Conference Transcript - NOW 2026-06-03

Kirk Materne, Analyst — Evercore ISI

Thanks everybody for joining us. Kirk Matern with Evercore ISI. Really excited to have ServiceNow with us this afternoon. Gaurav Ravori, who's the EVP of Global Marketing, Data, and Analytics. So thanks very much for being here. You know, just for some background for everybody, can you just talk about sort of your responsibilities at ServiceNow and then, you know, the elements of the data and analytics platform because, you know, I think it's something that's obviously up and coming at the company, but maybe not as familiar to everybody. So I'll let you kick off with that.

Gaurav Rivari, Analyst — Other

Thank you. I'm happy to provide some context, and thanks for having us. So yeah, I'm Gaurav Rivari. I'm EVP and GM, general manager of the data and analytics business, relatively new business for us at ServiceNow. And, you know, we've 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 sort of a serious area of focus. And so Bill and Amit reached out to me to join and to stand up our next multi-billion dollar business. So that was sort of the problem statement. And I think the motivation was, 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. And the second is just it's incredible relevance to our AI success right right and you know you've all probably read the reports right the very sobering statistics around 95% of product of projects fail you know the MIT study and the other things from Gartner that are you know equally sobering and and if you actually like read that report it tells you that in most cases the reason for that are data issues, right? So this is, I often, you know, the data is all siloed. I don't know where what is. Even if I find it, I don't know what it means. There are five different versions of the truth. The quality of the data is suspect. You know, if I can clean it up, how do I keep it clean? Then I derive insights on the data, but you've got one version of a definition for return on invested capital. Andrew's got another one. Which one do I believe? And on and on and on, right? So these kinds of issues, you know, we like to joke, it seems like the path to agentic AI heaven goes through some form of data hell, right? And so we said, okay, we looked ourselves in the mirror and we said, look, if you'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. AND I'M JUST DELIGHTED TO SHARE THAT SORT OF 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-DOLLAR-PLUS IN ARR IN JUST A FEW QUARTERS HERE. THAT'S GREAT.

Kirk Materne, Analyst — Evercore ISI

VERY FAST RAMP. CAN YOU JUST REMIND PEOPLE, YOU KNOW, THE PRODUCTS INVOLVED IN THE DATA AND ANALYTIC SIDE, OBVIOUSLY RAPTOR DB IS A BIG ONE, BUT WHAT ELSE FALLS WITHIN YOUR SORT OF PURVIEW?

Gaurav Rivari, Analyst — Other

GOOD QUESTION. Yeah, no, 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 four Cs. You know, your first order of business is just connect all your data. And it's really important that you provide connections to all systems of record, all data platforms, etc., right? So that these AI agents can learn what they need to learn, so that they can do what we want them to do, right? And they can't just be ServiceNow data. So that connect layer is hugely important. The second layer is, okay, I've connected it, but, like, it's not enough to just, you know, connect the data. I need to be confident that it's trustworthy. So I need to help clean the data, and you need to do that on an ongoing basis. So there's governance. that's required. So 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. So that's context. That's a big investment area for us. That's the third C. And then the fourth is just think about it for a second, right? AI isn't just about assists and co-pilot type of initial co-pilot kind of capabilities, right? It's about actually taking action. That's where AI is today. and so how can we have a world how can we have an architecture and an infrastructure where like the system of where you get insights is completely distinct from the system we actually take action you've got to bring those together and that's the fourth C converge okay and so our products map into that workflow data fabric is connect and control we have a new analytics product line that we've just announced that helps with the context and the context engine and finally the converges raptor okay out of curiosity when when bill came

Kirk Materne, Analyst — Evercore ISI

up to you and offered this opportunity why was it exciting to go to service now do this meaning data there's a lot of companies that are involved in data yeah um you know what what did you see at service now that that gave you sort of or gives you permission to win in this area and help customers with this i was kind of curious because it's not a trivial task to try to build a data a business, it's hard. There's, you know, a lot of companies are trying to do this.

Gaurav Rivari, Analyst — Other

Yeah, yeah, yeah. Look, I'll just sort of speak very frankly here. I wasn't initially intending to go to ServiceNow. I'd sort of deliberately, I think, chosen in my career to, you know, alternate between big companies like Oracle and then startups, just to sort of stay humble. And And so I was actually headed to a startup, and I spoke with Bill, and I suppose he did the old Jedi mind trick on me or whatever, but, like, I was so fired up at the end of that call, I've got to tell you, that I basically said yes on the call, and I hadn't spoken to my wife before doing that, so there was an interesting conversation that evening. Of course, she was very, very supportive, but I'll tell you, there were three reasons that just compelled me, like, to say yes. One is I've always had a soft corner 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? So we have a structural advantage. So, you know, even when we're in the age of AI, that notion of a single code base, a single platform, with a single security model, single user experience, The painful discipline required to just invest in that gives you leverage, right? And what did Archimedes say? Give me a lever long enough. I will lift the moon. You know, that's what architectural purity gives you. So I've had a soft corner for that. And then 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. So those are the things. Plus the old Jedi mind trick, I suppose.

Kirk Materne, Analyst — Evercore ISI

He's known as a pretty good salesperson, so you're not the first, I'm sure it is. You know, talk to us about, you know, as you obviously have a great customer base that has used you and trusts you for managing a lot of workflow, you know, how does the discussion about getting these data products, you know, getting them to view the data products as something they want to expand with you, you know, just walk me through maybe an example of a customer, you know, where that, you know, sort of conversation starts and what it ultimately ends up looking like when they say, hey, look, I like your strategy, let's go?

Gaurav Rivari, Analyst — Other

Great question. And I think that gives me an opportunity to make a really important point, is I think that we are 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. And the way we do that is by saying, hey, dear ServiceNow platform owner, dear line of business head that uses service now for workflow orchestration across the board would you like to have your operational workflows and your analytics run ten times faster right and if you do we'll run a POC for you we'll show you the results sign up and we would have sold Raptor DB Pro no conversation about speeds and feeds no conversation about columnist or indexing no column no conversation about parallel processing it is the value in the outcome that we sell. Then 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, right, data from not just ServiceNow, but 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? And so, and lastly, we've gotten into analytics now. We are not going and saying, okay, let's talk to you about, because I used to be at Oracle, at MicroStrategy, et cetera, I ran products for those companies, right? We're not going to have a conversation about, hey, let's talk about slowly changing dimensions and ragged hierarchies, which is the di-speak. We're going to say, you know, 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. And why we can do that is because we're going to take our analytics product and bottle it up into workflow-based solutions. So, fundamentally, we are actually selling solutions with data and analytics products underneath the hood, and then the phase two 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.

Kirk Materne, Analyst — Evercore ISI

Okay, that makes sense. You know, Raptor DB Pro, you know, it's positioned to run, you know, both transactional and analytical queries on the same data set. You know, how much of an advantage is that for customers in terms of just price performance? And, you know, that alone, does that get you in the door to have the conversation, I guess, in terms of what they say?

Gaurav Rivari, Analyst — Other

A hundred percent. A hundred percent. You know, look, I would say that Raptor DB Pro in its first innings, and we've got a few lined up, is largely about saying, initially, was largely about, hey, if you have a certain volume of transactions that you're running, certain number of workflows that you're orchestrating across your system, we will speed those up without you knowing, right? Like, that's what Raptor DB Pro brought to the table in the initial innings. But then 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, HCM, that, you know, execute transactions and get work done. And then you have, if you have questions you need answering, you know, 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, but now imagine a world where you have not a thousand, but millions if not billions of AI agents acting and thinking on your behalf, right? How can you have a situation where they're going to be acting on stale data and stale insights? Because, you know, moving that data over introduces what's called latency. So 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. So it's fundamentally that value proposition that we find that our customers love. And 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 Raptor DB, 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. And when you think about workflow data fabric,

Kirk Materne, Analyst — Evercore ISI

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 chicken or egg. Are they trying it out and then realizing and the data doesn't work, so they've got to come back and deal with it after the fact. Like, is it just the central discussion around, you know, 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?

Gaurav Rivari, Analyst — Other

And you're saying versus the more traditional, like, I want to upgrade my analytical infrastructure. Yeah, you know, good question. I'd say that certainly the need to get your data estate ready for AI is the why now motivator. I mean, I've been going to this Gartner Data and Analytics, you know, conference they have for longer than I care to admit. You know, I had a full head of air when I started, right? And I got to tell you, like, 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? no one went to data quality master data management data like those were not sexy at all no like last year two years standing room only same people same problem really and they're standing room only because their cios and ceos are telling them listen clean this up yesterday right so that urgency is definitely tied to getting your data ready for ai but in so doing you know 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.

Kirk Materne, Analyst — Evercore ISI

YOU KNOW, YOU MENTIONED EARLIER, YOU KNOW, THE SERVICE NOW CUSTOMER BASE, YOU KNOW, THEY'RE TRYING TO FIGURE OUT HOW TO GET TO THAT AGENTIC LAYER. WHEN THEY THINK ABOUT SORT OF SPENDING ON RAPTOR DB PRO, DOES IT COME MORE FROM SORT OF THE, IS IT JUST A BROADER VIEW OF WORKFLOW, AND SO THIS IS SORT OF ALMOST A NEW BUDGET FOR THEM, or the data people getting involved sort of after the fact, they're like, oh, yeah, like ServiceNow has 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. It might be all of the above.

Gaurav Rivari, Analyst — Other

It is all of the above, but principally I would say it's the ServiceNow platform owner and our existing e-buyer that sponsors, you know, the project around, okay, I have, like, 5,000 reports I'm running, and they can run 10 times as fast if I have Raptor DB Pro under the hood. So that's sort of the land motion for Raptor DB. But 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 Raptor DB? 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, etc. etc. So the cost of defining and maintaining those data pipelines goes away. So it self-funds itself. And guess what? Because you're hitting Raptor directly, you get live, real-time analytics, 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? And then we'll 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. So once again, the cost of doing that goes away if you go with the Raptor option. It's self-funds.

Kirk Materne, Analyst — Evercore ISI

Right. And I guess, you know, when you guys obviously have a lot of products like Now Assist and others that are agentic in nature. Yeah. You know, does, you know, is the data discussion fundamental in those as well now? I mean, is that when people are thinking about that, is sort of like if you really want to get to sort of more autonomous agentic you're gonna need to make sure that the data is set up so you know are you getting

Gaurav Rivari, Analyst — Other

pulled into those discussions essentially 100% and increasingly so I mean I'll be perfectly candid in the early days it would then the story was largely around connectivity of data quality of data governance of data is I gotta do it it's like washing my hands five times a day okay I get it I got to do it. But everything has changed with this context thing, where demonstrably you can show that the quality of your AI agents, you know, reducing hallucinations and bias is tied to how rich the context is that you can give to your AI agents. Then, the three Cs, connecting the data, controlling it, and then contextualizing it becomes crucially important. That's all in large part done through workflow data fabric. So suddenly it's like, okay I gotta buy this too as a prereq okay and I you know a vision I would say on context engine is quite unique because a lot of people may not know that like ServiceNow's initial special sauce was this CMDB and this whole knowledge graph that was built that powered the CMDB and so we've been in this business forever like which is mapping the smallest IT software hardware component all the way up to a business service, right? And understanding, like, the lineage, the impact analysis, etc. And so 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 VESA acquisition, about assets from our RMS acquisition. And so suddenly you've got something that is, it's the graph of graphs. That's what our context graph is. That's really interesting.

Kirk Materne, Analyst — Evercore ISI

Are there any questions? I've got a ton more, but I'm happy to make it interactive as well. I'll keep going. You know, the data.world acquisition, can you just talk about what you guys are doing sort of on that front in terms of the data catalog, governance capabilities? You know, I think it fits into what you said about the four Cs, obviously, but I'd love it for you.

Gaurav Rivari, Analyst — Other

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. And 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. And we just fell in love with this one because of the way it was architected. And, you know, it's wall-to-wall deployments at places like McKinsey, WPI, etc. So 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, you know, where did this field come from? Can it be trusted? What was the last time it was modified? What is its lineage? And ultimately, bless it. And so from that, we create data products that are really metadata, and that tells any user, including an AI agent, these set of things are on this topic and can be trusted, right? So 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. So that's sort of a big piece of the puzzle. But that's the first step in a longer journey. And 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. So data quality, data observability, MDM, data harmonization, data enrichment are all things that we will both build and partner with. So 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. So that is, once again, a very different approach relative to the other players. And so that's what, and we're calling, we're giving a fancy name, autonomous data governance. But really, that's what it is.

Kirk Materne, Analyst — Evercore ISI

Okay. And you mentioned sort of, you know, you guys have zero copy partnerships with some of the other data providers like Snowflake, Databricks. How should we think about those relationships in general? Is it, you know, is it all just about openness? If someone has, you know, most of the, you know, Snowflake's going to be their core data repository, maybe they have you all sort of just running under the ServiceNow stack, for example. I guess, how do you think about that from a, you know, there's, I'm sure, some coopetition to some degree, especially as you get into analytics. But, you know, how should an investor kind of frame your position in data versus the ones that are maybe more centralized around that area?

Gaurav Rivari, Analyst — Other

That's a very nice question. And I think that, honestly, it harkens back to one of the reasons I shared with you I felt compelled to join ServiceNow, which is going back to first principles and figuring out how to architect this for today's needs. And 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 If you'd like to we'd love it. Thank you very much We feel flattered, but you don't have to so if you want to leave your data and SAP the ERP systems Or if you want to leave it in Snowflake Databricks Google BigQuery Oracle data data 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 push down the query, federate the query, and push it down to these underlying data warehouses and data lakes. They're happy because we continue to drive data processing consumption there. We are happy for another reason. It's because we say to our customers, just like we are the platform of platforms, as

Kirk Materne, Analyst — Evercore ISI

Bill likes to say, for system of action, we're also the platform of platform for insights.

Gaurav Rivari, Analyst — Other

And AI agents need insight and action. It is our position in the stack that allows us to do what we do. And 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 an AI if you can't, you know, get data gravity. And our position was, that's nice, but it's not necessary. What matters to us more is knowledge gravity. And we believe we can do that even if we're sitting atop the data warehouses, data lakes the systems of record. So 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. You know, I think ServiceNet has always,

Kirk Materne, Analyst — Evercore ISI

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. You're not going to announce anything, but I'd imagine as customers think about building agents that are cross-functional, things like that, you know, the data foundation sort of helps support that view or that vision for you all. Could you just

Gaurav Rivari, Analyst — Other

talk about that a little bit? Absolutely. I think that data and knowledge foundation, as we just talked about, gives us the framework and the fabric, no pun intended, in place so that you can do powerful things on top and once again it's a logical fabric so not all the pieces involve moving the data over some can stay in place so we play nice with the other systems of record and the data platforms so 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 and you know that we have for this business and our ambitions by continuing to sell into our existing ebuyer more and more data and analytics capabilities, but positioned as outcomes that matter to them, right? But the time will come, and you know, 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 eBusiness Suite. The customer often didn't know that they were using a BI platform underneath. We, you know, blew that past a billion, billion and a half in revenue. And then after that, the customer was like, I kind of like this. Can I use it for other things? And we said, sure, you can. And that was the expand motion. It is our belief that exactly this will happen. You know, what Mark Twain said, you know, history doesn't repeat itself. But it rhymes.

Kirk Materne, Analyst — Evercore ISI

How about just the go-to-market for these products? I assume is this, you know, from a rep perspective, they understand, you know, the benefit of bringing data into the conversation. Do you have specialists that just are sort of, you know, that come in along with the sort of account manager? You know, how do you make sure that, you know, these, you know, the assets you have in data analytics are represented in conversations? Because I'm sure you're still, you know, introducing a lot of your customers to these capabilities.

Gaurav Rivari, Analyst — Other

No, no, great question. And look, I think it's the latter. So 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. And 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. And we've got specialist AEs and SCs as well. But now, you know, 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 5, 10, and now 15, you know, faster than anyone else. So we have to ask ourselves whether the time has come where we have a dedicated, not a specialist, but like dedicated Salesforce just for data analytics, or do we wait a little bit? So those are the discussions that will happen. back off of 26. Interesting. Interesting. Any questions? I keep polling, but I can keep going, too.

Kirk Materne, Analyst — Evercore ISI

You know, analytics. What do you think the secret sauce is for you in that area, right? I mean, we've all seen it. You're at Oracle, done that. We've seen, you know, analytics is, I don't know, it almost takes on sort of a, you know, people are like, oh, analytics, who cares? You know, but there's obviously value to that. Is the value in the analytics really just the whole stack that comes along with it from ServiceNow, like, you know, it feels like it's a layer that people think is somewhat commoditized, which might not be fair, but it's, you know, the view. You know, how do you make sure, or I guess, how do you monetize value at that, at that layer?

Gaurav Rivari, Analyst — Other

So I don't think that's fair. And as in, like, I think that proclamations of the death of BI are greatly exaggerated, as they say. And I think that it's never been more relevant. But there is such a thing called modern BI, right? And 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? And look, here are the three things that are happening. Number one is we now have a world, a genetic AI world, right, Where we want these AI agents to think and act on our behalf, right? And so, like, just as humans need trusted business metrics, you better believe that these AI agents need, you know, not the Monday afternoon versus Monday morning definition of return on invested capital, but the official, governed, curated, blessed version, right? So they need authoritative business metrics just as much as humans do. That's number one. 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, and they need real-time analytics in the flow of work. That's the second big thing that's happening. And then 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, and you want to have AI agents analyze the results for you, interpret it, spot outliers, bring them to your attention, and then, because we are serviced now, trigger workflows. So you detect risk, and you remediate in one platform. Nobody else can do that. And 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. 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 we're bringing insight and action together. That's the pyramid acquisition that we made two months, three months ago, something like that.

Kirk Materne, Analyst — Evercore ISI

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. And I know this might not be perfectly within your purview, but data seems to play a really important role in sort of the value of scribing up these layers and what you can do with data, you know, is sort of a differentiator versus just model intelligence getting better. I guess, how should we think about that with the data, you know, sort of offering at ServiceNow? Meaning, does it help you all have, like, does having the data platform make that sort of orchestration harness layer even more powerful to some degree as we, because the models are going to keep getting more intelligent. That's going to happen. So the differentiation has to happen in terms of your ability to understand data, take actions on data, things like that. So I feel like it sort of feeds into that broader discussion, but I'd love your sort of take on that.

Gaurav Rivari, Analyst — Other

No, no, for sure. You know, I think that, you know, we talked about this new context engine that we have that, you know, 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, 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? And so Decision traces to figure out why that was done and what the outcome was is context 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 so the in parlance you know common parlance that's called the semantic layer so we got that through uh through pyramid but that's going to fold into our context engine as well so these are ways in which the data products that we have become extraordinarily relevant to our AI story and to AI adoption with customers, which I think is what you were asking.

Kirk Materne, Analyst — Evercore ISI

Yeah, no, it's exactly, I think, yeah, we're all asking the question of, like, we all know the models are getting more powerful. How do you add value on top of them? And I think, obviously, you get data.

Gaurav Rivari, Analyst — Other

Yeah, so one is the unique context that we have and we provide, and then the second is what you were getting at, which I forgot to speak to, which is this notion of, you know, what Bill likes to call the rules and the rails, so the control paradigm and the harness. And I think that extends to data as well. That's what that autonomous data governance piece will give us, that we're building out. And data.world, the data catalog, is the first piece of it. So we create these data products that are blessed assets for AI agents and humans to use. And unless they're blessed, they can't be used. That's a harness. That's a control mechanism. In fact like we I'd wanted to name that before it got named autonomous data governance I was I wanted to name it the data control tower. It got shot down. It's going to be only one control tower. Only one control tower. Yeah, yeah, yeah. Stand in line. So yeah, but but in essence, that's what it is. Okay, and you all now have a much more

Kirk Materne, Analyst — Evercore ISI

Fulsome stack of data and analytics products right now. You know, is there a cadence that that's normal? area that's early, is there a cadence of, like, is it RaptorDB first, then workflow data fabric, then analytics? I mean, how do you think about it from a customer adoption perspective? Maybe it's too early to know that, or there's not a good, you know, sort of path? There is one or two

Gaurav Rivari, Analyst — Other

patterns. 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, largely because we are already in 95% of the Fortune 500 doing the take-action piece, so they're using the data integration write-back capabilities, and so they're already workflow data fabric customers. That's why we have more than 6,000 already, and then it's about jumping up tiers in our pricing model with them. Would you like to also tap into through zero copy, data break, snowflake, etc., well then step up to another tier. That's workflow data fabric. Raptor in the first innings was, hey, do I want my workflows to run 10 times faster? If so, you know, I'm in. So the bigger companies with, you know, a lot of workload, they're the first to gravitate towards it. But with LiveConnect, LivePerform, you know, some of the new capabilities I alluded to, I think we'll see more broad-based adoption of Raptor earlier. Okay. And analytics is the baby of the family. Like, it's only just rolling out.

Kirk Materne, Analyst — Evercore ISI

Taken off? Yeah. Okay. Of the, maybe I got a couple last ones, but, you know, of the of the data platform when you think about, you know, there's a lot of things I think bringing it in and having it be part of ServiceNow with the, you know, the change management database, things like that. Is that what's durable? Meaning I think everybody's wondering what like moats are and stuff right now. So when you think about your data platform and what's durable, that's, you know, very unique to ServiceNow as we think about sort of, you know, everybody's wondering about terminal value and all that in the world. You know, when you think about your business in particular, you know, the things that are going to be almost impossible for someone to sort of replicate or replicate easily, you know, what comes to mind? I'll give you three things and

Gaurav Rivari, Analyst — Other

tell you why neither of those are the answer to your question. The first is that that converged database you talked about, right, where you can do both operational execution as well as analytical execution in the same database without needing to move data. That's hugely differentiated, like and which can you think of like which no one else has it right at our scale so that's number one number two is the ability to like federate out the process of understanding the data and taking action without necessarily having to move the data over from external sources that's a crucial differentiation the third is the CMDB which is I mean it's 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, right? That, unless you've been in this business, supporting 10 billion plus workflows with trillions of transactions, how are you going to get it? That's a pretty good moat, right? That's the third piece. But neither of these, and there's probably two, three more I could probably come up with, but neither of these are what I would put as number one. Number one is the fact that all of these gems are in a single platform. Single data model, single security model, unified user experience for everyone. No one else has that, and that's because Fred Luddy, when he founded the company, made that a defining characteristic of the company. And so, you know, that's number one. That's the true differentiation.

Kirk Materne, Analyst — Evercore ISI

And is that single platform, when you think about it from a customer perspective, is it just like the simplicity of it to some degree? I mean, what is, like, if I'm a customer, I could be like, all right, great, yeah. I'm glad it's a single platform. What's that mean to me? Does it mean, is it just performance-based? Is it, you know, the understanding of centralization of data? Like, you know, just take it another step further, so if you're talking to a customer about it, why that would resonate with them? I don't understand why it resonates with SERVSnow, but why would the customer say?

Gaurav Rivari, Analyst — Other

You know, lower cost of ownership, more accuracy in the results, 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, right? So it's the gift that keeps on giving, right, and 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, and suddenly anything you do in HR or CRM or any of the other lines of business, inherit that. You know, in alternate solutions, it's all siloed. So then you've got to go buy some other product to stitch it all together. That's not the case when you have a single platform, single data model.

Kirk Materne, Analyst — Evercore ISI

And I would imagine because of that, do customers that have bought multiple products from you, whether it's ITSM plus HR onboarding, are they the ones that almost see the value the most? Is it most obvious to them? Are those the easiest upsell customers?

Gaurav Rivari, Analyst — Other

100%. I think you alluded to it in one of your earlier questions is this install base is actually pretty happy. It's quite refreshing, actually. You go to knowledge and you just feel the love. you know. And I say that because if you're an install-based play like data and analytics is, it matters. You know, you're innocent until proven guilty. You're given a chance,

Kirk Materne, Analyst — Evercore ISI

and that's a big deal. That's a big deal. One last chance. Any questions? We've covered a lot of territory, so we'll probably end it there, but thank you very much. This was really interesting, and 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 thank you appreciate it thanks everybody