Good morning. Good afternoon. Good evening. Good. Whatever time it is, wherever you are in this amazing place we call Earth.

I’m Malcolm Hawker. I am the CDO of Profisee and the host of the CDO Matters podcast. Hey. I am thrilled you are here joining us today.

I’m also thrilled to be joined by Gavin Hupp. He’s the VP of technology with United Parks and Resorts.

Gavin?

Hi. Hi. Happy Monday. Happy Monday to you, Malcolm. How are you?

I’m I’m doing well. So as we record this, I think we’re gonna this will be episode seventy six or seventy seven. It will come out probably in the middle of May, but as we record this, it was Easter yesterday. So Gavin and I just spent weekends outside.

Gavin was chasing kids, and I was planting trees, which which which I usually do. But we both live in Florida. We’re only about an hour, hour and a half apart from each other, so that accounts for our bronze tone have having spent the, the weekend outside. But I I’m I’m looking forward to our chat.

We’re gonna we’re gonna talk about the theme park business a little bit, what what Gavin’s doing to to help manage some things related to disruption or potential disruption. We’re gonna talk about running in data and AI function. We’re gonna talk about trends. We’re probably gonna talk a little bit about AI.

We’re gonna talk about a topic near and dear to my heart, which is leadership and mentorship and development.

So with that, let’s let’s dive right into it. So I kind of, gonna maybe spill the beans here a little bit on on our first topic, but I’d I’d love to talk about some of the things that that you may be doing in leading a data function in helping to try to get ahead of or manage a lot of the disruption that we see going on in our world today. There’s a lot of stuff going on. There’s there’s there’s tariffs. There’s changes. There’s demographic changes in the world. There’s all sorts of things going on.

And when you when I think about it from a data perspective, you know, I I’d love to hear, like, so what are you doing? Is this something that you kinda planned for? Is this something you’re reacting to a little bit of both? What’s happening in your world, Gavin?

Yeah. Malcolm, that’s a really good segue and good intro because we do a lot of both, right?

As a theme park business, we have long term strategies, we have multi year plans, putting in a roller coaster or a new animal exhibit is not just an overnight thing. You flip a switch and it goes up. There’s a lot of planning that goes involved, a lot of investment, a lot of execution.

And so we have our long term strategy, long term plans, and obviously you have to take into account changes in the market and you have a reasonable expectation of what’s going to happen in the future.

We know that our guests are always going to want immersive and amazing guest experiences and wonderful animal experiences.

Those things aren’t going to change. And so we continuously, no matter what, always march towards those goals.

But we do know that everything else is likely to change in a moment’s notice. And so, there’s that balancing act of being responsive and reactive to market changes, and having our long term business and technology strategies in place. So you got to kind of dance between those lines, not being overly reactive, but having the right process to surface times where, okay, we need to react quickly, we need to make some fast changes, having the right capabilities in place to take action on those changes, and being able to measure the results.

So balancing that long term strategy, but also we are very metric driven and we have lots of granular data that we look at and we’re we’re trying to fine tune, in a in a in a very fast, timely manner.

Got it. So my my audio is blipping a little bit. Perhaps yours is as well. We will we will forge through on the conversation.

So I heard a lot about balance there. I heard the need for long term planning, and I also heard the need for short term kind of reactiveness.

When it comes to the the the role of of data in all of that, you know, there’s a lot of companies that are struggling to say that, hey, we’re we’re we’re struggling to be more data driven and we’re struggling to kind of weave data into all of both our short term and our long term.

Where do you see you are in in in that journey? And how do you how how do you think you’re doing and how do you think you could be improving?

Yeah.

It’s a journey. I I think no matter who you are, what company you’re with, it’s always a journey. You can always improve, I think, both your long term and your your tactical execution. No one is perfect and no one will ever be perfect. And by the time you’re perfect, something will change where you’ll have to adjust and react and just just evolve.

So I think for us, again, we’re on that journey, but we do have, especially in the last couple years or so, really improved our near time capabilities with near time data, near time analysis, and being able to inform faster decision making.

The data itself, again, if you think about a theme park, we are multiple lines of business inside of one location, right, or one physical location across our multiple parks. We have a very heavy e commerce footprint, where the majority of our tickets and passes are sold online. So, that’s our e commerce, you know, kind of line of business.

We have our admissions business, we have obviously our animal exhibits, we have culinary experiences from family dining to hawking devices in our stadiums, to retail.

And there’s a lot of data that we have available, and there’s a lot of data that we need to surface up.

And I think we’ve done a really good job of surfacing a lot of that data faster and allowing better business decisions to be made earlier in the timeframe. Of course, still always areas, again, with how large we are and also how much of a legacy organization we are. We’ve been around for sixty years, and we’ve got other parts that have sprouted up since then.

So, there’s still a lot to do, but we’ve made a lot of good progress.

Got it. Now, you you talked about the the amount of data. When when I think about the amount of data that could be getting generated in in any like theme park, or you talk about telemetry data, people walking around, Talk about just, like, cameras. I I suspect just from a security perspective, you got a ton of cameras. There’d be data there.

Patterns of people walking the park even. I mean, I’m I’m just kind of thinking about amount of data that you must be managing here, and my my brain is starting to explode a little bit by how much you could be tracking. Am I am I barking up the right tree here? Is it are you is this is this the kind of stuff that you guys are working on in in terms?

Okay. And to the degree that you’re comfortable sharing, I I don’t want you to obviously, there there are trade secrets that are gonna be here, but but and and things that you can’t just publicly disclose. But when I think about the amount of of data just at an individual person, right, not how long are they standing in lines, how much how much stuff are they buying when they go to the park, when are they buying, when are they not buying? I got like, I’m boom.

My my head is kind of, you know, popping a little bit here. Yeah. What can you share?

Welcome to my world. Yeah. So from the very first interaction that a guest has with our web and digital experience, there’s data.

As you’re going through the purchasing funnel, there’s data.

As you enter into our parks, data.

A lot of the interactions that you have within the parks and your devices, you know, it is In order for that experience to happen, there needs to be some exchange of data, right? It’s a transaction that’s occurring.

So there is a big opportunity to collect and store, obviously securely and with privacy in mind, lots and lots of data. One of the things though that I think we do as an organization pretty well is ask the kind of the so what question. And just because we have the ability to capture it and store it, So what? How does this improve either the guest experience? How does this improve ultimately revenue, decrease costs, or increase guest satisfaction?

And so I think as an organization, we do a very good job of of aligning those things back to really the fundamental reasons of why why we’re in business, is to provide amazing experiences, animal conservation, revenue, and and cost for, animal reduction, those types of things. So, yeah, lots of opportunity, but we we always try to siphon siphon this back to the key business decisions that we need to make.

Well, that that hearkens back to a conversation that we had, earlier when we were talking about relationships with folks in the finance side of the house. And when we were talking about ROI and cost justifications, and, you know, you you just basically suggested it there by saying, hey. We’re not just randomly hoarding data. We’re not doing data for data’s sake.

We are doing data for these mission critical pillars. You listed them off, guest experience, animal conservation, other things. So that’s kind of the north stars you’ve got. But when it comes to some of the ROI related and and and evaluating what gets investment and what doesn’t, Describe to me, you know, some some of the things that are working for you or maybe not working for you from the perspective of of getting some of these initiatives funded and the relationship you’ve got with folks in the finance side of the house.

Sure. Yeah. And it’s it’s not just, the relationship with finance. It’s the relationship with the the business owners Yeah.

Who have a goal to accomplish something.

And they need better data, or they need a better visualization, or they need something to help them achieve that goal. And so it’s my job and kind of responsibility to partner up and say, okay, well, what’s preventing you from being successful? Is it data? Is it something else?

And then making sure that we talk about kind of the end to end solution and that is it’s just part of the story. Now, there are sometimes challenges where there are maybe the unsexy things that aren’t easily tied to a business initiative, but this as data and technology practitioners, we know like there’s some technical debt we need to clean up or there’s, you know, some some other nice to haves. That’s where it’s kind of an art and a science of tying those foundational pieces to either a current business initiative or a future business initiative to kind of plant the seeds and say, if this is an, as an example, an enterprise capability that we need and you’re going to want x data set at this level of quality, these are some items that we’re gonna have to work on in in advance of that to make sure we can accomplish the things you want and and need to do.

And so it’s, again, trying to be a good partner with the business teams or the operations teams who have these goals and initiatives.

It’s working very closely with the finance department to make sure that we’ve got the right forecasting and the right cost structure and the right deliveries in mind.

And it’s just kind of being all over to kind of coordinate and get everybody together, make sure that we’re marching towards the same the same goal.

So organizationally, you’ve You know, I know that you’re the VP of technology, but we’ve been talking about data.

So organizationally, you’ve got a bit of a unique title and you’ve got a bit of a unique remit from the some of the systems that that that are under your your under your scope.

Can you share a little bit of insight there and and how all these things fit together?

I I’m kinda going back to some of the customer experience things that we talked about and some of the kind of marketing centric data that we’ve been talking about. And help help me understand how, you know, why you’ve got a bit of a different role and and how your remit is different than the average potentially CDO.

Sure.

Yeah. That’s a that’s a good question.

And and my background really is in entrepreneurship. I started my own e commerce business a long time ago and then that turned into some consulting and then that turned into really kind of a digital marketing perspective.

That at the end of the day, I always have been passionate about making an impact, making change, making things better and moving things forward. Technology was a means to get there. And as I spend more time and get my hands dirty and roll my sleeves up, the more involved and ingrained I got and became more proficient in technology, Similar with data, in that since the beginning, I have a strong background in data analysis and using data to drive business decisions and marketing decisions and helping it inform technology.

And so when you kind of take that background and my entrepreneurial spirit and I get into an organization that really I started at United Parks and Resorts to help improve its, its martech and data capabilities and customer capabilities.

And as I got more involved and ingrained in the organization, we saw that there’s lots of opportunities and I kind of managed to go where I’m needed.

And the most value that I can add to an organization, I try to get in and make my mark and make big improvements and help the organization grow. So, it’s been an interesting journey of how I transitioned and moved throughout the organization.

And, again, thinking about technology and data on the same level, and trying to elevate data to being a first class citizen when we think about our business and technology.

Data should also be at the table. I think organizations as we’re moving forward, not just ours, but what I’m seeing across various industries, data, I don’t even want to say the terms and phrases, but data is very, very important, very critical, and it should be treated as a first class citizen. And I just feel fortunate that, one, I’ve got great teams that help enable the things that we’re trying to do, and we’ve got to have that organizational structure to have some of the technology and some of the data side.

I’m just really intrigued by the combination of MarTech and data.

And if historically, usually, there’s a certain degree of tension between kind of marketing operations or or or marketing broadly around CRM data, but it doesn’t necessarily have to just be CRM. It could be all all marketing technology. There’s there’s generally a lot of tension there because the marketing data comes over the fence, lands in the CDO’s lap, and there’s this, what is this? Right?

Like, you know, salespeople are doing crazy stuff in a CRM, or I can’t make any sense of this. And there’s there’s to me, there’s always been this this tension between, you know, marketing organizations, marketing technology organizations, and CD organizations. And and having those things close together makes a ton of sense to me, particularly if you are rapidly focused on customer, right, and and and and understanding customer. Now, of course, finance may continue to have a a different view of the world, and that’s fine.

They usually do.

But I’m I just find the org alignment very intriguing. You know, you’d mentioned that, you know, you you kind of wanna solve the hard problems and go where you’re needed, but at the same time, I I see an interesting combination there that would help galvanize things really, really nicely just around the idea of customer to customer first.

I I agree a hundred percent. Yeah. The the most important thing in in, I think, any business is, your customers.

Your customers, what they’re doing, what they’re buying, and how you can make their experience better.

And that obviously requires understanding the customer, which means you need to have data, you need to be able to take action on that data.

And it’s not just the customer data. So I forget where I heard this, but something like, you know, you can’t have CRM without MDM in a ways. Right? Because just having that that clean customer data by itself is great, but you need to understand all of the other things that they’re doing. What are they buying? What what are they visiting? What what are what are the other data elements that you need to be able to create great segmentation, great audiences and craft that story?

And so, yeah, I think it makes perfect sense. Maybe I’m biased, but to have your enterprise data and MarTech data close, close together, close together.

Well, I’m an MDM guy. So you you had me you had me at MDM. I and I and I and I I I tend to agree you need you need both because you need to understand the complete reach of all those relationships. Right?

And and if kind of customers then is the is the lead node you know, you talked about it in the in the parks. They’re they’re buying things. They’re walking places. They’re they are going to certain attractions.

They’re not going to certain attractions and on and on. And it’s and it’s it’s it’s the sender is all, of course, the customer.

Let let’s talk about about you and, you know, you’d mentioned you’re not technical. Right? I’m kind of putting two and two together here. You that you’re kind of learning the technology as you go.

What do you what advice would you give to somebody kind of starting in in in our world? Right? Who is maybe, you know, maybe twenties, maybe early thirties, trying to figure out, you know, should should I go back to school to get to get to to learn SQL, or what should I double double down on? I’m excited about the whole data thing. I’m excited about the whole AI thing.

What sort of advice would you have for somebody who who may be just embarking on a career that kind of looks like ours, and bear traps to avoid, paths to maybe take, not take, what would you do?

Yeah. That’s a really good question.

So I I think the the advice that that I would give, and I would give it to anybody at any time, is stay curious.

Stay curious.

Keep learning.

I think it’s very important to understand the foundational elements of whatever it is that you are trying to learn or try to embark on.

So get the fundamentals down, get the basics down. So if you want to get into data, you should learn about the concepts of data governance.

So I think you posted recently about the beloved, the wheel. The wheel. Yes.

Get started there. Learn the basics and go through the greats of the of the data world from the Inmans to even yourself.

So do do research and kind of look to see Somebody behind me?

Sorry. Just need to make sure.

And, and there’s a lot of great content creators and a lot of great experts that are out there, that are on LinkedIn, that are putting out some really good content.

Start figuring that stuff out. I mean, it’s important to differentiate between people that are hyping up technologies or hyping up solutions versus here’s kind of the foundational pieces that you need to understand and that you need to learn.

Then I think depending on your natural desires or natural abilities, if you want to go down the more technical route, that’s great. If you want to become a more business or data driven business person, that’s one one track.

I don’t think AI is going to go away.

I think it’s important to to still understand SQL. I I read something today actually where like, oh, SQL is going away and now you need to understand natural language queries. It’s like, well, oh, okay. But I think it’s really important to still understand how databases talk to each other, what the relationships are. Those are some fundamental things that you should have at least an understanding of. And understanding Python, I think if you’re going to be using Excel, then Python makes a good balance. You want to get into other areas.

Get the foundations down, study the historical stuff, because a lot of the what’s old is new, maybe not exactly, but the elements of what’s old are still popping up and will continue to evolve. But get the foundations down, then figure out which track you want to go on. Make sure you’re listening to good folks in your corner that have a lot of experience, and are hands in. They’re hands on keyboard today. So, yes, I’m not the most technical person in the world, And that’s one of the reasons why I make sure that I’ve got a great team to do that as well. So, I can’t be the best at everything.

Try to surround myself with people that are better than me in all the ways really.

Matthew Piepko (zero twenty seven:forty seven): Oh, I like that.

Surround yourself with people who are better than you. That starts to get into talking about leadership and mentorship, and I do want to go there in a little bit, but just I want to circle back on a couple of things.

The first answer that you gave was be curious. And then the next couple answers you gave basically said be a learner. Be be an insatiable learner.

This is something that actually Satya Nadella, the CEO of of Microsoft is a big believer. And and I read I read his book recently, and he was talking about becoming the learner in chief. This all ties to something kind of, you know, high level known as a growth mindset. But but just keep learning and keep learning and be curious, ask questions.

Don’t think you know it all because you never will know it all. None of us ever will. But as long as you keep learning and stay interested and stay curious, I think that I think that’s I think that’s great advice.

I also think I kinda heard you say I’m paraphrasing you now a little bit, but and this is a this is a pretty maybe a fairly major paraphrase, but maybe don’t be afraid to to walk through open doors. Like, if you’re if you’re cur if you’re curious and something comes up, may may maybe don’t be afraid to see where the path takes you. Because when I look back on on my career, I thought I had it when I was in my twenties, I thought it all figured out. I was gonna do this, and I was gonna do this, and I was gonna do this.

Like, and I had the I had it all nailed. Right? Like, I knew exactly where I was going and and where and none of that came to pass. None of it.

What what what happened was is every now and then, these doors would would kinda swing open a little bit, and every time I went through them, generally, good things happened. Is that how how has that been from your experience? How did you did you have a plan and and did you stick to it or did you just kind of have to evolve and adapt as you went? Yeah.

Oh, that’s that’s a that’s a great question. So, I had a loose plan, I still do.

And that there are some goals that I want, but I’ve got some kind of for the longest time, I’ve had these kind of guiding principles that I try to live my life by.

So it’s things like just show up every day, try to make a big impact, build and cultivate relationships, stay curious, you know, keep learning.

And in doing so, lots of different doors have opened up and, I’ve, you know, taken some leaps of faith. I’ve obviously had my wonderful and amazing wife to to help support in in those endeavors, especially when it involves me moving across country, which I’ve done a few times.

And out of every one of these major experiences, either they’ve been fantastic and amazing and have made great friends, great relationships, and made great progress with some really amazing companies.

And or I’ve had some learning experiences where I said, oh, okay. I’m gonna do something different next time. And each iteration of that, try to learn, try to do something different, try to make make, yeah, more or or better progress to go go in the right direction.

So, yeah, it’s didn’t quite have a plan. And if you asked me when I was in if you asked anybody who knew me in in high school, they definitely would not expect me to be, in this position that that I’m in today. So it’s it’s pretty cool.

Oh, my God. High school? You’re a complete mess.

I mean, I yeah. Me in high school, you would have looked at me in high school and you would have thought, like, no. No.

No. Not not not gonna happen. But you know what? I I I thank you for sharing that because I I really love this idea of here are some of my guiding principles. Right? Here here are here is my blueprint for for being a a a great employee.

Right? And I I I just love that idea because you can kind of align that to the companies that you go talk to. And how how closely does this align to to companies that you wanna work for or or not align? And the minute you start to get out of alignment is the minute you could say to yourself, well, hey.

You know what? This isn’t working for me anymore. So I I I I I love that. Instead of just saying, hey.

You know, here’s my progression. I’m gonna be a manager, a senior manager, director, senior director, and I needed to keep doing all these things.

If you follow what you just recommended, it’s the right way to say this. It’s it’s it’s more maybe more framework driven? I don’t I don’t know what I’m saying, but I do know. My my my dad has told me forever and ever and ever, the cream will rise to the top.

The cream will rise to the top. And that may be true, but what I’ve learned is that it’s not The cream is the output. How you work, how you create the cream. Right?

How you work, how showing up every day. Like what you just said, as hard as you may be or as deflated as you may be, showing up every day. Man, I love that. I love that.

Thanks for sharing.

Matthew Piepko (3two forty seven): Of course.

Yeah.

Matthew Piepko (3two forty seven): So let’s get back to leadership and kind of mentorship and building a team.

When building a team, when hiring people, what are some of the things that you look for?

You mentioned building this team of people that are smarter than you and can do the job better than you.

That’s obviously one of the the things that you use to evaluate, but, you know, what are some of the big things that you think about when when building out the team and developing your team?

Yeah, for sure. So I learned this from from my, boss actually when I was at PetSmart and and through my own experiences and and through reading, but before you try to assemble your team, it’s important to understand and define out the architecture first.

Because we think about Conway’s Law and you think about, you know, how or teams are organized, your architecture will likely mimic or mirror how the team is set up and so that maybe isn’t the best way. So we start with what is the work? Obviously, what are the business outcomes? What are we trying to solve?

What’s the work? How do we organize the systems and data in the right way to deliver the end result in the short term and also in the long term and what needs to shift. Then it’s looking at, okay, what are the resources that are needed to achieve and to accomplish the goal from end to end, not just the functional requirements, but the non functional requirements. How does the system need to behave?

So think about all of those things and make sure that we’ve got kind of the roles lined out. So, okay, we need shifted towards a data product oriented organization. So that’s one area that, you want to talk about what we can. But, so kind of think about data as a product because, you know, it really is in my mind.

There’s platforms and products and this is a thing that has a, it’s an asset, it’s tangible benefit, it can be defined.

So, anyway, so identify what is it you’re doing, the right roles, the right skills that are needed for the role, and those are kind of like the the the the basics of of of the job.

Then on the other side of it, there’s things like drive and determination, grit.

Their, you know, the the teammates ability to bring fresh perspective to the organization. I want to bring people on that want to be empowered and want to go on this mission and journey with us and grab it by by the horns and and kind of run with it. And I I make sure that I provide the the context and the communication, the guardrails in in areas where it makes sense.

But I want them to to to to run with it and make it their own, kind of add their their their flare to it.

So it’s kind of, one, making sure I’ve got the right job line description lined up, two, that I’ve got the right right skills, and then three, that we’ve got the right the right kind of soft skills and things that are maybe harder to measure, but making sure that we also include diversity and thought and diversity of experience because I think there’s such wonderful things that that happen there.

And, you know, it’s one, making sure that two, that I’m completely transparent with the folks that we’re bringing on board and say, okay, here’s our mission, our vision, here are all the things we’re working on, here are the challenging parts and this is, you know, while it’s, I like to have fun, and we like to have a good time, and we all work well, there’s some challenges. It’s not always easy.

Of course, we can just go from the office and go pet a dolphin, or look at a baby dolphin, or go on a roller coaster and come back, but it’s not always it’s not always perfect and great. So making sure that I’m giving a clear transparent look at, at what the work is.

And we also leverage a number of organizations and partners to help us with staffing, to help us find just amazing, amazing folks to to bring onto the team. And so, you know, those those those couple of things we make sure are are lined up.

Yeah. I mean, a lot of people are out there going to ax throwing, but you get to go pet baby dolphins. So I like that. The offsites, the team offsites, I don’t even know why you would ever go off-site. You just stay on-site when you’re literally in a theme park. It’s amazing.

I loved the first couple of answers you gave from the perspective of behavioral traits that you’re looking for. You used the words grit and resilience.

I love it. I think those are important traits for a lot of different reasons.

I’m not going to say that you get beaten down in the data role because I don’t necessarily think you do, but I love working with people who are just insatiable, crazy problem solvers.

And if the if, you know, the front door is locked or bolted shut, we’ll try to go around to the back door. And then if that’s shut, try to go around to the side door. That’s that’s the kind of person that I love working with. And that seems to be suggestive of of if you feel the same way.

Alright. You mentioned data products.

Gotta go there.

Well, I was actually I wanted to just on that front, so, around, you know, always finding a way. Yeah. And I I agree.

There’s but there’s always a way to do it, effectively and, you know, safely and not as haphazard. Right? So we we have a a process, we kinda call it skateboards, where if there is a a a really hard problem to solve or we’re just not quite sure about it, you might others might refer to it as a spike or or or MVP. I don’t really like the term MVP, especially when it comes to data. This kind of goes to the data products conversations too. But we try within a two week sprint to deliver some working, some tangible data product or data delivery solution to get in the hands of end users so they can touch it, feel it, give feedback, and then we can iterate and and and move forward.

But we also make sure that we have our data architecture team and folks involved in that from the get go. So we’re not building some one off throwaway thing that is, you know, complete waste of time, but we try to do it in a way that we’re learning, we’re experimenting, and we’re also keeping up with our enterprise data standards and using enterprise data model.

So that way we don’t get this kind of Frankenstein approach or sprawl, especially with with data, it seems pretty possible and and evident that the data sprawl is is certainly a thing, especially with so many new tools that are available out there. So we try to ring that in, definitely problem solved, but we try to do it in a way where we’re following our enterprise standards and deliver value quickly.

So there are guardrails. It’s not like, by any means necessary, go solve the problem. But you were saying, Hey, there are guardrails that would make sure that we’re all adhering to specific governance policies, adhering to specific architecture, adhering even to the the kind of the core corporate pillars that you mentioned earlier on at our discussion.

I I I love it. Right?

And that’s and, honestly, I think that’s all that every employee ever really needs, right, is to understand what are the limitations, what are the guardrails, what can and I can’t do, and then freedom within those guardrails is what I’m hearing you say.

Okay.

Before we get into data products, what’s the problem with MVP? What what don’t you what don’t you like about MVP? Too limiting?

Yeah. It’s it’s too I I think it’s too limiting.

In some areas, I think it’s overused.

I I I would rather, like, a rapid prototype.

Okay. Versus versus that because it the the other thing too is if you’re delivering a a a product out into to the world with with customers customer facing stuff, it needs to be more thoroughly, in my mind, in my perspective, more thoroughly baked, than a minimal viable product. I want something that people love.

So, or do a prototype and do something to get rapid feedback even even faster.

It’s just a nomenclature thing. I would pet peeve.

No. No. I I I get it. I I get it. So you’re suggesting in in your experience that if you wanna wow customers, if you wanna deliver that you use the words tangible benefit when you were describing products. You said tangible benefit. If you want to drive meaningful, tangible benefit, in your experience, what you’re saying is you may need to go a little bit beyond just something that works and get into something that has been maybe gone through some sort of user centric design process or maybe gone through some testing or gone through some of the iterative stuff that you’re talking about.

Maybe go a little bit beyond what most people would say as an MVP. I get it. I totally I get it.

Okay. Okay. Good. Thanks.

But that does align. That’s interesting, though. So that does you’re what you’re describing sounds to me as a product person. Okay? So I I I spent my first fifteen years on a product track. I was a chief product officer once. What you just described sounds very much to me like a finished product, like something you could put a price tag on and put on a shelf.

Agreed? Yeah.

Mhmm.

Yep. Okay. Alright. Well then okay. Well, this is a short conversation.

There there there is this little bit of a, a kind of a a schism, as it were, in the world of data products between people who see them being these very small, lowest levels of kind of atomicity as close to source as possible, you know, actually don’t get end user specific requirements because that will, you know, impact your ability to to scale. There’s there’s one school of thought. It’s a valid school of thought to a certain degree, especially when you’re talking about AI as the as the consumer. The other end of the spectrum is where I fall and scenes where you fall, which is more of a product management approach, I would argue, than just building something you’re calling a product is slapping a product label on it.

So, okay. So how long have you been at that and how’s it going? The data product journey? Yeah.

A little over a year.

Okay.

And it’s it’s going well. So we we’ve got we’ve kind of organized it into a couple of different data product areas and therefore data product owners.

And, you know, we’ve we’ve, I guess, shipped isn’t I don’t like to use the word shipped, but we we’ve made available, certain data products that have been in in use and are adding business value and we’re, you know, because it’s a product and not a project, it is ongoing, it has a life, and it continues to either evolve or we add new new products to it to make it better and and more more applicable and add more insights and adding more domains and, and so on and so forth. So it’s a journey too. It’s, you know, a big shift in in in mindset in a lot of areas, but creating kind of an open and collaborative product management mindset and taking the things from, I guess, Agile methodology. It’s really, you know, I like the I like the principles of Agile.

Delivering iteratively, you know, face to face feedback, working software is the thing that we we value the most. Other things are important, but these are things that we work on.

I think it’s added a really beneficial approach and layer into what we’re doing on on the data side.

Joshua Neiman (zero fifty three:forty nine): So you’d mentioned the necessity for different mindset, and and you followed that up by saying more of a product management mindset.

Is that the big mindset shift you’re talking about, is being more focused on product management and being focused on customers and, kind of, value add from a product perspective? Is that the mindset shift that you’re talking about?

Collum (zero:forty seven:forty seven): Data. So the data product management, managing our data initiatives as a product and- Okay.

Collum (zero:forty seven:forty seven): Working on the agile operating model within the data teams to make sure that we’re working with the right stakeholders, the right end users.

We’re we’re building usable and and value added products that, you know, if we’ve got one area so, again, we’ve got multiple theme parks, multiple lines of business, and if one group say, hey, this is some data that we need to make x y and z better or this type of decision, we take a look at it and kind of look at across the enterprise and do our, you know, data bus matrix and all the all the data modeling things. But then we say, okay, who else needs access? Where else would this be beneficial?

How do we then kind of deliver or create a roadmap and deliverables iteratively that we can say, okay, we can satisfy this area while also thinking about the other areas that are going to need this. How do we then incorporate this, make it part of our consumption and visualizations? And how do we manage that end to end life cycle of the initial data idea or the data request and turn it into this ongoing value adding solution?

Greg Foss (zero twenty four:forty four): So it’s interesting.

What you just described apart, not all, of course, but a part of what you just described was reuse.

Part of what you just described was scalability and reuse and multiple consumers for one product. And it’s interesting because the folks that are kind of on this, what I would say, the shift left side of the world also talk a lot about scalability, but they talk about scalability through a different lens, through through more of, you know, scalability from an architecture perspective versus scalability from a consumer perspective. So I I find that interesting.

Alright. In our last few minutes, let’s let’s let’s talk about, you know, the the the shiny thing in in in AI. You’ve mentioned a few times now in our conversations, you know, your fondness for agility, your fondness for kind of prototyping and being iterative, is that how you’re approaching AI as well?

It is. Yeah.

So, you know, obviously, everyone’s talking about it and everyone has different they’ve progressed in different ways on their AI journey.

For us, it’s, you know, we’re looking at the key use cases and areas that we can add more business value or customer value to our organization.

And it’s, you know, kind of working through to say, what can we test quickly and safely and securely?

Is AI even the right solve for that particular problem?

And sometimes AI, at least in the areas that we’ve looked at, is not quite there just yet. But when we’ve got business users and folks excited about data and excited about technology and really they’re excited about innovation.

I think that’s really what it boils down to that they’re excited that there’s a new capability that’s being talked about that can allow people to think about the problem, think about the solution entirely from a different lens, from a different perspective, and get excited about it and really try to identify bottlenecks in their business process or identify like, gosh, I wish this was easier or I wish, I wish we could do this.

Those types of things are really great to kind of surface and cultivate demand for data and technology.

And so, you know, you you you organize those problem sets and you come up with a solution and maybe there’s an AI component that can add value or other times it’s some good old fashioned business intelligence work or, there’s an ML model that would solve it just fine or maybe it’s a software change or maybe it’s an integration.

And so I think AI is a really, especially now, great conversation starter, great thing for collaboration in many areas.

It’s great for improving efficiencies, making lives easier.

Also kind of is a mirror and kind of or or a light and surfaces areas that, you know, maybe AI is the right solution. We’ve got some some groundwork or some other things that we can work on.

I I love it. You and I were both at, the Gartner Data Analytics Summit, I don’t know, a month ago, two months ago. All the all the all the days are melting together. But I saw a couple of different presentations there when talking about AI, kind of this progression of AI, where a lot of the focus today is around individual productivity, then group productivity. And what you touched on is the kind of the next level, which is process optimization.

Because you had mentioned that as well. And I think I think there’s a lot of very interesting things we could be doing, even just internally focused, where, you know, a lot of the risk of hallucination, a lot of the risk perceived risks with with AI, we can mitigate a lot of that.

And and by being iterative and being collaborative and focusing on some things that are a good fit Mhmm.

And for others saying, hey. They’re not a good fit. But I love the idea that that you said conversation starter.

Right? And even if AI is is helping open additional doors just to have those conversations about how we as data people can be providing value. Man, that sounds awesome to me.

Yeah. And and and too, like, if you think about the the full data supply chain or or just an enterprise capability map, right? So you’ve got your your your meaningful outcomes that you’re trying to to derive.

Those are accomplished by business process. Business process is enabled by a business application.

Business applications are fueled by data and technology. And then you got your infrastructure, whether it’s, you know, your own or hypervisor, whatever. And in each one of those areas, I think every single vendor and every single provider is trying to incorporate AI into their solution.

And so, you know, I think there’s a lot of great things that, you know, you lean on your SaaS providers, lean on your technology providers, to incorporate AI in those areas to see process improvements.

And those are kind of incremental enhancements.

And then I think the other part of it is kind of taking a step back and just reimagining the entire organization, the entire experience overall and say, how can we make all of this better?

So that’s, I think, a really exciting area to think about too.

I’m with you.

There is a lot of exciting times ahead as disruptive as these days may be or feel.

Gavin, thank you so much for sharing your wisdom, sharing your insights, the the things you shared about, you know, you know, what what makes you tick and you and your kind of your guidelines for your career. I I absolutely love that. I need to be thinking a little bit more about that sometimes. I I think way too much about kind of the next thing without thinking about kind of what are my four kind of operating principles.

That’s that’s something I’m gonna be chewing on tonight. Thanks for our conversation. So thank you. Really appreciate it.

My pleasure, Malcolm. Thank you so much.

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Bye for now.