to enable next-generation architecture and support the full lifecycle of data. I covered data integration and engineering along with related components, including connectivity and CLAIRE, on this slide. I'll now hand the mic over to Brett Roscoe to cover Data Catalog. Thank you. All right. Thank you, Pratik. Thank you. All right. Well, hello, everybody. My name is Brett Roscoe. I have the honor here at Informatica to be the general manager of our data governance and cloud operations here, and I have been here for about four years, and I have the honor today of really sharing some insights into the Informatica data governance and privacy portfolio and how that solution is having a great impact and value into this market space, so let's set some foundations, because one of the things I do want to make sure we talk about is the fact that organizations' data environments continue to become more complex. We see data coming from on-prem, from the cloud, hybrid, from SaaS applications, more and more diverse data, more and more volume of data coming at customers. A great example of this is one of our large telco customers has over 20,000 databases across on-prem and cloud. They have three data lakes across AWS, Azure, and Google, and they have petabytes' worth of data that they're managing. Now, you might say, well, that's one example. We recently did a survey with our customers, and it's reinforced, right? The survey illustrates that over 40% had an average of over 1,000 data sources, and nearly 80% said those data sources were continuing to increase and the complexity was continuing. Mike alluded to this in some of his opening presentation. Now, the data warehouse and data lake vendors, including the cloud service providers, continue to build out their offerings. They continue to attract data scientists and data analysts to their platforms. And several of these vendors have started ventures and building things, even open-source projects, to build catalogs, to build governance capabilities, and other complementary tools to simplify the experience of using their data warehouse and data lake environments. And this is great value. I mean, it's always good to have contextual information of the data in your environment. It helps you manage that, helps your customers manage that. But we know that they're very much focused on the use cases within their platforms. And so Informatica is no stranger to partnering with these ecosystem partners. Pratik talked a lot about that with the cloud service providers. This value of us being vendor neutral, of being the Switzerland of data, as Pratik mentioned, brings great value to them and to our customers and obviously to our business. I want to talk about three areas where we add tremendous value on top of this, and I'll elaborate on each of them. The first one is really comprehensive coverage, covering the data estate from end to end. The second one is about how we drive multiple personas to use our products together. How do we have projects with different people in the company working together on the same data? And the third one is the ability to provide augmented data management tools driven by AI and accelerating business outcomes for our customers. So let's dive into each of those. The first one is about comprehensive coverage. Again, this vendor-independent solution, this Switzerland of data, which allows us to work across vendors and with our partners. Now, if we go back to the local catalog discussion for a second, they have obviously been working on building some interoperability among them, right? Because it's difficult for somebody like a Snowflake or Databricks to offer catalog but not offer connectivity to something like S3 or an ADLS, where much, much of their customers' primary data is. They want to connect to that to make sure they can pull data into their data warehouses and lakes from those sources. But again, the primary goal is to simplify the experience for their focused use cases. Now, their business model is to win in the data warehouse and data lake market, which by definition kind of puts them at odds with each other, making it very difficult to provide connectivity or end-to-end across the entire ecosystem support for all of those data sources and data targets. Now, to complicate the issue, there continues to be thousands of production data sources on-prem, as well as SaaS-based applications that are continuing to produce different kinds of data. There's even unique and kind of tailored databases for specific verticals, like think healthcare or maybe some financial services. And now, most of these born on-prem data sources can potentially be hosted in the cloud, although the cloud service providers may or may not be aware of them because they're probably just an infrastructure play for them. So minus an enterprise global catalog, this leads to multiple tools to cover this environment from end to end. Mark Beyer is one of the leading experts at Gartner who spends much of his time discussing and analyzing the importance of metadata. Mark talks about this issue of multiple metadata stacks. The utilizing multiple stacks perpetuates silos. It perpetuates the isolation of data management within the enterprise. And you can imagine the kind of problems that can cause. Informatica, with our data governance and privacy portfolio within the Intelligent Data Management Cloud, provides the most comprehensive view and control of the entire data estate. Informatica covers a broad set of cloud, on-prem, hybrid data sources and the ability to extend those capabilities. We can leverage SDKs with our partners and with our customers to even build custom scanners to get rich metadata from unique databases. And we work with all ecosystems, all cloud service providers to provide the most federated data environments, a centralized way to control, govern, and provide privacy and security. Even IDC agrees, right? They believe a comprehensive multi-cloud and hybrid approach to data management is the right answer for most enterprises. So our mission is to bring comprehensive data coverage across the entire customer landscape. We support, as I said, all major ecosystems. We even connect and support a lot of these third-party or local catalogs. It allows us to pull, extract metadata through partnership into our catalog to provide a catalog of catalog, if you will, a way to have a localized catalog supporting a federated environment with centralized views. Now, the second area I want to talk about is the richness of metadata, right? How covering breadth and depth of sources is not enough, but the amount of metadata, the richness of your metadata is equally important. Now, let me start with a little bit of how our customers use our products. Our modern data governance portfolio is made up of four tightly integrated but persona-focused solutions. Our data governance catalog service provides simplified and consolidated views of our catalog and governance systems. It enables data profiling and lineage. It provides both technical and business views of that metadata, proactive alerts, integrated tools to discover, to provide glossaries and definitions, classifications, and it's all to derive this intelligent metadata. Our data marketplace is our storefront. It's where data consumers think somebody in a line of business, maybe somebody in marketing or HR, is able to go search, discover, explore, and even procure data products or data assets for their analytics and AI projects. Also, as centralized teams are implementing more DevOps and FinOps, right? We have evolved to provide more visibility and proactive quality remediation across the data state. So our data quality observability service allows customers to know when and where their data is being used. It provides tools to optimize around cost or performance, the ability to proactively improve quality or identify anomalies across the entire organization. And lastly, our data access management lets you secure sensitive information while still building trust and drive data sharing across the organization. It's a global data access management solution that leverages metadata to set policies. And those policies can be set all the way down to a data asset or even a row or column level within data sets. And it allows you to set access by location, by role, by data attribute, or any attribute you can imagine set in a policy with metadata. All right, with that, now how do you power all that, right? Powering a scaled, complex, multiple set of products requires rich metadata. And we leverage a single common multi-tenant cloud-based repository collecting metadata from all of our customers and all of their data sources and all of their operations. This allows our customers to utilize this portfolio of tools that allows each of these personas basically to have a different lens into the same metadata. Now, all of that with security and privacy and controls built in. But to support a single metadata repository, you need rich, high-quality metadata to better understand the data within that environment and expand the use cases. Now, we've all heard that metadata is data about data. But I'll tell you, not all metadata is the same. The richness of metadata can help enterprises better understand how to manage and utilize their data assets, right? It provides an amazing amount of intelligence on the data. It's history, it's sensitivity, it's quality, it's purpose, any classifications it needs, its locations, its sovereignty. All of those things can be extracted to make sure rich metadata can fuel projects across the organization. Now, because we want to have multiple personas and teams using metadata, we know that using disparate, separate, multiple stacks can lead to multiple data metadata repositories. Now, let's talk about the problem of that. Because as soon as you have multiple metadata repositories, by definition, they're going to get out of sync. And at that point, you start to wonder, well, what's the one single source of truth? Again, we rely on a cloud-native microservice, multi-tenant-based, single metadata repository that serves all of our tools and all of our services on the Intelligent Data Management Cloud. Now, a great example of how we create value from that is auto cataloging. Every time one of our customers does an operation in the integration side, we automatically collect metadata to provide almost a real-time view for our customers on the changes to that data state. Great value in terms of having all those people work together. Now, let me provide a quick example. This is one example of lineage on a single data element that's being used to populate a BI report, in this case, customer ID. As you can see, capturing the right level of metadata is a comprehensive task spanning many sources, many data transformations, and operations. Now, any lineage that's not captured through this scanning, through this deep scanning process, has to be filled in with manual labor. Even then, it's probably going to have gaps, which is going to lead to errors down the line, right, as that data moves into these analytics and AI projects. So data analysts for us, data stewards can catalog and classify data. Governance and security people can drive access and compliance policies. Data consumers can search, discover, and procure data through the data marketplace. Additionally, we provide collaboration. We have communication tools. We have rating systems for consumers to rate data assets. We have the ability for them to chat or request additional assets if they don't have access to that already. And data owners and data stewards can, through our data access management, automatically grant access. And that takes the time of what typically took manual labor of email or even going and talking to somebody, which would slow the process down. This provides a very fast way to get data into the right hands of the right people very quickly. Now, let's talk about this third element. To enable this level of scale, this level of metadata to be used across the organization, we believe an AI approach must be taken. One that provides AI to simplify, automate, recommend task, provide insights that only in a non-AI environment would require an enormous amount of human capital. Now, we believe that an AI architecture that collects this extensive metadata will enable more teams on more projects with more capabilities. After all, data governance and catalog has evolved far beyond its original roots of really providing regulatory and corporate compliance. All these things of data sharing and observability continue to fuel the additional use cases for governance and catalog. Now, our data catalog and metadata repository is the fuel of the engine behind CLAIRE. Pratik talked about CLAIRE. He talked about the different things we do on CLAIRE. He talked about some new things coming on Copilot and integration. Today, CLAIRE Copilot is our end product experience. It provides AI and generative AI to drive automation recommendations, all kinds of tasks, and auto glossary assignments, auto classifications, things that typically take hours of manual labor. With CLAIRE GPT, new sets of customers with less technical skills can drive data management tasks through a natural language query interface. By simply asking questions, you can now search, explore, discover, build pipelines, improve quality through CLAIRE GPT. It is a great way to bring in more users into the platform. Our customers value each of these. They use Informatica's solution for an increasing number of use cases and projects across their enterprise. In fact, I will tell you the evolution of governance and catalog continues to evolve our investments. It continues to keep us on our toes to drive new capabilities so that we can expand their use cases and the things that they're doing with data across a broader set of users in their organization. Now, Pratik showed a slide from this IDC research just a little bit earlier, but there's another data point that I thought was worth talking about. I didn't talk much about the impact of AI and generative AI on governance, but it's been big, right? Top of mind challenge for enterprises is making sure they have governance and privacy around the data being used for these projects. Because we've seen projects go awry. We've seen in social media, we've seen in mainstream media, some of the outcomes when you run too fast with a generative AI project that's not using unbiased, relevant, and highly understood data. Informatica is uniquely positioned to work with our customers and provide end-to-end solutions spanning the enterprise for these AI and GenAI projects. And let me wrap up by just saying, look, all of this is delivered in the Intelligent Data Management Cloud. So imagine all of the integration in our governance portfolio that I just discussed, integrated even further with our MDM, our 360 applications, our integration business. The goal is to design a solution that provides the broadest end-to-end data management experience across a multitude of sources, applications across on-prem, cloud, multi-cloud, multi-hybrid architectures. The goal is to deliver trusted, secure, and high-quality data from all of those data sources to all of the data consumers, your data scientists, your data analytics team, your data consumer. With that, I'm going to hand it back over to Mike, who's going to wrap us up. Thank you for your time, and I'm sure I'll be back for the Q&A. Thanks, Brett. Thank you. We've covered a lot of ground today, and I want to thank Pratik and Brett for shedding some light on what our software does, where we compete, and why we win, and I'd remind everybody that we're only able to cover, in the interest of time, two of the big areas that we participate in. We didn't cover master data management. We didn't cover API and application integration. We didn't cover data marketplace. Perhaps in a future webinar, we'll have a similar session. As we wrap up, I want to make a few key points. Cloud computing has driven massive changes in data management. Complexity has increased, vendors and competitors have changed, and multi-cloud is the norm, and that is increasing, not decreasing. In that environment, enterprise-grade data management from an independent, best-in-class vendor is every bit as essential in the modern cloud data world as it was in the on-premises era. Informatica's born-in-the-cloud IDMC platform is uniquely able to serve the needs of enterprise customers in the cloud today and in the future. Informatica's value proposition in the cloud is unique, and it cannot be fully replicated by the competitors we face today. Not the point providers, not the open-source and roll-your-own solutions, and not the cloud data warehouse and hyperscalers themselves with their proprietary tools. The IDMC, which is a collection of the best cloud data management products on the industry's only cloud data management platform, acting as the switch and load of data serving our customers' multi-vendor, multi-cloud, and hybrid needs, allows us to deliver the differentiated value proposition that we've been discussing today in cloud data integration, in cloud data catalog and governance, and in MDM and the other categories where we participate. Now, I'm going to end with a little bit of data that I think validates this assertion. It's our own usage data from our IPU customers on the IDMC and their usage of four of our leading cloud data management services on the platform. As you can see from this data, we are the leading cloud data management solution provider today. So I'm going to end it there, and let's turn it over to Q&A. I'd like to invite my colleagues up here to kick that off. All righty. We're going to jump into Q&A. Thanks for sending me your questions. Keep them coming. The first one I'm going to direct to Pratik. Zero-ETL. Seems like Zero-ETL is mainly for ad hoc queries. Where is the processing of the query happening? Is it happening in the CDW? Could it be possible for the CDW to provide certain ability to join data in the transactional systems directly to answer complex questions? Or would that be processing, latency, or cost prohibitive? Yeah, very good question. So as we covered, Zero-ETL does, in fact, work really well for ad hoc queries. And it's a very good question in terms of where the processing happens. There are two places that the processing can happen. What it allows us to do, and as I covered briefly in my talk, the underlying data format is generally the open table format that we talked about for Zero-ETL, which allows various engines outside of CDW to also access that particular data. So it can be processed in any execution environment. Generally speaking, it will be processed within the data warehouse context or directly by the analytics layer directly. And the solution or the use cases it's trying to address is the ad hoc query. The other part of the question was, can it be further joined to address that? That's where the latencies will come in. And that is why, for enterprise-scale analytics, that's not really an ideal solution if you have to actually look at the data that is sitting in somebody else's cloud, and now you are bringing that data set over, joining it, and then surfacing the value. For real analytics, you want that data to be present where it is done. Security constraints, governance constraints are other reasons why those architectures are required. But just purely speaking technically, that is a hard thing to do, to join, to filter, to do all the processing that generally you do for analytics as part of Zero-ETL. So the second part of that question is, what about federated data or data sharing? What does it mean? How does it work? Federated data and data sharing also is relied on the same thing. Zero-ETL has a lot of terms today. Some vendors are calling it zero copy. Some are calling it as links and references. This has been around in the past as data virtualization technology. So it's not really a new tech as such. And all it talks about is making that data available with a reference rather than actually physically copying the data, which is generally needed for large-scale analytics projects. Great. Let me ask Brett a question. How widely adopted is data catalog within your customers, and how much of their data is cataloged within Informatica? Great question. We don't talk specific numbers. I will say that we have a very large chunk of our IDMC customer base that is using our catalog and governance solutions in one way or another. In terms of how many of their data, how much of their data estate they cover, it widely varies. I mean, we often talk to customers and say, look, start simple. Start with one data element and expand. And then we have customers using it to basically create catalog and data sharing across their entire organization. In that case, they have millions of data assets sitting in the catalog. And so it varies by customer quite a bit. But what we're seeing, especially fueled by GenAI, is that many people are running to this to make sure that, man, I need to ramp up how much data I share across my organization, which means I need to have control over it. I need to have governance around it. So we're just seeing a great demand and a great adoption here lately, fueled by a lot of these GenAI projects. Great. Pratik, can you please talk about the risks and opportunities for Informatica with the emergence of open tables? As I said, formats are continuously evolving. For the last two decades, there has been a number of formats. Those are horizontal formats that I showed in this slide, like XML and JSON, that then gave rise to Avro, Parquet, ORC. And then now we are talking about Iceberg and Delta. With every generational shift, those formats have been able to do more. In fact, to convert from one format to another format, you need a data integration tool like Informatica. So we see that as net positive for Informatica. Then another question. With the hyperscalers leading the charge with LLMs, is there a risk that they capture more customer mind share, including being more competitive with Informatica? They are not competitive, so LLMs serve very specific purposes. Informatica is in the business of data integration and data management, so that's what we do the best. And good data is needed for good AI. And all of this data, 90% of the data that customers want to solve in the context of their AI project, is sitting within their firewall. And that's what Informatica is able to unlock and then blend with the LLM data, the general models, to provide the value that is very specific to that enterprise, so from that context, Informatica don't see that as a threat or a competition. In fact, we are partnering with all the hyperscalers to bring that blueprint in terms of what the customers need to implement a good AI project internally. And Vic, maybe I'll add on to that a little bit. We do compete against the hyperscalers and the cloud data warehouse providers to some extent. They have data management tools, and they're pretty good. As we talked about today, they generally work within the four walls of their environment, and their connectivity with external sources and targets is limited. But if you have a relatively simple use case and it's on a single cloud, those tools are sometimes sufficient. We don't even try to compete for those situations, for those use cases, because that's not where our value add lies. Our value add lies in the complexity and the multi-cloud world that the modern enterprise lives in. And where that is the situation, we compete, and we generally win. And we partner with those hyperscalers and cloud data warehouse providers for their piece of the puzzle. Great. So turning to catalog, does Apache Iceberg provide a metadata layer? Could continued open-source development in that project lead it to evolve into an independent enterprise catalog over time that people gravitate towards? Yes. I mean, obviously, there are capabilities to develop a catalog on top of an open table format. A great example of that is Snowflake introduced Polaris and then introduced it as an open-source project to extract metadata from that format. But another great example of how that maybe just the function of having so many different data sources in that environment is they use a different horizon to do all of their cataloging for other areas of their data sources and data lakes. So I think that's a perfect example that we don't see this consolidation of one format all of a sudden driving a consolidation in the catalog space. If anything, the formats. Pratik spent a lot of time on this. The formats continue to kind of expand and follow that pattern, which means that our IP, our ability to scan all of those different sources to extract rich metadata and do it in a consolidated way is what our customers continue to find valuable. I'll just add on that to reiterate a point I made in my presentation, that convergence is a popular concept, and it sounds good creating simplicity with a new standard or a new way of doing things. That's just not the way enterprise data works. It's not what we've seen in the past. It's not what we see today. It's not what independent analysts and third-party surveys say is going to happen in the future. The world of enterprise data is going to stay complex, likely get more complex, and it's going to be more multi-cloud, not less. That's where we play. So Mike, staying on you for a second, I got a question. Mike, you mentioned your historical strength in gold-tier data. Can you talk about more of the specific technical investments that you are making that could expand the universe of integration and data catalog to other tiers of data? Or is that not as relevant to your use cases? Sure. Maybe I'll ask Pratik to fill in any of the technical details I miss because I'm not a technologist. We are investing in our cloud data integration engineering capabilities all the time. We introduce on a regular cadence at Informatica World and, again, a big release this fall that exposed additional capabilities that we've been investing in over the last year or two. That's going to continue. Stay tuned for Informatica World because there will be some exciting things that are displayed there. In terms of the actual management and modification of the data within the data warehouse, within the data lake, within the data lakehouse, as you can see from Brett's presentation, we do that. We support that. That's a core way that our customers use that with an ELT push-down format and other categories. Absolutely, it will be a continued level of investment, and you will see us unveiling new capabilities of that nature on a regular cadence. Yeah. And Mike, with respect to the specific question about whether we do play a role beyond the gold tier, actually, we play a role across all the tiers in preparation to the gold tier. So bronze, silver, and gold, that is what I covered in this slide as to how we help prepare and refine the data for the gold tier. Now, that's just one part of it, which is for analytics and for data warehouse. What we did not cover today is how reverse ETL helps to get that data back and take it back into the system of record. We do data synchronization between two systems. We do application integration and data integration. So from an integration solution point of view, Informatica provides value way beyond just the creation of the data warehouse. It allows you to do operational data integration. It allows you to do application data integration. It allows you to do real-time data integration. So all the various what I call as patterns of integration. Yeah. And similar on catalog, I showed that Lenny's chart. So we capture all data. So as it moves through and progresses through these tiers or it progresses into a data warehouse, data lake, we allow you to track all that and know where that data came from, what operations took place on it, how that data is related to other data in your environment, and make sure you can govern it, make sure you have control over it, visibility over it, et cetera. So Pratik, going back to tables, are you seeing customers adopt Iceberg and Delta tables? Yes. We see customers embracing this new format. It has a lot of promise. The unique differentiation, as I covered earlier, which was not the case in the previous formats, was Iceberg and Delta both allow you to transact on that particular data store or the format. So you are able to do ACID transactions. You are able to do CRUD operations, which is acting like a database. And hence, you can do read, write, filters, queries, all of that on top of that. And I believe that has a lot of value for the customers to further democratize the storage tier into the lakehouses, which can then be made available for analytics as well as various other use cases. At the risk of sounding like a broken record, I want to remind everybody again that we support Iceberg. We embrace it, and we view it as a net positive for our business in the long term. Exactly right. So switching topics, where does real-time streaming fit into data architecture? Real-time streaming is another pattern of integration, which actually processes data in real time. There are two parts of it. One is what is called real-time analytics. Customers want data to be brought in as soon as possible for doing real-time analytics, and for certain workloads, that's very, very important, so we do solve that in two ways. One is all the connectors that I showed in my slide. There were a lot of real-time connectors that allow sources and targets to be received, so the real-time sources and targets, we are able to extract the data in real time and take it to a real-time data store, such as messaging systems, whether it's Kafka-based or Kinesis-based or Azure event-based. We can take to any of the event stores, and that allows us to participate in the real-time architecture. The other one is we support real-time processing. So that is called streaming sources as it comes in, whether it's from IoT or whether it is from change data capture. Our engine is able to process that workloads at scale in real time, processing that and taking it to the target. So both use cases, whether you are connecting to real-time sources and targets or whether you are processing real time, Informatica is able to do that both ways. Great. So another question. We touched on this a little bit earlier. It came back again. We understand that some hyperscalers have their own data management capabilities. Can you talk about what Snowflake and Databricks are doing? Do you compete with them? Where do you see them? On what particular area? The question is on data management. Oh, data management, what are these vendors doing? Yeah. So both Databricks, Data Warehouse has a data platform. They started to provide a very strong analytical data science and now AI use cases. So in that context, they have to work. Their core competency is in how they manage the data store and how they process that data. Now, in the context of the data, they are investing a little bit on some parts of data management, as Brett covered earlier. That includes cataloging. That includes some level of data transformation that can be used once the data is landed in those systems. So we see them investing in those areas, but their core priority is ensuring that they take the analytics, data science, and AI use cases. We partner really well with Databricks. We partner really well with Snowflake. In fact, this year, Databricks named Informatica as their data integration partner of the year. It may sound like we compete against them and we have duplicative surfaces here and there. And as Pratik talked about, yes, they do a good job inside their environment in doing the data management necessary for some very important workloads, but we're not in a converging world. We are not in a converging. We're in a world of complexity that's increasing. We're in a multi-cloud world that is becoming even more multi-cloud. That's where we come in, and that's the value we add. And that's why we partner so well with all of those providers in that tier. I think they recognize our strength in the cross-ecosystem, right? So when they come to us, it's usually because, oh, we realize that you can help us work with customers who have these more multi-ecosystem type environments. And so that, to me, is what fuels a lot of their interest in us. Part of the reason AWS is a great partner is, again, you said earlier, if they're using only them, if they're only using one ecosystem, it's where all their data is. It's where all their data stores are. We're not trying to steal that business. We are trying to be a value add for cross-ecosystem where they see us as well. Our enterprise value, back to the slide that I showed, the five-value proposition, especially in data integration and engineering, is about the high productivity. It's about the scale that we provide. It's about the various connectivity options both within the enterprise, outside the enterprise where they support. The workload management was a huge differentiator. And those are the reasons why we continue to provide that high value. And hence, we partner with them closely when customers are looking for that type of enterprise-grade solution. Great. So Brett, a question for you on data governance. How are customers thinking about data governance for their day-to-day regular use cases? And then how are they thinking about it for their AI initiatives? Great question. So I would say, look, enterprises, they live in a world where all of their data is under some kind of regulatory or even corporate-driven compliance guidelines. And that's kind of the level setting of, hey, I need to start having control and making sure that I'm compliant, that I can prove that we're taking care of this data and protecting sensitive information. So that's kind of any data. Now, I mentioned this earlier, which is that AI has been around for a while, but generative AI, certainly, the new GPT stuff is driving much more kind of data feeding these models in a much higher volume. One of the things when I talk to customers, I find that they are seeing people inside their companies who, a year ago or two years ago, would not be asking for data or suddenly asking for data. They want access to the data, well, how do you, and so if you had manual processes where you were trying to, hey, get people the data that should have it and make sure it was protected, those manual processes don't scale as all of a sudden you have hundreds of people asking for it, so what we're seeing is, hey, maybe I used governance for some of my regulatory stuff, but now I need to scale up my data sharing, right? We talked about kind of data regulatory. We talked about data sharing. We talked about observability, well, as I need to share more, I got to bring in more of these tools, and as I'm sharing more, I also want to have observability as to what I'm sharing, how it's being used, and am I in compliance, all those kinds of things. So I find that generative AI has turbocharged, if you will, the need for catalog and governance. But catalog and governance is needed for all data for most enterprises. And I would just point you to the IDC survey data that was just released yesterday. We showed some sample results in the presentation today. At the beginning of our little show here, we started with our corporate video and our tagline, "Everybody's ready for AI, except your data." Our customers are coming to that point of view very clearly. And all the survey data, all the conversations we're having with enterprise customers is that they agree. Great. Well, that's all the questions that I've received. I don't know if we have any closing remarks we want to leave the audience with. I'll turn it back over to you, Mike. I'd like to thank everybody for joining. And please continue with questions through Victoria or myself or IR channels. And stay tuned. We only covered a couple of the key parts of our business today: cloud data integration engineering and master data and sorry, and cloud data governance. There's a lot more that we do, especially master data management, application integration, API management. And in 2025, we'll plan to have another session like this to shed more light on those parts of our business. Thank you again for joining, and happy holidays, everybody. Thank you. Thank you.