55. Quality Data is the Foundation for AI with Qlik – Tech Field Day Podcast Spotlight

AI ought to be able to help businesses derive value from their data. But not all AI applications have a solid foundation. This episode of the Tech Field Day podcast looks forward to click Connect 2025, featuring delegates Gina Rosenthal and Jim Rinky discussing the importance of data with Nick Magnuson of Qlik and myself.

Welcome to the Tech Field Day podcast, where we bring together a group of IT experts to discuss a single idea about key concepts in the industry. This podcast features a variety of perspectives from members of the Tech Field Day community, and is often recorded in association with one of our events. This particular episode is being recorded in association with our forthcoming upcoming event where we are gonna be joining click on, uh, stage, uh, in person in reality in Orlando, Florida for Click Connect.

And if you'd like to come, there's still an opportunity to book your travel for that one as well. Tech Field Day is part of the Futurum Group, and this podcast is also published on our sister company's site, tech Strong tv. So let's talk a little bit about data.

Data is the, well, I, I guess people have said data is the new oil, data is the engine. Uh, data is all sorts of things, but the truth is that data has been very, very challenging for companies to get business value out of except now that AI is here. In fact, a lot of AI technology is, uh, predicated on the use of data and the idea that somehow, some way some we'll be able to build a, a foundation of quality corporate data, and we'll be able to leverage AI to get real business value out of that data.

That's the topic of this podcast, but it's also the topic, in my opinion, of Click Connect. So we shall see as this conversation unfolds. Uh, well, I guess how that's gonna work.

Before we start though, let's meet who's on the panel today. Hello, I'm Gina Rosenthal. I am actually a native Floridian stuck in Austin.

So happy to be going to click again. And also happy because I love data, I love making information, and I, I love all of these conversations and all the conversations we have when we're there. Hi, I'm Jim Rinky.

I am Chief storyteller at Zero Defect Computing Incorporated, and, uh, proud to be a delegate. Uh, I'm actually just wrapping up another event that was talking all about AI and data and everything else. Uh, and, uh, with a 25 plus year career as an Oracle database administrator and applications developer, I too, as Gina has said, love data and all of its myriad.

Well, great. That leaves me, um, Nick from Qlik. I'm the head of ai, uh, responsible for setting our strategy and vision around everything that we do with ai, and then ultimately turning that into deliveries from a product standpoint.

Also excited to be at Connect. We're gonna announce some really cool things. So, Steven, as you said, if you haven't booked your travel and you tend to come, I encourage you to do so.

There's still time. And I'm Steven, uh, Steven FoST. I'm the event lead for Tech Field Day, uh, at Click Connect.

I'm, I'm, I'm excited to be going back. Click is one of those companies that I just love to work with because frankly, it's one of those, uh, click Connect as an event and Click as a company is one of those companies that really, uh, resonates with my belief. Now, I'm a, I'm a nerd, but I believe that all this stuff that we're doing should have a reason, and that reason should be rooted in sort of, uh, value to the business, value to, uh, end users, that sort of thing.

Uh, click definitely has that attitude, and that has been my experience with that, with everybody at the company, with the people at Click Connect. Essentially, everybody is trying to figure out how do we actually do things with data? You know, it's not just about storing data, it's not just about organizing it.

It's about doing. And, and for me, that's the topic that I'd love to kind of dive into here because obviously it's 2025, everybody's all about that ai, but everybody's also terrified of, uh, hallucinations and sort of AI run amuck click proof to us. Last year, they were one of the first companies that demonstrated to the tech field day community a, a rag system that used corporate data and was, um, reliable enough to say, I don't know the answer to that.

That's not in my dataset. And I was thrilled to see that last year. I was also thrilled to see Qlik, uh, surface really interesting, um, elements from the dataset that it was fed.

And, and I assume that we're gonna be going in that direction now, Nick probably can't pre-announce anything here, but, uh, let's talk about that. So, enterprise data, we've got this great enterprise data. If only we could do something really magical, as Gina would say with that data.

Um, and maybe AI can work some magic. What do you think, Gina? Yeah, I, I, the magical thing is pretty funny, right?

Because, um, you see some big vendors showing some really cool things that they're doing with data and, um, may happen to have all the tools, but when you try to do the same things, it doesn't work because your data's not lined up exactly the way they created those products to do things. So, um, I think there are magical things that can happen if you've got historical data from a long time. Even my data, I've been in the business for a while.

If I took all my blog posts and put them together, there's probably some interesting threads that go through. You know, it'd be a great way to, to write a historical memoir on the evolution of data since, um, the late nineties. But, um, there's also just, you know, you want something good to happen and muck, muck, muck, Gina, I, uh, I, I agree with you.

Uh, you know, uh, certainly one of the things I'm looking at right now, uh, are things like knowledge graphs and sparkle, which is something I couldn't even pronounce until about a week ago. Much less spell is again, looking at the data about the data, about the data, you know, so that you can make intelligent sense outta what is still embedded in, you know, what we would still consider to be relational data, right? Because ultimately, oh, look, I've got these great, uh, pictures of, you know, whatever automobile crashes because I'm an insurance company.

Well, wait a minute, what's this guy's policy number? What's this lady's, uh, you know, uh, length of coverage and things like that. You ultimately have to go back to, you know, the, the what we would typically call the OLTP data, the transactional data.

Like, uh, have they paid their insurance bill last month or last quarter, right? So it's really interesting when you're trying to wrap all of that in and then all the other marvelous forms of data that, you know, companies are using to delve into kind of experimental things. Uh, everything from new compounds, uh, for medicine to the best design for a fusion reactor, which, you know, we didn't even think we could do five years ago.

So, uh, it's just amazing the amount of data, but also the quality issues of data, right? Uh, why is that field null in 60% of the cases is a real pain point, right? Yeah.

And I'll, I'll layer on that. There's a, a saying that our CEO Mike Capone likes to use that, you know, AI is great, but you gotta do the work. And I love that saying, 'cause it has the, um, connotation that AI is magic, but it's only when you've put the work in to get the data in the right place for the right use cases that you're trying to solve for.

So, Steven, yeah, you're right. Last year we made a big, uh, move into unlocking unstructured data for our customers with that retrieval, augmented generation solution, click answers. Um, we have a longstanding heritage of working really well with structured data.

Uh, so not to, uh, give too much of a clue as to what you'll see at Connect, but like we brought these two things together purposefully so that we can unlock insights and value of all sorts using ai. Uh, but predicated on bringing all that data together, all data matters, uh, and providing tooling to make sure that when you're using that data, you have the transparency, you have the confidence because we're showing you the quality of that data and being able to observe that over time. 'cause data is, is very, uh, non-static in nature.

And so, you know, you've gotta have all those things in place and then, yeah, AI makes it look like magic, but really you did all the work at the data level, uh, to make that happen. I think that that's the real challenge, isn't it, that so much of ai and, and, and, you know, we've had these conversations at Tech Field Day, we had them last year at Click Connect. So much of AI is predicated on data, and yet people aren't doing, I don't think they're doing it right so far because they're not thinking of the data first.

They're not thinking of sort of the, the, this foundational approach. And, and, and instead they're just in love with the magic that these LLMs can generate, you know, truthy sounding, you know, bits of, of, of speech. What's the point of that, right?

If you don't have data plugged into it, it's just, it's not an application, it's just, it's just a parlor trick. Right? Definitely.

I mean, I, I think that's, goes back to, I love that saying AI is great, that you've gotta do the work part of the work. And I would imagine, well, I know besides just cleaning the data and get it ready is like, what is the data? What should we be using?

What are we actually going to see at the other end of this big, you know, magical experience? What do we hope to discover? So, um, that engineering bit, um, has to come along with it.

I, I'm not sure people are doing that. I mean, I get caught up in it too because it's like, let's go and see what happens. I can't figure out what I wanna do with all of these words.

And then it's like, well, these aren't the right, I didn't give it the right, um, information to learn from. So lemme take a step back. What am I actually trying to do, and how do I want to, you know, plug the right data in to get a better result?

I think it's a great example, Gina. Um, you know, I think it was the father of machine learning said something like, all models are wrong, but some are useful. And I heard an extension of that recently, which was, AI always produces hallucinations.

Some just sound better than others. She, it, you know, it's true from a certain perspective, uh, with a human in the loop, you can get some pretty good sounding things, but, uh, I think a lot of times we forget that we have to keep a human in the loop so that they can easily evaluate if that hallucination wasn't too hallucinatory. Does that, I, I'm not sure if that makes sense.

Um, and it, it is actually pretty easy to fool an AI model, uh, even a large language model to do some nasty things depending on which model you decide to use. Yeah. And, and that's exactly why we went the route of, uh, grounding these models using Bragg as a, as a technique for that.

Um, 'cause it was really clear at the time, and again, a lot has changed since, uh, just even a year ago, that models will hallucinate if, if given the opportunity. So you really do need to ground them in, um, you know, your enterprise data, contextually relevant enterprise data that, uh, like you said, human in loop. Like it's not gonna be perfect every time, but like any AI model that anyone's ever built, it's never perfect out of the box.

And you iterate and you refine and you refine. So the human in loop element is, I think, a very key component to the iterative nature of making AI work for your organization. Um, and certainly like everything that we do at Qlik in terms of whether it's predictive ai, generative, or now agentic, we have that at the back of our mind that it's gotta be something that you can continually refine, maintain, and improve upon over time.

'cause uh, you know, what I would hate for a lot of people to do, which I do see often is they're so scared that they don't have the data just right, that they never even start the journey. And the truth is, you've gotta start the journey somewhere, because I've seen customers where they start building models in the data and the model that they get, it helps inform the data strategy. So they're like almost symbiotic in that, you know, AI will tell you that these things actually aren't really mattering and you're still like putting them in reports and sharing them with executives when the underlying factors that are driving the business are something else.

And AI has, I think, a great, uh, quality to it, that it can find patterns and data that, that humans are simply not able to do. So there, there's a lot of lessons to be learned from starting small with these AI projects and learning from them and then refining, uh, and then scaling them up over time. That was a good example then.

Awesome. Yes, Gina, you nailed it. Yeah.

Hey, Jim, you know, you've had a long career in the data industry, and one of my criticisms as a storage guy of the world of data people is that they seem to be so focused on micromanaging and, and kind of structuring data that sometimes they forget that it's, it's supposed to be useful, and all of the people now are going to attack me and punch me at, uh, click connect, I think for saying that. But, you know, DA data people have this reputation as really being, you know, having, you know, a meticulous and, and taking care of it and so on. And then there's analytics people and analytics people seem to be a different sort of animal, and they're much more interested in like, how can I dive in and explore this wild world of data?

Does does that resonate with you? Is that how, or am I completely off base? Well, speaking as a DBA, Steven, no, you can't have access to that.

Uh, but it's, you know, somebody once said, DBA stands for don't bother asking. Um, but it's, oh my gosh, I Thought that was just my opinion. Um, thank you Steven.

Uh, it's a joy to be here. Uh, one of the things that, you know, we really haven't talked about much in terms of, you know, accessing the data. I think what people are nervous about it also, right, is the idea of making sure that only the proper data gets out the door to where, you know, again, if you're on premises and you're completely in a self-contained, uh, private cloud, and you have your own models and all that kind of good stuff, you're fine.

But again, the DBA in me goes, what could somebody do if someone got, you know, beyond the footnotes on our annual report? Or what's the problem with putting out the formula behind our, you know, uh, unique molecular structure for our shaving cream? You know, to pick something, you know, that you normally wouldn't, perhaps a data scientist might not think about that, but, you know, the DBA who always gets blamed if the proper data or improper data leaves, uh, you know, leaves the firewall.

That's another huge aspect. Uh, and making sure that as we're curating, I like that term, curating data, right? That we've got the right security around it so that not just anyone can ship it outside to say, open ai or maybe not even internally, right?

So there's a lot of things going on in that space as well. So I think that's where some of the denial of you can have access to this, Stephen, uh, you know, first tell me why, you know, is because people have been burned and, you know, we hear stories every day of something that went out through the firewall they shouldn't have. Well, that's what I worry about when it comes to ai because it, it reminds me a lot of that inherent conflict between data people and analytics people between the world of data.

And, and, and Nick Qlik sits right there. I mean, you know, this is the company that has data products, but also has analytics products and also, frankly, is, as I said, focused on sort of that business value. And when it comes to ai, I, you're the strategy guy.

What do you think of this, this inherent conflict, and how do you break down, how do you integrate these people? Well, it, it, it is a, it is a big topic. In fact, if you look at, like, you know, the studies that have been done on why people aren't adopting AI more quickly or more rapidly, uh, one is they're, they're still getting educated.

And every time, like we go from generative to agentic, there's a new hype cycle, and they all feel, oh, I, I don't know where I'm exactly at. The other one is governance and the fear of hallucinations and data getting out that shouldn't. Um, and so, you know, Steven, you're right.

Like we sit at the very precipice of, of that where we have data products, we have analytics products, and we serve both, uh, both sides of that fence that you were talking about there, Jim. Uh, so there's a couple things that I think are super important. One is, um, from a technology standpoint, building in proper access, guardrails and controls so that you can fence off, uh, certain parts of your data that you don't want either AI touching or other personnel within the organization touching.

Uh, and, and we, we've invested across both of our product sets to, to enable that. Uh, I also think that something we've started to do and started to prescribe to customers is building AI policies that also govern who has access to what data, what data can be used for AI models, who has the authority to change things in an AI model? What sort of, uh, protocols are in place to approve those changes, um, so that you have a, a governance layer that isn't just in the technology, but also sits across your compliance.

And, uh, you know, we have an annual, uh, train program where we have to go through that every year just so we understand, uh, you know, those processes. So, uh, I think there's a couple different ways we've approached it. Certainly technologically, there's a, uh, you know, that's a really good place to start because if you are kind of putting a stake in their gun that this data's not accessible to ai, uh, that's great, but I think the policies also matter because then people are held accountable to that, uh, as well.

So that's quite the dichotomy though, right? Because you go from saying, uh, you just gotta get started to hearing, and, and you kind of hearing that, you know, through different channels on my side too, that people are hesitant to get started because of the hype cycles and the governance problems. So how do y'all, how do y'all keep people grounded so they can do both at the same time?

Yeah, it's, it's a dichotomy. It's a really think, uh, uh, instructive term for the state that we're all in. Um, so my coaching has always been start small.

So start with a very small subset of, of either a project or use case or set of data that you're gonna work with. Uh, typically that should be something that is well understood. It's data that you, you know, well, you know, the ramifications and start with a use case that has, you know, uh, I would say if things don't go perfectly, like it doesn't tear the business down, right?

Um, oftentimes I look at those use cases as being internally focused versus ones where you're putting a model and it's outputs out in the wild. Um, so you know, if it's a generative use case, it might be starting with the corpus of documents in your marketing department and starting to work with those where if the solutions aren't perfect, yeah, maybe your marketing's not quite great, but it's also not like, uh, you know, you, you had a catastrophic data leakage issue where, uh, you know, now you're looking at lawsuits, et cetera. So, um, but the point is, if you start small in the right use case, you're gonna learn a lot from that.

Other people in other departments can learn from that. They can start their own, uh, small projects. Um, and again, we're all learning at the same time.

So I think that that is an important thing to, to factor in, is that you, you do need to get started, uh, but it's being pragmatic about where you start. And I thought it was interesting, Nick, that you used the word precipice because we've talked about other quantum leaps before, but that, that's an extremely apt description because of that. And Gina, like you said, right?

That kind of fear gap of, well, what if I do it wrong? Uh, and with, especially with the need to keep A GPU busy as near to 100% of the time, which, you know, is like inverted from the way we think about, uh, you know, any other type of computing where, oh my gosh, it's at almost 100%. You know, it's really interesting that, you know, you've gotta show value almost right away when you commit that mo uh, that data and the model and everything behind it, right?

Um, the training cycles and everything else, because it is so expensive to run most of these, right? And you get the 70 billion parameter range, you're certainly looking at A GPU and most certainly, right? So how does that factor into that?

Does anybody have an insight on that? Well, yeah, I mean, I think one of the things I've seen over the last year, and I think we'll continue to see is, uh, more efficient ways to train models, um, so that you can get them, um, to a productive state with less resources. You've probably seen in the news recently, uh, both AWS and Microsoft and others pull back massive investments in these data centers.

And you also saw deep seek come out. And although they claimed it only took them $6 million to build that model, um, the fact of the matter is they used reinforcement learning for a lot of it, they used model distillation. These are all techniques that aren't brand new, but they made that sort of apparent that it, it may not require the type of laws of scaling that we're all afraid of, uh, for some time where, you know, unlimited compute and then all this data, like, you know, there may be more efficient ways to go about that.

So I do think there'll be a tailwind where the cost side of this continues to come down over time. Um, you've got small language models now that are highly performant, more performant than like a generation ago models that were considered of the largest of the type. So I love those tailwinds 'cause it just means as we build on top of them, we're gonna get, uh, better performance, uh, lower cost, uh, and better intelligence out of, out of them, again, with the, with the right, uh, foundation set in place.

So One of the things that occurs to me though, you know, you're talking about your precipice here, um, and, and you're talking about starting slow. Well, there's also this whole agentic revolution, and that's a little nerve, uh, nerve wracking, nerve inducing. I don't know the idea that, that we would have autonomous agents that are capable of performing actions running without human supervision.

That's gotta scare some people, right? I mean, how does a company like Qlik approach the agentic revolution? You know, what's your perspective on that?

Because, you know, you have this foundation in data quality and governance, and yet we're about to turn these agents loose. How do you deal with that? Yeah, yeah.

I, I, I was hoping we'd talk a little bit about agents. Um, we built our first prototype on an agent two and a half years ago. So very early on to the point where, uh, Lang chain was just Lang chain.

There was no lane graph. Harrison hadn't built out that product, which is now the major orchestration platform for a lot of these ag agentic systems that are built. Uh, and I, I was blown away at the time, like in my head I thought, you know, this is gonna turn into a network of intelligence that you can orchestrate and do really complex things to solve really challenging business problems.

Now, the reality is, yeah, like it scares the, you know, bejesus out of most people when they think of, I'm gonna turn this autonomous thing loose on AI that I barely trust at this point. Um, so a couple things. One, when we think about agents, we wanna give them very, very specific instructions on a very limited set of tasks or things to do, um, in a domain that, that, you know, is very confined.

Uh, and this is typically how you would start with any AI project, very confined, um, that way, you know, you're, you're kind of the blast. Radius is minimized, if you will. The other thing, and Jim, you touched on it, uh, earlier, uh, at least for the foreseeable future, there has to be human in the loop.

You can have these autonomous tasks going on, but there's either gotta be reporting out to a human, there's gotta be human, uh, intervention to do something when, when needed or just approval, right? Um, and so, you know, you've seen some, uh, agentic things come out where they are fully autonomous, but again, they're very narrow in scope. And then there's other things you've seen that come out where, where human and loop is required.

And so, uh, we, we are building our products in a way in which we understand that human loop is probably gonna be required, uh, probably gonna be desired by our users. So, uh, you know, so that they can, uh, they can act and, and kind of maintain and control, uh, what the, what the, as systems are doing. I think that adds to the, to the anxiety people have, um, about using any of ai.

But the idea of a agents being unsupervised, talking to each other, supervising each other, I mean, it's kind of how it's been built too. So it's refreshing to hear a, you know, a lot of, um, guardrails, put it around it, somebody talking some scents around here. Yeah.

And I think in some cases, you could consider human in the loop an agent within that agent's, you know, multi-agent system, so that while these things may be communicating, rationalizing, and putting a plan together, like there's human agent that has to say, okay, yeah, that makes sense to me. Um, that human loop I do think is, and, and again, I think there's a very long like trajectory on agents, so it's probably gonna be multiple years before we see real production type use cases in place. But again, you gotta have that human in the loop so that as we go from the first use case to the second use case, there's an increasing level of confidence and trust in the systems that are being built.

Yeah. You know, that you're building. Um, I, I feel like, I don't know if you think this, but I feel like age Gen X stuff is being so hyped that we're gonna see something really bad happen, some like really bad, um, implementation of it, and no one's gonna wanna touch it for a while.

It's kind of, what do you think about that? Kind of like what we saw with LLMs at first and, and, and, and generative ai, there's been so many examples of sort of AI face palms, uh, the agent face palm has got to happen, but yet people didn't really backpedal there. They, I, I guess some smart people are being a little more careful and cautious, but I, I don't think the, the technology has really slowed, has it?

Uh, the technology's only sped up, if anything. Yeah. I mean, it's do this.

Yeah. Uh, yeah, I mean, history repeats itself. Like Gina, I think that's a fairly, uh, uh, it's, it's a statement that has a lot of probability to it, is what I would say.

That's a very political way to say it. Well, I hope it doesn't happen, but I mean, it just, it's just ripe to happen. And, um, I know everybody is a little bit conscious, a little bit about repeating the AI winner, but it doesn't seem like anyone's pulling in any of the claims to, to make it real and, you know, make it reasonable, at least for businesses that are trying to figure out how implement this, Well, at least Qlik seems to be aware of the issues and focused on trying to deliver, you know, maybe a better solution.

And I think that that comes to be honest, from this being a company with Roots in Data and not a company with, that's just sort of like, Hey, let's build an AI playground and see what happens. And, you know, and then this, the, and, and hopefully that's reflective of, of the rest of us, I guess. Um, final thoughts here.

Um, Gina, Jim, uh, what are you looking forward to at, uh, at Click Connect next month? What I like the best when we went last year was, um, hearing the customer stories. So, um, you know, definitely seeing the technology, the new things that Qlik has dreamed up, but like listening to how customers have actually used the products and how they put things together and what they're doing.

So it makes everything real. So this is like one way to say, yeah, people are doing AI for real, and it's working, and here's how they're going about it. I'm looking for more of those stories.

That was good. This will be my first time, and I'm, I, I agree with you Genia, it's the customer stories. And I, I think it's okay when customers say, you know, we really struggled with this and we had a few failures, but with Click's help, we succeeded.

And here's why. When you hear those kinds of stories, because it's not all what unicorns and fairy dust out here, you know, it's hard, there's hard lessons to be learned, and yet there's gonna be failure, but there's also gonna be a lot more success, I think. So I'm really looking forward to hearing, you know, some of those stories as well, that, you know, we, we were able to make it happen.

Yeah. And I, you know, you, you will hear those stories, uh, so hopefully, you know, you, you make it to the sessions where those are told as, as I described, we launched, uh, that click Answers product last year, uh, middle of the year just after Connect. And now we've got customers up and running.

They're, they're actually using it for, uh, material use cases across multiple industries. Um, where, you know, for instance, they're using it to, um, you know, like a, a, a facilities management company where they go in and they, like, they clean a stadium at the end of, uh, an event. Like they have a lot of people in there that are putting together solutions to clean things to put together, and they have to go and look at manuals to do that, or have to have been trained on that.

And now they can use a solution like answers to basically say, Hey, you know what? I'm cleaning this surface. I think it's this solvent and this solvent.

And it gives them like the answer on the fly. And you get these big productivity gains, you get a consistency of, of, of the, you know, service that they're providing. And those are the types of use cases that I hope you guys can hear about, both from our, our generative ai as well as our predictive ai, which has been around a lot longer.

And, and, and we've got, you know, over 2000 customers using that product now today. So yeah, those stories should come out. I, I welcome you to engage with those customers.

'cause they've, they've been some of the pioneers with Qlik in terms of, you know, using AI on our, on our platform. Well, thanks so much Nick and, uh, Gina, Jim, can't wait to see you in person in Orlando. Um, before we go, uh, I wanna give you all a chance to give a little shout out, Nick.

Uh, what are you gonna be presenting at Click Connect? Uh, which, which sessions, uh, do you know yet? Um, I will be meeting with analysts in media for the most part.

That's where, uh, they dedicate my time, but I encourage, uh, the main stage. We're gonna demonstrate a lot of the stuff that's coming out of the, the RD teams that, that I support. Um, and I don't wanna reveal too much about it, but like I said, we've had a heritage with structured a heritage now that we've built with unstructured data.

Of course, we're gonna try and bring them together, uh, and agents are a big story behind that. So, um, we're super excited that, because ultimately that's what our customers have been asking for is, Hey, I love talking to my data. I love talking to my unstructured documents.

Can I do it all in one experience? And so, um, yeah, that, that should be interesting. I, I'm looking forward to the reaction when, uh, we make some of those revelations.

Um, so yeah, that's, uh, that's gonna be the highlight for me is, is, is seeing that come to life on stage. Excellent. Yeah.

And we're gonna be doing some, uh, uh, live coverage. We're gonna be doing some tech field day sessions. We're gonna be involved in things, gonna be recording, you know, videos and reactions and that sort of thing.

Uh, Jim, uh, Gina, uh, is there anything specific that you guys are interested in seeing at, uh, click Connect? I know we just talked about that a little bit, but, but more like, like the event, you know, what's your, what's your thoughts on that? Yeah, I wanna, I, I'm, I like to go to the Community party because that was really fun last year and there were really good people to talk to there.

So that, that's gonna be good. I hear it's gonna be at Animal Kingdom this time. I know.

So probably, I was talking to one of the other delegates earlier on a call. And, um, we are also looking forward to going on walks. We did that last year to see the alligators and stuff, so, yeah.

Oh, alligators. Okay. It's for us.

Cool. Well, and will they be at the bottom of the precipice? Well, if you, if you need any help with the alligators, I hear that, uh, Katie Ledecky is also gonna be there.

I don't know if y'all know her, but she's gonna be speaking, uh, doing a keynote session, and I think she can, uh, as an Olympic medalist to jump over them or beat them up or in some way away from them. Yeah. Uh, subdue them.

Uh, and, and, and, and we're gonna have some other sessions as well, uh, some pretty big keynote sessions, including some customer presentations. So can't wait to see that. Well, we'll see you guys there.

Um, keep an eye on the socials. Uh, keep an eye on, uh, the, uh, tech Field Day site, uh, techron tv, uh, this podcast, and, and we'll have a lot more coverage coming out of Click Connect as well. Uh, thank you for listening to this episode of the Tech Field Day podcast.

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