Season 6 - Episode 6 - Moneyball for Golf: How a lean team can punch above it's weight!

Speaker A: So we had the raw data sets were unbelievable. Like the capability to capture data was very high. So we had 25 years of historical data which was sitting in this big legacy stack and that's feeding handicap calculations. I think AI is just a, it's a force multiplier. I think if you, for people that uh, already have a background in data analytics and in some cases like software engineering development, it just allows them to do more with less time.

Speaker B: Welcome back to the Lead With Data podcast. I'm your host, Reena Gammi. I'm co founder of Connexus, who specializes in data analytics, recruitment, working with technology and data leaders to identify the right talent for your organization. This podcast is about the real work of leading with data. Not just the technology, but the judgment calls, trade offs and experiences that sit behind the scenes. Each episode features thoughtful conversations with leaders across data technology and the business, exploring what they've learned, what's been challenging and what's truly made a difference. I would also like to say a sincere thank you to our sponsor, uh, Experian Australia. Through solutions like Aperture Data Studio, Experian helps organizations govern, improve and trust their data so it can drive real business impact. To learn more, visit experian.com au or feel free to get in touch with me and I'll happily connect you with one of the fabulous members of the Experian team. And to all our listeners, thank you for tuning in. I really appreciate your continued support. Please keep sharing the topics you'd like me to bring to the show. Welcome back to the Lead with Data podcast. On the show today we're going to talk about a really interesting topic in relation to where data and the sporting world come together. Most data, uh, leaders I talk to tend to come from an enterprise level environment. They have a mandate from a board, they have teams that they can actually manage, control and influence. From my understanding, my guest on the show doesn't really have that. They run the data function for a national sporting body, which means the data that they get is dependent on what's created by clubs everywhere. He doesn't necessarily have an opportunity to influence what data is coming through the stakeholders and everything that they're doing is based on what is made available to them. But he's also sitting on one of the few data sets in the country that has, I think almost half a million data points where Australians interact with that personally and have very strong opinions about. So if you've ever argued about a handicap thing, this guest's data. So whether you're here because you're leading a data function and just want to see what's possible when you don't have big budgets or whether you're just a sports fan curious about how data and AI are actually changing the game. I think this one's for you. So welcome on the show. Jared Jarrod's the head of data and digital at Gulf Australia.

Speaker A: Thanks Reena. Great to be here.

Speaker B: So, Jarrod, I know it's taken us a while to get you on here because you are obviously a very busy person like most of the leaders I talk to. So I do appreciate you taking the time to jump on here. I might kick kick off by first asking you, how did you get into data analytics? Was there like a moment that you thought, this is what I want to do? Because my experience with dealing with people who work in sports is there's usually a bit of a story into how you landed into data and then also why you landed into sports.

Speaker A: Yeah, I've got a bit of an unusual entry into the space. I actually started out in aviation. I was studying aeronautics and pretty quickly during that journey worked out that what I love most about aviation wasn't the flying, although that was pretty cool component of it, but it was all the systems and the data sitting behind it. So then the question for me was, well, what systems and do I actually want to spend my career working on? And for me the answer was people and systems related to people. And I'd always had a real love for sport growing up. So for me, sport science seemed like a natural next step for my career. So which for those that don't know sports science is essentially the study of human systems and all of the data that runs through it. So it's a combination of that systems and data, which, you know, which really drew me in. So I worked in sport for a little while following that, so training athletes and working in some different high performance programs, which was really interesting. And then I got an opportunity to move more into research, which is something I hadn't done before. So I pursued an honours degree and then a PhD with Golf Australia. And the way postgrad research works, it's honestly, it's a little bit like dating. So you go and you meet different project leads, you have a chat and you just see if there's a good fit. And I talk to different people about lots of different projects and some of them are really cool. But the one that really grabbed me was golf. So there was a golf project and I played a lot of golf growing up. So for me that was really interesting. And the project sat, ah, sort of the intersection of human performance, sports psychology and data science. So there were the three big things that, that really interested me at the time, but still now. And that project was where it all came together for me. So the deeper I went into it, the more I realized that the data side was where the real leverage was and a lot of the value. So then I went away and built the technical skills, analytics, engineering through postgrad and always with that human performance lens underneath though, and that's something I'm really passionate about. So the PhD ended up being my way into Golf Australia and data analytics. And then I started working in the high performance team. So I guess very long way of saying I never really chose data analytics specifically, but for me I was just chasing questions like answers to questions and the best way to address them. And the questions were how do we make the best possible decisions? And often that's using data in different ways and then how do we support people in getting better or getting the best out of their performance? And data and analytics turned out to be the answer to both. And then somewhere along the way there I worked in various roles. I found a love for building products and systems that people genuinely enjoy using and that support those goals as well. So yeah, it's a bit of a roundabout way, but a different entry than most people into the industry.

Speaker B: Love that. And I think it's such an exciting space to be in, like quite a few other industries where there's such a huge opportunity with everything that we've now got available. So I think it's, it's a great place to work. So, uh, thank you for that. And for the listeners who don't know the golf world, what does Golf Australia actually do? I mean, I suspect it's a bit more than people assuming. I don't think I really understood it before I met you.

Speaker A: Yeah, I mean, to be completely honest, as someone who grew up playing golf, I still at the time didn't really have a good understanding of what Golf Australia does. And I think since then we've done a much better job of telling our story. Golf Australia is the, is the governing body for the sport in Australia. So what that means is that our mission is to grow the game in all of its forms and somewhat uniquely our product is golf. And the core asset, I'll often say, is the people playing it. And it's quite funny, uh, if I often go to industry events and with other heads of data and technology and the conversation often moves towards commercial growth, profit margins, and we do have those conversations as well. But we're being a not for profit. We still do look to generate revenue to reinvest all of it back into golf. But the difference is what we're actually growing as a product. So for us, our product or what m we're trying to grow is more Australians playing, playing more golf. So that's really our what exists to support and we do that in a lot of different ways. So we run participation programs. Our participation program for juniors is called My Golf. So similar. A lot of your listeners and maybe you too have heard of Oz Kick, which is sort of the AFL Junior grassroots participation program. My Golf is very similar to that. So it's quite a big program now and there's a lot of integration into schools as well. And the idea is just introducing golf and making it accessible to people at a young age. Traditionally golf had a bit of a barrier to entry and we try to, we try to smooth and create better entry points into the sport. We also do a lot of work with clubs and facilities. So we support clubs and facilities, advocacy, work with government. Obviously my background or entry into golf was through our national High Performance Program, which is a development and supporting program. Australia's most promising golf athletes at various levels all the way from first entry into the amateur, amateur levels of the sport up to the highest professional tours globally. And then we also run events as well. So we run amateur events, professional events, including the Australian Open, which is one of Australia's major golfing events. But I think most people probably know us best for the handicap system as you, I know you mentioned in your introduction. So if you're one of the roughly half a million golf club members who have a. An uh, official Golf Australia handicap, you're at some level in our database and you probably a typical experience for you would be putting your scores in through or submitting scores during competition rounds at your club or through like a third party app. And those scores make it to us in some form and then we apply all these different calculations and logic to return a handicap which for those who don't know, handicap is essentially a uh, like a leveling mechanism. So it's for players of. The aim is to provide a way for players of different ability levels to compete together fairly on the, on the level playing field. So in Australia is interesting in golf in that uh, like golf is now one of the biggest sports in Australia, but it's different in different parts of the world. In Australia we have a real competition culture. There's more rounds of competition golf played in Australia on a weekly basis than most other parts of the world. A lot of other parts of the world. It's a much more. So it's social like it's social here as well. We do have that component, but it's very competition focused, which maybe that's part of the values of the, of Australians, but it's very competition focused at a club level. So I guess, yeah, we're covering the full spectrum. So it's really the full game from a seven year old picking up a club for the first time right through those professional top level tours.

Speaker B: Thank you. And thanks so much for uh, explaining that. It's really interesting because I always, I think growing up thought it was a bit like horse riding. I thought posh people played golf. Like I think that was always my view on golf. But to hear it and then I think when we moved to Australia I actually realized there's a lot of people uk, it's so cold and miserable that I didn't see it as much or the people that I was around and the opportunities. But yeah, no, uh, it's definitely become a bit more mainstream and people talk about it more and I see my son's friends as well, they talk about golf and they're like 14, 15. So it's definitely become something that people are more interested in.

Speaker A: It's really interesting. And you see other, other parts of the world. Obviously the golf is everywhere globally. But talking about the UK part of it is a seasonal component like it is if you grew up in North America. In the northern parts of North America, there's parts of the year where you just can't play on a course. So there's snow and ice on the course, so it's really hard to actually go out and play. And the courses are closed for big segments of the year. So golf is still played. And a lot of golf is played in Canada for example, but in different forms. So during summer there'll be lots of on course golf, but uh, you'll see people move to off course and simulated golf. And that's with the rise of off course golf as more of a accessible product and in many cases an entertainment product for people. That's a really, it's very different the culture around golf in different parts of the world. But I think golf comparing to North America and Australia and even the uk, Australian golf is much more accessible. Like I've got a lot of courses within 10km of my house where I can go play. And it's not necessarily cheap, but it's not expensive either. So you can have a day out with your family or your friends and it's quite accessible and it's not as gated as it is in some other parts of the world, which is I think is really good.

Speaker B: And we uh, definitely, I mean I think my only experience of golf was driving range in the uk. Like just standing on top and just hitting. There's no skill. I know people listening will be like, you do still need skill. You don't really. You just kind of hitting the ball. Yeah, no, it's so true. I mean everywhere you go here, every suburb has got golf clubs and things like that. So yeah, you're right. It's definitely more accessible. I might get you to kind of think back to when you joined Golf Australia and I know uh, you've had a couple of stints in and out, but maybe more recently, what was the data landscape like? Paint me a picture of what you inherited.

Speaker A: Yeah, it's an interesting question. We, for me coming from, I came from a research and I worked a little bit in sport technology before going into golf. So you know, I was used to more mature data ecosystems or with processes set up and governance and golf is a little bit different. I think a lot of sport is like that in Australia as well, or at least it's becoming different now. But at the time, let's say you know, 10, 15 years ago, it was a lot more. There were co. There's coaches and there's people who had really passionate understand their areas really well at an intuitive level. And I think you'd see this across lots of different sports from an operational and admin standpoint. And they're generally great at what they do. But the overall maturity, notice this at golf was how data is captured, stored, used was pretty low. So I first came into the high performance team, which is where I started with golf. And as I said, some of the coaches and the high performance managers, uh, knew the player stats better than I ever will, even as a performance analyst myself when I started. So the gap wasn't really like that individual knowledge, it's more like as a collective. There wasn't really a capability to grow that knowledge over time. And we were attracting athletes through the entire pipeline. So when someone first enters, I'll just use the high performance example, but when someone first enters into a program where we start supporting athletes who are uh, really good golfers all the way to them transitioning to their first professional tour. So turning professional, becoming a committed player who wants to play on like global tours around the world and then from there like There's a certain percentage that, that actually make breakthrough and then they end up playing on global professional tours like the PGA Tour, ah, or the DP World Tour. So we capture a lot of data in that full spectrum and I think that data that you build up can generally change how you structure and how you run your programs. So we had the raw data sets were unbelievable. Like the capability to capture data was very high, but the system to actually bring it to life and harness it wasn't. So that wasn't quite in place. So for me coming in, the early priority was more about building that foundational level. And we worked with different partners like we worked with, we set up a partnership with Amazon Web Services quite early on when I joined golf. They've done some really good work with swimming. So you know, we set up a similar partnership but we leveraged a lot of their technical knowledge and support and then built out those really strong foundations for golf. And I think, I mean just in the high performance team as a starting point like we, we landed in a really good place. I think coming in it was quite, really good people doing good things but low data maturity. And now where we are with if we're not global leaders, I think we're national leaders in terms of the way we strategically use data as an asset. I think it's funny like when I talk to coaches or people in the high performance team now, like they're almost talking, the way that they speak about data is more of a um, it's a strategic asset and data is dirty or this data is clean. Like it's. The lingo is just changed so much over the last five years which has been really cool to see. And I think it was almost similar with the rest of the organization. So I started in high performance and I moved across into the digital team and started looking after all of golf and the different departments from a data standpoint. And it was the same thing on just a bigger surface. So I think from an operational standpoint everything worked really well. People knew the systems and they knew how to, they knew how to do their job to a high level. But there was, it was that classic picture of data and teams operating in silo and no single owner of data as an asset, which I think has changed a lot in the last 10 years. Like we had a database for a website, the handicap system, four or five other systems and lots of duplicate data and records between them and they didn't talk to each other at all. It all worked fine, but it wasn't, we couldn't Scale to use that data strategically. And I personally like, I saw it as a lot of it was a big upside. So I was pretty excited about uh, the opportunity. And I think we've spent years solving those problems and I knew that the playbook worked in high performance and we've applied the same learnings to the rest of the business. And we're in a, we're in a pretty good spot now, which I think has set us up for growth. So we're at the point now we can actually start to really grow and use our data strategically.

Speaker B: And it's really interesting because when you think of sporting in general or fitness, people who uh, are on their fitness journey, everything's about metrics and progress and milestones. And so you'd think that those kind of data points would just come naturally. So to hear that the literacy around data was quite low when you joined, even though people knew and they knew their jobs well and knew what to look for, just the understanding of it. And I think even when you talk about silos in an industry like yours and you really know their own space, but once you can bring it all together and they can see the bigger picture and the impact of what they're doing on this and what that person's doing on this, that can be huge. So it's really exciting to see that because I think particularly if you are into golf or sports or anything like you focused and motivated by doing better and this just allows you to do it with the blink of an eye. Now being able to see that.

Speaker A: Yeah, a lot of it's interesting in sport that uh, particularly professional sports and individual sports, that because there's so many inputs into a high performance program, it's like if you, I worked a little bit in AFL and if you compare like a team sport like AFL and the way that data is managed, even like years ago, it's a lot easier to control like what's being captured, the compliance in terms of athletes entering data, uh, because there's everyone's in the same team, everyone's contracted, written into their contracts, often will be there's mandates around. We need you to enter this level of data. Uh, the purpose is because we need to track your performance and we're monitoring how interventions are working over time, things like that. Whereas in individual sports it's a little bit different. Like we still have athlete development programs at scale, athletes still train together, same with tennis. But it's a little bit different because the athletes don't need to, they don't need to be supported by A governing body, like it helps them. We think it's, it's a really good system and it offers a lot of value to up and coming athletes. But they can just go off on their own if they want to. Like they could just work with their own coach and they can turn professional. They don't need to be embedded in a program. Whereas in professional sports that are team based, like soccer or AFL or many others, they have to be in the team. So they have to follow the uh, they have to get with the program, so to speak. Whereas coming into high performance golf, it's very different in that uh, a lot of athletes have their own coaches external to the program. So and then there's also lots of other specialized service providers that work with the athletes on a day to day and work with the different programs. And there's different programs nationally as well. So there's state programs, there's regional programs. So the data kind of gets lost a little bit. Like everyone has like I think to your point before, people are capturing really good data, but it's all over the place. So we had to get a little bit creative around how do we, like you really have to sell the value. We had to do a lot of advocacy around, oh, why do we even want to centralize all our data? Like what's the point in doing this? So I think once we got that in place, then everyone understood, oh, like that, and that's where the systems start to come to support that. But it was interesting challenge to solve because it's like, well, we already have the data we need for our athletes. But the thinking of, but if we manage to capture that and build up a stockpile of data over five to 10 years, we can, we can really build a blueprint for athlete development.

Speaker B: Definitely. So fascinating. And I know over the last couple of years you've done some big transformational work. You've obviously spent a lot of time building the capability and the technology architecture and getting all of that stuff done. But you've done some really interesting transformation work since then. So maybe talk us through that. What's changed? What's the impact been across the business, the clubs and even to the golfers themselves with some of the stuff you're doing.

Speaker A: Jared, we finished up late last year. I mean, I guess you could call it a whole of organization, but really more of a whole of industry transformation project that last few years. So we, our team was working on that almost in isolation of other things. So there was a lot of big pieces of work undertaken simultaneously. So in terms of like the change management program, it was a lot of which is really heavily used for uh, handicap monitoring. So those half a million golf club members, if you're wanting to, if you submit scores on the weekend and you want to see how your handicaps change, usually you would download and log into our app and you can see your handicap change over time as well as your friends do the same thing on our website. But a lot of people using our app as well. We also built a new website so golf.comd which is a, uh, for the first time we have a joint website with Golf Australia and the PGA of Australia, which is the other major uh, golf organization in Australia. So we're on one single website now. So it's one single shop front for Australian golf, which is great. And we also onboarded a new technology partner as well as lots of other things. So new software, the handicap system had a bit of an uplift. New software for managing the game and it was provided to all clubs and facilities. So a lot happened all, all at once. And also part of what my team led was the. One of the work stream was the data migration. So we had 25 years of historical data which was sitting in this big legacy stack and that's feeding handicap calculations. And with that with very little documentation, as you can imagine with a lot of these legacy systems, they're almost run, they run their own pattern over time. So the way that it operates, you often need to reverse engineer them to be able to replicate them as you transition over. And that was one of the probably the most challenging projects I've worked on. And we stood up a team and we brought in external support. It was a lot of hours and learnings and. But yeah, so I think with things like that, with some of those big, particularly in sport, like those big transformational projects beyond the new website, which is like the shiny front end and the app, it looks like not much has changed. A lot of it happened under the hood. So I think the point was really more about getting again its foundation. So it's. To a lot of people it's like the boring part, but really like the point of the project other than those other major digital touch points and the uplift relating to those was setting Australian golf up for the future. So we know the future or the face of golf and we just talked a little bit about it, but the face of golf is changing. So we're seeing younger players, a huge percentage of new members and people into the game are actually much younger than what we've seen in the Past like we still have that core membership base as younger players, more women, more families getting involved, more non traditional forms of golf. So I think you mentioned at the start as well, more like traditional golf is thought of as someone goes out, joins an exclusive golf club and then plays on the course on the weekend. But it's still, that's changing. There's a lot more off course opportunities. Like we talked about driving ranges before as one of them, simulated golfs as well. So you can go to a simulated facility and just play golf virtually with your mates on the weekend. And a lot of people are doing that who don't really haven't. They're not traditional people who play golf, so they've never been a member of a golf club. A lot of them do both. But some people just use golf as an entertainment product. So rather than going bowling, they go play golf. So the traditional game's still at the heart of it, but the game's changing so our systems need to change with it. Uh, so I think because we've done a lot of that foundational work, maybe it didn't look that like that much change, but because we own our data end to end now we're actually positioned ourselves for growth. So I think about it like that launch wasn't necessarily the end of the, it's sort of the start. Like now that we've got, we've moved over, uh, we've onboarded a new partner and now we can start building from here.

Speaker B: Yeah, yeah. And look, Simon, I know we talked about this before we jumped on the podcast. A lot of the data that you generate is generated by the clubs and state bodies, right? So you don't necessarily have control over what you're given or you might to some degree be able to request certain types of data. But how do you, firstly, how do you overcome that challenge? Because I'm sure as a data professional you're going, I wish we had access to this because I think we could build this. And are you doing that? And do you get, have you had success with that? But then secondly, really, how do you get quality and consistency? Because there's no kind of rules or regulations, uh, like I suppose the banks have or other companies that have standard processes have. So how do you mandate or how do you manage the quality and consistency when you can't mandate that?

Speaker A: Sort of an ongoing challenge for us in terms of the way that we receive data. So we have a lot of third party providers that work with clubs and they integrate through our ah, systems, our API layer and use an example where a club may use a third party scoring app, like a digital scoring app, so the members of their golf club can enter scores and that will get. There's. They're working through a third party provider that isn't us. You would enter scores, they would get synced to the club's management software that's ran through that third party. And then those clubs, the scores from that competition will eventually get sent up to our central database and that's where the handicapping occurs and that's the data that we can access. But one of the challenges, as you mentioned, is that we don't necessarily have tight controls in place for the way that data is entered. So often there, there's often issues with the data or coming in and it might need to be changed later. Uh, I wouldn't say all the time, but sometimes the. Because we're receiving data from that third party, but there's another third party and there's multiples of those differences in the quality of the data. So in terms of the way we bring all that together, one thing that's been challenging as we now own our data end to end and we have this full ecosystem, is trying to get our data quality rules in place that we can apply at an enterprise level, but then also building out our semantic model as well. So how do we talk about the various data sources internally and to make sure we're all having the same conversation? Because there's, there's, there's data coming from clubs. There's also data. I mean if you, we talked about the example before of Ozkic and my golf, which is uh, our junior participation programs, we run other programs as well. So there's obviously direct relationships with Golf Australia where you go and participate in one of our participation programs and you just want to learn more about golf and get, and you might do that at a driving range, for example. And there'll be like a, there'll be a deliverer who's representing Golf Australia and some of that data is just simply you've registered for a program and how often are you registering and we can access that so we can sort of understand who our participant base is. But then there's also the transactional data, then there's all the data from High Performance, which is more on athletes. So for us we are doing a lot of work at the moment around governance and like putting some, some best practices in place for how we structure all the data. And I don't think there's necessarily a right answer for this, but it's more Just something we're working on day to day because it's, it is a little bit more challenging. We don't have, like you said, we're not a financial institution where there's transactional data coming in really clean from credit card providers for example. We just have to do a lot of work with modeling and bringing all the data into one, one single schema that we can work with.

Speaker B: And that's why I was curious to understand because yeah, I suspect that would probably one of the areas that you do have to spend a fair bit of time with, especially given some of the stuff you're doing. And um, we'll talk about it a little bit later around some of the stuff you're doing around AI. And obviously when it comes to AI, it's about having good quality data. I want to touch a little bit on because I know some of the listeners will be curious as well to understand a bit more about the performance and the elite side of golf. How is data changing how players are identified and developed? What can you measure now that you perhaps couldn't five, eight years ago?

Speaker A: I mean a lot has changed. I would say the biggest change for us. So there's macro industry changes at like a wholesale level which impacting all sports which is relating to AI and data availability and the way that we work with data. But then there's organizational change as well for golf Australia. So in the high performance team, I mean coming in we decisions were made quite subjectively. Often there was intuitively data was used but a lot of it was based on. I mean I think the example that I often use when I talk to people is if, I don't know if you've seen the movie Moneyball?

Speaker B: I have, yeah.

Speaker A: It's a great movie. And the premise of the movie is essentially the rise of sabermetrics which is uh, the way that professional baseball teams developed a system to use data which is all based around using data in a strategic way to make really good decisions around not just picking necessarily the best player as in they don't look like they're the best player which historically and a lot of sports have operated this way. Scouting is a big thing in sports. So using ex players or someone who knows the sport really well to go out and watch players participate or engage in sport. And from there they would make these subjective assessment on oh he, he has a really good swing or you know, can get on first base. In baseball or golf often you'd hear that they just have the X factor. They look like they, they look like a Golfer. So it's more like very subjective and very hard to test whether or not is the criteria we're using actually the best criteria to get one, the best athlete, the best bang for buck. And also is it in team sport? It's more about is it what we need? Because team sports need different types of athletes. But for golf it was the same. It's the uh, we came in and was very subjective and I think I was coming more from a data driven standpoint of there's actually a lot of inputs we could use and if we start looking and evaluating. And part of my PhD was in decision science. So it's how do we actually make decisions, which is as an organization I think it's sometimes interesting to sit down and think about how you do that. And I would say that the learning there was, that it was different each time. There was not always a consistent template applied, but there was a lot of really good inputs like coaches really knew their athletes and there was a lot of data available. So I think for us what's really changed is we can actually, because we built a really strong foundation for uh, I think at the time we called it getting all our data. Getting our data in one place was a really simple tagline. So it's how do we work out what data is important and then how do we capture and manage it in the best way. And now that we've done that, now we can really understand, we can run experiments retrospectively and we can do modeling work to understand like what uh, are the features that make the biggest impact in terms of if we were to select this athlete over this athlete. Because one of the key things for sport, particularly golf, is that there is very limited resource in terms of how we can support athletes. So support may come in different forms. We provide direct funding to go to a tournament or it could be, I want to see this special swing coach and that's going to, there's going to be some investment required there. So a lot of the decision making is around how do we actually make sure we make really good decisions and we're really transparent about how we fund and who we fund in terms of how we support athletes. So yeah, I think we're at the point now where because we have years of data and we understand how we made decisions and we have the data that's attached to those decisions, we can actually build really strong models around how do we know what's most important and how we find and identify the best athlete given our limited funding and also what's worked and what hasn't? So I think if we don't have the data, then we can't measure. And that's the big thing now. Now we can actually do that measurement at scale. And for us, that's really, I mean, the other big thing for sports is how do you structure your program? There's a million different ways to structure a program that supports athletes. Like you could have. You can be. Usually there's a pyramid analogy, so you can structure more of your. You could have a reverse pyramid or a typical structured pyramid where you've got. Do you put most of your funding down the bottom of the pyramid or at the top? And now we actually have the data. We have the success rates of different athletes that we've invested in. And we can see, like, what's the best way to structure our program to have the biggest return on investment? Or if our mission is to transition athletes to the highest levels of performance in golf, what's the best way to do that? And now we actually have the data to back that up. And I think that's only really been possible because we've been able to capture it over an extended period of time. So that's made. That's opened up a lot of doors for us.

Speaker B: Definitely. And if I was to ask you, and I know you've given us other examples, but when you're building this capability and you're starting to really work with the data, do you have a specific example of a decision that, uh, Gulf Australia made differently because of the data to what they used to and where the numbers helped change the call of something they were about to make?

Speaker A: Yeah. One of the things, without going into too much detail, one of the biggest inputs or one of the areas we can support the most is that we have a program in the High performance team, which is called the Rookie program. So basically what that means is that in the first few years, or first sort of one to five years of an athlete's professional career, we provide targeted support while they turn professional as they start playing on tour. And the reason that's important is in golf as a professional, individual sport, generally athletes turn pro. And there's a bit of a perception in golf that athletes, everyone's really rich and there's lots of money, but that's at the very top, pointy end of the sport. As you are, uh, just starting to play professional golf, generally you don't have a lot of backing or a lot of resource. So. And that's the most vulnerable period for an athlete. They have to start. I mean, it's kind of like the analogy is if you think about when you're traveling, if you have to travel for a couple of months at a time, if you're by the end you kind of like, um, I'm getting, I'm kind of ready to come home now. I've been away. I'm kind of tired. Like, there's a lot of travel just sort of takes it out of you a bit. If you think about that's just the athlete's life forever. Like they're always on the, on tour, uh, they're always traveling. It sounds like a great, A lot of fun and it is in some ways. But in the early parts of your career, when you're away from home, you don't necessarily have a team traveling with you yet because you can't always afford that. So for us, we give a lot of support for those athletes in the early parts of their career. So a lot of the decisions we make and the most impactful decision we can make is around who we fund at that part of their career or during that period and then how we fund them in what ways. So for us, one of the things we looked at is we looked at 10 years of decisions that were being made around how we fund athletes. So who's being funded, when and why, like what was actually the input and where were they, where was their ranking, what was their performance at when we actually funded them? And we looked at the outcome of those decisions. So if you track someone for, if we made a decision five years ago, where is that athlete now? Because we have all that data, uh, in like a easy to use format now we can do these types of experiments really quickly. So that I think what made a big difference was looking at both men and women, the decisions we made around funding at different periods and what worked and what didn't. And there's some general rules we've developed that I think in a lot, in almost every case have held true. That if we make a certain decision at a certain time, it's generally a worse decision than if we were to make it earlier. I probably can't share exactly that. It's more around, um, there's ways we can fund athletes at different times and understanding, like having that historical data and really building a model for what works and what doesn't, that's made a really big impact on how, I guess the goodness of the decision we can make

Speaker B: and also the journey of that player and individual, because you're able to make decisions that are probably going to come with the best Outcomes at the right times because you now have access to that. So, yeah, no, thank you for sharing that. I guess I also wanted to touch on AI. Where's AI showing up in your world right now and where is it? Still a hype in your opinion?

Speaker A: Yeah, it's an interesting question. I think we recently ran an internal survey. So I guess the starting point for this conversation is uh, we're still pretty early in our journey around AI so more of our product engineering data teams, like we're all using AI day to day. I think its ability to. I just think about it and this is coming from my background, looking at it through like a human performance lens. I think AI is just a. It's a force multiplier. I think if you. For people that are already have a background in data analytics and in some cases like software engineering development, it just allows them to do more with less time. So which for me is great because I'm always looking for ways. At golf, we don't have huge teams like we. We operate at an enterprise level, but we don't have really big teams of analysts or developers. So we're always looking for creative ways to get an edge and be more efficient. So I think our ability to scale our output has increased like tenfold in the last couple of years. A lot of that's doing owing to AI and I supported development, lifecycle, things like that. So I think that's really powerful. The challenge for us now is that everyone wants to use AI so everyone in the organization wants AI at some level and they hear, uh, everyone's got different networks and they're attending conferences and they're hearing and watching things relating to AI m and hearing a lot about it. And I think there's a bit of fervor and hype around act it can have. So for us probably the challenge is not so much. I think in certain areas we're having a, uh. It's having a lot of impact. I'll use an example where we ran an internal survey recently and it was around how many people are using AI in the organization and how are they using it. And this is a lead into our enterprise AI strategy that we're rolling out at the moment. And we got more responses in one hour than we usually get in a week with an internal survey. So almost everyone came back and everyone's very excited, which is great. I think it's good to see and. But I think for us, the results of the survey, almost everyone is using some form of AI. So for us it's not really? And I think some organizations have a lot of probably differing views around the effectiveness. I think there's not necessarily an adoption problem at golf, but it's more of a governance problem. It's like, so how do we, it's almost a runaway train. Like how do we govern something that's moving so quickly? So that's for us, where more of the challenge is. We have some areas of the business using AI really well and make use of it, but how do we make it accessible for everyone? So. So people that aren't technical necessarily, but still could actually. I mean I see a lot of value in AI for uh, more for admin and automation. So like people that are reporting, for example, and it takes hours and it probably doesn't need to take hours, it's something that could take 20 minutes. So I think there's like lower value things that we can take off people's plate and they can spend more time on like really high value work that I think the challenge for us, and this is where we are at the minute, is how do we get everyone upskilled and how do we support their development in that, uh, given that it is changing so quickly.

Speaker B: And it's also, you don't want to quash people's enthusiasm for it either. So it's just that balance, isn't it Jared? Because you're a progressive organization, you want to, but you want to do it responsibly and you want to encourage people to have a play. And I think that is the constant challenge. Are you guys mainly using AI at the moment for internal sort of productivity gains or are you starting to look at ways in which it's going to serve your customer and client base, your kind of stake? I, uh, suppose your stakeholders.

Speaker A: Yeah, we're still exploring different ways to use it. I mean it's mostly like you said, internal productivity for us at the minute. So the way that we've structured our strategy is essentially internal productivity. First get everyone really comfortable and comfortable in understanding AI at a baseline and then once we have the foundation there, we can start to think about, oh, um, how might this be applicable to different areas of our business, many of which are customer facing. So there's, I can already see some customer facing applications. Like if you think about, there's apps out there in market that have done a lot in terms of their ability to influence behavior. Like Strava. Like Strava for running. People get addicted to running because of Strava. Duolingo for language. My partner is always on Duolingo and is scared to miss a day because you lose your streak. And the same thing for like wellness apps like Whoop.

Speaker B: Yes.

Speaker A: Done a lot for people's uh, understanding of sleep hygiene and like their ability to monitor wellness. So I kind of think about the way we can build products relating to like we have a lot of data on people in terms of like people get really engaged when you offer their own data back to them. Yes, that's uh, the learning out of those products like the distravas and Whoop of the World. So what we're thinking about is how do we build products where people can better interact with their own data. And the aim is to get people more engaged with golf and to be honest, addicted to golf. Because we think golf's great. We want to help them understand their data and get really engaged. So there's, I think there is applications there that AI can help with some of those things but we're just in the exploration phase.

Speaker B: No, absolutely, absolutely. Now you're obviously providing a service to people who are uh, the weekend golfers, regional clubs, obviously elite players, the government, I guess commercial partnerships, industry bodies, etc. A common issue or a challenge that a data leader has an enterprise level, the demand in which the business is asking for stuff. How do you decide what gets prioritized when you've got so many different people asking or ah, biting at the chomp for data?

Speaker A: It's a very good question. I think that one really never goes away for us. Our team is growing quite quickly but I think our capacity hasn't really kept pace with ambition yet with on the back of that digital transformation piece. There's so much we want to do as a team and an organization and even as an industry. So I think we're still in that uh, tension period with how do we budget day to day work or business as usual work with project based work projects. Yeah and it's hard like when we probably haven't found the right balance yet. And I think it's actually ironically almost got worse that problem with as we have more wins, we get more runs on the board, we've shown what's possible with data. Now uh, we start to get more and more ideas coming from different parts of the business, parts that haven't really been that interested in data and analytics and things like AI in the past. So it's, I think it's a good problem to have but it is still, for now we're still very much focused on foundational work. So getting all our integrations right like making sure like back to that issue of data, like, how do we ensure that? I think AI is a good example. Like AI is not going to be that helpful unless we have really clean, well governed data. So, so that is still the priority. But we're playing this balancing act of trying to support various projects that are starting up all over the business while still working on some projects that we need to get done before we can get to the next level and start growing. So I think the other thing is we are starting to think a little bit more deeply at an enterprise level about, uh, with the executive as well. How do we actually prioritize big projects coming in now that we're starting to. Because I think when I first started at Golf, I was having to come up with ideas and then approach different functions to say, oh, it would be cool if we maybe this could help you solve this problem. Or I've got some ideas for how this might address or create opportunities for your team. But now it's the other way where actually it's the classic story of, you know, guarding your data team and how do you do that? But also the balance of you don't want to guard them too much because you want people to, you want to add value as well. And we just want to help them work on different projects. So I think we haven't quite got that right yet. But it's something we're learning.

Speaker B: And look, it is a great problem to have, I think, and I've had said this many times with guests on the show, that I think the best position you can be in, Jarrod, as a leader, is to make your team indispensable and where the business depends and relies on you because everything they do, they can't do without the support of your team. And it sounds like you've created a great environment and capability at Golf Australia. So now it's the next challenge of figuring out how you're going to deliver to the demands that are increasing. But I did have one question. I mean, organizations like yourself don't tend to have massive teams and endless budgets, right? So a lot of our leaders or listeners that we're sitting on might, might m. Sit on slightly more enterprise level budgets. But how do you genuinely run a data capability when you've got a fairly lean team? How have you managed to do that?

Speaker A: It can be very challenging at times. I would say we're still, we're in that period now where, as I said before, because we proved some of the value out, we're starting to get more opportunities for investment into the team. So there's some roles that I'd really like to add in and I think that there's, we're getting more support now and people can, you know, we're seeing more understanding around the value of that type of role versus this role and like how we can continue to deliver projects if we have more, more capacity. But I think it's uh, in the past we've had to be a bit creative about how we, how we get after projects. So part of that is like many organizations using like a scale up, scale down model. So we have some organized awesome vendors externally that we work with and they do a really good job and that will come in when we need support and peel away when we don't. So we don't always have like for example, during the major digital transformation project, at one time we would have had our team plus an additional six or seven other people, but then as the requirement dropped away, that team would scale down. So we do a little bit of that. Scale back, scale back. Yet in the past I've also, I've really obviously coming from like academia and working a lot with research partners. I've tried to engage as many different research organizations as possible because there's always opportunities to, I mean sport. We've got a bit of a privileged position where if we go talk to Melbourne University or Monash University data programs, often there's a lot of interest to work on something that's a little bit different. Like uh, oh, do you have any postgraduate students who would be interested in doing like an internship or Maybe even a PhD with our organization we've done that in the past. So if we have a really well defined project, often there's a lot of opportunities and people that put their hand up to say, oh yeah, we love to work on something that's a little bit different. And a lot of people love golf and sport in general. So I think that can be really valuable if you play to your strengths a little bit. Like we don't have the big budgets, but we have a really interesting subject matter. So people often want to work on it just because it's different. So I always try to use that to our advantage a little bit as well.

Speaker B: Absolutely. So just as we wrap up the conversation, Jared, I've just got a couple of quick questions for you. With everything moving so fast, what's the one thing that keeps you up at night in the role that you do?

Speaker A: I think the one thing that's keeping me up at night. I mean it. I know we've already kind of talked about it, but it's probably coming back to the prioritization piece. Like we have. There's so many things we want to work on and also like there's two parts of this. Like one part is that there's so many projects we want to work on, there's so many good ideas coming in from across the business. How do we actually support all those things with limited resource? So that's one thing that I'm always trying to budget and use the team's time as effectively as we can. And then the other part to that is because we're almost the internal leaders or subject matter experts for data and AI and people want, are starting to want more and more support around thought leadership or like just understanding what options are out there and how we can best use data. And so that's around. We have to keep up to pace with everything changing really quickly so that we can continue to add value from that standpoint. So it's both, it's like how do we keep working on good projects and add value but also how do we keep up to pace with all the changes in the industry? So I think balancing those two is, is getting pretty challenging. So that's one thing that. But it's actually a good thing I think. Yeah, learning being able to be in a position to teach different teams, like that's what makes you better I think and keeps our team on edge and sharp. So I think it's an opportunity. But it's also something that's a challenge.

Speaker B: It's a double edged sword, isn't it? It's you're doing, you do some luck with anything, you start doing some really good stuff and people just want more, you just want more for yourself. And so yeah, it's always a double edged sword. And lastly, if you could get every sporting organization in the country to do one thing better with their data, what would that be?

Speaker A: Thinking about this, Julie, before ask this question, I know we talked about it before the interview, but I think one, one observation for me is I don't work as closely with other sports as I used to, but I still stay connected to other sporting organizations. I think that one thing I've seen is that other organizations, golf is a little bit different in that our data team and our engineering team works across all areas of the sport. Whereas what I've seen from other sports is that uh, data and engineering capability, it either sits in like more commercial and digital or it sits in high performance. But it also, and sometimes can sit in both. So they can actually be Separate teams and they can be walled off from each other. I think that in some cases there's a good reason for that, but in other cases it could be a bit of a missed opportunity because in, in sport, like all the different functions, so commercial. Most sports are set up in a similar way in Australia. So you have a commercial team, commercial and partnerships, events, a digital data team, product, team marketing. And I think a lot of those functions are more similar than they are different. When I worked in high performance, like the problems we were, we were addressing, like how do we make better decisions, how do we bring all our data together and better manage it, how do we build products that people really want to use and all the change management that comes along with those things. I think what makes a team good in that area is actually the same as what makes a team good working with digital and marketing and other problems across the business as well. So I think that you can actually carry that pattern through. And because they're also related as well, like the data that's interesting to people, like commercial partners and customers as well, like and fans is actually the data that's often being used to support high performance. So if you can package some of that up and then reuse it, you can actually get a lot of engagement through the different stakeholder groups, both external and internal. So I think that the classic model of splitting those up is sometimes there's an opportunity there to actually bring them together. That's where the challenge comes around. How do you balance priority? So I think, I think what you said before was right. It is a double edged sword. But I think talking to a lot of other sports there is an opportunity for like horizontal service delivery across the whole business. And there's opportunities there that might be missed if you have two separate teams or teams working in complete isolation.

Speaker B: I love that and I think, yeah, uh, it definitely applies well to organizations who are in that sporting sector. So yeah, thank you for that. I mean, look, I think for me what I took from this conversation is really that every business has constraints, right? And organizations like yours, particularly when they're not for profit as well, and you have lean teams, there's a limitation or a lack of data literacy. All of those challenges can make it an interesting journey to bring you into the world that we're in now. But I think you've just through that conversation every time I talk to you, Jarrod, you're so passionate about what you do in golf Australia, that comes through every time I have a conversation with you. So I think you've not let the budgets and anything hold you back and you've turned this into a really good operating model where they're able to make some really, really good decisions. And so, you know, I think there's a lesson there for, uh, any data leader, whether they're in sporting or enterprise. So for Jared, anyone that wants to follow you and the team at Golf Australia and what you're doing, where's the best place to find you?

Speaker A: I'm Fairly active on LinkedIn, so yeah, feel free to send me a message and I'd love to chat to anyone about any of the things or more that we've talked about today.

Speaker B: Uh, fantastic. Thank you so much for coming on the show, Jared.

Speaker A: Great. Thanks for having me.

Speaker B: Reyna, A big thank you to our listeners. Your support helps us share these valuable conversations with more people. If you're enjoying the podcast, please give it a like or follow.