All right, I guess we can get started. Welcome, everybody. My name is Koji Ikeda. I am one of the software analysts here at BofA. I am super, super thrilled to have Rick McConnell, CEO of Dynatrace, here with me. Thanks so much for doing this. Super, super appreciate it. I guess first question, always the level set question, you know, from a very, very high level, for those in the room that are new to Dynatrace and for those on the webcast that are new to Dynatrace, what do you guys do? What's the opportunity you guys are, you know, going after? What is observability in 30 seconds to a minute, and tell us a little bit about yourself.
In one minute?
Yes, please.
Including the setup. Oh, that sounds like a good start. Welcome, everybody. Thanks for joining us. Dynatrace exists to help create a world in which software works perfectly.
Mm-hmm.
We do this for enterprise customers. The market that we access to do this is observability, which we define as a $50 billion or so market for observability and application security. It really gets fueled off of this notion that digital transformation and cloud migration is fueling a huge explosion of data with a massive increase in complexity. The combination of those elements mandates automated response, automated review of that data so that it becomes more actionable. That's what we do at Dynatrace that is differentiating relative to others in our space.
Got it. Got it. I wanna ask you a couple of, you know, set questions that I've been asking every management team. One about the macro, another about AI. This first question on the macro. How does the environment feel for you from your lens out there, you know, June 2023 versus maybe January 2023, and then the year-ago period, June 2022? Does it feel different? Does it feel the same, or is the end market looking at observability tools differently? You know, just any sort of help of what it feels like out there.
Well, if I play that through chronologically, I would say that January 20... Obviously, we're still in it. This really started in earnest, for it to become slower, I would say, in the June timeframe of 2022, and we still are seeing this in June of 2023. The expectation that we have relative to our FY 2024 plan is no notable improvement.
Mm.
We're assuming status quo, parity in the macro environment through our fiscal year.
Got it. Got it. Moving on to the topic of AI, very topical here. Been asking every management team, AI, generative AI, in three different flavors. The first question is: you know, how does Dynatrace think about leveraging AI within your offerings?
Well, Dynatrace, if I play back to the answer to my first question, Dynatrace uses AI to really provide the precise answers-
Mm-hmm.
Automation that we use in our platform.
Yeah.
Unlike most, we're not new to AI.
Mm-hmm.
We, we've been using it really as part of the core platform of what we deliver and have been doing so for a very long time. To clarify, at the next double-click level down, we use what we refer to as, or think of as, Causal AI. This is different from generative AI, which is ChatGPT, large language models, et cetera. The way to think about generative AI, is that it is an enhancement to productivity, whether it's writing term papers or film scripts or code, for example. I can use generative AI to create more code faster, deliver more workloads, more applications, more capabilities, more elements to be observed, if you will.
Mm-hmm.
We do view generative AI as a tailwind to the opportunity generally in observability for those reasons. Having said that, Causal AI, what we do, is using data that is constructed in real time. These are traces, routes, logs, metrics, a number of data types generated by IT ecosystems that we are constantly creating and evaluating using our AI engine to deliver very precise answers on how your environment is operating. The combination of the two, generative AI and Causal AI, we believe are very synergistic, highly symbiotic, because if you can use generative AI and natural language to query Causal AI, then you can actually use generative AI to, through our AI capabilities, deliver very precise answers.
That makes a lot of sense. No, thank you for that.
Really?
Yeah, yeah.
That's good.
Yeah. I wanted to follow up of the end market that you're addressing and how they are thinking about what AI and generative AI means for their businesses, and the pain points that they're trying to address with observability tools. You know, when we look back at January 2023, I don't think. You know, it would be hard to imagine the kind of awareness that generative AI has today. Presumably, the end market, you know, your customers are starting to look at, or maybe not, the way that they're structuring their tech stacks or data stacks or observability solutions. Has the conversations changed in the way that your end market is looking at?
I was in Canada with in customer meetings, roundtables, last week with many of the largest banks in Canada, retail, training companies, a variety of them. I got a very real-time update from maybe 20 customers in the last week on their perspective on generative AI, because it came up in basically every conversation I was in. What I would say is the common thread is strong feeling that it will be a significant productivity enhancement, uncertainty as the timeline over which that might happen. Everybody's looking at it, everybody's evaluating it, how to find ways to accelerate productivity. I said at the outset, generative AI, it's really about productivity gain.
Got it.
Yet it's accessing a data store that is largely a static data store, that, the AI results are only as good as that data store.
Mm-hmm.
I think that organizations are really working hard to figure this out in real time. Nobody wants to fall behind because your competitors have found a way to get more productive faster than you.
Mm-hmm.
At the same time, there is reservation to sort through exactly how it is best utilizable by your company.
I wanted to dig into that a little bit. And you're saying about customers having a little bit of reservation. Can you dig into what is the reservation? Is it data security, privacy, or, you know? Anything that they were talking about that is, you know, maybe causing a little bit of hesitation from the end customer's perspective?
Well, one element is that generative AI is never gonna deliver a perfect result.
Mm-hmm.
You're still gonna have to provide some sort of manual work on top of that to work through the result. You can't just ask it to develop a code snippet and be assured that that code snippet is gonna work perfectly. It just isn't that simple. I think that organizations wanna make sure that they don't get ahead of themselves, and whether it's for security reasons, integrating more viruses due to code libraries that might be called, that may not be compliant, for example, or, for one of our other sets of reasons. I think they wanna proceed with some degree of sort of explicit view as to what they're getting themselves into and how to utilize it most effectively, given that it's so early.
Okay. Last question on generative AI or AI?
I don't believe you.
Is?
Hey, I'm just...
Is the monetization of AI for Dynatrace. You know, how do you guys think about it? Is it just embedded in the platform? You get it when you purchase the contract? Does it drive premium SKUs? Could it even drive new products for you over time?
A little bit of all of the above. As I mentioned at the outset, what I would say is that generative AI enables more workloads, more applications to be created faster. This notion that data is exploding and that it's harder to manually evaluate through those network operations centers, where you have an army of people staring at a sea of screens and glass, looking at dashboards. They're looking for alerts. Those alerts tell you red, yellow, green, and that is. You see red, and then you begin a triage process. That is a difficult process to figure out precisely where something is not working the way that you want. This problem is exacerbated by generative AI.
If you can now take a situation in which data's exploding, resources are constrained, I already can't get to the answer quickly enough, and now I'm gonna make it worse by adding or accelerating the number of applications? Problematic. I'd say the biggest way in which generative AI helps Dynatrace, at least in the immediate term, is a catalyst that comes out of what we see anyway, which is data explosion and these other elements. By making that problem worse, it's gonna drive automation being at the forefront of objectives of these organizations. As automation moves to the forefront, that suggests Dynatrace, because that's what we do better, we believe, than others in our industry.
Got it. Got it.
Now, there's another aspect, Koji, which I would just say, which, we also do believe that this notion of connecting generative AI to Causal AI really is valuable, and that's something that we will drive as well. That should also be a catalyst to business opportunity.
Got it. Okay. I wanna move the conversation over to Grail.
Okay.
Yeah.
It's good.
Big, big topic of conversation, Grail. We've definitely heard a lot of positive reviews as we're talking with partners and customers out there, and the one thing that we hear a lot about is, this thing is fast, and we just don't know why it's so fast. Maybe could you help us explain or help explain, why, from an architectural standpoint, Grail is just so good at what it does?
Well, to step back a level, for those of you who don't know what Grail is, Grail is a massively parallel -processing data lakehouse that we've constructed. We began work on it about four years, or now 4.5 years ago. We designed it because we couldn't find anything in the industry that did what we needed to do, that we could leverage and integrate into our platform. We built it ourselves. It uses hypergraph technology which keeps all data types in massive scale, in context with one another. We believe strongly that ultimately the right answer in observability is end-to-end observability. That means applications, infrastructure, application security, end user monitoring, log management, all of these elements will be holistic in providing a single, integrated, unified platform. That's what we can deliver at Dynatrace.
If you deliver a fully unified platform, that platform needs access to a fully integrated set of data types. Meaning logs, traces, routes, metrics, real user data, behavioral analytics, metadata, et cetera. That data gets stored in context in Grail, and Grail was built with an architecture which doesn't ever do re-indexing. The result of it is, it is extraordinarily fast. In fact, for complex queries, what we see is 5x-100x faster than the capabilities on the market today to do analytics and queries on that data. That's, that's how we did it.
How difficult is that to replicate when you talk about a massive data lakehouse and hypergraph technology and massive scale? Is it just, you know, if I, if I were to go out there, raise some money and, you know, try and build this myself, I mean, how long would that take for me to replicate?
Well, I'm pretty sure, Koji, that you could build it yourself, but I'm not sure about anybody else.
Thank you. Thank you.
The answer to your question is, well, it took us four years to do it, and we started with a very consummate goal in mind. There are two challenges. One is just building the capability from scratch, which is time-consuming. The second is The Innovator's Dilemma problem of how do you figure out how to shift data repositories from one to another? We sort of built Grail with that in mind. I think it is a substantial undertaking to replicate.
Okay. Okay. What are you most excited about from this Grail platform over the next six months, and then maybe over the next five years?
Well, the answer is probably similar in both cases, which is I get really excited about what Grail can deliver by way of delivering an observability unified platform.
Mm-hmm.
An end-to-end observability platform, covers all data types and covers all of the use cases that I described earlier, be it AppSec, be it log management, be it end-to-end observability and tool consolidation. These are all use cases that our customers come to us and say, "I want that." They're not necessarily coming to us saying, "I want and need Grail." They're asking us for is, "I want a unified observability framework. Too many tools, too much dispersion in access to those tools, and it's too complicated to manage. Moreover, I need scale, performance, et cetera." This is what we're focused on delivering, an answer to those requests for purchase behavior and use cases. Grail is an enabler to that.
Got it. I wanna ask you one more question and then open it up to the audience to see if there's any Q&A out there. There is a microphone. Please wait for the microphone to make sure that we get your question for the webcast. My last question for you before Q&A is, you know, I think about Grail, I think about large data ingestion. I think about, you know, the fast speeds that it has, I also think about margins. We've talked about this before, I wanted to dig into it a little bit more. You know, just, hey, if you're ingesting all this data, it feels like it could be a potential impact to gross margins as you're ingesting all that data. Walk me through what Grail means for gross margins as it continues to scale.
The net of it is, we don't expect it to have any material impact, one way or the other. To be on Grail, you need to be on our SaaS platform. If you're already there, we expect the margins are already reflected. If you're not, you're on a managed environment, you move to a SaaS environment, there's gonna be a modest uptick in pricing from managed to SaaS, which should make up for any cost differential. Net of it is, we're not projecting any material gross margin impact from Grail.
Got it. Thanks, Rick. Any questions from the audience? Please raise your hand, and we'll get a microphone over to you. Got one question over here, please.
What's the state of competition in the industry right now? I guess, for a long time, it was thought to be a relatively crowded space, too many players. I guess, is there a feeling that there's gonna be M&A consolidation in your industry, that subsector? Thanks.
I'm not necessarily projecting any major consolidation of major players. I do think we're already seeing separation of organizations that are competitors in our space. I think that our view of that is that the end-to-end observability and unification of an approach is the way that we have been competing, the way we will compete, and I think that's differentiable as we look at it.
As I think about the competitive landscape, you know, the observability category. It's easy to sometimes bucket certain companies and competitors within certain aspects of observability. Dynatrace, very good at APM from the onset. Other competitors may be log management, infrastructure monitoring, et cetera. All these competitors, when you go to the observability space, when you look at their websites, it feels like they all have platforms.
Yeah.
You know, walk me through when you're talking with your target end market, the top 15,000 out there. What are they looking for? How are they thinking about consolidation? You know, is the platform value proposition becoming more meaningful for them?
In my view, the platform proposition is absolutely becoming more meaningful. I'm talking to customer after customer where they say, "I've got open source here, internally designed observability monitoring and monitoring here. I'm using one particular tool here, another one here," and it is unmanageable. I mean, in a network operations center, you can't have 15 different tools, which is what we see sometimes. BT, great example, British Telecom. They onboarded within the last year or so, tens of thousands of host units. They had 16 tools. For observability, they told us. They wanted to drastically narrow that down. As a result of doing that to Dynatrace, to get a much more comprehensive platform, they put out metrics to us that said they reduced incidents by 50%, five, zero.
They reduced MTTR, mean time to repair, respond by 90%, they projected saving GBP 28 million over a three-year span. These are the kinds of metrics that our customers want. They want meaningful observability capabilities that are going to make material inroads into the metrics that they're concerned about, like number of incidents, MTTR, and the like. They want to pull this down. The other fact I'd share with you all is that last quarter, we had more than 20 competitive takeout deals of $1 million TCV, total contract value, or greater, that occurred in that quarter. These are competitive takeout deals from others in our industry. We asked our sales team: Why did this happen? What was the driver? Typically, the answer to it was automation, automation, automation.
This notion of manual processing of red, yellow, green alerts and dashboards, replaced by an automated capability that can be delivered by Dynatrace, using the combination of our end-to-end platform and our AIOps engine, really does have appreciable and differentiable impact.
Is that the first thing that customers see when they adopt the Dynatrace platform, coming from, you know, call it a basket of best-of-breed point solutions? It's that immediate, you know, meantime to fixing a problem? Or what are the ROI that they're looking at? You know, what is it? Just they're seeing having higher visibility first or incident response is better? I mean, what do the customers really feel first as they go to the platform versus a basket of point solutions?
What they want is they want near instant response of a better ability to get to the heart of any issues. Moreover, what they want is more proactive analytics looking ahead, so that they don't have problems in the first place. Our vision is, as I indicated or have indicated, to make the world's software work perfectly for our enterprise customers. That means it couldn't break in the first place. It wasn't about rapid break pace.
Mm-hmm.
It was about avoidance of a problem. Using the analytical framework within the Dynatrace platform, we believe that we can drive that set of capabilities, that you're getting more predictive capabilities to avoid the problem in the first place. That's what they're after. The short answer to your question is, they want reduction of incidents, more rapid ability to isolate and identify issues, and then resolve them much more rapidly when they occur.
Got it. Wanted to ask you one quick question on cloud optimizations. You know, very topical for a long time, still topical today. Have you been seeing any change in pace in the cloud optimizations out there? Maybe why or why not would Dynatrace be more shielded than others out there?
Well, we've been saying for six to nine months now that, while we haven't looked at cloud optimization necessarily as a huge tailwind, I don't wanna overstate it, we still like the cloud. Precisely what we do. We enable much more efficiency out of your cloud deployments. We want your cloud deployments to work better. We even call our tagline, we even talk about our tagline as, or our mantra as Cloud done right. We offer more sophisticated and automated data-driven analytics to enable that to happen.
Yep.
I wouldn't say we're immune from cloud optimization, but what I would say is that it is an objective which we aspire to deliver against.
Got it. I can't believe I forgot to ask you the question. I was gonna ask this right at the beginning. You had an announcement, new Chief Revenue Officer.
Yes.
Yes, yes. I guess the big picture question, you know, why now? What does the new Chief Revenue Officer, you know, maybe bring to the table?
Sure. Well, Steve Pace has been our Chief Revenue Officer for the last 7.5 years. He's done an exceptional job growing, I think, a great company at Dynatrace at the rate of growth that we're delivered, enabled us to surpass the $1 billion, and then some, ARR mark. Huge kudos to Steve. He's at an age when he wants to retire.
Yeah.
This is a well-planned transition, highly thought through. He and I have been working on this now for some time. The time has come when this is a good time to complete the transition. We opened a search a little bit ago, and we sought to add a CRO with certain capabilities. One of them being scale, global capabilities, partner capabilities, good cultural fit, great leader and proven leadership talent. We found all of those in Dan Zugelder, comes from VMware. Super excited to have Dan joining in early July. Can't wait to have him part of the team. Steve will stay on board through a transition period, through our fiscal Q2, through the end of September, early October, to assist Dan in getting up to speed.
We'll make this as seamless a transition as we possibly can.
Got it. Now, thank you for that. Before I ask a couple more questions, just wanted to open up to the audience. Any questions from the audience before we... Yeah, we got one up here. Please wait for the microphone.
Do you guys have any plans of introducing Copilot-like solutions to interact with the data that you already have, leveraging that generative AI capabilities? Is that something that just doesn't make sense in the industry for whatever reason?
The capabilities. Well, we would rely upon partners or third parties mainly to do that, I think at this juncture. We would benefit from more code capabilities coming out of those types of initiatives, but probably we wouldn't be generating those directly.
Rick, I wanted to ask you a question on security. You know, security, you've recently talked about kind of a milestone goal of $100 million in a ARR by fiscal 2025. You know, are you seeing any, you know, certain patterns or trends within the customers that are adopting security today? How are you thinking about go-to-market for security over the next 12 to 24- months, and what gets you most excited about security?
Well, security is, security is an area where I believe that application security, or certain aspects of application security, and observability are converging at a rapid rate. We are not focused strategically on being all things to all people in security. We will leave that to the mega security vendors and others. What we are focused on are areas of application security, where observability data adds differentiable value, and vulnerability management being the first one, but areas like runtime application protection, maybe over time, even SIEM capabilities. We believe that bringing not just logs, but logs, traces, metrics, all the data types together, can, in many ways, deliver a better SIEM than SIEMs on the market today. These are elements in which we'll be investing going forward. I've.
The good news is I've had lots of personal experience in Application Security from my background in growing more than a $1 billion business in security. I've seen the movie before. That doesn't mean this movie plays out the same way. We'll, we'll see how it adjusts, but I do believe that it is possible, and I think that possible in the sense of growing a notable business, I can't say to what level. At least at the moment, that $100 million plan over a three-year span is on track. We're one year in, two more years to go against that plan, and we feel good about it. We are today about 10% penetrated in AppSec into our customer base.
No reason that can't continue to grow to 20%, 30%, 40% as we look at it.
Awesome. Rick, we're all out of time.
All right.
Thank you so much for doing this. This has been a great conversation.
Great.
Appreciate it. Thank you so much.
Thank you.
Thanks all.