So, you are a musician. If you've seen TNK, he's also a prolific, musician, particularly around data, and you're also obviously running a data and AI strategy at ThoughtWorks. But perhaps more importantly for this discussion, you're also the author of this humanizing data strategy, which is a book all about the people side of data and how to make it into a strength in your data strategy in organizations. So I'm really grateful for you that you'll be up for this conversation. It's it's a really messy topic, which you talk about in your introduction, but you also talk about as a complete strength and a way to turn insight, which is really valuable and often a neglected part of how people think about data. Before we dive in, let's just ask you a bit more about who you are and how you got to this. So maybe to start off with the icebreaker question we love asking people is, when did you decide that you were gonna be a data person rather than a person using data? When did you know that data was gonna be in your career? Yeah. I think that's was actually very early on in my career. I would even say during my high school time because I was fascinated by advertising at the very beginning. So I really wanted to see why is these kind of little movies and sounds and stuff convincing us to buy stuff that seems very interesting, and I kinda wanted to know behind the scenes. And I was good with computers and math and stuff. So I was like, when I would study, let me try to go in that direction. So I chose industrial engineering, which was a mix of business and engineering topics. And my focus was marketing and the database systems to bring the worlds together. And this is where I learned during my studies already that I can actually make this topic of advertisement and marketing also break them down to data and information, and I can actually use a structured approach to understand what's going on or I learned about market research. I learned about web analytics, social media analytics, paid media analytics, all these things. And then when I graduated, I basically ended up right in the analytics world of things. But I I remember that during my studies, I was most fascinated by that part. To make this kind of creative world of marketing actually more data driven and being able to make it rational and break it down to data, that really made me, like I wanna be that person that is able to translate between the creative world and the data world and make sense of it. That's that's great. I mean, I I love that you started so early. Most but you've done the the traditional I would say the very common thing, is getting to a business problem, getting to business domain to into a real world concept, and then find the way you tackle that problem is in the data and the the the inquisitiveness of data, and then realizing that data itself is a wonderful thing. We've done that, sounds like, much earlier than most perhaps back in back in education, which is really quite novel, actually. So tell us we're gonna spend a lot of time looking looking through your book, thinking through the different themes there, but maybe if you could paint for us your career to date. Like, maybe it's like, where have you how have you taken your career? Where have you gone with it? And then maybe then from there, what made you think that this book wasn't a thing that the industry, the community needed to to read? What where's that how's that journey gone for you? Absolutely. I mean, I can just pick up right away from where I just ended, right, by my sentence. So I basically ended up, after my studies, directly working in marketing analytics as a marketing analyst back then from an agency side for different clients, but for every different clients. So I was able to basically understand different industries and their marketing measures and the different target audiences, etcetera, etcetera. From there, I then went to a big sports company to take over various roles, like being a data product owner, being a capability lead, but also then switching on that side from the analytics and data science side to the data governance side. Because I was a big advocate for data quality, and I couldn't understand why the data quality is so hard to manage. Right? It's just correct the numbers, right, like I thought. And, I was so persistent towards the data governance team that they offered me basically to join them and to, basically, not just be part of the problem, but actually help solve it, basically. And I realized why it's a complicated Yeah. Exactly. But I also it opened a whole new world, like, all the way behind the scenes stuff. How does data work together? How where does it all come from? Why are so many people involved? Everyone has somehow some, impact on the data itself as an organization. That also lead led me to where I'm now, where I'm leading that strategic part of data and AI. Right? I thought it works as a consultant now again, but I'm able to apply my strategic thinking in the bigger picture to different client problems. Across all of that, I always realized that the biggest problems, and the reasons why data efforts fail are human. Right? So it's always like a miscommunication, a lack of knowledge, under evenly distributed expertise. Right? Something always is there where it lead led to a wrong decision, and the wrong decision then is impacted by how data is there, at least the compliance issues, etcetera, etcetera. And, I kept it with me from all sides. Right? As an analyst, I realized I am a weak link in the whole thing. Right? Because if I translate it wrong and I have it, then I'm the problem. It was for working and focusing on data quality. I don't know where it comes from, but human beings are manually entering data. And, of course, they are also human beings, that is a problem too. But, also, people in between don't talk to each other, so they don't know why the data exists the way it exists. So they just assume things and then misinterpret it too. Right? And also on a strategic level, right, change management overall is a big topic. And if you don't do change management well, then you're gonna end up with people sabotaging it or being really passive about change, and that itself isn't a problem. Right? And and all of this, I realized this is a big issue, but there still seem to be a lot of people actually writing practical advice about the human side of data. So I read all of these books that are related to it, mostly focusing on technology, a little bit about the human side. I was like, maybe I should actually write a book about the human side and try to tackle it in a more practical approach and give something to your community. And, yeah, then, basically, I I ended up writing that book. It's also not too long of a book. Right? And I always saw It's not. I've read it. I've actually read it twice since before since we started planning this session. Wow. Awesome. Not not look. Because it's because it is very digestible. It's also very well structured. They're very I would say this to you earlier before we jumped on that, like, there's so many hidden gems within each chapter that you can just take away something straight away. We'll hopefully come to that in a bit actually as well. But I'm just gonna read to you you read your own book back to you. But one of the things I just write I love that. Second paragraph of the introduction, I thought just kind of sums up really well, which is that which we try to be analytical, reasonable, and fact based, but the truth is that we're all emotional, irrational, and unpredictable. And I think yeah. In fact, it it is quite simply impossible to create an impartial objective truth. And I think that is at the cusp of this. Right? That if we don't address that the the weak link, you say, the human human side of this communication, then we're I mean, we got we've got that kind of barrier to the the value. They've got a big they're not gonna fully get it right, and that's kind of and, actually, the other way looking at which I think you talked about really well in your book is there's also huge amounts of opportunity in this space in data. Right? It's not just about get facts into people's heads. Actually, one of the things you talk about, which we'll come to again, is creativity, and I think that's a wonderful sole source of there's far more here than just being part of a kind of widget machine of insights that let's just get data into people's brains. You paint a wonderful broad view view of how all the different parts of humanity can really help. So, I mean, I'm as you can see, I'm I'm bought in. I love it. I Yeah. Thank you. Describe this. People who obviously watch the show, we talk a lot about the tenants of data value, operational clarity, problem solving, time to decision, and measure yourself. And I was trying to work out how these fit together. For the audience, I would say that what T and K is providing here is, like, the glue, the the thing which fits around these delivery of value, the kind of the mesh of, like, this is all the kind of imperfect human bits that you need to get right to make sure everything you deliver, everything you do through the whole value chain is working well. So I think it's a really wonderful complementary structure to the the tenants of high impact that that the the audience knows so well. So I I'd love us to just well, I guess I'd love to dive into the structure, give people a feel of, like, the different aspects of this human strategy. But maybe first, if you can indulge me, just tell me, like, just the mechanics. How did you go from like, how do you write a book? Like, how did you do it? How did you get the is this a spewing out your brain full of knowledge out of decades of working data? Did you research it? Like, tell us the process about how you built the book up and got to the meat of the topic. Yeah. That that's a great question. To be honest, I did have an approach, but I'm also not sure how others are writing their books. That seems to be a little bit of a we could all talk about it, but we all have our little secrets of how we write it. Okay. But to be very fair, I think I started very simply with a hand drawn mind map. And at some point, I went digital for it because bigger and bigger. But in the beginning, I was like, these are all my thoughts, and I think this is how they all connect to each other. Yeah. And this was the basis for me to think about, I could group be grouped into different topics. And it was just also a lucky coincidence that they will all fit under words that start with c because that's how I came up with the five c's of a framework. Right? And, then they all of the other thoughts that I had and the research that I did that was complementing what I already knew and experienced myself, they all fed well into these kind of five c's. And this is how I basically got it all together, into that framework. And, I actually from all of the book writing, I would say I spent thirty percent not actually writing texts. I was only brainstorming and collecting thoughts until I was happy enough to say, okay. All of that now feels comprehensive. Now I'm gonna start writing. And when I started writing, I didn't actually have that many additional ideas anymore. It was just putting the bullet points down and the mind map branches into text actually and make it, like, long form text, basically. And then I finally ended up, writing it. But, yeah, I it's hard to say how I came up with the mind map, though. I think it was a mix of being inspired every day, having experiences, remembering things, having inspiring conversations that led me to new thoughts. I was just capturing notes on my phone the whole time and then bring them at home back to the mind map again. I spent a lot of time doing that. I it's funny. It's so that's so this is a complete deviation as I just think about it, but, like, the the amount of time you put into the structure of the book, the mind mapping phase, the structure to then hang on the bits of knowledge that you you know and that you've you've found, discovered of a tunnel that you go and research is is actually now just to, like, how I think data needs to we we do data. Right? The more we have a structure to our analysis, the more we have a framework to follow, the faster, more efficient, the clearer our analysis is. And, actually, if you just go straight in, just go bottom up, that is is a part of that, which is important. But doing that without structure is complete is a complete nightmare. And I think that's a very very funny analogy to data actually in similar kind of way. Like, spending more time on structure is a a thing I keep finding is valuable, and it's proved apparently, it's good in book writing too. And Absolutely. Yeah. Mhmm. We we should move on because I think I want to make sure we get the audience to hear a bit more about the structure. You mentioned the five c's. It's a wonderfully concise structure, which is very memorable. And I I'm gonna pull it up actually just just to make sure I can talk to the right the right things because I though I'm I'm my memory is terrible, basically. But Okay. We've got the five c's. They are I'm gonna put them in order. Competence, collaboration, communication, creativity, and conscience. So would you mind just I'd love to if you could unpack those four, it's like, tell us how they fit together. What are they all what are they all delivering? And then we could dive into each of those a bit in a bit more depth on maybe pull out some examples. I think it's they make sense. It's a very neat structure, but it it it's helpful to unpack what each of those five definitions are and why they're important. Absolutely. So competence really is about human skill set. Right? And to actually have human skill set, that should be matched to intrinsic motivations of people to learn more and grow and to be able to be good at things. And, that topic covers also data literacy versus business acumen, right, that both should be balanced. If you want people to work together, you should have both to actually be able together. But it's also about how to support the talent life cycle, how to give people the opportunity to jump between departments to actually grow or to basically what the promotion career path look like, and also how to use academy concepts, for example, to actually teach in a structured approach and give people, like, a group learning experience to grow together. And all of that is part of competence. Collaboration itself is really based in the intrinsic motivation of human connection, I would say. Right? So it's really about we are human beings. We want to. We are social beings. Right? We want to actually work together. But for some reason in data and in other corporate environments, business environments, we seem to be motivated to do the opposite. Right? Yeah. And if we can basically build it by changing our mindset around it, then I think it's all better. And there's one important I think the most critical part of the chapter is about the theme of cocreation. Right? Because if we think about central service or self-service, it's always gonna be a trade off. But if we all had cocreation as a mindset, then we wouldn't need to basically always fight about my goals versus your goals because we have the same goals, and we work towards it with different motivations. Yeah. Then we have communication, right, where it's really about the communication of value of data. Why should we work together? What is actually the underlying reason? And there's basically a topic between organizational goals and value versus personal reward because we are only rationally driven by the organizational goals, but emotionally driven by our own reward. So that should be managed. Then we have creativity, which is, I would say, the human basis for organizational innovation. Right? Because innovation start with good ideas, and then ideas come from human beings. And the idea to also see data as a field where you can be creative and to have the environment for able to be able to have ideas, to put them into practical outcomes, and to have an experiment experimentation culture, basically. Right? And finally, conscience. Really, that's the critical thinking and human judgment part. Right? We want to proactively do the right thing. Right? We wanna be on the right side of history, but we cannot do it alone as a data expert. We need other experts to help us do that, like legal experts, information security expert, ethics expert, all these kind of people to actually make the right decisions, to be able to actually foresee the consequences and to make everything in our power to prevent them. It's great. I I think when I read the book and I I love the structure because it it it does feel like it's, like, mutually exclusive, like, mutually exclusive, be exhausted. It feels like it's very it feels like a very holistic view of all the different aspects. It's kind of a nice they don't they don't always very it's not like you can mathematically make that judgment, but I just feel like it does a lot. It's it's done really well. And one of the things I find really fascinating about it is what what I think was almost most valuable for me was that you you really signpost different trade offs. You mentioned things like self-service versus, like, serve like, self-service and just being insight driven. You you don't what I thought was elegant is you don't dictate a way of working. What you do in the book mostly is just discuss the trade offs between these things and signpost Exactly. All the areas that need to be considered in, say, you know, collaboration or in communication or and I thought it was really helpful. It's this book is great for someone who is struggling. I would say the person who needs to read this book most is person who's struggling with how to, as I said, deal with the people around them in their team, in their organization. They want to deliver that cultural change and engage with people, but they don't actually know where to start. I think you paint a wonderful you basically just paint the picture of the space in a really great way, but you don't and I think what's really important and why it's useful is that you don't a bit like with our tenants of value, you don't prescribe the way to get to a better outcome. You just Absolutely. Lay the groundwork, lay the trade offs, lay the tension. Was that was that an intentional thing for you that you you didn't deliver the operating model, like, must have these meetings. You must have this way of thinking. You're just painting the landscape well. Was that kind of intentional part of this? Absolutely. And the reason why that is is very early on when I decided on the title, humanizing their strategy, I thought I painted myself into a corner. Because if I'm really taking humanity seriously, then I need to acknowledge that every human being is different and unique. So that implies there is no one size of all approach because that would imply that all the human beings are the same, and there's one approach that would convince all of them to do the right things. But that just isn't true. Right? Yeah. So all I can do is based on experiences and people things and research that certain things always have these advantages and disadvantages, and finding what works for you and your organization needs to be your own effort. I can only give you the impulse and the direction of what you should aim for, but finding that way is on your own, which is also why exactly why I'm not prescribing anything. Right? I'm I'm not I cannot tell you what is exactly the right thing to you. I can go and give you ideas and examples, and you figure it out on your own. But I think with my book, hopefully, I gave you, like, an impulse in the right direction and you figure it out. That's also most of the feedback I get. It's like the I'm so glad that the people that are really smart because they take the ideas that I give. They're like, I already started applying today. It helped me to structure my thoughts about it. Now I know how I'm gonna operationalize this kind of thing that I was thinking about, for example. Right? So that's a great response too. Yeah. I I think it's it's it's funny actually, isn't it, how just provide again back to structure. Just a bit of structure to these concepts can really help. Like, it's data particularly is such a malleable discipline. It it does, you know, as a dis as a kind of a as a discipline, we often fill in the cracks of the organization to make the make people valuable, help people deliver more. And it's been funny for me with the kind of the tenants of value that, you know, people that show know really well. It's surprisingly high level. It's surprisingly they are just sort of sort of sort of key metrics or sort of, like, north star metrics to aim for. And but even providing that structure, this gives people such grounding to start making more tangible informed decisions. And I think the same is true for your book. I mean, I think you give a lot of to be clear, I think you do give a lot of helpful frameworks. You do go into lots of detail, but you you do paint options rather than painting this is the way to go. So Exactly. That was a very well put thing. There are incredible gems in the book, though. I I wanted to bring up a few of them because they Mhmm. If you don't mind, because I think they just are really tangible things that people audience could take away today and start applying. One of them, if I can throw it to you, know it's probably right in the weeds of it, but, like, follow your pain was a really wonderful thought experiment for how to sort of dig into the collaboration, how to start to co collaborate. Tell us more about that that frame up mental model because it was a really nice Absolutely. Yeah. I mean, the the underlying, assumption is that sooner or later, all of the data people are gonna be asked to justify the value of what they're doing. Right? And that's the typical thing. Like, what is the ROI of your the thing that you're doing right now? Yeah. And, that also leads to the impact that if you only work on abstract things, often in data governance, data strategy, work on policies and strategic operating model artifacts and so on, that if they're not being made alive, they're not bringing any value. Right? They're just the document that sit on SharePoints and in conference pages, etcetera, etcetera. So following the pain medicine means if you want to quickly show value, then you need to solve problems. And the way to solve problems is to find them first, and that means you need to follow where the pain is because you need to find those people that are actually struggling with data problems. And pain also means that certain teams, I assume, in organizations have been dealing with the same issue all over again. Nobody was able to help them. And they got so frustrated they started doing their own workarounds that they're maintaining. Right? Shadow IT or whatever you wanna call it. Right? But it's not it's a big time investment for them, and they would love to spend that time doing something else, but doing data cleaning all the time, for example. Right? So, yeah, working with them and working with those people that are sending the pain is good because they are so frustrated with the issue that they're motivated to do anything to help solve the problem. So they would happily work with you. And they actually because they know what correct looks like, because they have done the work around themselves, they have very clear requirements. They know exactly what they're asking for too. Right? What does correct actually look like? And so you have clear requirements. You have the urgency, and you can actually assess the business impact because they are investing a lot of time. They probably have assessed the impact over themselves. So you solve the issue. You generate value that is already before kind of defined, and you create word-of-mouth. So in many ways, that is, like, the right way. And then you hear about more pain. You can follow more pain points and you solve more and pain relief, basically, and this is the way you generate value more and more. It's, it's very startup mindset, isn't it? Like, is that if you're trying to set up a new company, often people say, go find it, but there's a real pain where people are trying to do workarounds because they've already proven the solution is required. It's a really lovely thing. There there are many more. I I wanna I can ask. The other one I love is the your data purpose. That can you just explain that to your I think, again, that was just a wonderful and it it just trying to there's many more of these in here, by the way, and these gems that sit within underneath the five c's. But that one also I felt was just a really good mental model to help people think about think about your purpose and where to go. So maybe just can you explain that as well if that's not throwing you into the into the depths of your No. Absolutely. Yeah. So, I mean, the the data purpose is basically a Venn diagram that was inspired by the ikigai. And for those who don't know what the ikigai is, right, that's like a diagram of finding your own purpose in your profession. Right? It's like what you're good at versus, what the world needs versus what you can get paid for and what you're allowed to do. Right? Basically, all of these things together. And I basically adapted, and I thought about if you want to drive data and use data for the right things, then there's a similar Venn diagram too, which in my case means what is exciting to do, what is aligned with your business objectives, what is allowed to do, and what is feasible to do. Right? And excitement, always we all have excitement. Right? Oh, we could do all these cool things with data. Let's do that. That needs to be then guided by what helps our business. Right? It cannot just be a random data use case, but something that actually helps what we wanna do as a business. What is allowed to do is, like, all of the ethical and compliance related requirements that are coming. We should not do something that's illegal or could hurt people. Right? And what's feasible to do is your own technological maturity. Right? Are we even able to pull this off with the technology we have, and do we have the right skill sets to be able to run this kind of advanced method? And it also implies that if you run through these four aspects, you cannot decide alone again. Right? You have to bring actually the right people in to help you justify and understand that this is possible or not. Does it fulfill that criteria or not? So when you then finally end up in the middle to say, we have now a use case that fulfills all the four aspects, the good thing is you already have talked to all of the stakeholders that are relevant for it. So it's also not gonna fail on its way. Right? You can run this, and it will most likely go to the end, and everyone will acknowledge the value of it because it was discussed before. It didn't catch anyone by surprise negatively, basically. Yeah. Yeah. It it's a really again, it's just a lovely way to sort of take out the soup of all these requests and all this kind of complexity that you feel on database and has really helped you frame it, work out where there's the path of least resistance to the maximum value. It's it's a really lovely little framework. There are many more. There are, like, the three musketeers of data quality. I love that one as well, but there's you can spend all time looking at going through every single page. But I I think what I what I wanted to thematically, one of the things I wanted to talk to you about before we sort of summarize is just that you do focus a lot on individual value like that. Your data purpose is a good example of this. Like, organizational value and individual value, and it's a kind of interesting not tension, but you do discuss both, and then you're often looking for the sweet spot between that as a way Yeah. It also sort of TLDL the book. It's basically saying humans are messy. We've gotta play to the humanity and also play to the logic value and find the middle ground between the two of them. Yes. Is that is that something which you as you went through the book, you realized that was a common theme? Is or is that just like which is it just makes sense that's there. And and is there any more like, how would you frame that to people about the kind of the the way to think about individual value and an organizational value? I I mean, that one is very much rooted in my own experiences, both the good and the bad, I would say. But I realized if I really want to account on someone's collaboration, I better address their personal ambitions and what they get out of it on an individual level. And that really means, right, I can always tell people, right, this plays into this big, KPI of profitability on organizational level. We should do it. And they might say, yes. Yes. But they're not really motivated. They might only, help half heartedly, so it's okay. But if I tell them, look. This might help you to get more people, and you might get promoted if you help me do this, right, that very bluntly saying, then the motivation is very different. They're like, oh, that's a really good point. I will actually look really good in front of the leadership. They might reward me with a promotion, and then I will look good in front of whole organization. Right? So if if you can be more honest about it and we understand each other's personal motivations, we can address those accordingly. And then if we find that sweet spot, we're not only doing it because we have to, we do it because we want to. And internally, motivation can be such a strong driver. Right? Proactivity of people is so nice when it works because we all know how passive people that need to be told every time they need to do and be reminded over and over again how nerve wracking that is for us on the other side. Right? So why not address it in the right way? Give them the motivation that they want, and then basically work together intricately. Find the path of least resistance in many ways and just Exactly. Strength. It's really it's really nice. K. One last question on on the five c's. One of the if you think about the five c's, not not that they aren't important, but, like, competence, collaboration, communication, I think the words that people would expect to be part of a strategy around humans. And I I and to be with you, I think once you think about conscience, you think, yes. Actually, there's an ethical part to this. Creativity is the c, which I think me, Pilbara, would be is pleasantly there, but it's probably not the thing that people would write down if they're asking to write down the five c's of data of data strategy. Where did that come from? Tell us more about what that looks like. And, obviously, you are an incredibly creative person. You really we will make sure we will get get your YouTube channel of your of your data songs as all the bloody outlets of your creativity. But tell us what you is that a personal thing that you'll tell us about that. It feels like it's a thing which is personal to you. You obviously see a lot of opportunity in that space. Maybe just help people think about that. Because I think it's a lovely positive vision that you can give people with it. Yeah. There I think there are two main reasons for it. So one is, of course, my own experience. I realized, and I experienced firsthand that if I bring my creativity in and I start doing things differently and doing things innovatively, that that helps me in my career, and that helps overall to make data more valuable. Right? That is just what I experienced. And I also experienced the other side, which is that most people I talk to in data, they would tell me, yeah. I'm not like you. I'm not a creative person. Right? That's why I chose to work in data. But that on itself is not contradicting, I would say. Right? Just because you're working in data doesn't mean you're not creative. You and even more, if you are working on structured things, you could also be creative with that structure that you have. And maybe within those guardrails, be creative the way you want to because necessity usually makes people creative anyway. And, the other side of it is more about the technology of AI moving forward, right, where we cannot generate so much with the prompts. Right? You can get pictures and texts and, videos and music out of AI generators. But for me, that is not, the same as human creativity. Right? Because it's learned from all of the existing stuff, which means it never will be hundred percent original. It will always just have mixed and matched things. But we as human beings have actual, truly disruptive, original thoughts, right, which we should never, underestimate. So, if we don't start practicing our creative muscle, let's say, now, then we will not give the right impulses to AI moving forward to actually help with that originality, but everything would become bland and the same. So in a way, it's both call to action, be more creative at work because that's gonna help you anyway in your career. But also for the sake of society, let's not forget what makes us unique as human beings because we are creative by nature. We just need to do it more. Yeah. I agree. I think people have people may people are always creative whether they believe they are creative or not. Like, if even if even if you're solving a particular difficult mathematical problem, it doesn't feel creative. Actually, there's certain the ingenuity spark that you need to solve the problem is creativity. Exactly. It's just not Exactly. It was painting a picture, and I think that's absolutely right. We need to keep that innovation spark and you lean into it. I think I love the way you're talking about it as a future defensive mechanism where everything else becomes a bit more automated. The creative can is the place that can flourish. I love it. It's a really great structure. I I want to sort of ask you we're running out of time, so I wanna ask you one other question which came from the community. I love it's a great question maybe to finish as well, which is the question that I I that that the person asked was, you've gone through this journey of really summarizing this book and creating this human data strategy concepts and putting it on the page, making it a thing, which is just so important and a missing gap as you've rightly identified. What is the one thing that you having gone through this journey that has surprised you or that you've you've learned doing it that you didn't know before or wasn't obvious before? Yeah. I would say days, days, people maybe. Yeah. Yes. Absolutely. No. I think, the part I got most surprised at was when I did research about the conscience chapter. And I realized if you dig deeper into how much damage actually data and AI is doing to the environment, then you get a bit more conscious of how you're doing it. And very simply put, nowadays, thinking about prompting something at OpenAI, for example, at ChatGPT, how much a prompt is, consuming an energy, which obviously then leads into water consumption and overall global warming and these kind of things, that is quite tricky. Like, it almost feels like, wow. What this little thing is causing so much damage to the world. That was really mind blowing to me. That made me really conscious about doing it, using it for the right thing. Don't do so much just playing around with it, but have at least some kind of a goal towards it. And, like, if we all just play with it and waste energy and, consume so much things, resources, and was that really good thing? That, I think, changed me for good. That was the part where I was like, I I think we need to be all more careful. Everyone is encouraged to do more research on it, but that's something that is always top of mind nowadays for me also when we talk about technology and innovation. Yeah. That's a great answer. I mean, I think also just paints for people how how thoughtful and how broad team Kai's book has gone. Like, you have really covered out almost every aspect of humanity and data. I really recommend it. We've got to finish there. It's been a wonderful discussion. Thanks so much. So you we've covered off the five c's, the structure of human data strategy. I really recommend Ting Kai's book. It is really great. It is it is a very dense book, but a very easy read. It's very digestible. As I mentioned, there's lots of hidden gems in it, and I think it's a wonderful complement to the tenants of value. It's the glue. It's the enabler in the human side of it. So it's a really wonderful book to complement. I'm grateful for your conversation, your generosity, and sharing your ideas, Tinkai. Thank you so much for being here, and I hope everyone's really enjoyed the discussion. Yeah. Thank you.