Yeah, we have time? Hurry up. We'll get going. Thanks everybody for joining us. Kirk Materne with Evercore ISI. Really excited to have ServiceNow with us this afternoon. Gaurav Rewari, who's the EVP of Global Marketing, Data, and Analytics. Thanks very much for being here. Of course. Just for some background for everybody, can you just talk about your responsibilities at ServiceNow and then the elements of the data and analytics platform because I think it's something that's obviously up and coming. Yeah the company, but maybe not as familiar to everybody. I'll let you kick off with that. Thanks. Sure. No, happy to provide some context and thanks for having us. Yeah, I'm Gaurav Rewari. I'm EVP and GM, General Manager of the data and analytics business. Relatively new business for us at ServiceNow. We'd done things in data and analytics before. We had embedded reporting, we had data integration products, but it was fairly scattered, and it wasn't a serious area of focus. Bill and Amit reached out to me to join and to stand up our next multibillion-dollar business. That was the problem statement. I think the motivation on their part was twofold. One was what they were hearing from customers, which is, Look, we'd love for you to take data seriously. The second is just its incredible relevance to our AI success. Right. Right? You've all probably read the reports, right? The very sobering statistics around 95% of projects fail, according to the MIT study. Sure. There are other things from Gartner that are equally sobering. If you actually read that report, it tells you that in most cases, the reason for that is data issues, right? The data is all siloed. I don't know where it is. Even if I find it, I don't know what it means. There are five different versions of the truth. Yeah. The quality of the data is suspect. If I can clean it up, how do I keep it clean? I derive insights from the data, but you've got one version of a definition for return on invested capital. Right. Andrew's got another one. Which one do I believe? Yep. On and on and on, right? These kinds of issues, we like to joke about. It seems like the path to agentic AI heaven goes through some form of data hell. Right? We said, okay. We looked at ourselves in the mirror and we said, look, if we're serious about driving business transformation through agentic AI, we've also got to be serious about being in the data and analytics space and making sure that our customers have the tools and the support that they need to get their data estate to be AI ready. Okay. I'm just delighted to share that the product line that we built to support that has met with a very strong reception, and we're on track to break a billion dollars plus in ARR in just a few quarters here. That's great. Very fast ramp. Yeah. Can you just remind people of the products involved in the data and analytics side? Yeah. Obviously, RaptorDB is a big one. Yeah What else falls within your purview? Good question. Yeah. Look, the framework with which we think about the scope of data and analytics in the new world of AI, which is fundamentally different, we believe, from yesteryear. are what we call the 4Cs. Your first order of business is just connecting all your data. it's really important that you provide connections to all systems of record, all data platforms, et cetera, right? that these AI agents can learn what they need to learn so they can do what we want them to do, right? Yeah. They can't just be ServiceNow data. That connect layer is hugely important. The second layer is, okay, I've connected it, but it's not enough to just connect the data. I need to be confident that it's trustworthy. I need to help clean the data, and you need to do that on an ongoing basis. There's governance— Yep that's required. that's the second C. The third is you can connect your data, you can keep it clean, but you can still really not know what it means and what ties to the other. that's context. That's a big investment area for us. That's the third C. the fourth is, just think about it for a second, right? AI isn't just about assists and initial copilot -kind of capabilities, right? It's about actually taking action. That's where AI is today. how can we have a world, or how can we have an architecture and an infrastructure where the system of where you get insights is completely distinct from the system where you actually take action? We've got to bring those together, and that's the fourth C, converge. Okay. Our products map into that. Workflow Data Fabric is connected and controlled. We have a new analytics product line that we've just announced that helps with the context and the context engine. The convergence is Raptor. Okay. Out of curiosity, when Bill came up to you and offered this opportunity, why was it exciting to go to ServiceNow to do this? Meaning data- Yeah there's a lot of companies that are involved in data. Yeah. What did you see at ServiceNow that gave you, or gives you, permission to win in this area and help customers with this? I was curious because it's not a trivial task to try to build a data business. Yeah. It's hard. A lot of companies are trying to do this. Yeah. Look, just speak very frankly here. I wasn't initially intending— Okay to go to ServiceNow. I'd sort of deliberately, I think, chosen in my career to alternate between big companies like Oracle. startups, just to stay humble. I was actually headed to a startup, then I spoke with Bill, and I suppose he did the old Jedi mind trick on me or whatever. I was so fired up at the end of that call, I got to tell you, that I basically said yes on the call. I hadn't spoken to my wife before doing that. There was an interesting conversation that evening. Of course, she was very, very supportive. I'll tell you, there were three reasons that just compelled me to say yes. One is I've always had a soft spot for this company because from the Fred Luddy days— they always go back to first principles and think about how is it that we can architect our product to win, right? we have a structural advantage. Even when we are in the age of AI, that notion of a single codebase, a single platform with a single security model, a single user experience, and the painful discipline required to just invest in that gives you leverage, right? what did Archimedes say? Give me a lever long enough, and I will lift the moon." That's what architectural purity gives you. Okay. I've had a soft corner for that. Second is it's a very collegial place. It's somehow managed to keep a very sort of startup-y, innovative environment going even at scale. That's amazing those are the things. Plus the old Jedi mind trick, I suppose. Good. He's known as a pretty good salesperson. You're not the first; I'm sure there is. You obviously have a great customer base that has used you and trusts you for managing a lot of workflow. How does the discussion about getting these data products, getting them to view the data products as something that they want to expand with you— Yeah just walk me through maybe an example of a customer, where that sort of conversation starts and what it ultimately ends up looking like when Yeah they say, "Hey, look, I like your strategy. Let's go. Great question. I think that gives me an opportunity to make a really important point: I think that we're on our way to becoming a data and analytics juggernaut by initially, and even in the midterm, I would say, never really selling directly to data and analytics teams. The way we do that is by saying, Hey, dear ServiceNow platform owner, dear line of business head that uses ServiceNow for workflow orchestration across the board, would you like to have your operational workflows and your analytics run 10 x faster? Right? If you do, we'll run a POC for you. We'll show you the results. Sign up." we would've sold RaptorDB Pro. No conversation about speeds and feeds, no conversation about column store indexing, no conversation about parallel processing. It is the value and the outcome that we sell. the next port of call is, Okay, so now you're embarking with us on this journey for agentic workflows. Would you like to make sure that your AI agents have, for their training, data from not just ServiceNow but also related data from Workday, from Snowflake, from Databricks, et cetera? If so, we've got the right thing for you. It's Workflow Data Fabric with its Connect and contextualize layer. Right? lastly, we've gotten into analytics now. We are not going and saying, "Okay, let's talk to you about it Because I used to be at Oracle, at MicroStrategy, et cetera, ran products for those companies. We're not going to have a conversation about, "Hey, let's talk about slowly changing dimensions and ragged hierarchies," which is the BI speak. We're going to say, "You put changes into your production systems. Do you want us to help you predict which changes are likely to fail, and where the incident volume is likely to spike, and which businesses are likely to be impacted?" Why we can do that is because we're going to take our analytics product and bottle it up into workflow-based solutions. Fundamentally, we are actually selling solutions with data and analytics products underneath the hood. The phase II of our journey is to say, Well, we've earned the right then, at some moment in time, to go directly to the data and analytics office. Okay, that makes sense. RaptorDB Pro is positioned to run both transactional and analytical queries on Yeah the same data set. Yeah. How much of an advantage is that for customers in terms of just price performance, and that alone, does that get you in the door to have the conversation, I guess, in terms of 100%. 100%. Look, I would say that RaptorDB Pro in its first innings, and we've got a few lined up, is largely about, Hey, if you have a certain volume of transactions that you're running or a certain number of workflows that you're orchestrating across your system, we will speed those up without you knowing. That's what RaptorDB Pro brought to the table in the initial innings. This notion, as you call out, about a converged infrastructure is profoundly important. You see, most of enterprise software's history has been about saying, you have these what are called OLTP systems, online transactional processing systems, think ERP, CRM, and HCM, that execute transactions and get work done. you have, if you have questions you need answered or business intelligence, analytics, you would typically forklift that data out into a data warehouse or a data mart and then analyze it over there, right? Okay. Now imagine a world where you have not thousands, but millions, if not billions, of AI agents acting and thinking on your behalf. How can you have a situation where they're going to be acting on stale data, on stale insights? Because moving that data over introduces what's called latency. If you can have the same workhorse database perform both operational tasks and analytical tasks, you give them real-time insights. not insights that have that latency. it's fundamentally that value proposition that we find that our customers love. what we have chosen to do, and this is once again how ServiceNow is distinctly different, is we've said, "Yes, we have an analytics tool, but if you want to point your favorite analytics tool, Tableau, Power BI, et cetera, directly against RaptorDB, we'll let you do that, too. It's okay. We'll embrace the choices you've made, just as we've embraced the choices they've made on the systems of record layer as well. When you think about Workflow Data Fabric, how much of the sort of early demand for that is people just trying to get ready for agentic? Meaning, I kind of wonder it's always sort of the- Yeah chicken or egg. Are they trying it out and then realizing the data doesn't work, so they have to come back and deal with it after the fact? Yeah. Is it just this central discussion around an agentic enterprise driving more, I guess, understanding of the need for a technology like that that can help centralize data and basically inform the agents in a much better way? you're saying versus the more traditional, like I want to upgrade my analytical infrastructure. Yes I need that. Yeah. Good question. I'd say that, certainly the need to get your data estate ready for AI is the why now— Okay, yeah motivator. I've been going to this Gartner Data and Analytics conference they have for longer than I care to admit. I had a full head of hair when I started, right? I got to tell you, the sessions that used to be the most packed were the ones on analytics and dashboards. That's where you got the whistles from the gallery, right? Yeah. No one went to data quality or master data management. Those were not sexy at all. Last two years, standing room only. Same people, same problem really Yeah they're standing room only because their CIOs and CEOs are telling them, "Listen, clean this up yesterday. Yeah. Right? That urgency is definitely tied to getting your data ready for AI. In so doing, I honestly do feel you're solving a lot of the problems you need to solve anyway to get a more robust, semantically richer analytical infrastructure in place. Yeah. You mentioned earlier the ServiceNow customer base; they're trying to figure out how to get to that agentic layer. When they think about spending on RaptorDB Pro, Is it just a broader view of the workflow, and so this is sort of almost a new budget for them? Are the data people getting involved sort of after the fact? They're like, Oh, ServiceNow's got a lot to go here. I'm just kind of curious who the buying audience ultimately ends up being at the end of it. Yeah. It might be all of the above. It is all of the above, but principally, I would say, it's the ServiceNow platform owner— Yeah our existing eBuyer that sponsors the project around, okay, I have 5,000 reports I'm running, and they can run 10 x as fast if I have RaptorDB Pro— Yeah under the hood. That's sort of the land motion for RaptorDB. We have just announced some additional capabilities, like one I alluded to, which is, hey, what if I have Power BI or Tableau in-house and I want to point it directly against RaptorDB? Right. What does that mean? That means you don't necessarily anymore have to take out your data from Raptor, put it in Snowflake, put it in Oracle, put it in BigQuery, et cetera. The cost of defining and maintaining those data pipelines goes away. Right. It self-funds itself. Guess what? Because you're hitting Raptor directly, you get live, real-time analytics. Okay. Not with that latency. We've done the same with something called Live Archive, where what we're saying is, if within Raptor, you want to offload some data to lower -cost storage, we'll let you do that. Right? We let you actually query both the hot and cold data seamlessly. Today, a lot of companies take the data out and put it in a backup and archival system. Once again, the cost of doing that goes away if you go with the Raptor option. Yeah. It self-funds. Right. I guess, when you guys obviously have a lot of products like Now Assist and others that are agentic in nature— Yeah Is the data discussion fundamental in those as well now? Is that when people are thinking about that, is it sort of like, if you really want to get to sort of more autonomous agentic- Yeah you're going to need to make sure that the data's set up. are you getting pulled into those discussions, essentially? Yeah. 100%, and increasingly so. I'll be perfectly candid. In the early days, when the story was largely around connectivity of data, quality of data- governance of data is like, yeah, I got to do it. It's like washing my hands five times a day. Yeah. I get it. I got to do it. everything has changed with this context thing, where demonstratively, you can show that the quality of your AI agents, reducing hallucinations and bias, is tied to how rich the context is that you can give to your AI agents. collect the three Cs, the connecting the data, controlling it, and then contextualizing it. becomes crucially important. That's all in large part done through Workflow Data Fabric. Yep. suddenly it's like, okay, I got to buy this too as a prerequisite. Okay. Our vision, I would say, on Context Engine is quite unique because a lot of people may not know that ServiceNow's initial special sauce was this CMDB, and this whole knowledge graph that was built that powered the CMDB. We've been in this business forever, which is mapping the smallest IT software and hardware component all the way up to a business service, right? Understanding the lineage, the impact analysis, et cetera. To that, we've added context from your data platforms, like Snowflake and Databricks. We've added context from identity and about users through our Veza acquisition and about assets from our Armis acquisition. Suddenly you've got something that is. It's the graph of graphs. That's what our Context Graph is. Okay. That's really interesting. If there are any questions, I got a ton more, but I'm happy to make it interactive as well. All right. I'll keep going. The data.world acquisition, can you just talk about what you guys are doing sort of— Yeah on that front in terms of the data catalog, governance capabilities? I think it fits into what you said about the 4Cs, obviously, but- Yeah love to hear it from you. Yeah, no. Happy to spend a minute or two on that. Look, I think that was the first move we made, an inorganic move that we made. It was a knowledge graph-based company for data cataloging, which was unique. We looked at all the other- companies out there in the startup venture ecosystem. we just fell in love with this one because of the way it was architected. it's wall-to-wall deployments at places like McKinsey, WPP, et cetera. we spoke to a lot of customers. Fundamentally, what we said was, "We need a way to organize the data or catalog it so we understand where this field came from. Can it be trusted? What was the last time it was modified? What is its lineage?" ultimately bless it. from that, we create data products that are really metadata, and that tells any user, including an AI agent, this set of things are on this topic and can be trusted. Right? we knew that it was a seminal piece. We had not built that, so we made an inorganic move there. Happy to report we've just fully integrated it into the ServiceNow Platform and rolled it out at Knowledge in May. That's sort of a big piece of the puzzle, but that's the first step in a longer journey. Yeah. That longer journey is about saying, We're not just going to get your data estate AI-ready on day one. We're going to keep it AI-ready. Yeah. Data quality, data observability, MDM, data harmonization, and data enrichment are all things that we will both build and partner with. We have this notion called Workflow Data Network, which says, Look, if you want to use ServiceNow's data quality product down the road, great. If not, if you've got your favorite data quality product, you can plug it in." That is, once again, a very different approach relative to the other players. Yeah. We're giving it a fancy name, autonomous data governance. Really that's what it is. Okay. you mentioned you guys have zero copy partnerships. Yeah with some of the other data providers, like Snowflake and Databricks. Yeah. How should we think about those relationships in general? Is it all just about openness? If someone has most of Snowflake, it's going to be their core data repository. Maybe they have you all sort of just running under the ServiceNow stack, for example. Yeah. I guess, how do you think about that there's, I'm sure, some co-opetition to some degree— Sure especially as you get into analytics. Yeah. How should an investor kind of frame your- Yeah position in data versus the ones that are maybe more centralized around that area? That's a very nice question, and I think that honestly, it harkens back to one of the reasons I shared with you that I felt compelled to join ServiceNow, which is going back to first principles and figuring out how to architect this for today's needs. In so doing, I believe our position is unique in the market, right? We don't say you have to move all your data into our data cloud or into Raptor for the magic to happen. Yeah. If you'd like to, we'd love it. Thank you very much. Yeah. We'd be flattered. You don't have to. If you want to leave your data in SAP, the ERP systems, or if you want to leave it in Snowflake, Databricks, Google BigQuery, Oracle, or Teradata, we've got all of those. You can leave it in place. You don't need to move it. We will logically represent it in Raptor, and at the moment of the question, we'll federate the query and push it down to these underlying data warehouses and data lakes. Yeah. They're happy because we continue to drive data processing consumption there. Right. We are happy for another reason. It's because we say to our customers, just like we are the platform of platforms, as Bill likes to say, for systems of action, we're also the platform of platforms for insights, and AI agents need insight and action. It is our position in the stack that allows us to do what we do. What that meant was basically looking at where the industry was, where everyone, you might remember, was talking about data gravity, data gravity; don't play in AI if you can't get data gravity. Our position was, That's nice, but it's not necessary. What matters to us more is knowledge gravity. We believe we can do that even if you're sitting atop the data warehouses, data lakes, and systems of record. Okay. that's why zero copy is such an exciting thing for us, and it's important, and the reception has been really strong, and I think it's a distinctive architectural benefit. Yeah. I think ServiceNow has always, because you've done so well in your core ITSM, you've been sort of given permission by your customers to expand. Yeah. I'd imagine having data products allows for potentially more surface coverage for you all over time. Yeah. You're not going to announce anything. Yeah I'd imagine as customers think about building agents that are cross-functional, things like that Yeah the data foundation sort of helps support that view or that vision for you all. Could you just talk about that a little bit? Absolutely. I think that data and knowledge foundation- Okay As we just talked about, gives us the framework and the fabric, no pun intended- Yeah in place so that you can do powerful things on top. Once again, it's a logical fabric. Not all the pieces involve moving the data over. Some can stay in place. We play nice with the other systems of record and the data platforms. I think it opens up avenues for us, and I think I'm personally very content because we can blow past all our revenue targets that we have for this business and our ambitions by continuing to sell into our existing IT buyer more and more data and analytics capabilities, but positioned as outcomes that matter to them. Right? The time will come, and this was actually something we did at Oracle. We were very late to the BI platform space, so we built something called BI Apps. Basically, it was CRM analytics, ERP analytics, HCM analytics, and that's what we sold on top of PeopleSoft, Siebel, JD Edwards, and E-Business Suite. The customer often didn't know that they were using- Yeah a BI platform underneath. We blew that past $1 billion, $1.5 billion in revenue, and then after that, the customer was like, "I kind of like this. Can I use it for other things?" We said, "Sure you can." That was the expand motion. It is our belief that exactly this will happen. What Mark Twain said, "History doesn't repeat itself. But it rhymes. Yeah. How about just the go-to market for these products? I assume is this, from a rep perspective, they understand the benefit of bringing data into the conversation. Do you have specialists that come in along with the account manager? How do you make sure that the assets you have in data and analytics are represented in conversations? It's a fabulous question. I'm sure you're still introducing a lot of your customers to these capabilities. No, no, great question. Look, I think it's the latter. What we do is we have our core AEs, and the core AEs own the relationship with the customer. They're typically more schooled in the sort of bread and butter products of ServiceNow that we're known for, whether it's IT service management or the like. What they do is they know enough to be dangerous and have the first couple of conversations, and then they quickly pull in the specialists. Okay. we've got specialist AEs and SCs as well. Okay. now, we have to, as we go into 2027, ask ourselves, because this business is one of the fastest-growing businesses ever in ServiceNow's history, right? Within a company that has already broken past five, 10, and now 15 at faster than anyone else. we have to ask ourselves whether the time has come where we have a dedicated, not a specialist, but dedicated sales force just for data analytics, or do we wait a little bit? those are the discussions that'll happen back half of 2026. Interesting. Any questions? I'll keep polling, but I can keep going, too. All right. Analytics. What do you think the secret sauce is for you in that area, right? Yeah. We've all seen it. You were at Oracle, done that. We've seen- Yeah analytics is, I don't know, it almost takes on sort of a. People are like, "Oh, analytics, who care? Yeah. there's obviously value to that. Is the value in the analytics really just the whole stack that comes along with it from ServiceNow? It feels like it's a layer that people think is somewhat commoditized, which might not be fair, but it's the view. How do you make sure, or I guess, how do you monetize value at that layer? I don't think that's fair. as in like, I think that proclamations of the death of BI are greatly exaggerated- Okay as they say. Yep. I think that it's never been more relevant, but there is such a thing called modern BI, right? What is modern BI? Modern BI is the complete upending of a massive category. This is a $100 billion TAM category. I started my career at MicroStrategy back when the term BI was not coined, and we sort of evangelized it along with Business Objects, right? Look, here are the three things that are happening. Number one is we now have a world, agentic AI world, right? Where we want these AI agents to think and act on our behalf, right? Just as humans need trusted business metrics, you better believe that these AI agents need not the Monday afternoon versus Monday morning definition of return on invested capital, but the official governed, curated, blessed version, right? They need authoritative business metrics just as much as humans do. That's number one. Yeah. Number two is that this separation between the world of getting insights and taking action cannot survive in a world where you've got AI agents doing both. They need real-time analytics in the flow of work. Yep. That's the second big thing that's happening. The third is, I think dashboards will be greatly diminished as a consumption mechanism for BI and for analytics. It'll be conversational. You'll want to ask your questions conversationally, get results conversationally. You want to have AI agents analyze the results for you, interpret it, spot outliers, bring them to your attention. Because we are ServiceNow- Make change trigger workflows. Yeah. you detect risk, and you remediate. Yep in one platform. Nobody else can do that. Okay. that's why analytics is deadly important for us, and it comes at a time when every single chief data officer is looking at the old analytics tools and saying, "You know, their better days are behind them." Yeah. Like, we have to think differently in the age of AI. It is a moment of profound disruption in this $100 billion TAM market, and we are positioned to go in with bringing insight and action together. Yeah. That's the Pyramid acquisition that- Okay we made two months, three months ago, something like that. Okay. That's super helpful. One of the conversations I think we've been having at this conference and with investors the last few months is this sort of concept of a harness and orchestration layer at companies. Yeah. I know this might not be perfectly within your purview, but data seems to play a really important role- Yeah in sort of the value of structuring up these layers and Yeah what you can do with data as sort of a differentiator versus just model intelligence getting better. Yeah. I guess, how should we think about that with the data sort of offering at ServiceNow? Meaning, Does having the data platform make that sort of orchestration harness layer even more powerful to some degree? Because the models are going to keep getting more intelligent. Yeah. That's going to happen. Yeah. The differentiation has to happen in terms of your ability to understand data, take actions on data. Correct things like that. Yeah. I feel like it sort of feeds into that broader discussion, but- Yeah I'd love your sort of take on that. No, no, for sure. I think that we talked about this new Context Engine that we have, that is a graph of graphs. It combines the traditional ServiceNow knowledge graph that we've always had with an identity graph, a user graph. We've also built in sort of something we're calling a decision graph. Okay. Which is because we're sitting on 20 years plus of accumulated workflows, we are able to understand in a look-back fashion and a go-forward fashion, okay, when a decision was taken. Why was it taken? When an exception was made, who made the exception? Did it go through a chain of approval, yes or no? Decision traces to figure out why that was done. what the outcome was, is context- Yeah for the AI agents to make smarter decisions in the future. Similarly, we talked about that one version of the truth for your business metrics. In parlance, in common parlance, that's called the semantic layer. We got that through Pyramid, but that's going to fold into our Context Engine as well. Yeah. these are ways in which the data, products that we have become extraordinarily relevant- Yeah To our AI story and to AI adoption with customers. Okay. Which I think is what you were Yeah, no, it's exactly. I think, yeah, we're all asking a question of like, we all know the models are getting more powerful. Yeah. How do you add value on top of them? I think obviously- Yeah. One is the unique context that we have. Yeah We provide, and then the second is what you were getting at, which I forgot to speak to, which is this notion of, what Bill likes to call the rules and the rails. the control, paradigm and the harness. Yeah. I think that extends to data as well. That's what that autonomous data governance piece will give us that we're building out. data.world, the data catalog, is the first piece of it. we create these data products that are blessed assets for AI agents and humans to use. Okay. unless they're blessed, they can't be used. That's a harness. Okay. That's a control mechanism. In fact, I'd wanted to name that, before it got named autonomous data governance, I wanted to name it the AI Control Tower. Okay. It got shot down. It's going to be only one control tower. Only one control tower. Yeah. Standard line. Exactly. Yes. in essence, that's what it is. Okay. You all now have a much more fulsome stack of data and analytics products right now. Is there a cadence that's normal for an area that's early? Is there a cadence of, like, RaptorDB first, then Workflow Data Fabric, then analytics? How do you think about Customer, from customer adoption yeah, from a customer adoption perspective. Maybe it's too early to know that, or there's not a good, sort of There are one or two patterns. Okay. I mean, yeah, there's a lot of noise in the data, but a couple of distinct patterns are I think it's usually Workflow Data Fabric first. Okay. Largely because we are already in 95% of the Fortune 500 doing the take -action piece. They're using the data integration write -back capabilities. Okay. They're already a Workflow Data Fabric customer. That's why we have more than 6,000 already. Okay. it's about jumping up tiers in our pricing model with them, saying, Would you like to also tap into zero-copy Databricks, Snowflake, et cetera? Well, then step up to another tier. Okay. That's Workflow Data Fabric. Raptor 's first innings were, Hey, do I want my workflows to run 10x faster? If so, I'm in. The bigger companies with a lot of workloads are the first to gravitate towards it. With Live Connect and Live Perform, some of the new capabilities I alluded to, I think we'll see broader-based adoption of Raptor earlier. Okay. Analytics is the baby of the family. Yeah. It's only just rolling out. Taking off. Yeah. Okay. Maybe I got a couple last ones, but of the data platform, when you think about it, there's a lot of things, I think, bringing it in and having it be part of ServiceNow with the change management database, things like that. Is that what's durable? Meaning, I think everybody's wondering what moats are in software right now. Yeah. when you think about your data platform and what's durable, that's very unique to ServiceNow as we think about sort of— Yeah Everybody's wondering about terminal value and all that in the AI world. Yeah. When you think about your business in particular, what are the things that are going to be almost impossible for someone to sort of replicate or replicate easily, what comes to mind? I'll give you three things and then tell you why none of those are the answer to your question. Okay. The first is that converged database you talked about, where you can do both operational execution and analytical execution in the same database without needing to move data. Okay. That's hugely differentiated. Yeah. No one else has it at our scale. I'll ask a follow-up if you don't, yeah. that's number one. Number two is the ability to federate out, the process of understanding the data and taking action without necessarily having to move the data. Okay over from external sources. That's a crucial differentiation. The third is the CMDB, which is, I mean, a marvel of engineering, built over 20 years. With the accumulated workflow history that we have that allows us to do the things we can with the context engine. Yeah. That, unless you've been in this business supporting 10 billion + workflows with trillions of transactions, how are you going to get it? That's a pretty good moat. Right. That's the third piece. Neither of these, and there are probably two or three more I could probably come up with, but neither of these is what I would put as number one. Number one is the fact that all of these gems are in a single platform. Okay. Yep. Single data model, single security model. It's a unified user experience for everyone. No one else has that. Yeah. that's because Fred Luddy, when he founded the company, made that a defining characteristic of the company. that's number one. That's the durable differentiation. Yeah. Is that single platform, when you think about it from a customer perspective, is it just the simplicity of it to some degree? I mean, what is if I'm a customer? I could be like, All right, great. Yeah. Yeah. I'm glad it's a single platform. Well, what's that mean to me? Does it mean it is just performance-based? Is it the understanding of centralization of data? Just take it another step further, so if you're talking to a customer about it, it will resonate with them. I understand why it resonates with ServiceNow. Yeah. Why would the customer say? Lower cost of ownership. Okay. More accuracy in the results. Yep. The ability to have a core set of people within your IT department trained on using the platform that can then do, with the same skills, allow you to do magical things in HR, CRM, ERP, IT, you name it. it's the gift that keeps on giving. Right. The ability to say, Hey, you set up your security, and you have access to this kind of data. Andrew has access to something else. Suddenly, anything you do in HR or CRM or any of the other lines of business inherits that. In alternate solutions, it's all siloed. Right. you got to go buy some other product to stitch it all together. That's not the case when you have a single platform and a single data model. Okay. I would imagine because of that, do customers that have bought multiple products from you, whether it's ITSM plus HR onboarding— Yeah are they the ones that almost see the value the most? Is it most obvious to them? Are those the easiest upsell customers? 100%. Okay. 100%. I think you alluded to it in one of your earlier questions is, this install base is actually pretty happy, and it's quite refreshing, actually. Yeah. You go to Knowledge and you just feel the love. I say that because if you're an install-based play, like data and analytics is, it matters. Yeah. You're innocent until proven guilty. Yeah. You're given a chance, and that's a big deal. That's a big deal. Okay. One last chance. Any questions? All right. Well, we've covered a lot of territory. No, it was fun. we will- Thank you probably end it there. Thank you very much. My pleasure. This was really interesting. Yeah. We'll see how data ends up in the next year or so at ServiceNow. It'll be a lot to watch. Thanks a lot for being here. Thank you. Appreciate it. Thanks, everybody