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Data Ideas Podcast - Ep 68 Getting Clarity From Data w/ Oliver Hughes CEO @ Count
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Hey. Well, good morning. Good evening. Good afternoon. Depending on where you're joining from. Very early broadcast here from the central United States, but I know it's a lunch hour over in the UK. Episode sixty eight of the Data Ideas podcast. Welcome everyone that's joining live. Thanks to everyone listening to the recording. Joined today by Ali Hughes, who is the CEO at Count, over in the UK. Ali, thanks so much for joining. Thank you for getting up early to speak to me. Yeah. Absolutely. No. This is fun. I like this. Very cool. And for those listening live today, if you are, in the broadcast, say hello. Let us know where you're coming from. It's always good to see who's in the live broadcast and and where you're joining from. But, Ali, before we get into talking about your company, you were a practitioner before you founded your company, which is really cool. Can you talk to us a little bit about how you got interested in data and analytics, where your journey began? Absolutely. Yeah. I I think, like, a lot of people I've met, in data over the last few years, I'm kind of a similar story that I kinda fell into by accident. I started my career, in consulting. I had the joy of working in lots of different industries like manufacturing, health care, retail, in government as well. And really, what I what I loved was just solving problems. Like, at the core, I think I'm a problem solver. I just love the I I love that you could use data to find and find out things and investigate things quickly and give that kind of visibility into the into the business that sort of raised eyebrows and help the business coalesce around new idea and initiative. And that just led me down the rabbit hole of, like, more and more data, more and more things, particularly as the as the organization's got more complex and got the data got more, it lets you do more fun things. So, yeah, that that's kind of it at the hot core. I I just love solving problems, and data is the fuel for that. And it's, that's kind of my background really and how I sort of got into it. Cool. That's awesome. And, as as an entrepreneur, you obviously solve problems. So I think there's, like, a natural synergy there. Yeah. Solving problems with data, addressing problems as an entrepreneur. Can you tell us a little bit about how your experiences as a practitioner, like, led to you founding Count? Yeah. Absolutely. I think, I I used to love playing with these different tools. I have to often use these different, these different BI tools that were in my clients. So I bring a a BI tool to the space for my like, to to to work through the data. And not not to bad mouth anything, but I remember at the time I was working, with companies and Power BI had just been launched. At the time, I think Tableau was the big king, the kind of the big power tool for exploratory analysis and dashboarding. And then there's like, oh, wow. Microsoft's bringing up Power BI. This is this is gonna be different. What are they what are they bringing to this? And I'm not gonna make myself popular here. I'm sure people as people use Power BI and love it, but I just felt it was basically just a poor man's Tableau. It wasn't or at least it was exactly the same flavor of idea of let's go build dashboards and publish them. Let's get data out so that other people can see it. And all well and good in one way, but it wasn't innovation. It wasn't really innovative in the core sense. And more importantly, when you're trying to use data to drive decision making, make the business better, these tools are not ideal. I was often finding myself using a whole range of different tools to screenshot out of these these BI tools so I could explain what's going on, actually work through problems collaboratively with Sure. My clients. Mhmm. And I realized that these BI tools are not actually helping decision making or making the business better. They're helping you see the day to day. They help me make operational decisions, but not wider. And that was really my that's really what let let me into, actually, is there a better way? Like, it feels like these BI tools are not delivering as much value as they need to or could do. How do we work on on more than just the operational decision making and actually help solve and move business forward? Basically, is the the story. Yeah. That's a really interesting point because I remember many times as a practitioner myself, like, you know, you get questions from executives or peers in the organization, and they're very specific questions. And sometimes, you know, the individual doesn't know how to access the dashboard or wouldn't have, you know, an idea as to how to navigate to answer that question and or whatever. And whether it's a combination of, you know, they don't have time to do it or you haven't maybe trained them properly or what whatever the reason is, the reality is you get asked these very specific questions. And I've done tons of screenshotting and sharing back, you know, very specific, you know, filter sets applied to dashboards and zooming in and circling things and drawing arrows, which I know that you can do in your tool. But, and then you provide some commentary around it in the email. And sometimes that email trail goes on for ten or fifteen, you know, strings long, and you're continuing to do the same thing over and over again. So I can totally relate to that. Yeah. Exactly. And and who's to say the questions that you're being asked by your executive are actually good? Mhmm. I mean Right. How are you helping them think clearer and come up with the right questions? That's also Right. A big part of this as well. It's like this the kind of the fabric of the business' understanding. Is it generating high value or low value questions? And and, yeah, you're right. The whole workflow, as you painted there wonderfully, is actually really the bottleneck. Like, how do you make that decision making process faster? Yeah. It's all that. You're absolutely right. And why is it so hard to to do that? Like, why is it so hard to give that full full picture? It seems like that's kind of the holy grail to go after, and it it feels like it's so much it's it's it's there's a lot of, like, friction to to get there. I just that's how I feel as a practitioner over time. You know? It just is yeah. Yeah. I mean, I I it's it's very easy to I mean, I to be clear, I I don't wanna slam anything. I I think it's just evolution of the space that Mhmm. When I started out my career, like, getting a dashboard into hands of people in business was like a huge win. Just getting access to data at all was like a huge unlock. Totally. So and that was a that was that was awesome. That was a great time. Dashboarding was king. Just getting data in people's hands, giving them any chance to explore their world in data was was the win. And so we built tools to do that really well, but we're now in a different era, a different time, and we're going to even diff even more diff even a more different time with AI Yeah. Totally. Where, actually, access to data is no longer the problem. Businesses are flooded with data everywhere. Like, every software product they use gives them a dashboard of some description. There's spreadsheets everywhere. Like, with you know, people can access data in high level, low level, and every interaction they have. And so the tools that we have in BI are kind of actually fueling that problem now where there's Mhmm. There's more information noise alongside all the other tools that these dashboards are also you know, that we're competing in. And and that's the challenge. It's like these BI tools were serving a different problem, and now the problem is more about clarity, less about access, I would say. We've kind of almost got too much access people can make. And that's not quite true on this on a on a particular issue left and right. But as an aggregate, we've got way more data than we can cope with, and therefore, a different problem needs to be solved, which is clarity and visibility, as you say. Mhmm. I think that's really it. Makes sense. Yeah. And I think, the folks that can deliver that are are are the most valuable practitioners. Right? And you, I know have a lot of ideas for how you can deliver that kind of value. You know, you've got a framework that you've developed for it. Before we dive into that, I actually just wanted to prompt again. If folks are watching, well, I first of all, welcome. Welcome if you're listening to the podcast recording as well, when it becomes available. It'll be out tomorrow, actually, Friday, August eighth, on all major podcast platforms. But if you are joining live, say hello. Let us know in the comments. Let us know where you're coming from. It's always good to see who's who's joining live. But going back to that question, Ali, like, how can practitioners help solve this this challenge? Yeah. I I think, as I said, somewhat sometimes these there there is a tooling element to this, which is like some tools don't really help with these challenges. But Mhmm. Even with the tools we have, we can do more. And I think it's about the mindset, like, where does the data team's value come from? And data teams are unique because and their superpowers come from the fact they're natural problem solvers. Mhmm. They they are naturally collaborative and actually uniquely have access. Particularly now we have all these cloud data warehouses, have access to all the data across all the business in a way that most departments don't have access to everything. They can only see their silo. They can only see data related to them, whereas the data team has access to everything. So there was real responsibility to, like, say, well, how do I turn this all the information of this company into something which actually creates clarity rather than just, like, more informate more more than noise. Let's focus on clarity. So, yeah, that that's the core idea I think here is, like, what you what data team should be doing is thinking and this comes back to this framework you mentioned, something which we've been working with with tons of data teams to really work out, like, what are the most valuable things data teams can do. And there are four there are probably four things. One is what we call operational clarity, this idea of creating focusing on signal to noise ratio. How can you make the business as clear as it possibly can be with the least amount of information possible? So fighting against this information barrage that exists now, how does the data team become the source of source of clarity, not just adding to the noise? That's one very important principle. We can go into how to do that, but that is a very clear thing. This idea of clarity being a north star metric of the data team and minimizing information as much as possible actually is a really valuable ad thing. Secondly, problem solving, being the organization's problem solvers, not just answering every question that the executive team asks, but saying, no. No. No. I'm here to solve the biggest problems the business has, and I'm gonna focus on that. That's a very we all know that's what we should be here for, but it's very hard to get to that level where you're not just answering questions. You're helping the business think through the questions and leading them through a problem solve. It's a different dynamic to just answering questions the executive pings you. Instead, let's say, well, that'll get your question. What are you trying to solve for? Here's a few different ways we could figure out this problem. That's a level up in terms of how a data team can drive value. The the the the third idea is this idea of minimizing time to decision. We discussed before about how that thread of discussions you get when you have an ad hoc question Mhmm. Is really painful. Like, actually, as as what we're realizing is that the bottleneck for decision making is actually rarely the chart. It's actually the process and the discussion and how you get your organization to buy into the stat story and make a decision. That's the bottleneck to value. How can data teams recognize that the wider context that data data is being used in and help optimize that? That's a different North Star metric than just self-service or access to data. It's got a broader scope, and it's a very exciting one because you're you're playing with and helping the business move faster with data as the catalyst. And then the fourth one is kind of simple, but it's the idea of, like, measuring yourself. The the most the most painful or most expensive part of a data team is the payroll of the data team, not Sure. Costs, not BI tool expenditure. It's them and therefore, the time where you're allocating your time as a team, it really matters. And are you allocating the time of your team on things which are growing the business or things which are maintaining the business? And I think a lot of data teams spend too much time focusing on, like, maintaining data quality as as important as that is, but getting to a diminishing returns point where they're not actually helping the business move forward. They're just making this the day to day operations, the the operating envelope steady state as it were, better. But that's not the same thing as making the business grow on saving money or moving the business to grow sales or whatever it is. So how you spend your time is also a thing you should be measuring as a team, not to tell anyone else necessarily, but just to help yourself make sure you're working the most important things, and only good things can come from that. So that's the framework that we've been working with data teams on, those kind of four pillars of value. Mhmm. And they're just very high level, but North Star metrics of, like, this is what matters, driving clarity, problem solving, minimizing time to decision, and then measuring yourself that you know you're working on the business's biggest problems as much as possible. And that's a very powerful framework that we we we've seen hundreds of teams trying to adopt, think about as they're selling their OKRs, thinking about on a day to day week by week, quarterly basis. Just just was a north star for them to know they're doing the right things. Mhmm. That makes sense. And it's to your point, it is simple, but also it's powerful. And I think that if you actually adhered to this framework, you're gonna find yourself, you know, getting order of magnitude level results back versus not, doing you know, just pumping out, reports and dashboards and, you know, taking no action, not being proactive to your point, not going to the next level, not measuring your results, things like that. So I love it. I certainly see the value in that, having been a practitioner myself. Just wanted to give a shout out to, Kapil. Hope I'm pronouncing your name right. Coming from Seattle. Wow. I appreciate that because it is, I believe, four twenty one AM in Seattle right now. So Thanks for tuning in. That's amazing. Yeah. Thanks for tuning in. And, he also says, love the four pillars. I feel the biggest challenge for data teams lies in dealing with data quality. Most of the time, it stops them from bringing value to broader business. Yeah. I think that's a very good point. I I so data quality is obviously an enormous enabler of all those things, particularly the idea of bringing clarity to the business. If you're bringing the wrong data or it's inaccurate, that is damaging clarity and and that signal to noise ratio. So you're absolutely right. Data quality is a is a key enabler. The only thing that I see more often than not, I'm honest, is the diminishing returns Mhmm. On that. Like, there's obviously an effort to reward if you think about that logarithmic curve effectively of getting from ninety five percent to ninety eight percent data quality. Probably is is that really the most invaluable thing you spend your time on? If you're, like, twenty percent accuracy or quality, then that is a hundred percent the thing to focus on. It's just working out that that diminishing returns curve on data quality. That not that that that depends on your context completely. Right? So I don't I don't wanna speak to anyone's context, but I I find a lot of mature data teams who've got the kind of everything set up are still spending a huge amount of time of their payroll trying to finish off those niggles that bother them where the business even see the difference or even notice. Or they're trying to save BigQuery, you know, or or Snowflake credit spend, And, like, actually, the amount of time they're spending isn't worth the amount of credits they're saving. You know? There's some crazy diminishing returns happening, probably not in everyone's case, but as an as a as an industry, I see people moving too far away from business problems, basically. Totally. And just want to say hello to Sri. Thanks for joining from Indiana this morning. If you are joining live, let us know, you're here. Say hello in the chat. Let us know where you're joining from. And if you do have a question for Ollie, maybe we'll have time for a question or two. Yeah. Absolutely. Yeah. Well, I appreciate the comments shared there. So you've developed you're obviously very passionate about this topic and helping data teams create value using this framework. You've created, obviously, a company to to help with this. Before we get into maybe sharing kind of specifically how count helps address this, you know, when you work with with organizations and teams, are you working are you training kind of the leaders of the teams, or are you training, like, the team members? Or how is it a train the trainer model? Or what does that look like? Yeah. So to give you some context, count is a kind of Canvas based BI tool. So if you imagine a kind of infinite collaborative whiteboard tool like Miro or Figma, if you've used those tools before, it's the same kind of idea Yep. But based around data analytics, data modeling, and, obviously, business reporting. There's all the, it's basically a BI tool but in a in a collaborative space. Sure. And so we actually find, like, a lot of data teams, like, as soon as they start to do their own exploratory analysis and start to explore data and SQL, Python, and low code, they they start to realize how powerful that is just for their own working, using the Canvas to let them think clearer, lay out a story, work through numbers without gonna sort of switch between different tools and different tabs. So we actually find that data teams really get it quite quickly, and then they start to collaborate with the wider organization, which massively increases their iteration loop and time to value. And then they start to do sort of what we would consider more of the the kind of the really big use case, which is, like, let's start to map metrics, not just do dashboarding, but let's map our metrics and show how our business fits together. We call that from a business visualization rather than, like, data visualization. Like, let's make this clearer. And that's a very powerful use case. Yeah. Yeah. It's a very powerful use case of the canvas. You've got the space to sort of lay out processes as they actually are and put numbers to that process, and showing data in context is so clarifying to the business. And then you start to have this wonderful natural flow where by seeing the business clearer, seeing the bottleneck, seeing data in context, it's clarifying. You get better questions. You can then use the count canvas to collaborate and explore the answers to those questions, and then make a decision faster because you can sort of work peer to peer with the business as they need answers happening. And that's the big cycle. It's like driving improvement on by showing the business clearer and then problem solving collaboratively so you can reach answers and then decide quickly, and and that's the real feedback loop that we see. So So it's a it's an interesting journey, but you can start very, very much in the kind of exploratory analysis stage and then just branch out and grow into a a much broader kind of business improvement framework where you're really driving change and the data team isn't just moving faster, but is actually working better with the wider organization, helping the business think and improve faster. Makes a ton of sense, and I'm looking forward to seeing some of this in in action just wanted to highlight. Sorry, I can't see the name. I think there's a security setting here or something at play. But LinkedIn user says business visualization. Love that. And I mean, ultimately and you think back even to the example I was mentioning of these, you know, long email strings with all the different snapshots from the dashboards. I mean, that really is business visualization taking place, you know, in the form of a conversation. Right? Exactly. Snippets of dashboards being sent and commentary on it and things like that. Just wanted to say hello to Susan. Susan from the UK. Susan was actually on the podcast a few episodes ago, but good to see you, Susan. Thanks for for joining. And then also, Sekou, always good to to see you. Also a former podcast guest. Looks like he's a fan of yours. So thanks to everyone joining. Continue to say hello in the chat if you are joining live. But, Ali, do you wanna show us a little bit about how, you know, just how count, helps Sure. Yeah. I I think what what I wanna say first off is like these are equal pillars of value, you can apply them regardless of the tool you have. I I really believe that I've seen many teams adopting these ideas of, like, operational clarity and Yeah. Problem solving with the business, like, working with the tools they have available. But it it can be helped to sort of see visually how different it can be. And let me let's bring up like, let's start with the dashboard. Let's start with the kind of the enemy, as it were. Like and we just talk through this in a kind of very fundamental sense. I can show you the difference in contrast. So if you can if you can see this, you'll see what is actually just a normal dashboard because count you can still do all the things you can in normal BI tool. This is not its secret sauce. Can everyone see that, Dustin? Yep. Let me pull it up. There we go. Yep. So this is obviously a dashboard. It's a grid of numbers. This is what we've seen for decades. And don't be wrong. Dashboards have their place particularly for, you know, operational, very quick gut, like, instinctive decision making when you see a number. But let me just show you this in a different way. This is what I'm gonna show you now is exactly the same metrics as you can see here, but laid out in what we would call a metric map or a metric tree. So this is exactly the same numbers, same charts you saw in the dashboard view, but you can say we're laying these metrics out as a hierarchy. We're showing and putting these metrics into context of each other so the story and how the business fits together is being told, not just the numbers. And this is what we would call business visualization because how these metrics relate to each other is showing you how the business works. And you can instantly see the story here that, in this case, we've got actions and actions broken down into active users and actions per user. You can see that their active user is growing. The ratio of actions per user is dropping, and that leads to a flattening curve at the top level. And it's just that it sounds so simple, but it is just that ability to let the the the BI tool paint the the business, not just the metrics and a crude grid. And there are other examples of this. I could show you, what we call a a revenue tree. So it's showing you revenue broken down by the different levers that drive revenue so you can really understand how the business fits together. But it can also be things like if you have a mobile app or a software platform, you can sort of paint out each user journeys through your mobile app, in this case, with screenshots so you can see how the users are flowing through your product. But then you're annotating that with live data so they can actually see the usage of the users, the aggregate metrics, the time spent, the conversion rates, and sort of spot, like, why is this drop off rate at this step so low? And then you can dig down and go, right. Well, let's take this step, and let's explore the data collaboratively in this collaborative canvas to work out why. And so you can see already that this ability to contextualize your numbers is to it gives you this clarity, this operational clarity, which, you know, would normally be very difficult in dashboard view. This particular example here you can see has got about sixty different metrics, but it doesn't look or confuse you as sixty metrics would if it was in a dashboard of many pages. Right. Because you it's all too technical. I understand they all fit together. So you can take very complicated systems or which often businesses are and make them as simple as you can to remove the cognitive load from that executive maybe who's trick throwing you questions and say, here's your world. Here's the business you're dealing with. What questions do you now have? And then the questions you're gonna receive from them could be far better, more informed than they'd be if they're trying to work out what's really going on with the eight or nine dashboards they look at on a daily basis, and they can't quite, you know, see the wood from the trees. So it's a simple but very powerful concept, and it leads to many. All the work, all the exploratory analysis that therefore takes place is just leveled up as well, plus plus it being collaborative so you can actually, you know, discuss and work through this as a team, and it just makes the whole process much faster. Totally. And a couple questions here. Well, actually, first, I just wanted to let folks know, if you are listening to the broadcast, what Ali has up right now is a view into first of all, it's, his company's tool account, but it's a canvas that he has up. And it has it starts with on the left hand side of the canvas that we're looking at right now, you see, like, the total utilization, I believe, this is of a of an app. So there's, like, one number right on the on the far left. And then it breaks it down into you think about that example I used earlier of, like, okay. What might be the next question that someone might ask? Well, okay. How many of of those users are existing? How many are new? And so then this, on the canvas, you can see the breakdown of there's, like, these two lanes that divide. You have existing users and new users. And he's got an image of the I think it's the app that's up on there. I'm I'm zoomed out a little bit. But, yeah, yeah. This is just just screenshots of that of what the actual user's looking at is. Exactly. Yep. And then it just continues to answer additional questions, like, in a logical flow, you know, so you get this full picture of I start with this high level metric, and then I can go all the way down to answering questions that are actually gonna give me some insight, help me action the data, you know, help me do my job better, if I'm working at this organization, things like that. Ali, is it mostly in terms of those that actually play around with with the Canvas, you know, is it mostly data teams that are doing this, or do they ever make this available, like, to business users too? Or what does that look like? Oh, totally. Yeah. As we have, for example, a semantic layer, we call it count metrics, which allows you to define business metrics and dimensions, which means that anyone in your organization who's comfortable doing that kind of drag and drop exploration can come in here, drag numbers around, lay out their own process flows that that make sense for them, offer trusted data model, and start to collaborate. And maybe bring in the analyst team if they get stuck, but it's all about letting people interact to the level they are. So, you know, the power of this kind of interface is that you're really just using it as a space to think. And, obviously, one of the best ways to think about your business is to lay out the process, but, really, the campus is just a thinking space. And so though it this example here is very refined and very clean and is actually not, you know, is actually not custom soft often an operational report that's sent out daily. You can just use it as a very big much a scratch pad to just think through a quick question, lay out your ideas, adding qualitative information, not just business information. So we see our customers using it. Yes. Definitely, the data analysts using it for heavy SQL or Python analysis and doing advanced work and data modeling. Mhmm. But all the way down, just seeing a CFO adding sticky notes and post it notes into the canvas as they're thinking through questions and asking others to do the do the analysis. Or we also have, you know, FP and A managers, product managers doing their own exploratory analysis in low code. It kind of works at different levels depending on how comfortable you are. Very cool. And, if it's okay, we'll take a question here. Kapil has a good one. He's so he wants to know, how does the tool enable, building a logical view like this, and, like, what stops other BI tools from replicating a view like this? I mean, I I can't speak other BI tools. You have to ask them why they don't wanna make it easy to see see their business. I all I can say is for us, we've worked very hard to build a a rendering engine which allows you to see about seventy five charts in one single view. That isn't a trivial problem. It's kinda one of those things where you've gotta build a technology to enable the experience you want. You can't retrofit that if you haven't thought about it before. So that may be a blocker, but, I mean, I mean, love it if the the market adopts this idea. From our perspective, like, laying this out is as simple as dragging objects around the page. It's not meant to be any more complicated than that. You know? This is a this is just simply a sample of chart objects, which are live updating from a data source, post it notes, arrows, and diagrams, boxes to let you tell the story and paint those metrics. It's just that you've got this canvas which allows you to lay this stuff out as flexible as you need to. It's just it's actually surprisingly simple. Yeah. For sure. And it looks really nice. It's, it's just I mean, it's just impressive visually, as well. But so thinking about I mean, some folks listen to the podcast because they kind of want to learn from other practitioners that have kind of been there, done that, you know, advanced in the field, things like that. Yeah. How would you frame this to someone that maybe wants to they're, you know, a practitioner that kind of wants to move up in the field. They wanna make more impact, you know, with their work. I mean, you've touched on this a little bit as we've been talking, you know, I think implied, you know, how this can benefit an individual. But, how what kind of benefits can someone expect as an individual practitioner in their career if they deliver insights in this manner versus just kind of being a report factory or just focusing on coding? Like, what's the benefit of delivering a picture like this? Yeah. I I I think great question. And I I think it's I think the answer here is particularly said looking forward into an AI first world. What that what that really means is that a lot of the time that analysts are spending doing the kind of technical querying, the logical expert, like, coding, the kind of mundane data modeling work is gonna become increasingly automated. And then the question is, like, what is the data team's role now if, like, building a dashboard is no longer a big effort to do? And the answer is really that data teams have the opportunity with the superpowers they have of collaboration, the ability to just have all the data across the business, and being natural problem solvers that they can lean into being that driving force of business improvement. They can be the ones which, like, clarify the businesses together. So, really, I think the opportunity for data practitioners who want to go on this journey to say, I'm looking to use the fact I I have all this data to create that kind of clarity. Like, if I if I don't know what's going on in this business and what's really broken and what really needs to be fixed, fixed, then there's no chance anyone else does. And if I need to make that clarity true for myself, and that will make it better for other people, and then I'm also gonna work on problem solving. How can I be the best problem solver this organization has? How can I make sure that my statistical knowledge, my logical thinking, my critical thinking, and the ability to break problems down to chunks, how can I use that to help a business and my who have got a lot of little burns to think about operationally? How can I give them the space and the help them solve problems and think clearly? That is the role that identity should be leveraging into, and that's just a much more satisfying place to be. It's more valuable for the business than for you to do that. And I think we haven't got much of a choice because we don't do those things. Then we're just gonna be, we're just doing all the mechanical things that actually our labs are very good at, which is coding and and cleaning data. Absolutely. Yeah. No. I agree. And I I think too, I I just think of as you've been talking about your framework, this this problem of showing the complete picture. You know, I've been thinking about there was actually a Forbes article by, that Brent Dykes had put out where he showed that or, you know, kind of show that five percent of organizations really only reach the final mile or inch of analytics, whatever you wanna call it, in terms of actually, like, leading to action. Right? And I think, like, this type of a framework, looking at the whole picture, like, actually truly giving folks that view into the metric tree, things like that, that's where you start to get into the organizations that are in that five percent that actually take action. They're not just, you know, giving the dashboard that they never use or don't know how to use and don't know how to find the insights that they really need. So I totally agree. And the ROI, is totally different, when you take action and you have that right visibility and the complete picture versus, when you don't. Absolutely. It's I had I we worked out a very shocking statistic recently, which is that about eighty ninety percent of a data team's time is spent on things which don't move the business forward. Right. Right? Something about that. Ninety percent of your time is spent on things which maintain the business's view of itself and not making the business better, more cost efficient. Right. That's a lot like that's one in ten. Even getting to, like, two out of ten, doubling your ROI as a company as a team. Right. Yeah. And hopefully the idea is that AI is gonna let that ninety percent become way lower, and then you can fill more of your time with that. But then you've gotta work out how to apply AI to not create more information noise, but actually give you the chance to give clarity and then do that problem solving work. Yeah. So question for you is, being both a practitioner that I've got obviously got really passionate about using data to solve problems, and had success as a practitioner. You also are an entrepreneur, started this organization. You obviously have been able to get your ideas in front of other people kind of in both of those roles and capacities. Right? To be an entrepreneur, you have to get your ideas in front of clients and investors and things like that. To be a successful practitioner. You have to get your ideas in front of executives and peers and things like that. What advice I think that one thing the reason I'm asking this question is I think that folks sometimes struggle with getting their ideas in front of others, you know. Or to your point, I mean, your framework, you know, being proactive, you know, and sharing what you're seeing and sharing that picture. Like, what advice would you have for folks to to get over that hump and and be able to get their ideas in front of other people? I mean, good question. I I I wanna be very honest actually and say I'm not sure I've really ever cracked it. I I've typically got better. I think the key is I've always worked at it, had to work on it really to sort of get the business, traction, get it moving. But it's not my I don't think I'm a I I know a lot of people, all my friends who are incredibly good natural communicators who can weave a story so well, and I'm always in awe of those people. I've I I have to work at this and I think that's an important piece. It's just you gotta keep working at this. It's not a thing which you can tap a fingers and suddenly become brilliant at. I'm certainly still learning a lot as I go. I think the thing which has often really helped me is trying to work the empathy piece, which I think there are people who actually have a lot of empathy generally. Like trying to think about what's the other person's perspective, where are they, what else is going on in their world, how can I make their life easier? That is a way to get your ideas adopted by them because you're trying to position it in the most in a way they can really grip rather than just the way you think they should think about it. Mhmm. Work out how they are gonna cover it well. And I often think about, like, I think you now feel it more as my as I'm now running my in a home company with my cofounder, is I'm getting the other side of the coin that I was my clients were receiving, which is I'm bombarded with information all over the place. So my source for getting clarity and having people help me see things clearly Interesting. Is now kind of coming full circle. And that's kind of what's driving a lot of, like, you know, luckily, we're using Canon internally to help be that part of that possible. But if you think about that, your executive has got about eighty things going in their head and you're bringing that one thing. How can you make that one thing really fit their world and help make that as a lower the lowest cognitive load in their head? Don't make their brain work harder, make it less. It's true across everything. It's true when you're going talking to investors and to customers and to even internal stakeholders is kind of work out how try and be empathetic and work out how to help them most get what you're saying. Position it for their world, not what you think their world should be. Mhmm. That has been an unlock. There's many others, there's many other good books on good communication, which I I really love. Things like the pyramid principle, top down communication, lots of things like that, which I've used in my career. But ultimately, I think if you're peep if you're focusing on the other person and thinking about them and serving their them and what helps their brain unlock the story, you're gonna be not far off. I completely agree. I I love that answer. And I the only thing I'll add to because we could talk about this topic for probably a whole separate episode. But We can talk to you now, Jack. Yeah. But just in terms of thinking about the individual or the team that you're, you know, working with. Right. It does go a long way and it can really break down. I think it can be the difference actually between a successful project or data product and an unsuccessful one. And so just a tip I'd throw in there. Sometimes I would take a I'd I'd have a think, you know, before I met with someone. If I was doing a training with them one on one or if it's with a team, I would think about who was on that team. You know, maybe do a little bit of research. Maybe, like, look at their background, things like that, you know, and just use some examples that are relatable to them or their job, you know, and say, hey, you know, Jane or John, you know, you might being in this role, you might use this to do something like this. Or, you know, having this background, you might be interested in something like that. And, it's it makes a big difference. So I love that answer. Just wanted to highlight one question here. I have a perspective on this, but I'd I'd love to hear yours, Ali. LinkedIn user, again, sorry that the the name is shielded here. But it's just confirming that this has been said a lot, which I would agree with with that, You know? But it is really hard to get to. And LinkedIn user is asking, do we have any use cases where this is a success? I'm sure you can't speak to specific clients, you know, and things like that. But is there anything at a high level you can share where you're like, you know what? This was really helpful. I think I I I, I think most data teams are doing some degree of problem solving. Maybe not why is it are doing it well? Is it done in a way which is obvious, Or is it kind of, like, loosely held between the questions and the answers that exist in a kind of Teams or Slack thread? Like, I think it's happening. It's just is it happening? And is the business understanding the power and the value data team can bring to this? Right. And letting the data team lead the structuring of the problem. That's the difference, I would say. Absolutely. Like, yes. We love problem solving, and I think we are probably helping a lot with some of the stuff, but it's a bit hit and miss. Like, the value of the question being asked is maybe a bit opaque. Really, what I see when I see it done really well, it's when the the questions asked the data team and the data team goes, I get your question. Here's three or four different ways we could tackle this problem, which will make sense. And before you end your analysis, you're just saying there are four ways I could this problem could be broken down, or here's a way to think about the structure of this question or this, this problem. Before I even touch the numbers, which of these resonate with you? And that in itself is helping them think through the problem better by just structuring it, breaking it down. And I've seen that happen hundreds of times. And that is the smallest unlock, but it suddenly shifts the dynamic where the data team is not just doing numbers and throwing across the fence and hoping that the other person's gonna look at that, understand it, have thought it through logically. You're helping them structure and give information. You're guiding them through it. That's when I see it working really well, and that's much more satisfying than just taking the next, oh, how about this then question, which comes often, like, five questions down. I also think we've seen many, many examples in our customer base where when they have built a metric tree and have helped the business see itself clearer, that that solves a lot of questions you get asked. And you the the questions or the the problems that materialize have a lot more urgency, are clearly much more valuable. And so that that there there's a kind of virtuous cycle that that gets produced here with the more clear the business is, the better the questions that they're asking are. And and that's a very important virtuous cycle you wanna get into versus a a vicious cycle, which is when you just spew out information, you get asked more questions because they don't understand what's going on. So Right. I'm not sure it's a direct question, but I I see problem solving happening all the time. It's just the quality of the problem solved and the frequency of those problem solved over random questions. That's the thing we're told to hit. It's the percentage of time of quality that needs to go up, I think. I've seen that happen many many times as well where it's hard. It's it's it's a challenge to get over the hump of those kind of random questions and the poking holes in the data. And it's it's Yeah. You really wanna get to the business questions and the business visualization to your example earlier. And when you get to that level, folks, you know, trust the data. They get what what's being presented to them. And you can get into the business conversation around the data points. That's when the rubber really meets the road in terms of value add. So I love that example. Ali, really appreciate you joining this morning for me, this afternoon for you. Yeah. It's been this has been a great conversation. And That was good to get worth getting up for. Yeah. For sure. Absolutely. Yeah. Yeah. No. This is fun. Good way to start the day. Wondering how can folks follow you? You know, I know you put a lot of content out there, kind of a frequent speaker in the industry, things like that. How can folks follow you and, learn more about your company? Thank you very much. Well, yeah, absolutely. You can find our company at count dot co. That's our website domain. There's a blog on there with lots of these ideas and lots of examples of, like, business visualization if you're trying to be inspired, what that really means. Mhmm. And I'm I'm off to just on LinkedIn, putting up my thoughts and engaging with people about these kind of questions, so you can find me to search for all all of the queues I think I am on LinkedIn. You'll find me pretty quickly. Very cool. Awesome. Well, we'll put a link to both of those, count dot c o as well as Ali's LinkedIn profile in the show notes. So if you're working out or wherever you are, if you wanna go back later and, check out the links, They'll be in the show notes underneath the episode. But, thanks again for joining, Ollie. This was a great conversation. Thanks, Evan, Dustin. It was really good fun. Yep. Absolutely. Take care. Bye bye. Okay. Bye now.