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. Thanks. That's great. Yeah, Teradata, you could say, invented the enterprise data warehousing space from a technology perspective and you know, 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 our research and development, budget. It was focused 70% on-prem and 30% in the cloud.
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. I know 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. 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.
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 you know, a modern selling motion in terms of development expansion inside our 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. You know, as reflecting back, I think, you know it's, 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, you know, we will have increased our cloud ARR by tenfold over the last three years, and our cloud ARR should be over a third of Teradata's total ARR. Well on the path to achieving the goals that we set out for 2025. But that transformation, I mean, it's touched every single aspect. We've now had a solid leadership team in place for the last two years, with a new CFO, a new Chief Revenue Officer, a 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 and, 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 and the pivot.
Yeah.
You've seen in terms of the results of the company.
Yeah, absolutely. And I'd love to kind of get into 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, is 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 from talking to investors. You had a lot of these hyperscalers talking about cloud optimizations, you know, budgets being cut. What about that timing led you to really start to see an inflection in the cloud business?
I guess, are you maybe benefiting from some of the, the challenges that, say, a Snowflake or the Hyperscalers , have referenced in terms of cloud optimizations? Or just, just.
Yeah, I think.
Help us understand that.
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." 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.
Then we allow a 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. You know, American Airlines moved their entire production environment from on-prem to run on the Azure cloud. And it was funny, one of the executives from American Airlines, we asked him, "You know, what did your users see after that migration to the cloud with Teradata?" And his one comment was, "Hey, did you guys do something?
The system seems to be running quicker now.' And that is exactly... If you are an IT organization in 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, 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's 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, mission-critical workloads moving to the cloud with Teradata.
Yeah. Yeah. So, 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 expectation 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.
Yeah.
It might be good to just kind of clarify.
You can think about VantageCloud Enterprise as an abstraction of our software, our on-prem software, into the cloud. VantageCloud Lake is a complete rearchitecture, 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 compute and storage. But more fundamentally, it provides our customers with a choice. Teradata was well known, as I said, for creating 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, while matching it up with the pristine data that you have in your enterprise data warehouse. What we see is organizations want to make the most of their data, no matter where it is 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.
Yeah. Yeah. But so today, still a majority are on Enterprise then?
The majority are on Enterprise.
Yeah.
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. For example, if you are using Teradata, as many organizations do, to close your books on a quarterly or annual basis, then you, as the CIO, 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.
Right.
Yeah. 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 Lake capability to execute large language models from a GenAI perspective. I'm sure that questions are coming up from the, from a GenAI perspective.
The other thing that we announced last August, and it was a kind of precursor to all of the GenAI that you see getting the limelight now with ChatGPT and so forth, was a whole set of what we call in-database advanced analytics. And this goes back, Teradata in 2010 bought Aster Technologies. And there was no press or big, like, media attention that was given at the time, because if you think back to the 2010s, that was a data-oriented decade, not what we're in now, an analytics and AI-oriented decade in terms of information technology. But those core technologies we have taken as we rearchitected and implemented the product, we've put that advanced analytics right into our processing engine from a technology perspective.
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, combine time series data and geospatial data to actually make recommendations to how to serve, 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 one of the analysts said that we had far more than five times the analytic capabilities of our nearest competitor from an analytics perspective. I think that was probably Databricks.
Mm-hmm. Mm-hmm. Yeah, a lot, a lot of history of innovation that you've been able to kinda roll-
Yeah, taking that, taking that innovation, rolling it into our future architecture to give, you know, an unbeatable platform solution.
Yeah. Awesome. Well, so you brought up generative AI, so I figure we'll ask the, you know, obligatory questions-
Yeah
there. But I'm curious, I mean, training large language models requires a vast amount of data, a 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. You know, does it result in a certain percentage increase in spend? Just talk about what you're seeing on.
Yeah
on the GenAI perspective.
GenAI is certainly acting as a catalyst for all of the data players in the marketplace. But it, 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 you know it's querying the web essentially, and utilizing data elements that you know are modeled or not factually accurate, right? So enterprises really want to deploy AI and advanced analytic capabilities against trusted data.
Teradata stores the world's largest companies' trusted data in 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's a recent study that said 85% fail, 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 fail 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.
The reasons that fail is because you're introducing a very large corpus of data to those large language models as they move into production. Now, Teradata was born to process massive amounts of data, and so we do that very effectively and efficiently. No, I think, I was reading the other day that ChatGPT costs, I think it's $3 million a month, that in January timescale, it was costing $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. Teradata has proven time and time again that we are the lowest cost per query in the industry.
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. We actually enable that operationalization of AI technologies in an enterprise environment. I could talk about this for a long time. We also recently bought an organization called Stemma. Stemma was a very small technology organization, but 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 you can graphically map out your enterprise data and understand where the source was for that data and where it came from, and what 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 might otherwise not be trusted by enterprises. So we see that as a really game-changing technology capability that's gonna be built right into our Teradata platform as we move forward.
Got it. That's a helpful overview of the GenAI. I think one topic that we've heard in that realm is just kinda the idea of, you know, companies are very cognizant of keeping their sensitive data, you know, in house, right? That you don't want this stuff 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 I'm just curious your perspective on that. I mean, would you? Are you seeing that at all? And would. I assume you would benefit just because you do offer Teradata on-premise as well. But just curious if you've seen that.
Yeah
customer base.
I think from a data governance perspective, you know, Teradata is always, because we handle, you know, every, nearly every major bank's trusted data, you know, our data governance and data management capabilities inside Teradata are incredibly significant. And you need that for both trusted AI, ethical AI, but you also need it to ensure that, you know, 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, you know, 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 wanting to deploy large language models in a fixed ecosystem, both on-prem in some cases, but also utilizing large language models in their tenant in the cloud. And so we, I've 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 in 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 a highly governed data ecosystem.
You find few organizations talking about those capabilities because it's clearly a differentiator that we have in the marketplace. For a lot of organizations, it's a nascent capability that they've been developing over the last, you know, 5 or 6 years, whereas we've got, you know, 30 years of experience of handling the world's most sensitive data.
Mm-hmm. Mm-hmm, got it. So, so we often get asked about, competition in the space. Obviously, a very, competitive market. And, yeah, I think many would have been surprised by how large the, the cloud business has, has grown to. I guess, how are you seeing the, the competitive landscape today, change? I mean, on one hand, you have, Snowflake, which continues to grow at a, at a high rate, but, but certainly has, has come down. They seem to be pushing more into data, you know, their, their, Snowpark, kind of its data engineering use cases. Databricks has kind of been more in that data engineering use case, kind of moving more into, to data warehousing. Like, how a re, are those two companies kind of battling it out with, with each other, creating opportunity for you?
Or how would you kind of position Teradata and, you know, a lot of times you are coexisting with these vendors and these accounts, but how is there room for all of you, especially as these workloads continue to modernize?
Yeah, I think this is a huge marketplace, and it's a huge area of spend inside the customer organizations. And it's not gonna be a one winner take all. What we're finding is that our ethos from a data perspective, which is very different. If you think about 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 CSP's compute services or storage services, I don't really make much money on that. Yeah, 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 were 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 Data Cloud. Put all of the data in the world into the Snowflake Data Cloud." I'm sorry, that's not going to work, right?
Yeah.
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. 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, is a super important deployment mechanism. In fact, we announced a partnership with Dell recently to utilize their converged infrastructure as their private cloud solution, which is super interesting because it moves... And I realize we're in the hardware segment from a Citi perspective.
We might be moving soon, Tyler, to as we actually de-emphasize our hardware engineering capabilities and utilize partners like Dell to support our software usage, both on-prem and also linking that software from a cloud perspective to create that query fabric for our customers. And no other organization has 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 you know, we've always been the custodians of that essential enterprise data, and customers may have taken some data out into you know, a data mart 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 actually finding it very, very expensive to emulate Teradata's, Teradata's capabilities in the cloud.
So that enterprise cost performance, our built-in analytics, our open and connected ethos is truly a differentiator from us in a 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.
Yeah. Yeah, 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 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 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 quarters has been more because of timing stuff that is maybe closed a bit earlier, which isn't a bad thing versus true, you know, consumption upside or adoption of these new use cases faster than expected? Just help us unpack that.
Yeah, 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?
Yeah.
So that means that those workloads that we have in the cloud are expanding, yeah, 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 inside Teradata, and that is translating. Now that our cloud business, where the bulk of our growth is, 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.
Yeah. And then, so to specifically to your question around, is it timing? Is it strategy? It's a combination of both. The operational effectiveness that my management team have deployed, and this is a great team of individuals experienced in the industry, they are transforming how we operate and execute. You know, if you're a cloud business, you don't do all your transactions in fourth quarter. We are still heavily weighted towards second half of the year. But the more I can pull that out of fourth quarter and put it into the other quarters of the 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.
Yeah. Yeah. 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 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, you know, from a general budget perspective, Teradata is now in the kind of nexus of critical spend for customers. You know, if you ask, "How are you spending your money?" to any CIO, the first thing they'll probably say, and my CIO would be the same: cybersecurity.
Mm-hmm.
Yeah. Cloud, data and analytics, and AI is now coming up as a kind of separate-
Yeah.
Data and analytics, AI.
Yeah.
Right? So, you know, from a nexus perspective, our cloud focus and being in the data and analytics marketplace means that we have a tremendous TAM to address, and a growing TAM, in an area that organizations are seeing absolutely critical. There's no board in the world, and I include my board in this, that doesn't say, "So how are you utilizing GenAI?" And we look at that from a Teradata perspective as, you know, how can we, how can we utilize GenAI in terms of our product 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 we use GenAI in terms of our company operations 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.
Mm-hmm. Yeah. Okay. So going back to the go-to-market, certainly there's been a lot of changes, whether it's, you know, 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 have been over the last year? And then, you know, any tweaks you're thinking about making to the model in the next year, whether it's incentives or leaning more on partners versus direct.
Yeah. 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 Accenture and the Deloitte 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. It's low margin. It's not in our margin, 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.
We are seeing our partnerships with organizations like Accenture, like Deloitte, like KPMG, like some of the regional systems integrators, as driving a huge amplification for our business. You know, as we are executing these migrations, of you know, 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 demands 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, you know, 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 in 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.
Yeah. Yeah, absolutely. And, you know, in terms of the go-to-market leadership, you've also brought in some new marketing heads.
Yeah.
You know, since you've joined, Jacqueline.
Yeah.
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 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?
Yeah.
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 know, you can evaluate side by side against Databricks or Snowflake?
Yeah. Yeah, I think, you know, I think three years ago, if I'd gone out to the marketplace and said, "Hey, we're, 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 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, you know, what, what we're doing. We have Vodafone in the U.K., a great telco company, talking about how they're utilizing 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're looking to accelerate that. And I think that's what we're seeing in Q1 and Q2 results. You know, 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.
Yeah.
We're super excited about what we're going to be talking about in these events.
Yeah. 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?
Yeah, I'd encourage you, if you're in London next week, come in to the Teradata event. It's also in Orlando in October. Hillary, our Chief Product Officer, is going to be talking about 12 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's large corpus of data and 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 continue to be enhanced, that we're already utilizing with our customers. So lots 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. You know, we've spent a lot of our engineering talent over the last three years replatforming the Teradata platform to a cloud native.
We have now unleashed a fantastic amount of product engineering capability to true innovation, and I think the innovation you'll see at these marketing events is something that Teradata has never been able to stand up and deliver before.
Awesome. Well, with that, I think we'll have to wrap up. But Steve, thanks for joining, and thanks everyone for attending.
Thank you.