Teradata Corporation (TDC) Earnings Call Transcript & Summary

Hey, everybody. Thanks for joining us. My name is Tyler Radke, I co-head the software sector here at Citi. Welcome to Day One of our tech conference. And we're happy to have Teradata, we have the CEO, Steve McMillan here.

Steve, thanks for joining us. I think you've come to this conference a number of years, but it's been a little more than three years since you recently took over as CEO of the company. Maybe for investors who are reengaging with Teradata, I think people have an idea of who Teradata is. But just talk about the last three years, what were some of the biggest priorities for you? Where are we in terms of executing against those priorities and kind of what's the vision for Teradata for the next three years?

Sure. That's great. Yes, Teradata, you could say, invented the enterprise data warehousing space from a technology perspective and developed over many years, a whole set of patents and unique capabilities to process tremendous amounts of data, largely known for their on-prem capabilities. When I joined the company three years ago, we pivoted the entire company towards a cloud-first strategy, cloud-first but not cloud-only. So what we did was we changed and over that period of time, have changed every single aspect of the company. We started with our people, we introduced a new Chief Product Officer, and actually, with Hillary, our Chief Product Officer, we actually transformed the research and development budget. It was focused 70% on-prem and 30% in the cloud. And over the course of about 6 weeks, we actually pivoted that entire budget to be 70% focused on cloud development and cloud technologies and 30% from an on-prem perspective. That was a massive change for the company and I think what we're seeing recently is the maturation of the company and the technology is actually starting to appear in our results, and we'll talk a little bit -- I'm sure we'll talk about our results for the first half of the year a little bit later. But the technology was the start of the transformation because at our core, we're a technology platform company. And more so, we're also a software company and now we're moving into a true software-as-a-service company. And what that meant is we had to change nearly every aspect of the company from our go-to-market strategy. We changed our go-to-market strategy in terms of both our partnering strategy, working with SIs, ISVs, the cloud service providers. We changed the go-to-market in terms of having customer success managers who are focused on a modern selling motion in terms of development, expansion and say their customers. And we also changed some of our incentive structure to actually get the sales teams focused on taking that cloud message to the marketplace. As -- reflecting back, I think it's amazing the progress that the team has executed over the last three years. If we -- when we get to the point of completing this year, we will have increased our cloud ARR by tenfold over the last three years. And our cloud ARR should be over 1/3 of Teradata's total ARR and well on the path to achieving the goals that we set out for 2025. But that transformation, I mean, has touched every single aspect. We've had -- we've now had a solid leadership team in place for the last two years, with a new CFO, a new Chief Revenue Officer and new Chief Marketing Officer. But that team, I think, is now firing on all cylinders, and we've built the foundational capabilities inside the company. Really proud on the product releases that we've been executing over the last two years. And I think that those foundations are really setting us up for our future success. I think you can see that in terms of some of the results in the pivot you've seen in terms of the results of the company.

Yes, absolutely. And I'd love to kind of get in a little bit in terms of what we've seen. I know it's been a strong year for the company, but I feel like it's dated back even a little bit longer. Really the third quarter of last year, so almost about a year ago was really when you started to see kind of this inflection in the cloud business. And it was kind of an unexpected time to see that. I think at least that was the tone I got talking to investors, you had a lot of these hyperscalers talking about cloud optimizations, budgets being cut. What about it -- what about that timing led you to really start to see an inflection in the cloud business? And I guess, are you maybe benefiting from some of the challenges that, say, a snowflake or the hyperscalers have referenced in terms of cloud optimization or just help us understand that?

Yes. I think what we see from a Teradata perspective is we deal with the largest companies in the world. We're focused initially when I joined on the [ G1000 ] and we opened that aperture up to say, "No, we're going to focus on the top 10,000 enterprises in the world." And Teradata runs the most mission-critical workload for some of the world's largest organizations. That workload is very sticky to the Teradata platform. And the reason that it's very sticky to the Teradata platform is that cloud-native solutions find it very difficult to execute that workload. And the reason for that is pretty simple. Cloud-native solutions tend to solve complex problems by expanding the compute environment and it becomes very costly, very quickly. When Teradata solves a problem because we are born on-prem with limited compute capability, we actually make the most of that compute capability with some of our patented technologies such as workload management and query optimization. So our compute is actually utilized at incredibly high rates from an on-prem perspective over 99% in most cases to run these mission-critical workloads for the largest companies in the world. When we take that to the cloud, those compute environments continue to run at close to 100% utilization. And so the software itself is actually doing the cloud optimization that many organizations are having to do manually in terms of trying to control compute costs. And we actually contract with our customers in a way so that we actually contract for that fixed capacity and our recurring revenues, which becomes a very predictable and stable revenue stream for us. And then we allow our consumption model on top of that, where there can be some optimization in terms of utilization. But the core and by far, the bulk of Teradata's cloud revenues are actually in a fixed capacity contract, which means that it's a very stable revenue and our customers get the most out of that. And it's been really exciting to see some of these huge environments move to the cloud. American Airlines moved their entire production environment from on-prem to run on the Azure cloud. And it was funny the -- one of the executives from American Airlines, we asked them what did your users see after that migration to the cloud with Teradata. And his one comment was, the only feedback we got was, "Hey, did you guys do something, the system seems to be running quicker now?" and that is exactly -- if you are in an IT organization and constrained environments, and you want to take advantage of modern cloud-based technologies which every company does, especially with the GenAI catalyst that's happening in the marketplace just now. Then you want to get your technology to the cloud as quickly, efficiently, effectively, less risk, less cost as possible. And to do that, Teradata gives a lot of organizations that opportunity. And reflecting back, you mentioned third quarter, we do a lot of large transactions, obviously, with the biggest companies in the world. But we actually signed in the third quarter last year, the largest AWS ISV contract ever. So we became AWS' largest ISV provider in terms of the contract that we signed for one of our large banking customers. So super interesting environment, seeing all of these critical mission-critical workloads moving to the cloud with Teradata.

Yes. So also about a year ago, you -- on the product front, you kind of rolled out this idea of VantageCloud Lake versus VantageCloud Enterprise, you also released ClearScape Analytics. Can you just talk about the differences between Cloud Lake versus Cloud Enterprise and what's kind of your expectations for how customers will leverage both of those or maybe they ultimately migrate to Cloud Lake? But we get a lot of questions on that. So it might be good to just kind of clarify.

You can think about VantageCloud Enterprise as an abstraction of our software or on-prem software into the cloud. VantageCloud Lake is a complete re-architecture, keeping all of the great stuff of VantageCloud Enterprise, but rearchitecting that and making it fully cloud-native with all of the modern advantages you'd expect from that from full self-service, separation of computer storage. But more fundamentally, it provides our customers with a choice. Teradata was well known, as I said, for trading the enterprise data warehouse. But this new technology enables our customers to deploy not just an enterprise data warehouse, but a data lake or a data lakehouse. And so our customers utilizing the technology that they know today, now are able to deal with much more flexible data storage options, utilizing things like open table format, native object store, creating that data lake and getting the best out of the data that's in those data lake environments, whilst matching it up with the pristine data that you have in your enterprise data warehouse. And so what we see is organizations wanted to make the most of their data no matter if we're at, as in their ecosystem, the Teradata technology now enables them to do that. You're absolutely right Tyler. Over time, what will happen is we'll stop talking about VantageCloud Enterprise and VantageCloud Lake. Those technologies will essentially coalesce onto the lake architecture. But with all of the capabilities to deploy a data warehouse, a data lake or a data lakehouse.

Yes. So today still, majority are on enterprise...

The majority are in enterprise. And that, for our customers, has given them a really fast path to the cloud. We're seeing a lot of our customers now deploy VantageCloud Lake alongside VantageCloud Enterprise so that they can make use of that very flexible architecture for experimental workloads. So for example, if you are using Teradata as many organizations do to close your books on a quarterly or annual basis, then as the CIO, you do not want to interfere with that workload and go to the CFO and say, "Hey, we were running this marketing campaign. And I'm sorry, you couldn't -- we couldn't close your books." But what VantageCloud Lake enables a marketing team or an HR team or a product team to do is run advanced queries against the enterprise data warehouse while those mission-critical workloads continue to execute at a super high service level availability and performance characteristics. And then the Cloud Lake, you can utilize advanced analytics that are now built into the platform and we're actually seeing some of our customers start to use that cloud-like capability to execute large language models from a Gen AI perspective, I'm sure that questions are coming up from a Gen AI perspective. The other thing that we announced last August, and it was a kind of a precursor to all of the GenAI that you see getting the limelight now with ChatGPT and so forth. There was a whole set of what we call end database advanced analytics. And this goes back, Teradata in 2010 or Aster technologies, and there was no press or a big media attention that was given that to the team because think back to the 2010, that was a data-oriented decade, not what we're in now and analytics and AI-oriented decades in terms of information technology. But those core technologies we have taken, as we re-architected and implemented the product, we put that advanced analytics right into our processing engine from a technology perspective and what that enables companies to do is operate at tremendous scale, super advanced analytics. So we have this concept of 4D analytics where you can look at data over time and from a geography perspective, geospatial data, combined time series data and geospatial data to actually make recommendations to how to better serve a customer or how to optimize your supply chain or make your organization more effective and efficient. And all of that happens in database. I think of the analysts said that we had far more than 5x the analytic capabilities of our nearest competitor from an analytics perspective, I think that was probably Databricks.

Yes. A lot of history of innovation that you've been able to kind of...

Yes. Taking that innovation, rolling it into our future architecture to give an unbeatable platform solution.

Yes. Awesome. Well, so you brought up generative AI, so I figure we'll ask the obligatory questions there. But I'm curious, I mean, training large language models requires a vast amount of data, vast amount of compute. Teradata sits -- there's a ton of data that sits in Teradata. So how do you enable large language models? It sounds like you're already seeing some use cases on VantageCloud Lake, but just talk about that opportunity. Any way you could help size it or measure it? Does it result in a certain percentage increase in spend? Just talk about what you're seeing on the GenAI perspective.

GenAI is certainly acting as a catalyst for all of the data players in the marketplace. But it's super interesting how that is manifesting. We see a number of our customers already deploying large language models. GenAI and ChatGPT, GenAI is getting all of the hype and all of the interest. But the real implementation is actually in the large language models that sit underneath those technologies and then applying a corpus of data that can be trusted. Because if without great data, artificial intelligence just becomes artificial. I think you've all seen like some of the examples of that where querying the web essentially and utilizing data elements that are modeled or not factually accurate, right? So enterprises really want to deploy AI and advanced analytic capabilities against trusted data. And Teradata stores the world's largest companies trusted data and a very consumable way for these large language models. Interestingly, more than 65% of data science projects fail and I include in that all of the AI projects in the world. In fact, there was a recent study, it said 85% failed, and that's probably as a result of the catalyst around GenAI. But why do they fail? They fail not because you don't have a great idea for a GenAI product or capability inside your organization, that's called feature engineering. You don't feel because you can't train the model and actually have that model operate well. They fail because you can't move that model into production successfully. And the reason that fails is because you are introducing a very large corpus of data to those large language models as they move into production. Now Teradata was borne to process massive amounts of data and so we do that very effectively and efficiently. Now I think I was reading another day that ChatGPT costs, I think it's $3 million a month that in January time scale, there was cost of $3 million a month to run ChatGPT. If you are a corporation and you want to take advantage of these large language models against your production data, you need the lowest cost per query. And Teradata has proven time and time again that we are the lowest cost per query in the industry. And so in order to actually operationalize large language models, you need a really low cost per query because of the number of queries these large language models execute. And Teradata gives that solution in a very predictable way for our customers. And so we actually enable that operationalization of AI technologies in an enterprise environment. I can talk about this for a long time. We also recently bought an organization called Stemma. Stemma was a very small technology organization, but they were -- had a super interesting capability. Their value proposition is essentially enables organizations to search and explore the data inside their enterprise, be able to work out what the lineage is of that data. So that they can actually -- so you can graphically map out your enterprise data and understand where the source was for that data and where it came from and form it is currently in. By utilizing a technology like Stemma, alongside the technologies that we have in, say, Teradata, it opens up a whole corpus of data that may otherwise not be trusted by enterprises. And so we see that as a really game-changing technology capability that's going to be built right into our Teradata platform as we move forward.

Got it. That's helpful overview of the Gen AI. I think one topic that we've heard in that realm is just kind of the idea of companies are very cognizant of sensitive -- keeping their sensitive data in-house, right? You don't want to step out on the open web. And maybe this idea that people will actually start to bring stuff more on-premise to do these large language models, yes, it's -- there's trade-offs obviously. But -- just curious on your perspective on that. I mean, would you -- are you seeing that at all? And I assume you would benefit just because you do offer Teradata on-premise as well. But I'm just curious if you've seen that customer base.

Yes. I think from a data governance perspective, Teradata is always -- because we handle every -- nearly every major bank's trusted data. Our data governance and data management capabilities and say, Teradata are incredibly significant. And you need that for both trusted AI, ethical AI but you also needed to ensure that you don't let your data, which can be your organization's most valuable asset, leak out into the public domain. And we've seen some examples, even technology companies have that happen to them in terms of training a public model like ChatGPT with proprietary intellectual property means that, that intellectual property becomes available in the general model. And so what we see is actually customers and organizations want to deploy large language models and effect ecosystem, both on-prem in some cases, but also utilize large language models and their tenant in the cloud. And so we have actually been working with a CPG company in the United States, they wanted to optimize their supply chain using large language models. They do the feature engineering that idea around the GenAI or large language model in Teradata. They do the training and Azure ML, with all of the benefits that, that gets in terms of the investments Microsoft are making in their ML and AI capabilities. And then they actually deploy that model using Teradata technology. But it's all in a fixed ecosystem and in a highly governed data ecosystem. And you find few organizations talking about those capabilities because it's clearly a differentiator that we have in the marketplace, and for a lot of organizations, it's a nascent capability that they've been developing over the last 5 or 6 years, whereas we've got 30 years of experience of handling the world's most sensitive data.

Got it. So we often get asked about competition in the space, obviously, a very competitive market. And yes, I think many would have been surprised by how large the cloud business has grown to, I guess how are you seeing the competitive landscape today changed? I mean on one hand, you have Snowflake, which continues to grow at a high rate, but certainly it's come down. They seem to be pushing more into data of -- their Snowpark kind of the data engineering use cases. Databricks has kind of been more in that data engineering use case kind of moving more into data warehousing. Like how -- are those two companies kind of battling it out with each other, creating opportunity for you? Or how would you kind of position Teradata? And a lot of times, you are coexisting with these vendors in these accounts. But how is there room for all of you, especially as these workloads continue to modernize?

Yes. I think this is a huge marketplace, and it's a huge area of spend inside customer organizations. And it's not going to be one winner take all. What we're finding is that our ethos from a data perspective, which is very different. If you think of a Teradata from an on-prem perspective, our ethos was get all of the data in the world to run on Teradata systems because as you expand the Teradata system on-prem, you'll buy more storage from us, more hardware, more compute capability. It wouldn't surprise anybody in this room to learn that when I resell the CSPs, compute services or storage services, I don't really make much money on that. I make money out of our customers utilizing our software to get insights out of their data. And so our ethos as a company now is to open up our platform so that it can process much more data no matter where it is in the ecosystem, whether it's in a lake and it may be Delta Lake or from a Databricks perspective or it's in Snowflake or it's in an Oracle system or a Db2 system which we'd be happy to take out of a customer's technology stack for them. So no matter where that data sits inside their ecosystem, it's probably there for the reason that they are using some form of advanced service that, that provider gave. But keep that data where it is, and we will deploy a query fabric to ensure that our customers can link all of that data together no matter what technology got it there and actually process that data in a much more effective and efficient way. And so that ethos is actually resonating with our customers a lot more than a Snowflake ethos of, hey, we've got this thing called Beta cloud, put all of the data in the world into the Snowflake Data Cloud, I'm sorry, that's not going to work. You're not going to have all of the world's data in one cloud, right? What we see our customers deploying is a multi-cloud solution. They are -- some of our customers are actually recognizing the benefits of keeping an on-prem capability. So multi-cloud and I also include private cloud on that back into on-prem as a super important deployment mechanism. In fact, we announced a partnership with Dell recently to utilize their converged infrastructure is our private cloud solution, which is super interesting because it moves and I realize we're in the hardware segment from a city perspective, we may be moving soon, Tyler, to as we actually deemphasize our hardware engineering capabilities and utilize partners like Dell to support our software usage of both on-prem and also linking that software from a cloud perspective to create that query fabric for our customers. And no other organization is really got that ethos. I think Databricks is starting to have that ethos a lot more. What we see from a Teradata perspective is that we've always been the custodians of that essential enterprise data and customers may have taken some data out into a data mark like Snowflake to run marketing campaigns. But as they're going through cloud optimization and as they look at the lineage of their data, they're starting to ask the question, why are we duplicating that data in Snowflake or in Databricks when it's already sitting in the Teradata platform? So we're seeing a lot of customers recommitting to this new architecture that we have from a Teradata perspective, and we're seeing organizations that have tried to utilize a service like BigQuery or Snowflake. I actually find it very, very expensive to emulate Teradata's capabilities in the cloud. So that enterprise cost performance, our built-in analytics are open and connected ethos is truly a differentiator from us in the marketplace setting. And I think it's the reason that we get a lot of organizations committing to us in the cloud, and we're starting to see that acceleration.

Yes. Awesome. So talking about that acceleration, I guess, as we look at most recent set of results, I mean, this was a very strong first half of the year, especially in Q1, you had some really large deals. I guess, at the same time, we didn't see you raise the full-year outlook, even though there's been a couple of good strong quarters. So I guess the question is how much of the strength that we've seen out of Teradata over the last couple of quarters has been more because of timing stuff that is maybe closed a bit earlier, which isn't a bad thing versus true consumption upside or adoption of these new use cases faster than expected? Just help us unpack that.

Yes. So I think one of the key things to note about our first half results is that the bulk of our growth from a cloud perspective, was largely driven by expansion, right? So that means that those workloads that we have in the cloud are expanding with our customers as they want to utilize more data, they want to put out new capabilities on their systems. So fundamentally, it's expansion. However, I think what we're seeing, Tyler, now is the fruition of our strategy and consistent execution of our strategy. We have built the foundations to be a truly cloud Software-as-a-Service company and say Teradata. And that is translating now that our cloud business, where the bulk of our growth is becoming more and more meaningful in terms of a percentage share of the overall business. That number is now starting to impact our total growth as well yes. And then -- so specifically to your question around, is it timing, it's a combination of both. The operational effectiveness that my management team has deployed, and this is a great team of individuals experienced in the industry. They are transforming how we operate and execute. If you're a cloud business, you don't do all your transactions in the fourth quarter. We are still heavily weighted towards the second half of the year. But the more I can pull that out of fourth quarter and put it into the other quarters a year through fantastic operational execution and discipline, I'll certainly do that. But what's really driving the growth of Teradata is that expansion, those expansion motions, which are actually increasing our general presence in all of our customers.

Yes. And just given what you've seen -- I mean, certainly, IT budgets are still under a lot of pressure. Maybe they're starting to stabilize or loosen up a little bit. I guess what are your thought -- what have you seen from customers in terms of signals? Is there going to be a potential budget flush this year? And then do you think 2024 is kind of a recovery year? Just curious if you have any perspectives on that.

Look, I think from a general budget perspective, Teradata is now in the kind of nexus of critical spend for customers. If you ask how are you spending your money to any CIO, the first thing they'll probably say my CIO would be the same, Cybersecurity, yes, cloud, data and analytics and AI is now coming up as a kind of separate data and analytics, AI, right? So from a Nexus perspective, our cloud focus and being in the data and analytics marketplace means that we have a tremendous TAM to address in a growing TAM in an area that organizations are seen absolutely critical. There is no Board in the world, and I include my Board in this that hasn't said -- so how are you utilizing GenAI? And we look at that from a Teradata perspective as how can we utilize GenAI in terms of our products and making our product better. How can we utilize GenAI in terms of enabling organizations around the world to have successful artificial intelligence programs? And then how do you use GenAI in terms of our company operations to become more effective and efficient, like product engineering, generating code? So every organization, I think, is looking to take advantage of that. And I think the spend on data and analytics will continue to be stable and grow as we move into 2024 as organizations realize that they really need to get the benefit of their data if they want to truly compete and be a company of the future for 2030.

Yes. Okay. So going back to the go-to-market, certainly, there's been a lot of changes, whether it's engaging more with ISVs and system integrators. But also internally, you mentioned some of the changes you're making to drive better linearity throughout the year. Could you just kind of recap what some of the big go-to-market changes has been over the last year? And then any tweaks you're thinking about making to the model into next year, whether it's incentives or leaning more on partners versus direct?

Yes. I think like one of the things I think is super important is our relationships with systems integrators. So let me tell you a little bit of the history of Teradata, and what we've transformed over the last three years in terms of our relationship with Systems Integrators. When I started with Teradata, I set out a clear mandate that we were going to be a technology platform company at our core. That was a really important message for Teradata because in previous years, we'd actually had a strategy of building up a consulting and services organization and capability that actually competed with the SIs. The Accentures and the Deloittes of the world. If you are competing with an Accenture and a Deloitte or a Kyndryl now or an HCL or an Infosys or a Wipro and those organizations are actually making the technology recommendations to a CIO, it's unlikely they're going to recommend Teradata who competes with them for the SI and the services work. So by setting out a clear strategy that we were going to be a technology platform company, a software company, we weren't going to be a services company. We were going to strategically reduce our services revenues over time to enable our SIs to take that business at low margin. It's not in our target margin footprint to take that business so that they saw Teradata as a true partner. So cloud-first, partner first became incredibly important to how we execute. And we are seeing our partnerships with organizations like Accenture, like Deloitte, like KPMG, like some of the regional systems integrators is driving a huge amplification for our business. As we are executing these migrations of the world's largest companies to the cloud, we don't have enough services people internally to be able to service that. We need a vibrant partner community both to help us position the Teradata technology inside our customer, but also to service the demand that, that will place in terms of providing people to execute these projects to transform the data and analytics capabilities of our customers. So that was a key part of how we have transformed Teradata over the last three years. Another example is our customer success motion. Looking at how our customers are actually utilizing the platform and taking the best industry capabilities. So with Accenture, for example, we're developing what we call solution accelerators and a number of different areas like customer experience and actually deploying those as a solution on top of the Teradata platform. And so working with these SIs in very targeted ways is driving great expansion for the platform as we move forward. And that's just one aspect of our go-to-market transformation but a really exciting one.

Yes, absolutely. And in terms of the go-to-market leadership, you've also brought in some new marketing heads since you've joined Jacqueline, Jacqueline joining. And I guess along those lines, you've launched these new series of conferences. I think you did one in Singapore, you have one in London coming up next week and then Orlando Teradata is possible. Can you just talk about -- I mean, one of the things that I think has always been true about Teradata is it's always had a perception challenge, right? And to your point earlier, the prior management team was not focused on new logo acquisition. So I guess, where are we in that journey of convincing the next generation of developers that this is actually a modern cloud-native platform that you can evaluate side-by-side against Databricks or Snowflake?

Yes. I think three years ago, if I had gone out to the marketplace and say, "Hey, we're a cloud company," I would have had no credibility, right? We are executing a strategy in terms of the transformation of the company, and we're at the point where we have now built all of the foundations. Now that we have hundreds and hundreds of millions of dollars in ARR in the cloud, and we have hundreds and hundreds of customers running in the cloud with us. We have customers that are like American Airlines that are willing to stand up and be a reference in terms of what we're doing. We have Vodafone in the U.K., a great telco company talking about how they utilize in Teradata. Now that we have all of these proof points, now is the time that we are looking to amplify our position in the marketplace. We've built the foundations, we've got the credibility, we've got the testimonials, and now is the time we are looking to accelerate that. And I think that's what we're seeing in Q1 and Q2 results. We're growing our cloud business at really a substantial rate, but it's impacting our total ARR growth as well. And I think it's just the fact that we have laid the foundations in terms of each of the pillars of our business and are now ready to accelerate and amplify that as we move forward. And that's why we're investing in these marketing events around the world. We're super excited about what we're going to be talking about in these events.

Yes. I guess in the last minute or so, do you -- any sneak peeks you could give us into what's to come from some of these events? Or just any closing thoughts you have for the rest of the year into 2024.

Yes, I'd encourage you if you're in London next week coming to the Teradata event, also in Orlando in October. Hilary, our Chief Product Officer, is going to be talking about a dozen new capabilities inside the Teradata platform. I'll just highlight some of them. We have a GenAI interface to improve how to access and utilize Teradata sitting on top of our new cloud console. We have semantic mapping, which takes an organization, a large corpus of data and work structures that data in an autonomous way. It's a super exciting technology. We have a serverless compute engine that we're launching in private preview which is a SQL engine that can be spun up in the AWS marketplace. It's a very friendly developer-oriented, still got access to all of our analytic capabilities, but it's serverless. If you'd said to me three years ago, Teradata would be talking about serverless infrastructure, I wouldn't have believed it. And so super exciting. We've got large language models, capabilities that are going to be continuing to be enhanced that we're already utilizing with our customers, so a lot's going on open table format, embracing open table formats and the large corpuses of data that's in those open table formats is going to be super important. We've spent a lot of our engineering talent over the last three years, replatforming the Teradata platform to cloud native. We have now unleashed a fantastic amount of product engineering capability to true innovation and I think the innovation you'll see in these marketing events is something that Teradata has never been able to stand up and deliver before.

Awesome. With that, I think we'll have to wrap up. But Steve, thanks for joining, and thanks, everyone, for attending.

Thank you.