Hi, everyone. Welcome to more than numbers live, a data show focused on turning data teams from support functions into engines of growth. I'm Ollie Hughes. I'm one of the cofounders of count dot co. We're a Canvas based BI tool helping data teams move away from the dashboard factory and become their organization's problem solvers. Each week, we have a chance to interview someone in the data world who's helping people drive more value from data. And this week, we've got Tristan Burns. Tristan Burns is one of the world's leading data coaches helping data leaders understand and improve the way they're driving their organization and their team towards value. He's worked with hundreds of data leaders at a time, and so we're gonna work with him to understand exactly what are the challenges that are common for their leaders to be facing and how he can help us go about solving them. Tristan, hello. Thank you for joining us. Hi, Ali. Thank you. What an introduction. It's great to be here. I haven't oversold you. You've I'll you and I have actually met from just both riffing on LinkedIn about the pains of being a data leader and the pains of the support function. So I've always wanted to get you on the podcast because I believe what you have to what you're doing, how you're tackling it, obviously, count very much from a technology perspective. But, obviously, what you're doing, helping the people to engage with these problems is just a great mix. So thank you for being off for it. My pleasure. Yeah. It's great to be here. And, yeah, like I said, it's great to, continue the conversation. Absolutely. So my first question, I I have prepared you for this, is my favorite icebreaker question, which is when you were, in a practitioner, because you've worked in many other many big brand names in your time, what was the data tool that really took your heart, which really made you realize that data was the place despite all its challenges that actually data was the the industry you want to be part of? I don't think your listeners are probably gonna hear this one too often. I know that when I said it to you the first time around, you I think you fell off your chair, with with excitement. My answer is Domo. Domo. Domo. Exactly. Yes. I mean, Domo is one of those things where, it has a very veil, very loyal fan base, but it's just never broken into the mainstream. It's like it's not Totally. It's not in the typical place you'd go. I mean so tell me why you love it. What is it that that about it that really took your heart that made you realize it you wouldn't don't wanna go to Tableau or the Power BI route. You wanted to go Domo and be like that black sheep, the data place. What was it? Well, when when I first encountered it, I'd never actually heard of it myself. Obviously, I was familiar with Power BI and I was familiar with Tableau. I'd used Tableau in the past. I know you kind of asked me what was my first, but I'm gonna answer this, that Dom was my favorite rather than my first. Yeah. I probably get into this a little bit, but I'm allergic to Microsoft products. I immediately, you know, didn't want to go down that route. I worked in, at the time in a in a kind of a big organization, but it kind of had a very startup vibe. And I think we wanted to use something that was cool and different and, you know, it followed the ethos of a startup. And we came across Domo, and I just fell in love with the with the usability, the look and feel. It's very clean. It's very minimalist. It's kind of scandi, in that regard. And I just really like like Domo. And compared to some of the other tools out there, I think it's just very nice. There you go. I mean, if Domo, you wanna give me some endorsement for recognizing a different BI tool, then here we go. Thankfully, I think, it it it has a very loyal but very narrow use base. But if if it's cool I'm not actually you know what? I've never even used that. I've never even seen it. It's one of those walled off rocks you can't even get your hands on unless you have an enormous budget. So, there you go. If you wanna if you have a lot of pockets, then maybe, and you didn't think count was the future, which it is, then Domo's the place to go. It's great for us to have a different answer. I I love it. Well, look, I wanna jump straight straight to it, Tristan. Like, you've worked with, you know, many, many data leaders. You have an amazing perspective on the challenges of being a data leader. There's so many if you go on LinkedIn for more than about ten minutes, you'll see someone talk about the challenges of being a data leader, the challenges of impact, talking about the average tenure of day leaders being really low. That's one of those challenging positions to be in in an organization, and you get that, like, full full shotgun blast to the face every day when you're meeting people, helping people. Yeah. So I just wanna start just talking to you about what you're seeing. Like, when what are you hearing from data that you're talking to, working with, coaching? What are the typical problems? Then I'll yeah. Let's go into some of the things that you'd help people think through how they can approach this because I imagine people generally have very similar topics or very similar challenges. I don't know. You tell me. They they absolutely do. I tend to find myself having the same conversation over and over and over again. I think it helps that, we talk about this online quite openly, and and and it resonates with people. And and, we attract people who have those problems and and help them solve them. In in terms of what specific problems I'm seeing over and over and over again, it's things like, data teams feeling like there is just a service desk rather than something that's that contributes strategically to the organization. And, in that regard, then seen as a cost center rather than a value add. I don't think it's very nice to feel like you're, you know, a liability on a balance sheet rather than an asset. And I think data teams can be assets, but data leaders need to find ways to make that work and to and to prove that they can provide value. And that that's that's difficult for people who've never been trained to do that and don't have necessarily the skills to do that. Other things, that I come across are, kind of tangential to that, but not being included in strategic conversations. I think that data actually needs to come before strategy, and then also data needs to come after strategy in terms of being able to reflect on how that strategy is performing. How can an organization determine what its strategy is going to be for the next however many years without being properly informed by the data they've collected and have seen in the market and so forth, in the first place? And if data individuals aren't, you know, included in that conversation, then are we getting the right inputs into the strategies that we create for ourselves? So I think the data people need to make a case for being in strategic conversations, as another, as another point. They're not currently, typically, and that's a big problem that the people I speak to experience. They would like to be, and they feel that they should be. They're not. Yeah. Those, I mean, those are amazingly I I mean, they are very existential questions. Right? If you're not at the table helping drive your business strategy, they're kind of they're self reinforcing on me. Like, either you're not at the table driving business strategy, helping form strategy, then you're only ever gonna be seen as an input to someone else's strategy, and seems like cost center there. But they're kind of existential. Like, those are the topics which, you know, if you yeah. Yeah. They are big problems. Right? If you're seen as that way, obviously, this whole podcast is focused on the exact challenge of that. So I'm sure there's also other kind of more tactical ones we can dive into as well, but let's focus on these two. And I'd love to understand how you help how we help a a a dev leader, like, unpick that. How do, you know about the tenants and how we talk about the operational way of working towards operational clarity, problem solving, time to decision. But that that's quite you know, that's a operational thing. Where do you start when you're talking to someone about the the how they move out of being that support function? How do you coach someone that way? Yeah. So I think it really starts off as as a maturity concern for the organization. Data data people often well, data teams rather often start within an organization at the very, very bottom. They're brought in because someone needs a report. So we'll hire a junior data person. There's already a lack of strategic thinking around that. It's not, well, let's bring in a really super experienced senior data person who can help us think very globally about how we use our data. It's actually, no, let's just get some young junior person to come in and start reporting for us. So it's a it's it's the wrong foot to start on to begin with. And a lot of organizations, even large ones, make that mistake. That person in that organization or those people in that organization are therefore, they don't have the authority. They don't have the leverage to make a case for being strategic and they just become, reporting functions usually and service desks. So I have a question about data. I'm going to send this to those guys. Guys, you've got fifteen minutes to come back and hear the response kind of thing. And this trend will start. And, and it's very, very, very difficult for them to get out of that dynamic. The way that I kind of coach people to, to approach that is you need strong leadership. You need someone who can, at some point in time, just say, okay, pause, we need, we need to really reflect on, is this process working? Is it adding value to the organization? Is our data team as contributing as well as they we know that they can be. Are the right procedures in place, right processes in place to limit their exposure to kind of ad hoc low value work? Can we actually, put a cap on that and then help them or get them to work on more, longer term vision strategic type stuff? So that would be a good yeah. Just assessing the situation and putting some limitations in place. It is a difficult conversation to have, and a lot of people hate having it. It it's quite hard to sort of own up to the fact. Like, we we've obviously, we we we both know the problem really well. We're trying to solve it in two different ways in many ways. And, we I I did a I did a piece of work recently where I asked data teams how much of their teams' time do they think actually drives business impact, and it was about ten percent. They literally, like, cheaper fee admitted that only about ten percent of their their time as a data team is spent, like, moving the moving the business forward. And ninety percent of that time is spent just maintaining the envelope performance, maintaining reporting, or maintaining questions that keep the business sort of ticking over. And that's kind of a shocking thing to end up to and go to the business and say, this isn't working. I'm being I'm I generally am a support function. That's not what that's not where I wanna be. That's quite a big the risk is they say, oh, you're right. Yeah. We should get rid of you. So you gotta, like, go in there, like Unless they can take what you've just said and make a business case for why they why they ought to be, seen as a more value add proposition, then, yeah, I would I would probably say, what's the point? Why do we have data teams if all they do is ask questions that those people who are asking them could probably find out for themselves anyway if they if they bothered to. That that's why I mean, that's why I I did do this podcast. It's why I believe in the the the tenants that, you know, the four pillars of value because if you don't give, the business a an answer which is more appealing or at least exciting, then you are just the thing which they can automate away, that you are ultimately a middleman to the raw answers that the business has to hold. And you need a data team which actually can drive clarity, drive solve problems for the business. Otherwise, you're just you're just in the way of the business doing that instead. Exactly. And what you're what you're talking about there, I believe, is a business case and a strategic vision for the data team at the organization. If data people can't understand how that organization makes money, what that organization's strategy is, then it can't clearly make a case for no longer being a service desk. How can it add value to the organization unless it's very clear on on what that looks like and where the effort needs to be applied and what the ROI potentially is as well. Yeah. You know what? One of the things that I I really believe as an industry we've gotta solve is is there is a really poor definition of what a data team's role should be. Again, it's something I think we I would love to help be part of this help that solution because if you're, like, a VP of product or VP of marketing, like, the business what you think your role is and how you add value is probably pretty similar to what the CEO thinks your job should be or the CTO thinks your job should be and where marketing fits into the organization responsibility. For data, people have everyone has a different opinion what a data team's role should be, and that that is true as different data leaders, as I keep hearing. It's definitely true for these different CEOs, different CTOs about how they view data. Some have had experiences of a data team very high functioning, literally driving forwards on the biggest initiatives of the business and being, like, intrinsically part of the solution. Others have seen it just as, as you say, like that service desk model. Yep. And if you've been if if you've only experienced service desk model, then your expectations are exactly that. So we've gotta have a a more exciting narrative, which we can just teach the rest of the world to understand. Yeah. That's a very interesting point, actually. You talked about the other other departments or divisions within an organization. You've got things like finance, marketing, operations, sales, and so forth. Those are traditional business functions that have been with had a seat on the board forever, that have that have come up through the modernization of business since or whatever. Data isn't, data is new on the block, so to speak. We don't feel that new. We've been around here twenty, thirty years, but we're still, we still haven't carved out an identity for ourselves in the same regard that those functions have. In fact, we've let others determine what that is largely. And another contributing point to that dynamic is that largely people in the data profession start their careers as individual contributors, not business people. And that's necessary when you're early on in your data career because you do need to be able to analyze data. You do need to be able to work with systems. But as you go up in your career, that is no longer helpful to you and you're actually as helpful and you actually need to be much more business minded. Whereas someone who's come up in finance or someone who's come up in marketing throughout their entire career, from day one, they've been business centric. Right? Yeah. We learn that skill on the go, and no one's teaching us. I agree. And you know what? That's I love what you're saying there. You it's a good point to remember that. You're right. Data is a kind of a new function. So there is there is a there is a kind of maturity curve attached to that, a kind of like a decade long maturity curve, like what is data for, which we're we're all learning a bit and getting. So that's a helpful kind of so it just makes us feel a bit less bit calmer that actually is a problem that probably will get better over time. That's a helpful refrain. And then you're right about the domain expertise. And we actually just interviewed, Fibo, who is leading analytics and data at Deezer, and he has come from you know, he didn't come from data. He came from m and a. He came from strategy, and then took over the data function. And so this was, like, off the bat, exactly how to use his data or resources and his team to drive value. And it if you haven't done that pivot from business to data, which is difficult in other ways. Right? Different problems there. But if you go your way around, then, yeah, you're right. You've gotta you've gotta you've gotta learn that quickly, and probably no one's really gonna tell you because then they assume it instead of assuming they need to help. Yeah. Exactly. Exactly. Yeah. What is there so that that is that's a that's a there's a lot to unpick there, and I love the idea of, like, building that business case for, like, that vision. If you for those of you who are following the podcast, you should go back and look at other interviews and look at the tenants in more depth because there's a lot that I hope you can help. And Tristan's got, is very heartfully shared as a resource about how to build maturity curves and your organization, which he's gonna put into the show notes. There's more to dig into that essential question. Chris, maybe you can dive into some more of the kind of well, I don't I say fun in the in the wrong way, but, like, some of the smaller but very painful examples of where you get problems which are a bit more tactical. I think one of the ones that I think about is, like, the idea of, like, shadow data teams, which I think is quite common. Like, people desperate to get data, and then suddenly it's out of blue. Marketing has formed a data team, and it's like a rival. How do you like, there there are things which are, they sound funny when I say it, and they are a bit less existential or high level. They're much more tactical. Like, do you see those kind of things? Is that a thing which you have to how do you help with that kind of situation? Shadow data teams is a very common problem. Well, let's be careful with the language that we use because k. It's not always necessarily a bad thing. It can be an indicator of something, but it is largely bad. So, yeah, shadow data teams that pop up within the organization and that typically the data leader who is, let's say, the central data leader, in most cases, isn't aware of. So you might say, for example, a marketing team has gone and hired an analyst. The HR team has gone and hired an analyst. And, yeah, you you ask yourself, well, why do these teams feel like they need this this additional support? Are they not getting that support centrally from the data teams? Typically, they are not. That could be down to the data team taking a bit of ownership and saying no to certain requests and and and saying, look, we don't have capacity or we've got more strategic important stuff to be focusing on. And if you are doing those things and those teams, which I feel are right things that data leaders should be focusing on and those teams therefore, feel they're not getting the support and they go and hire their own person. It's not necessarily a bad thing. Right? For sure. Yeah. Yeah. Dangerous is where data governance and definitions and consistency throughout how data is looked at in the organization starts to fall apart when there's not a very, constructive relationship between a centralized data function and a shadow data team, for example. There's also a lot of ego bruising that can happen when someone goes to hires or an analyst and the data leader doesn't feel like they were consulted on this. Because that's their domain and you go and hire someone with the expertise that they would usually be looking after or managing. So that that's always a scary thing for them to to come to terms with. But yeah. Yeah. That's I love that reframe. That is actually an indication of a a positive side of your wanting data, wanting to use data, and it's then like, it's the governance. It's the kind of it's the connectedness that you need to have between those embedded teams and the central team. It actually it's actually something we picked up on with Philip from Monza, and we talked about the analogies between finance and data that you actually you could have, like, a financial controller working in a different part of the business. But if they're working different terminology to the central finance team, you've gotta have a whole host of problems at a very kind of, you know, fundamental level to your finance team. The same principle price of data. Just you've gotta have that connected dots Yeah. To empower and move people forwards. Yeah. If you are the data leader in this scenario and you do you come to know that there are shadow data teams popping up, I would do my best to bring them under the, umbrella in terms of defining measurement and and KPI definitions because it's gonna blow up in your face if that person sits in a meeting and says, actually, my numbers are different to yours. Let's let's go with let's go with that person's numbers. Because, you know, that's never what you you never wanna be having that scenario. So you wanna gain control or at least consistency across across that, challenge. You also wanna be making sure that you guys aren't, you know, taking on the same piece of work and doing and and and duplicating effort on something. If if there's projects that can be delineated between the two functions that that that and it make in a way that makes sense, then most certainly, this is still a positive thing. I'm not not anti I I I I anti the shadow piece rather than the the piece that Yeah. Yeah. I think it's the shadow roles on the I I mean, the the discussion about centralizers embedded data teams is an is it gonna never end? Excuse me. A question either way. It's a shadow piece, isn't it? It's the it's the it's the fact that they're it's not, it's not as you the win they'll you're missing win wins by having people work under the same kind of consistency. I think that's a that's a great way to put it. I I love that I love that framing. I mean, one of the things that makes me think about is you mentioned the things like anxiety and, like, and bruising of ego is how much of your coaching is data specific, and how much of it is just sensible management coaching and, actually, a lot of lots of doodown your expertise, but, like, actually, a lot of this is actually true of any manager and any function. Like, I think that's quite a helpful thing with data team. They'd lead us to hear is actually Yeah. They aren't that unique or, actually, maybe they are. How do you reform that? How does that come across in your the way you work? Certainly. So I I touched on it earlier where I talked about people who come into data typically coming from an IC background and moving up through the ranks. People who become managers and leaders of teams and so forth are thrust into a new entirely new environment where they're now expected to not only be a data expert, probably also contribute somewhat as a, an individual contributor, but they're also now a stakeholder manager. They're a people manager. They're doing a whole bunch of different things that they probably aren't particularly well skilled in. So to answer your question, where do I mainly focus? It's it it's probably to to a pretty large extent agnostic of actual data and and it's looks more like management, but it is specific to to the problems and challenges that are, very much central part of the experience of data leaders. Because they don't have they don't have it. They have not really had to, fight these battles in the past. Yeah. No. That makes sense. There's definitely a as we're describing, like, the fact that as an industry, we have a bit of a identity to work through generally what what it what it is to be a great data leader, which I think we're solving. That just layer is not a level of complexity, and there's another needs the the the layer of the the coaching that you'd bring or how to help them influence. That makes a lot of sense. That's really cool. I I I I appreciate this is great. Is there is there when you is there anything which you, always try and land with the daily menu coach? If you had if you had a, like, a one minute to speak to any data leader, what would you can I ask you what you'd say? Like, is that is that is that is that generic you can talk about, or is there anything you wanna that you think our audience should definitely hear from you? One thing that bothers me every time I speak to someone new is I ask them what's your organization's strategy, and they can't tell me. And whether or not that's because they're not aware or because that organization has not clearly articulated it, or even the organization doesn't actually have one. But in any case, it's concerning to me the sheer volume of people who who can't articulate an organizational strategy. So my question is then how can you lead a data function without that knowledge? That is, one of the best piece of advice I think I've heard on this podcast like that is Really? You tell me. I mean, just think how much how much of the time that if you don't know that, how difficult it'd be to make a business case, how difficult it would be to justify any of the work you're doing if you don't know where it's actually where your business is going. You're it sounds like reactionary. That's that's crazy. Yeah. Yeah. Great. I use the example. Right now, a lot of organizations, I guess, with economic times and and macroeconomic environment, profitability is a strategic pillar that is a common one right now for a lot of organizations. Post COVID and last few years, it's been growth, and it's been growth at all costs. People have gone into debt. They've raised money in equity and, invested heavily in growth, but now money isn't so easy to come by. Interest rates are higher and so forth. So now profitability is the key, is a key strategic pillar for many organizations. So then I when I hear that the company wants to focus on being profitable and I ask the data leader, what are you doing to help the organization be more profitable? They're like, what could I do? Or can I possibly be Well, what do you mean? Like, we're just doing we're doing our piece over here. And I'm like, well, the organization has said profitability is really important. So why haven't you re recalibrated what it is that you're working on to focus on helping deliver that strategic pillar? And I I and the response just to clarify those listening, the response is not, I'm gonna go save compute costs. I'm gonna go get you with the iTool, by the way, in case you have. The the, because that is just leading you to the more the service desk support, and then it'll just keep chipping away. There's always more cost to take out. You've gotta drive value. I Yes. I completely agree. I completely agree that, you've gotta lean in and work out where to drive the value, not just drive down the cost. Yeah. And we have the data to find the answer to those questions, so let's use it. Exactly. Yeah. Yeah. Love it. Well, if those those people this is kind of a self reinforcing thing. If you're listening to this podcast, you care about driving value, and therefore we're speaking to the converted in some respects, it's just been great. The last thing I wanna ask you, Justin, just just a general pulse question if I can, is just on the AI topic, a thing which I I talk about, because it's in topical and people are thinking about it. But, like, from your perspective, I I see two sides. I see people being very anxious. I also feel very excited. Where do you find the people you're coaching landing with AI? Do you have a sense of pulse on that? Because it is you can see it as an existential question Jeremy Pearl wrestling with. Have you got a sense of where people are? Like, is there kind of population of data leaders? Yeah. I think in the short term, there's some excitement, and in the long term, there's some anxiety. So what what's gonna happen in the long term, what's gonna happen to our careers? Where where where does data play a role going forward in the future? Where does AI place pressure on our careers? It's not necessarily you will have heard this before if you're reading up, you know, the media at LinkedIn or reading articles about AI, that it's not necessarily going to be that AI will replace the entire job that you have, but it will replace components of it. We'll find ways for you to be more efficient in those areas. So what does that look like? Where does that apply pressure? In the short term, I think there's excitement. I think a lot of people are happy to get their hands on projects. And, one cool thing that I think is really important that a lot of people are using this opportunity to do is to bundle together AI projects with, like, data governance projects, which have been long ignored. So when the CEO, whomever comes down and says, hey. Let's do some AI projects. Here's a bit of budget. You say, fantastic. Part one is data governance because we need to be in a position to at least data will never be perfect, but at least do the thing that we've always put off doing in terms of data quality. Yeah. Yeah. Just driving simplification and clarity just generally and using using AI as the buzzword to get there. Yeah. That's pretty cool. I also I do with a word of caution, don't don't blow the opportunity to to to learn and upscale on AI and just, focusing on data governance quality. You wanna you wanna get, you know, you wanna horse in the race. Fair. Tom is running away from this. This has been this has been a great conversation. I wanna ask you, two more things. One is just, like, what's the piece of advice you give to anyone getting into data? Like, what would you say you've been on a a path, with the big the biggest companies and the smallest. Now you're coaching data leaders. What would be the piece of advice for someone starting their career in data that you think wish you'd learned at the start? I would say, personally, I would avoid corporates. I worked for some of the biggest companies in the world, like you said, early on in my career. When you are a data person in a big company, you are a very, very small cog in a very, very, very large machine. It's great for getting the reps under your belt. It's great for doing the same thing over and over and over again until you're really, really, really good at it. It's not great for understanding the broader picture, understanding what that business does, who its stakeholders and customers are. All those things that I think are really important if you wanna be a strategic data leader down the line. So Makes sense. Startups, small businesses, get close to the founders, be in the room with those people if you can. You just it just require you don't yes. I get you. You're saying you can't the the bigger the company, the smaller the cog you become and the smaller specialist you become and the less you get that breadth, which is gonna help you later on to actually understand and be great at be that kind of business acumen that you you need exposure to to succeed. That's cool. I love that. I think it speeds it up. Yeah. Yeah. Exactly. It speeds it up. Actually, it is the it is the fuel that drives value from the data in the first place. I've got one other my other question I'm gonna ask you is, my anonymous data leader's question. This is the letter from the community where people are told I was speaking to you. This is what people were asking. I've got a letter here from you for you to tackle. This one is, what's Tristan's take on the curse of the CDI, I think, the chief data officer? There used to be a period of time when companies were adding chief data officers onto their board. It felt like this never really took off. Does Tristan have any idea why why this could what this could be because of? So there you go. Maybe that's a a failed CDO or somebody's where making the title. Any thoughts on that? Yeah. Well, I'm not I'm not a CDO myself, not been one. So it's the best guess from what I've observed and the conversations that I've had with people. But my my interpretation of the challenge is that, again, we are seen as a technical function rather than the business function. So we're we're we're hiring people. If you look at any CDO job job description out there right now, you're gonna see a litany of technical skills and experience with such and such tool and blah blah blah. It's the wrong thing to be prioritizing. We need to be hiring Those skills that if you have them fantastic, but we need to be hiring business centric, strategically minded individuals into these roles. When you hire technical people in, it's kind of like a hammer and a nail. Everything and when you're hammer, everything looks like a nail. When you're technical, everything looks like a technical problem. Upstream from technical solutions needs to be data culture and data strategy. If those people those CDOs, those people can't, first of all, address those things and improve those things, any technology they build is the wrong thing to have done. So So I think there's an ROI issue that comes heavily with, with CDOs. They they don't stick around and they don't succeed and they get let go because they've not proved the value of data and the value of their solutions because they've not integrated them with strategic thinking at the forefront. I agree. I think that's a really good good summary. It links back to what we're saying before on maturity of data just generally that the the roles of CTO, CMO, CEO have been around for decades more than the idea of data being core to your business. And it feels like if you don't know there's not an industry wide or very, very clear purpose for data to be strategically understood and valued by an organization. But I guess more generally in the industry, then you just got a role without purpose, which isn't science gonna be a successful one. So I think that's why when the big data rush came, people put them on the board. They knew it was important, but they didn't really get what that role should be other than data needed to be covered. I'm sure there'll be a chief AI officer in a in a in in a in the next decade. We'll have a similar problem because we're not really into what that means. It's than that. I think in the next year or so, we're gonna see that. A lot of that. That's a that's a hot prediction right there. Yeah. That's great. Tristan, thank you so much. This has been a really great conversation. I thank you so much for sharing with people the challenges that I'm sure they feel very have a lot of affinity to in the audience and some of the way to think about tackling it. It's been a a real joy. If you wanna follow-up with Tristan, he shared both two things to mention. He runs an amazing book club for data leaders, which I recommend getting signed up to. And, of course, he shared, kind of a white paper document about how to think about data strategy. Both of those things are in the show notes. Head to cal dot co slash m t n. If you wanna learn more about Cal and about how we help data teams drive, impact from the BI technology perspective, head head to cal dot co as well. We, obviously, the technical technical solution to Tristan's coaching solution Together, we're a great great com great combo. Tristan, thanks so much for being here. I I'm grateful. I hope you have enjoyed the the conversation, and thank you all for checking us out and enjoying the joining the conversation. Thank you for having me, Ali. It was absolute pleasure to be here. I really, really enjoyed the chat.