ServiceNow, Inc. — Call Transcript 2026

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 10x 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 10x 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 the4Cs, 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

Speaker 2: 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. Yeah, we have time? yeah we have time Hurry up. hurry up We'll get going. we'll get going Thanks everybody for joining us. thanks everybody for joining us Kirk Materne with Evercore ISI. kirk materne with evercore isi Really excited to have ServiceNow with us this afternoon. really excited to have servicenow with us this afternoon Gaurav Rewari, who's the EVP of Global Marketing, Data, and Analytics. gaurav rewari who's the evp of global marketing data and analytics Thanks very much for being here. thanks very much for being here

Speaker 1: Of course. Of course. of course

Speaker 2: 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. 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. 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

Speaker 1: Yeah Yeah yeah

Speaker 2: the company, but maybe not as familiar to everybody. I'll let you kick off with that. Thanks. the company, but maybe not as familiar to everybody. the company but maybe not as familiar to everybody I'll let you kick off with that. i'll let you kick off with that Thanks. thanks

Speaker 1: 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. Sure. sure No, happy to provide some context and thanks for having us. no happy to provide some context and thanks for having us Yeah, I'm Gaurav Rewari. yeah i'm gaurav rewari I'm EVP and GM, General Manager of the data and analytics business. i'm evp and gm general manager of the data and analytics business Relatively new business for us at ServiceNow. relatively new business for us at servicenow We'd done things in data and analytics before. 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. 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. 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. That was the problem statement. that was the problem statement I think the motivation on their part was twofold. 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. 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. the second is just its incredible relevance to our ai success

Speaker 2: Right. Right. right

Speaker 1: Right? You've all probably read the reports, right? The very sobering statistics around 95% of projects fail, according to the MIT study. Right? right You've all probably read the reports, right? you've all probably read the reports right The very sobering statistics around 95% of projects fail, according to the MIT study. the very sobering statistics around 95% of projects fail, according to the mit study

Speaker 2: Sure. Sure. sure

Speaker 1: 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. There are other things from Gartner that are equally sobering. 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? 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. the data is all siloed I don't know where it is. i don't know where it is Even if I find it, I don't know what it means. even if i find it i don't know what it means There are five different versions of the truth. there are five different versions of the truth

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. The quality of the data is suspect. the quality of the data is suspect If I can clean it up, how do I keep it clean? 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. i derive insights from the data but you've got one version of a definition for return on invested capital

Speaker 2: Right. Right. right

Speaker 1: Andrew's got another one. Which one do I believe? Andrew's got another one. andrew's got another one Which one do I believe? which one do i believe

Speaker 2: Yep. Yep. yep

Speaker 1: 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. On and on and on, right? on and on and on right These kinds of issues, we like to joke about. 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. it seems like the path to agentic ai heaven goes through some form of data hell Right? right We said, okay. 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. 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

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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. 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

Speaker 2: That's great. Very fast ramp. That's great. that's great Very fast ramp. very fast ramp

Speaker 1: Yeah. Yeah. yeah

Speaker 2: Can you just remind people of the products involved in the data and analytics side? Can you just remind people of the products involved in the data and analytics side? can you just remind people of the products involved in the data and analytics side

Speaker 1: Yeah. Yeah. yeah

Speaker 2: Obviously, RaptorDB is a big one. Obviously, RaptorDB is a big one. obviously raptordb is a big one

Speaker 1: Yeah Yeah yeah

Speaker 2: What else falls within your purview? What else falls within your purview? what else falls within your purview

Speaker 1: 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. Good question. good question Yeah. 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. 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? are what we call the 4Cs . 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? 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

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. They can't just be ServiceNow data. they can't just be servicenow data That connect layer is hugely important. 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. 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 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— I need to help clean the data, and you need to do that on an ongoing basis. i need to help clean the data and you need to do that on an ongoing basis There's governance— there's governance—

Speaker 2: Yep Yep yep

Speaker 1: 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? that's required. that's the second C. 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. 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 a big investment area for us That's the third C. the fourth is, just think about it for a second, right? 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? ai isn't just about assists and initial copilot -kind of capabilities right It's about actually taking action. 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? 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. We've got to bring those together, and that's the fourth C, converge. we've got to bring those together and that's the fourth c converge

Speaker 2: Okay. Okay. okay

Speaker 1: 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. Our products map into that. our products map into that Workflow Data Fabric is connected and controlled. 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. 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. the convergence is raptor

Speaker 2: 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- Okay. 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? 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- meaning data-

Speaker 1: Yeah Yeah yeah

Speaker 2: there's a lot of companies that are involved in data. there's a lot of companies that are involved in data. there's a lot of companies that are involved in data

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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. What did you see at ServiceNow that gave you, or gives you, permission to win in this area and help customers with this? 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. i was curious because it's not a trivial task to try to build a data business

Speaker 1: Yeah. Yeah. yeah

Speaker 2: It's hard. A lot of companies are trying to do this. It's hard. it's hard A lot of companies are trying to do this. a lot of companies are trying to do this

Speaker 1: Yeah. Look, just speak very frankly here. I wasn't initially intending— Yeah. yeah Look, just speak very frankly here. look just speak very frankly here I wasn't initially intending— i wasn't initially intending—

Speaker 2: Okay Okay okay

Speaker 1: to go to ServiceNow. I'd sort of deliberately, I think, chosen in my career to alternate between big companies like Oracle. to go to ServiceNow. to go to servicenow I'd sort of deliberately, I think, chosen in my career to alternate between big companies like Oracle. 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— startups, just to stay humble. 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 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 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. i hadn't spoken to my wife before doing that There was an interesting conversation that evening. there was an interesting conversation that evening Of course, she was very, very supportive. of course she was very very supportive I'll tell you, there were three reasons that just compelled me to say yes. 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— 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. 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. 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? 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. give me a lever long enough, and i will lift the moon." that's what architectural purity gives you

Speaker 2: Okay. Okay. okay

Speaker 1: 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. I've had a soft corner for that. i've had a soft corner for that Second is it's a very collegial place. 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. it's somehow managed to keep a very sort of startup-y innovative environment going even at scale

Speaker 2: That's amazing That's amazing that's amazing

Speaker 1: those are the things. Plus the old Jedi mind trick, I suppose. those are the things. those are the things Plus the old Jedi mind trick, I suppose. plus the old jedi mind trick i suppose

Speaker 2: 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— Good. good He's known as a pretty good salesperson. he's known as a pretty good salesperson You're not the first; I'm sure there is. 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. 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— 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—

Speaker 1: Yeah Yeah yeah

Speaker 2: 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 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 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

Speaker 1: Yeah Yeah yeah

Speaker 2: they say, "Hey, look, I like your strategy. Let's go. they say, "Hey, look, I like your strategy. they say "hey look i like your strategy Let's go. let's go

Speaker 1: 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. Great question. 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. 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 10x faster? 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? 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? right If you do, we'll run a POC for you. if you do we'll run a poc for you We'll show you the results. we'll show you the results Sign up." we would've sold RaptorDB Pro. 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. 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. 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? 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. if so we've got the right thing for you It's Workflow Data Fabric with its Connect and contextualize layer. 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. Right? lastly, we've gotten into analytics now. 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 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 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. We're going to say, "You put changes into your production systems. 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. 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. Fundamentally, we are actually selling solutions with data and analytics products underneath the hood. 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. 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

Speaker 2: Okay, that makes sense. RaptorDB Pro is positioned to run both transactional and analytical queries on Okay, that makes sense. okay that makes sense RaptorDB Pro is positioned to run both transactional and analytical queries on raptordb pro is positioned to run both transactional and analytical queries on

Speaker 1: Yeah Yeah yeah

Speaker 2: the same data set. the same data set. the same data set

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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 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 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

Speaker 1: 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. 100%. 100%. 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. 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. 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. 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 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? 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? 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. Okay. okay Now imagine a world where you have not thousands, but millions, if not billions, of AI agents acting and thinking on your behalf. 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? 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. 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. 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. 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. 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. 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. 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

Speaker 2: 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- 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? 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- meaning i kind of wonder it's always sort of the-

Speaker 1: Yeah Yeah yeah

Speaker 2: 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? chicken or egg. 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? 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

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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? 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? 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

Speaker 1: you're saying versus the more traditional, like I want to upgrade my analytical infrastructure. you're saying versus the more traditional, like I want to upgrade my analytical infrastructure. you're saying versus the more traditional like i want to upgrade my analytical infrastructure

Speaker 2: Yes Yes yes

Speaker 1: 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— I need that. i need that Yeah. yeah Good question. good question I'd say that, certainly the need to get your data estate ready for AI is the why now— i'd say that certainly the need to get your data estate ready for ai is the why now—

Speaker 2: Okay, yeah Okay, yeah okay yeah

Speaker 1: 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? motivator. motivator I've been going to this Gartner Data and Analytics conference they have for longer than I care to admit. 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 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. 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? that's where you got the whistles from the gallery right

Speaker 2: Yeah. Yeah. yeah

Speaker 1: No one went to data quality or master data management. Those were not sexy at all. Last two years, standing room only. No one went to data quality or master data management. no one went to data quality or master data management Those were not sexy at all. those were not sexy at all Last two years, standing room only. last two years standing room only Same people, same problem really Same people, same problem really same people same problem really

Speaker 2: Yeah Yeah yeah

Speaker 1: they're standing room only because their CIOs and CEOs are telling them, "Listen, clean this up yesterday. they're standing room only because their CIOs and CEOs are telling them, "Listen, clean this up yesterday. they're standing room only because their cios and ceos are telling them "listen clean this up yesterday

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. Right? right That urgency is definitely tied to getting your data ready for AI. 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. 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

Speaker 2: 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. yeah You mentioned earlier the ServiceNow customer base; they're trying to figure out how to get to that agentic layer. 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? 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? are the data people getting involved sort of after the fact They're like, Oh, ServiceNow's got a lot to go here. 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. i'm just kind of curious who the buying audience ultimately ends up being at the end of it

Speaker 1: Yeah. Yeah. yeah

Speaker 2: It might be all of the above. It might be all of the above. it might be all of the above

Speaker 1: It is all of the above, but principally, I would say, it's the ServiceNow platform owner— It is all of the above, but principally, I would say, it's the ServiceNow platform owner— it is all of the above but principally i would say it's the servicenow platform owner—

Speaker 2: Yeah Yeah yeah

Speaker 1: our existing eBuyer that sponsors the project around, okay, I have 5,000 reports I'm running, and they can run 10x as fast if I have RaptorDB Pro— 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— 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—

Speaker 2: Yeah Yeah yeah

Speaker 1: 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? under the hood. under the hood That's sort of the land motion for RaptorDB. 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? 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

Speaker 2: Right. Right. right

Speaker 1: 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. What does that mean? 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. 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. the cost of defining and maintaining those data pipelines goes away

Speaker 2: Right. Right. right

Speaker 1: It self-funds itself. It self-funds itself. it self-funds itself Guess what? Because you're hitting Raptor directly, you get live, real-time analytics. Guess what? guess what Because you're hitting Raptor directly, you get live, real-time analytics. because you're hitting raptor directly you get live real-time analytics

Speaker 2: Okay. Okay. okay

Speaker 1: 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. Not with that latency. 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. 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? right We let you actually query both the hot and cold data seamlessly. 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. 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. once again the cost of doing that goes away if you go with the raptor option

Speaker 2: Yeah. Yeah. yeah

Speaker 1: It self-funds. It self-funds. it self-funds

Speaker 2: Right. I guess, when you guys obviously have a lot of products like Now Assist and others that are agentic in nature— Right. right I guess, when you guys obviously have a lot of products like Now Assist and others that are agentic in nature— i guess when you guys obviously have a lot of products like now assist and others that are agentic in nature—

Speaker 1: Yeah Yeah yeah

Speaker 2: 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- Is the data discussion fundamental in those as well now? 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- 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-

Speaker 1: Yeah Yeah yeah

Speaker 2: you're going to need to make sure that the data's set up. are you getting pulled into those discussions, essentially? you're going to need to make sure that the data's set up. are you getting pulled into those discussions, essentially? you're going to need to make sure that the data's set up are you getting pulled into those discussions essentially

Speaker 1: 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- Yeah. 100%, and increasingly so. yeah 100% and increasingly so I'll be perfectly candid. i'll be perfectly candid In the early days, when the story was largely around connectivity of data, quality of data- 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. governance of data is like, yeah, I got to do it. governance of data is like yeah i got to do it It's like washing my hands five times a day. it's like washing my hands five times a day

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. I get it. 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. 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. becomes crucially important. becomes crucially important That's all in large part done through Workflow Data Fabric. that's all in large part done through workflow data fabric

Speaker 2: Yep. Yep. yep

Speaker 1: suddenly it's like, okay, I got to buy this too as a prerequisite. suddenly it's like, okay, I got to buy this too as a prerequisite. suddenly it's like okay i got to buy this too as a prerequisite

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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. 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? 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. understanding the lineage the impact analysis et cetera To that, we've added context from your data platforms, like Snowflake and Databricks. 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. 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. suddenly you've got something that is It's the graph of graphs. it's the graph of graphs That's what our Context Graph is. that's what our context graph is

Speaker 2: 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— Okay. okay That's really interesting. that's really interesting If there are any questions, I got a ton more, but I'm happy to make it interactive as well. if there are any questions i got a ton more but i'm happy to make it interactive as well All right. all right I'll keep going. i'll keep going The data.world acquisition, can you just talk about what you guys are doing sort of— the data.world acquisition can you just talk about what you guys are doing sort of—

Speaker 1: Yeah Yeah yeah

Speaker 2: on that front in terms of the data catalog, governance capabilities? I think it fits into what you said about the4Cs, obviously, but- on that front in terms of the data catalog, governance capabilities? on that front in terms of the data catalog governance capabilities I think it fits into what you said about the 4Cs, obviously, but- i think it fits into what you said about the 4cs obviously but-

Speaker 1: Yeah Yeah yeah

Speaker 2: love to hear it from you. love to hear it from you. love to hear it from you

Speaker 1: 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- Yeah, no. yeah no Happy to spend a minute or two on that. 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. 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. it was a knowledge graph-based company for data cataloging which was unique We looked at all the other- 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. 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. 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. 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? can it be trusted What was the last time it was modified? 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. 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. right we knew that it was a seminal piece We had not built that, so we made an inorganic move there. 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. Happy to report we've just fully integrated it into the ServiceNow Platform and rolled it out at Knowledge in May. 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. that's sort of a big piece of the puzzle but that's the first step in a longer journey

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. That longer journey is about saying, We're not just going to get your data estate AI-ready on day one. 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. we're going to keep it ai-ready

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. Data quality, data observability, MDM, data harmonization, and data enrichment are all things that we will both build and partner with. 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. 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. 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

Speaker 2: Yeah. Yeah. yeah

Speaker 1: We're giving it a fancy name, autonomous data governance. We're giving it a fancy name, autonomous data governance. we're giving it a fancy name autonomous data governance Really that's what it is. Really that's what it is. really that's what it is

Speaker 2: Okay. you mentioned you guys have zero copy partnerships. Okay. you mentioned you guys have zero copy partnerships. okay you mentioned you guys have zero copy partnerships

Speaker 1: Yeah Yeah yeah

Speaker 2: with some of the other data providers, like Snowflake and Databricks. with some of the other data providers, like Snowflake and Databricks. with some of the other data providers like snowflake and databricks

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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. How should we think about those relationships in general? how should we think about those relationships in general Is it all just about openness? is it all just about openness If someone has most of Snowflake, it's going to be their core data repository. 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. maybe they have you all sort of just running under the servicenow stack for example

Speaker 1: Yeah. Yeah. yeah

Speaker 2: I guess, how do you think about that there's, I'm sure, some co-opetition to some degree— I guess, how do you think about that there's, I'm sure, some co-opetition to some degree— i guess how do you think about that there's i'm sure some co-opetition to some degree—

Speaker 1: Sure Sure sure

Speaker 2: especially as you get into analytics. especially as you get into analytics. especially as you get into analytics

Speaker 1: Yeah. Yeah. yeah

Speaker 2: How should an investor kind of frame your- How should an investor kind of frame your- how should an investor kind of frame your-

Speaker 1: Yeah Yeah yeah

Speaker 2: position in data versus the ones that are maybe more centralized around that area? position in data versus the ones that are maybe more centralized around that area? position in data versus the ones that are maybe more centralized around that area

Speaker 1: 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. 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. 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? 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. we don't say you have to move all your data into our data cloud or into raptor for the magic to happen

Speaker 2: Yeah. Yeah. yeah

Speaker 1: If you'd like to, we'd love it. Thank you very much. If you'd like to, we'd love it. if you'd like to we'd love it Thank you very much. thank you very much

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. We'd be flattered. we'd be flattered You don't have to. 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. 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 can leave it in place You don't need to move it. 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. 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

Speaker 2: Yeah. Yeah. yeah

Speaker 1: They're happy because we continue to drive data processing consumption there. They're happy because we continue to drive data processing consumption there. they're happy because we continue to drive data processing consumption there

Speaker 2: Right. Right. right

Speaker 1: 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, We are happy for another reason. 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'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. 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. 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, 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. That's nice, but it's not necessary. that's nice but it's not necessary What matters to us more is knowledge gravity. 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. we believe we can do that even if you're sitting atop the data warehouses data lakes and systems of record

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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. 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

Speaker 2: 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. 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. 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

Speaker 1: Yeah. Yeah. yeah

Speaker 2: I'd imagine having data products allows for potentially more surface coverage for you all over time. I'd imagine having data products allows for potentially more surface coverage for you all over time. i'd imagine having data products allows for potentially more surface coverage for you all over time

Speaker 1: Yeah. Yeah. yeah

Speaker 2: You're not going to announce anything. You're not going to announce anything. you're not going to announce anything

Speaker 1: Yeah Yeah yeah

Speaker 2: I'd imagine as customers think about building agents that are cross-functional, things like that I'd imagine as customers think about building agents that are cross-functional, things like that i'd imagine as customers think about building agents that are cross-functional things like that

Speaker 1: Yeah Yeah yeah

Speaker 2: the data foundation sort of helps support that view or that vision for you all. Could you just talk about that a little bit? the data foundation sort of helps support that view or that vision for you all. the data foundation sort of helps support that view or that vision for you all Could you just talk about that a little bit? could you just talk about that a little bit

Speaker 1: Absolutely. I think that data and knowledge foundation- Absolutely. absolutely I think that data and knowledge foundation- i think that data and knowledge foundation-

Speaker 2: Okay Okay okay

Speaker 1: As we just talked about, gives us the framework and the fabric, no pun intended- As we just talked about, gives us the framework and the fabric, no pun intended- as we just talked about gives us the framework and the fabric no pun intended-

Speaker 2: Yeah Yeah yeah

Speaker 1: 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. in place so that you can do powerful things on top. in place so that you can do powerful things on top Once again, it's a logical fabric. once again it's a logical fabric Not all the pieces involve moving the data over. not all the pieces involve moving the data over Some can stay in place. some can stay in place We play nice with the other systems of record and the data platforms. 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. 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- Right? right The time will come, and this was actually something we did at Oracle. 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. 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. 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- the customer often didn't know that they were using-

Speaker 2: Yeah Yeah yeah

Speaker 1: 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. a BI platform underneath. 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. 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. 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. it is our belief that exactly this will happen What Mark Twain said, "History doesn't repeat itself. But it rhymes. what mark twain said "history doesn't repeat itself. but it rhymes

Speaker 2: 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? Yeah. yeah How about just the go-to market for these products? 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. 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? 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? how do you make sure that the assets you have in data and analytics are represented in conversations

Speaker 1: It's a fabulous question. It's a fabulous question. it's a fabulous question

Speaker 2: I'm sure you're still introducing a lot of your customers to these capabilities. I'm sure you're still introducing a lot of your customers to these capabilities. i'm sure you're still introducing a lot of your customers to these capabilities

Speaker 1: 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. No, no, great question. no no great question Look, I think it's the latter. 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. 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. 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. 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

Speaker 2: Okay. Okay. okay

Speaker 1: we've got specialist AEs and SCs as well. we've got specialist AEs and SCs as well. we've got specialist aes and scs as well

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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? 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. 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

Speaker 2: Interesting. Any questions? I'll keep polling, but I can keep going, too. Interesting. interesting Any questions? any questions I'll keep polling, but I can keep going, too. i'll keep polling but i can keep going too

Speaker 1: All right. All right. all right

Speaker 2: Analytics. What do you think the secret sauce is for you in that area, right? Analytics. analytics What do you think the secret sauce is for you in that area, right? what do you think the secret sauce is for you in that area right

Speaker 1: Yeah. Yeah. yeah

Speaker 2: We've all seen it. You were at Oracle, done that. We've seen- We've all seen it. we've all seen it You were at Oracle, done that. you were at oracle done that We've seen- we've seen-

Speaker 1: Yeah Yeah yeah

Speaker 2: analytics is, I don't know, it almost takes on sort of a. People are like, "Oh, analytics, who care? analytics is, I don't know, it almost takes on sort of a. analytics is i don't know it almost takes on sort of a People are like, "Oh, analytics, who care? people are like "oh analytics who care

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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? there's obviously value to that. there's obviously value to that Is the value in the analytics really just the whole stack that comes along with it from ServiceNow? 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. 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? how do you make sure or i guess how do you monetize value at that layer

Speaker 1: I don't think that's fair. as in like, I think that proclamations of the death of BI are greatly exaggerated- I don't think that's fair. as in like, I think that proclamations of the death of BI are greatly exaggerated- i don't think that's fair as in like i think that proclamations of the death of bi are greatly exaggerated-

Speaker 2: Okay Okay okay

Speaker 1: as they say. as they say. as they say

Speaker 2: Yep. Yep. yep

Speaker 1: 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? I think that it's never been more relevant, but there is such a thing called modern BI, right? i think that it's never been more relevant but there is such a thing called modern bi right What is modern BI? what is modern bi Modern BI is the complete upending of a massive category. modern bi is the complete upending of a massive category This is a $100 billion TAM 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? 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. look here are the three things that are happening Number one is we now have a world, agentic AI world, right? 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? 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? 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. They need authoritative business metrics just as much as humans do. they need authoritative business metrics just as much as humans do That's number one. that's number one

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. 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. 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. they need real-time analytics in the flow of work

Speaker 2: Yep. Yep. yep

Speaker 1: 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- That's the second big thing that's happening. 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. the third is i think dashboards will be greatly diminished as a consumption mechanism for bi and for analytics It'll be conversational. it'll be conversational You'll want to ask your questions conversationally, get results conversationally. 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. you want to have ai agents analyze the results for you interpret it spot outliers bring them to your attention Because we are ServiceNow- because we are servicenow-

Speaker 2: Make change Make change make change

Speaker 1: trigger workflows. trigger workflows. trigger workflows

Speaker 2: Yeah. Yeah. yeah

Speaker 1: you detect risk, and you remediate. you detect risk, and you remediate. you detect risk and you remediate

Speaker 2: Yep Yep yep

Speaker 1: in one platform. Nobody else can do that. in one platform. in one platform Nobody else can do that. nobody else can do that

Speaker 2: Okay. Okay. okay

Speaker 1: 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." 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." 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."

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. Like, we have to think differently in the age of AI. 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. 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

Speaker 2: Yeah. Yeah. yeah

Speaker 1: That's the Pyramid acquisition that- That's the Pyramid acquisition that- that's the pyramid acquisition that-

Speaker 2: Okay Okay okay

Speaker 1: we made two months, three months ago, something like that. we made two months, three months ago, something like that. we made two months three months ago something like that

Speaker 2: 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. Okay. okay That's super helpful. 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. 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

Speaker 1: Yeah. Yeah. yeah

Speaker 2: I know this might not be perfectly within your purview, but data seems to play a really important role- I know this might not be perfectly within your purview, but data seems to play a really important role- i know this might not be perfectly within your purview but data seems to play a really important role-

Speaker 1: Yeah Yeah yeah

Speaker 2: in sort of the value of structuring up these layers and in sort of the value of structuring up these layers and in sort of the value of structuring up these layers and

Speaker 1: Yeah Yeah yeah

Speaker 2: what you can do with data as sort of a differentiator versus just model intelligence getting better. what you can do with data as sort of a differentiator versus just model intelligence getting better. what you can do with data as sort of a differentiator versus just model intelligence getting better

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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. I guess, how should we think about that with the data sort of offering at ServiceNow? 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? 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. because the models are going to keep getting more intelligent

Speaker 1: Yeah. Yeah. yeah

Speaker 2: That's going to happen. That's going to happen. that's going to happen

Speaker 1: Yeah. Yeah. yeah

Speaker 2: The differentiation has to happen in terms of your ability to understand data, take actions on data. The differentiation has to happen in terms of your ability to understand data, take actions on data. the differentiation has to happen in terms of your ability to understand data take actions on data

Speaker 1: Correct Correct correct

Speaker 2: things like that. things like that. things like that

Speaker 1: Yeah. Yeah. yeah

Speaker 2: I feel like it sort of feeds into that broader discussion, but- I feel like it sort of feeds into that broader discussion, but- i feel like it sort of feeds into that broader discussion but-

Speaker 1: Yeah Yeah yeah

Speaker 2: I'd love your sort of take on that. I'd love your sort of take on that. i'd love your sort of take on that

Speaker 1: 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. No, no, for sure. no no for sure I think that we talked about this new Context Engine that we have, that is a graph of graphs. 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. 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. we've also built in sort of something we're calling a decision graph

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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? 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? when an exception was made who made the exception Did it go through a chain of approval, yes or no? did it go through a chain of approval yes or no Decision traces to figure out why that was done. decision traces to figure out why that was done what the outcome was, is context- what the outcome was, is context- what the outcome was is context-

Speaker 2: Yeah Yeah yeah

Speaker 1: 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. for the AI agents to make smarter decisions in the future. 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. 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. 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. we got that through pyramid but that's going to fold into our context engine as well

Speaker 2: Yeah. Yeah. yeah

Speaker 1: these are ways in which the data, products that we have become extraordinarily relevant- these are ways in which the data, products that we have become extraordinarily relevant- these are ways in which the data products that we have become extraordinarily relevant-

Speaker 2: Yeah Yeah yeah

Speaker 1: To our AI story and to AI adoption with customers. To our AI story and to AI adoption with customers. to our ai story and to ai adoption with customers

Speaker 2: Okay. Okay. okay

Speaker 1: Which I think is what you were Which I think is what you were which i think is what you were

Speaker 2: 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, no, it's exactly. 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. i think yeah we're all asking a question of like we all know the models are getting more powerful

Speaker 1: Yeah. Yeah. yeah

Speaker 2: How do you add value on top of them? I think obviously- How do you add value on top of them? how do you add value on top of them I think obviously- i think obviously-

Speaker 1: Yeah. One is the unique context that we have. Yeah. yeah One is the unique context that we have. one is the unique context that we have

Speaker 2: Yeah Yeah yeah

Speaker 1: 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. 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. 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. the control, paradigm and the harness. the control paradigm and the harness

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. I think that extends to data as well. 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. 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

Speaker 2: Okay. Okay. okay

Speaker 1: unless they're blessed, they can't be used. That's a harness. unless they're blessed, they can't be used. unless they're blessed they can't be used That's a harness. that's a harness

Speaker 2: Okay. Okay. okay

Speaker 1: 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. That's a control mechanism. 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. in fact i'd wanted to name that before it got named autonomous data governance i wanted to name it the ai control tower

Speaker 2: Okay. Okay. okay

Speaker 1: It got shot down. It's going to be only one control tower. It got shot down. it got shot down It's going to be only one control tower. it's going to be only one control tower

Speaker 2: Only one control tower. Only one control tower. only one control tower

Speaker 1: Yeah. Standard line. Yeah. yeah Standard line. standard line

Speaker 2: Exactly. Exactly. exactly

Speaker 1: Yes. in essence, that's what it is. Yes. in essence, that's what it is. yes in essence that's what it is

Speaker 2: 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 Okay. okay You all now have a much more fulsome stack of data and analytics products right now. 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 that's normal for an area that's early Is there a cadence of , like, RaptorDB first, then Workflow Data Fabric, then analytics? is there a cadence of , like raptordb first then workflow data fabric then analytics How do you think about how do you think about

Speaker 1: Customer, from customer adoption Customer, from customer adoption customer from customer adoption

Speaker 2: yeah, from a customer adoption perspective. Maybe it's too early to know that, or there's not a good, sort of yeah, from a customer adoption perspective. yeah from a customer adoption perspective Maybe it's too early to know that, or there's not a good, sort of maybe it's too early to know that or there's not a good sort of

Speaker 1: There are one or two patterns. There are one or two patterns. there are one or two patterns

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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. 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

Speaker 2: Okay. Okay. okay

Speaker 1: Largely because we are already in 95% of the Fortune 500 doing the take-action piece. Largely because we are already in 95% of the Fortune 500 doing the take -action piece. 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. They're using the data integration write -back capabilities. they're using the data integration write -back capabilities

Speaker 2: Okay. Okay. okay

Speaker 1: They're already a Workflow Data Fabric customer. That's why we have more than 6,000 already. They're already a Workflow Data Fabric customer. they're already a workflow data fabric customer That's why we have more than 6,000 already. that's why we have more than 6,000 already

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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? 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. well then step up to another tier

Speaker 2: Okay. Okay. okay

Speaker 1: 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. That's Workflow Data Fabric. that's workflow data fabric Raptor 's first innings were, Hey, do I want my workflows to run 10x faster? raptor 's first innings were hey do i want my workflows to run 10x faster If so, I'm in. if so i'm in The bigger companies with a lot of workloads are the first to gravitate towards it. 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. 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

Speaker 2: Okay. Okay. okay

Speaker 1: Analytics is the baby of the family. Analytics is the baby of the family. analytics is the baby of the family

Speaker 2: Yeah. Yeah. yeah

Speaker 1: It's only just rolling out. It's only just rolling out. it's only just rolling out

Speaker 2: Taking off. Taking off. taking off

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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. Okay. 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. 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? is that what's durable Meaning, I think everybody's wondering what moats are in software right now. meaning i think everybody's wondering what moats are in software right now

Speaker 1: Yeah. Yeah. yeah

Speaker 2: when you think about your data platform and what's durable, that's very unique to ServiceNow as we think about sort of— when you think about your data platform and what's durable, that's very unique to ServiceNow as we think about sort of— when you think about your data platform and what's durable that's very unique to servicenow as we think about sort of—

Speaker 1: Yeah Yeah yeah

Speaker 2: Everybody's wondering about terminal value and all that in the AI world. Everybody's wondering about terminal value and all that in the AI world. everybody's wondering about terminal value and all that in the ai world

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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? 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? 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

Speaker 1: I'll give you three things and then tell you why none of those are the answer to your question. I'll give you three things and then tell you why none of those are the answer to your question. i'll give you three things and then tell you why none of those are the answer to your question

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 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. 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

Speaker 2: Okay. Okay. okay

Speaker 1: That's hugely differentiated. That's hugely differentiated. that's hugely differentiated

Speaker 2: Yeah. Yeah. yeah

Speaker 1: No one else has it at our scale. No one else has it at our scale. no one else has it at our scale

Speaker 2: I'll ask a follow-up if you don't, yeah. I'll ask a follow-up if you don't, yeah. i'll ask a follow-up if you don't yeah

Speaker 1: 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. that's number one. 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. number two is the ability to federate out the process of understanding the data and taking action without necessarily having to move the data

Speaker 2: Okay Okay okay

Speaker 1: 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. over from external sources. over from external sources That's a crucial differentiation. that's a crucial differentiation The third is the CMDB, which is, I mean, a marvel of engineering, built over 20 years. 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. with the accumulated workflow history that we have that allows us to do the things we can with the context engine

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. That, unless you've been in this business supporting 10 billion + workflows with trillions of transactions, how are you going to get it? 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. that's a pretty good moat

Speaker 2: Right. Right. right

Speaker 1: 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. That's the third piece. 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. 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. number one is the fact that all of these gems are in a single platform

Speaker 2: Okay. Yep. Okay. okay Yep. yep

Speaker 1: Single data model, single security model. It's a unified user experience for everyone. No one else has that. Single data model, single security model. single data model single security model It's a unified user experience for everyone. it's a unified user experience for everyone No one else has that. no one else has that

Speaker 2: Yeah. Yeah. yeah

Speaker 1: 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. that's because Fred Luddy, when he founded the company, made that a defining characteristic of the company. that's number one. 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. that's the durable differentiation

Speaker 2: 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. yeah Is that single platform, when you think about it from a customer perspective, is it just the simplicity of it to some degree? 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 mean what is if i'm a customer I could be like, All right, great. i could be like all right great Yeah. yeah

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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. I'm glad it's a single platform. i'm glad it's a single platform Well, what's that mean to me? well what's that mean to me Does it mean it is just performance-based? does it mean it is just performance-based Is it the understanding of centralization of data? 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. 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. i understand why it resonates with servicenow

Speaker 1: Yeah. Yeah. yeah

Speaker 2: Why would the customer say? Why would the customer say? why would the customer say

Speaker 1: Lower cost of ownership. Lower cost of ownership. lower cost of ownership

Speaker 2: Okay. Okay. okay

Speaker 1: More accuracy in the results. More accuracy in the results. more accuracy in the results

Speaker 2: Yep. Yep. yep

Speaker 1: 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. 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. 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

Speaker 2: Right. Right. right

Speaker 1: 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. The ability to say, Hey, you set up your security, and you have access to this kind of data. 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. andrew has access to something else Suddenly, anything you do in HR or CRM or any of the other lines of business inherits that. 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. In alternate solutions, it's all siloed. in alternate solutions it's all siloed

Speaker 2: Right. Right. right

Speaker 1: 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. you got to go buy some other product to stitch it all together. 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. that's not the case when you have a single platform and a single data model

Speaker 2: Okay. I would imagine because of that, do customers that have bought multiple products from you, whether it's ITSM plus HR onboarding— Okay. okay I would imagine because of that, do customers that have bought multiple products from you, whether it's ITSM plus HR onboarding— i would imagine because of that do customers that have bought multiple products from you whether it's itsm plus hr onboarding—

Speaker 1: Yeah Yeah yeah

Speaker 2: are they the ones that almost see the value the most? Is it most obvious to them? Are those the easiest upsell customers? are they the ones that almost see the value the most? are they the ones that almost see the value the most Is it most obvious to them? is it most obvious to them Are those the easiest upsell customers? are those the easiest upsell customers

Speaker 1: 100%. 100%. 100%

Speaker 2: Okay. Okay. okay

Speaker 1: 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. 100%. 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. 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

Speaker 2: Yeah. Yeah. yeah

Speaker 1: You go to Knowledge and you just feel the love. You go to Knowledge and you just feel the love. 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. I say that because if you're an install-based play, like data and analytics is, it matters. i say that because if you're an install-based play like data and analytics is it matters

Speaker 2: Yeah. Yeah. yeah

Speaker 1: You're innocent until proven guilty. You're innocent until proven guilty. you're innocent until proven guilty

Speaker 2: Yeah. Yeah. yeah

Speaker 1: You're given a chance, and that's a big deal. That's a big deal. You're given a chance, and that's a big deal. you're given a chance and that's a big deal That's a big deal. that's a big deal

Speaker 2: Okay. One last chance. Any questions? All right. Well, we've covered a lot of territory. Okay. okay One last chance. one last chance Any questions? any questions All right. all right Well, we've covered a lot of territory. well we've covered a lot of territory

Speaker 1: No, it was fun. No, it was fun. no it was fun

Speaker 2: we will- we will- we will-

Speaker 1: Thank you Thank you thank you

Speaker 2: probably end it there. Thank you very much. probably end it there. probably end it there Thank you very much. thank you very much

Speaker 1: My pleasure. My pleasure. my pleasure

Speaker 2: This was really interesting. This was really interesting. this was really interesting

Speaker 1: Yeah. Yeah. yeah

Speaker 2: 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. We'll see how data ends up in the next year or so at ServiceNow. we'll see how data ends up in the next year or so at servicenow It'll be a lot to watch. it'll be a lot to watch Thanks a lot for being here. thanks a lot for being here

Speaker 1: Thank you. Thank you. thank you

Speaker 2: Appreciate it. Thanks, everybody Appreciate it. appreciate it Thanks, everybody thanks everybody