[00:00:00] Speaker A: Hey, everyone. Welcome back to episode four of Couch Confidentials. And if you thought I've said everything there has to be said about a specific writer, you might be wrong. Have I done all the promotion possible for a specific writer, you might be wrong as well. But again, this topic and the person behind, well, my next guest is so fascinating, I just had to invite him. And being the person that he is, he said yes. So I'd like to welcome Chiang Kaifeng to Couch Confidentials as number four. [00:00:30] Speaker B: Hello. Hello, everyone. Thanks so much for having me. Matthew, I already mentioned in the prep just before. [00:00:35] Speaker C: Right. [00:00:36] Speaker B: I liked all of the interactions we had so far. I feel almost a little bit embarrassed about all the promotion that you did for me, but it's more authentic, actually, than me doing it on my own behalf, so I can only thank you for that. [00:00:49] Speaker A: Well, great to be here. Me being Dutch. You'll probably get an invoice in the mail pretty soon, right? [00:00:55] Speaker B: For all the promotional services. Here you go. Exactly. [00:00:58] Speaker A: Yeah. And next week we have a. We have a. I guess a fiance. Yeah, yeah. [00:01:05] Speaker B: Dehumanizing Data strategy. [00:01:06] Speaker C: Right. [00:01:06] Speaker A: Dehumanizing data strategy. But for those of those of you who don't know who Chiang Kai Feng is and who have not been watched or watching their feed very closely in LinkedIn, Chiang Kaifeng is the author of the book called Humanizing Data Strategy. Not to sound or look impolite to you, Chung Kai Fang, but I think we've spoken enough about this. I want to get to know you a little bit more. The first thing that did hit me, and it's not about the book, but a one star review. Who would do that? I saw it on your LinkedIn profile. Somebody wrote you a one star review, but the way you responded to it was. Was great. But. But was that your. Was that your true feeling at the moment you saw it? [00:01:46] Speaker B: Well, I mean, I think that what I wrote down in the text was truly how I felt. Because it starts with first being really upset about it. [00:01:53] Speaker C: Right. [00:01:54] Speaker B: And not being sure how actually it would. Oh, that's right. [00:01:57] Speaker A: Yeah. [00:01:57] Speaker B: Realizing it doesn't make any sense for me to be upset all about it for two reasons. One is that first of all, that reviewer didn't leave any text. Right. It was just that one star rating and without any context. So I don't have any clue about why that even happened. Right. And the second one is, anyway, that if someone doesn't like my approach or disagrees with me, that's always a fair point. [00:02:22] Speaker C: Right. [00:02:23] Speaker B: And I realized that it's better to create some kind of emotion, good or bad, than no emotion at all. Right. Because at least it means that. That I'm hitting some kind of nerve. [00:02:31] Speaker C: Right. [00:02:31] Speaker B: In all of this and in a net way. And the net positive is still good. [00:02:35] Speaker C: Right. [00:02:36] Speaker B: In the end, I still have a lot more positive reviews than negative reviews. So all in all, I feel like it's still valuable what I did, but being publicly available for co criticism that way is still something I have to deal with and get used to. I feel like it's not often talked about how vulnerable an author feels when your book all of a sudden is everywhere and everyone can feel free to have opinions about it. And it's really nerve wracking to think about, oh my God, anyone could just say my book sucks and I have to just face it. And I don't know how to face that. [00:03:07] Speaker C: Right. [00:03:08] Speaker B: So all of those feelings went through my head. I feel like it made me grow in a way. [00:03:12] Speaker C: Right. [00:03:12] Speaker B: That negative review made me realize, okay, this was a small thing in the bigger picture. But I also have a feeling now how I deal with criticism generally about my content and my book. And I feel that's a good thing. Yeah, yeah. [00:03:23] Speaker A: And I remember a long time ago when I was in the. Just kind of a little bit off topic when I was in the military, it was kind of the first time I did something wrong and I got criticism. And my sergeant at the time, he took me apart. He says, listen, this criticism is. Is more focus is towards you in your role as, you know, the soldier that I was and not you as a person. [00:03:49] Speaker C: Right. [00:03:49] Speaker A: I think. Would you see it the same way that the criticism, criticism you got in the book is more towards you and your thought matter than you actually as a person? It's a very thin line though. Yes. [00:04:02] Speaker B: I think I would say it's more towards my role, but at the same time, because I'm writing so much about personal things in the book too. Right. About my heritage, about my anecdotes from my life, I can't help but think it might be something personal as well. [00:04:18] Speaker C: Right. [00:04:18] Speaker A: Okay. [00:04:19] Speaker B: But I would like to believe that since it's still a business book and a book about data in the very end. [00:04:23] Speaker C: Right. [00:04:24] Speaker B: It's more about the approach that being criticized and not ignoring who I am as a person or disagreeing with my experiences, because these are not debatable. [00:04:33] Speaker C: Right. [00:04:34] Speaker B: So I think it's more of a just thought process for myself to come to the conclusion business. [00:04:39] Speaker A: I wouldn't be surprised if you find this book in A self help department at some point. Because I think, I mean for me personally, I kind of dabbled with data strategy indirectly. So you know, analyst role, I got into tag management. So you're trying to improve data quality and now with customer data you're trying to get more value out of data because analyst is very passive. You're doing work and generating insights, but you're not making changes to where we are now. And I think with data strategy it takes a step even further because you're trying to paint out the big picture. But I think the approach that you took is, yeah, the human part. But why would you do the human part? I mean you say it's a business book, but you speak so much from the heart and the power of humans. What got you that far? [00:05:31] Speaker B: I think, I mean, maybe two reasons actually that led me to this. One is that across my, along my career working in data, the human side was always a passion of mine anyway, right. So I always made sure that whatever I'm analyzing and what it says I'm generating are being convincing. And then actually it has an impact for people and they believe it, they're convinced by it and they're changing their mindset and their actions by my kind of insights that I'm generating for them. So that was always important to me. [00:06:02] Speaker A: And when you did those insights, is that purely quantitative data? Because I recognize something you're saying, but that's when I started working with the qualitative side. User feedback and user testing, etc. [00:06:12] Speaker B: Both, I mean I started very much as a marketing analyst, so a lot of it was web analytics and you know, paid media analytics. So that's I think more quantifiable. Quantitative, quantitative. And then when the social listening started and it was more about text mining and so on, then got more qualitative as well. [00:06:27] Speaker A: Yeah, social listening, that's a good one. [00:06:28] Speaker B: Exactly. But either way, right, it was all about insights have to be convincing and have to lead to some kind of actions to have an impact. And if it's not reaching that goal, then it was all really for nothing. [00:06:38] Speaker C: Right. [00:06:39] Speaker B: You just made beautiful graphs and beautiful slides, but no one actually knows and, or is influenced by anything. [00:06:44] Speaker A: So. [00:06:45] Speaker B: And then going to data management, data strategy was of course even more about it, that people needed to work together to make it work. And the other side of it though was that whenever something was failing and something was not working, I felt it was always traced back to wrong decisions, wrong collaboration, wrong information and these kind of things. And I realized that is human root causes too Right. So whenever something is failing in data strategy, it goes back to human factors. So why are we not talking about this more? [00:07:13] Speaker C: Right. [00:07:13] Speaker B: We talk so much about the tools and the technologies about it. And why are we not dealing in more detail with practically about the human side of data? And if you look in literature, right, there's a lot more books about the technical side of data than about the human side of data. And that is something I wanted to contribute to, to just have that aspect and area more presented in the way that I know how to write and in my own writing style and being a bit more vulnerable and bit more approachable in what I'm writing. So that's how it all came together. [00:07:44] Speaker A: And looking at the responses, it's, you know, it's definitely visible that you are, you're very approachable, even getting you on this podcast right now. And I think you're right about the technical side. I mean, if you were to weigh up the O'Reilly books against, you know, your book, it's, it's way out of ratio. So it's interesting for you to say that. So other things that we've been seeing on LinkedIn is the whole music part. And the question is great, by the way, to those 17 listeners who have listened to you in the past year. I saw that little. Is that what you get from Spotify kind of notification? [00:08:19] Speaker B: So if I'm listed as an artist on Spotify, you get your own little wrapped as well. So you have like the other side of it, like how many people listen to you from where in Sunny Sun. [00:08:28] Speaker A: So, yeah. And the amount of time they've listened as well, kind of. All right, but so, so from a analyst point of view, what's your year on year growth there? [00:08:37] Speaker B: Actually it was 20% more streams than last year, which is, as I said, really surprising because I didn't didn't release any new music this year on Spotify because I only put my original music on there. Nothing of the parodies. Right. And last year had created like two songs or something at least this year I didn't create any new songs. But somehow the kind of listen behavior even increased, which is really nice. It feels like it's more built on the evergreens in my music portfolio than anything else. And it's actually still the data songs that are creating most of the kind of traffic to my music. So it's really cool to see. [00:09:14] Speaker A: Are you going to put the parodies on there? Because the last one that I saw about Christmas, what was it? [00:09:19] Speaker B: Nicola Ascent? [00:09:20] Speaker A: Yeah. Yeah, that was Funny. [00:09:22] Speaker B: That is very good. I think there's some kind of complications about the legal side of it doing parodies and licensing it and something I haven't really quite figured out yet. So I'm just a little bit more cautious about that side of things. That's the only reason why I haven't put it up yet. [00:09:37] Speaker A: You can't get any copyright strikes on LinkedIn. On LinkedIn now at the moment, I believe. [00:09:41] Speaker B: Exactly, exactly. [00:09:43] Speaker A: Yeah. So you also had the TALK ACT guide. And I'm going to be a little bit, I don't want to say self centered here, but the. I love the acronym tag. I came from a tagging background. [00:09:57] Speaker B: Oh, I haven't even thought about that acronym. You blew my mind right now. Oh my God. No, I didn't know. I mean I have not called it tag. [00:10:03] Speaker C: Right. [00:10:03] Speaker B: But it makes so much sense. [00:10:06] Speaker A: I had, I had, I had another one recently when I was talking to a company and they're there. The company initials was PR DCT Predictive, but they shorten it to predict and they're a CDP company. But if you rearrange it, you have rt, cdp Real time cdp, which is what Adobe uses for there. And it's like, all right, this world is getting too big already because we can't even come up with any fresh acronyms. [00:10:34] Speaker B: Acronyms. Yeah. [00:10:36] Speaker A: So can you share it? I mean, this is kind of the overall question that I have for all of these questions. You seem to like to have so much experience already. You've been analysts. You're now up to kind of head of data governance and data strategy. But around tag, how do you see that? Because there's two sides that I see to it. We need people who actually can do that. But some companies are still stuck in a kind of a generation gap where there are leaders who did not necessarily grow up with the amount of data that we have now. So what are the examples that you had at a young age to kind of inspire you around tag? [00:11:19] Speaker B: That's a really good question. I think actually how I came up with that framework is purely based on that talking is not enough. And if talking is not enough, then what is missing? [00:11:30] Speaker C: Right. [00:11:30] Speaker B: So I kind of try to just complement that framework of talking is not enough, but then what is enough? And that is how came up with ACT and guiding. [00:11:39] Speaker C: Right. [00:11:39] Speaker B: And it's really more about that. Saying data is important is one thing, but you have to act like it and then guide others to take it seriously too. And that leadership role modeling on that particular aspect is really important for people to actually follow you. [00:11:56] Speaker C: Right. [00:11:57] Speaker B: And to actually do it in the right way in the end having cultural implications. I think my experience with it is that you don't need to be really hugely knowledgeable about data to be doing it right. As long as you believe in it and you trust your data experts in your organization that this is the right thing to do and you're engaged with it. That itself can already work as an acting and a guidance for others. [00:12:22] Speaker A: Right. [00:12:22] Speaker B: Just by even acting that you are meeting, let's say ahead of data as a CEO, for example, once per month is more than nothing, and you're acting accordingly. And if you tell regularly other departments to please consider the data and work with the data team on this, then that's also guiding, right? [00:12:40] Speaker A: Yeah. [00:12:40] Speaker B: And that doesn't require you to be like a deep expert that is familiar or not familiar with the data. It's just more of a matter of prioritization and recognizing that it's important. [00:12:50] Speaker A: Is it kind of a. Maybe we're going getting too philosophical around this, but kind of does it need to be part of someone's DNA to especially the G part, the guiding, the acting and the guiding. I mean, like you said, talking is not enough. We need to lead by examples kind of the acting part. And then we also need to make sure that we share our knowledge, we guide people along the way. There are plenty of memes on the Internet about now or online right now about leadership that you have the leader where he's pulling the cart along with the coworks and there's a boss who's sitting in the cart yelling at his coworkers. But is it, is it maybe human? Does it need to be in someone's behavior or DNA to be able to guide? I mean, I'm looking at you as well, because look, I think. Don't think you wrote the book to become a millionaire. [00:13:45] Speaker B: No. [00:13:46] Speaker A: There's something in you that's inherently there to say, hey, I want to share this information, I want to help. Does that make a difference? [00:13:57] Speaker B: It does make a difference. But at the same time, I would hope that any person that gets into a senior leadership role has a mandate to guide anyway. [00:14:08] Speaker C: Right. [00:14:09] Speaker A: That would be beautiful world, right? [00:14:12] Speaker B: Independent if it's in them or not. They need to make decisions and delegate and share priorities and guide teams to do the right things. [00:14:21] Speaker C: Right. [00:14:21] Speaker B: That is part of being a leader. So I would hope in an ideal world that everyone in a C level role is of course used to guiding. So guiding in a direction that is focusing more on data shouldn't be new to them. It's just applying the same skills that they have just in a different context. [00:14:38] Speaker C: Right. [00:14:39] Speaker B: I would say. But in reality, of course, we have probably all seen leaders that are not really guiding people and are more talkers than anything else and somehow always get away with it with more promotions and more rewards without any actions made it right. [00:14:52] Speaker A: Yeah. [00:14:53] Speaker B: But I would hope at least that any, in any reasonable setup and any rational decisions about leadership that these leaders need and know how to guide. [00:15:04] Speaker A: Yeah. I was just about to say, I don't think anyone has ever lived that Disney fairy tale where we've only been surrounded by people who match this, you know, this tag framework. It's. Yeah. So there was another question here, which I had to relate to someone who I've personally always looked up to during my career as analyst as a. In web analytics. So I think it's referred to now as digital analytics. And the guy was called Avinash Kaushi. Yeah, yeah, yeah. [00:15:37] Speaker C: Okay. [00:15:38] Speaker A: I'm glad he rings a bell with you. And so I had this question here and I have to kind of peek here. I'm getting older, I can't do anything. [00:15:45] Speaker B: No worries. [00:15:46] Speaker A: And I wrote it down. I said, so you have this trust spillover from human relationships to data systems, and it goes both ways. The better the data, the more trust the humans will. But how can organizations actively cultivate this transfer? And why I took the example of Avinash back in there is because I think it was around 2005, he was at a conference here in the Netherlands and he said, if you want to find out who your champions are around data, who are really using that data, he said, turn off all the report exports. So he was talking about Google Analytics at the time, and you had this report function where like daily or weekly it would send out reports to a distribution list. And he said, turn it off. The people who come knocking on your door are the people that you need to work with. And I've always used that. And I didn't switch out the reports, but I was fishing for those people who, who, who would advocate for me to build, help me build that trust back, give feedback, honest feedback, instead of just saying, hey, no, everything's fine, we've got all the data, but how do you. How can businesses do that more effectively? [00:16:58] Speaker B: Yes, I mean, I think the, what you're describing, I think is a really interesting way of doing it, but it also, it does, it does feel more like I'm cautious about who to trust and I will find out through this method, who I can actually trust. [00:17:12] Speaker C: Right. [00:17:13] Speaker B: But it's rooted a little bit in skepticism, however. [00:17:15] Speaker C: Right. [00:17:16] Speaker B: It's a little bit like it's not. [00:17:17] Speaker A: A polite way to do it. [00:17:18] Speaker B: Exactly, Exactly. So. And what I'm thinking is it should be actually the other way around. Instead of trying to test and probe who you can trust, the goal should be to establish trust with as much as many people as possible. [00:17:31] Speaker C: Right. [00:17:32] Speaker B: And that is the ultimate goal. The more data professionals and data team members are being trusted, the more the area itself is being trusted, and then the systems behind it and the efforts behind it are being trusted as well. So what I'm saying is it should be actually more of a proactive effort and not like a passive one. [00:17:49] Speaker C: Right. [00:17:49] Speaker B: Like waiting, what something to break, and then see who I can actually trust and build the trust with people. And you will realize it will be easier with some than with others. But building trust is really important. And that really starts very simply with, like, active listening and understanding each other, I would say. Right. Really, simply put. And the really most basic thing in trust is understanding where we come from, from a motivation point of view and in our ambitions to then say we understand each other's ambitions and that's why we should work together, because we are supporting each other in this. Yeah. And as long as you have and you are sure that the other person has your interest in their mind too, then this is like the ultimate fundament foundation for trust. [00:18:34] Speaker C: Right. [00:18:34] Speaker B: And you can like, okay, I trust you because I know you have a what's best in mind, what's best for me in mind too. And I know what's best for you in mind. So let's work together. [00:18:43] Speaker A: And you wrote about this in your book as well. And I'd boil it down to building empathy. I think you mentioned about that companies should not necessarily force, but enable people to work with other teams to learn about the culture within a specific department, understand the problems that they live in. And you really made me reminisce. And I know I'm interviewing you and I feel like I'm telling my story, but you really made me reminisce about my times in the military where we would do this thing called cross polling, cross pollination, cross decking, where I would be on a Dutch ship and I would spend a few days on a Norwegian ship or Spanish or German just to understand, doing the same role that I did on my ship, but then in a different culture. [00:19:35] Speaker B: Interesting. Okay. [00:19:36] Speaker A: And you see the bridge environments, you see the radio. The radio environments. I was in Communications. And it was so the next time you spoke to someone on the line or you were sending messages to that ship, you could, you could visually place yourself in that environment, you know, where they were coming from, to pick up the message. And I found it so fascinating that. And I'd never thought about it from a business perspective. Have you actually seen this in practice before or are you enforcing it? [00:20:03] Speaker B: Absolutely. I did it myself. Like, there was one example where I was working in a digital analytics team that was a centralized team, and it was about setting up brand KPIs, like how to measure marketing effectiveness, basically. Right. So I spent time, one day per week to shadow the brand strategy team on how they work, and I spent time with them. I was part of the team meetings just to see how they make decisions, how they interact with each other, and what it means to them to have, like a measurable brand, so to say. [00:20:33] Speaker C: Right. [00:20:33] Speaker B: And this gave me all of the information I needed. Like, I would have never found out in workshops or meetings. I just needed to be with them, to actually fully empathize with them and know how to do things. So that was one of the ways I really valued basically just immersing myself in a different environment and understanding it, rather than just asking cognitively about it. [00:20:53] Speaker A: Right, yeah, well, yeah, because you could, you could put yourself into that situation a whole lot better. But here's a kind of devil's advocate question, though. As I just mentioned, the whole, you know, Disney fairy tale situation, it doesn't happen everywhere. And I'm sure you've come across or have been in situations and jobs where you said, well, this is just not working. Has that ever happened to you before or that you couldn't make that change? [00:21:22] Speaker B: Yeah, of course. Absolutely. All the time, I think. [00:21:25] Speaker A: All the time. [00:21:28] Speaker B: I mean, in human nature. I think I talk about that in the book, too. Change is always hard, right? And if you don't see the valiant change, then you're going to resist as much as possible and it's always going to get tricky. So if you come up with, especially when you point to human reasons being the factor, then it seems, first of all, very personal to people. And it might be that senior leaders feel that it's their personal fault that they're being blamed for something that they should know better about. [00:21:58] Speaker C: Right. [00:21:59] Speaker B: Like, I'm a leader. I should have known this. I cannot admit that I made a mistake about this or haven't paid attention enough to it. So I'm not gonna let you convince me that I made a mistake or that Anything is going wrong, everything is going great. Right. This is kind of the political side reaction. Exactly. And I think it's more about as soon as they realize themselves that this might be the reason and if we move away from finger pointing to who is actually's fault it is but that we all contribute to a solution and we make things better as a team, no matter where the problem comes from. Right. Then it's easier to deal with it. [00:22:34] Speaker C: Right. [00:22:34] Speaker B: And to see the change and the benefit of the change itself. [00:22:38] Speaker A: Yeah, yeah. Because you also talk about the. Celebrating the achievements, not the larger ones, but the smaller ones as well. [00:22:45] Speaker B: Exactly. [00:22:46] Speaker A: And would that also, you know, if we, if we look at. I think it was just a LinkedIn post. Just the other. Well, half an hour before we got onto the call and we were talking about the gen. The influence of gen AI on marketing where we are. There are two sides to that coin where on one end we have companies still struggling to get the basics right. But you're also creating. There are also companies who are really adopting this gen AI. And he pointed out saying the end users are becoming less and less critically or critical minded because they're leaving all the decision making, all the creativity to AI. So that's kind of a segue to AI. But will this have an effect on how, you know, on team cohesion when they are being led by ideas that are generated in this way, even insights. I mean we've seen examples of data analysis with AI. [00:23:46] Speaker B: I think it could be. But on the other hand, my hope is that by actually allowing others to see what you generated with AI as an insight, there's more eyes on it that can critically review if it's right or wrong. Right. Many of the things is because we individually do our own thing, we ask a chatbot, it gives an answer, nobody reviews it. I don't know the answer myself if this is right or wrong because I don't know more about it. So I'm just going to use it and go straight ahead. [00:24:16] Speaker C: Right. [00:24:16] Speaker B: But if it's part of a team culture and like part of a team effort, then there's going to be more eyes on it that is reviewing the content of each other. [00:24:25] Speaker C: Right. [00:24:26] Speaker B: And of the ultimate outcome of it all together. So in a way I hope that working on things genai in a team actually would allow you to be more critical because there are more expertise, more expertise from different areas involved that can really spot that something is not right in the outcomes. Either it's a hallucination or it's formulated in the Wrong way or phrased in a different way. Either way. [00:24:49] Speaker C: Right. [00:24:50] Speaker B: Any kind of things. I would hope that it's just in our human nature that we want to call out something that is wrong and let people know that this should be corrected. [00:25:02] Speaker A: You talk a lot about teams when we give examples or we're talking about kind of ways to improve. But what would you say to people who work like as a one man army, as a one man data team to maybe reflect on ideas that they're having is. It's especially like you said, sometimes change is hard. And I think in some organizations there's still leadership in place who are rusted in the past, who this person could probably not talk to. Have you experienced or found any online resources or communities? Because communities is also a strong place to connect. [00:25:45] Speaker B: Yeah, I do think. Yeah. I mean there's first of all, lots of communities, especially for data scientists and data analysts. I think that where they can exchange with each other. [00:25:54] Speaker A: Yeah. [00:25:54] Speaker B: But you're right that it can be quite a lonely job too. [00:25:57] Speaker C: Right. [00:25:57] Speaker B: If you're like the only analyst and only data professional on a bigger team and you're the owner who knows everything. [00:26:03] Speaker A: That's why I do podcasting because I'm a contract freelance contractor. I get lonely, man. [00:26:09] Speaker B: Yeah. [00:26:10] Speaker A: I don't even publish these discussions. [00:26:12] Speaker B: It's just, it's just for yourself to stay social. No. Good. No, what I'm. What I'm thinking is that those. So they have all of the responsibility but really not a lot of the reward. Right. So there's two things they need to take care of at the same time. One is doing the right thing when it comes to data because they have only their own conscience to be accountable for it. And no one else is going to review and has expertise review if what you're doing is right or wrong. So you need to be really confident about it. But at the same time you need to market yourself as well because nobody is doing it on your behalf. That what you do is valuable in a team. [00:26:46] Speaker C: Right. [00:26:46] Speaker B: Because otherwise you're always the back end data guy. That nerd that sits in the corner that's in the basement. Exactly. That's just crunching tables. But it's more than that. [00:26:58] Speaker C: Right. [00:26:58] Speaker B: And then you have to even communicate it on your behalf that you're doing something important. So it's really an ungrateful kind of job at the same time. But I think by focusing on these two things. Right. It might be a little bit better. And again, focus more on the human aspect of it all and not Just the hard skills that is needed I think is even more important if you're a one man show. [00:27:19] Speaker A: Yeah, I forgot his name. I'm going to cut this out because I just want to grab a book. Hold on. [00:27:23] Speaker B: Yeah, sure. [00:27:24] Speaker A: So it's definitely a lonely team and I think as the one man army within a company, you need to push yourself. And you made me think of, I had to go grab the book so it's going to be cut. And your response was really, really nice. I hope we can kind of do that again. So here's a book from a guy I used to work with in the past, People Skills for Analytical Thinkers by Gilbert Eklebo. I'm going to make a note of him to invite him out on the show, but it's great podcast as well. And I mean, is this what you're thinking about when you're that one man person that you need to be able to communicate? I'm thinking from my intro, I don't want to put labels, but I'm relatively introvert. The more in the corner I am doing my thing in a flow, the happier I am. But I guess that approach wouldn't work, right? [00:28:14] Speaker B: It can work. It's more about how you prioritize your time. Right. And realizing that being in the flow is important and getting things done. But it shouldn't be 100% of your time. [00:28:26] Speaker C: Right. [00:28:26] Speaker B: I would say at least maybe have 30% of your time invested in communication and relationship building to actually tackle the other side of it. [00:28:37] Speaker C: Right. [00:28:37] Speaker B: And this way it's really just a rough estimation. But you kind of every week, when you look in your calendar, block your flow times and when you need to get things done, but intentionally set up times with people to communicate. Set up time where you write down stuff and communicate things in a wider audience and so on, but make an effort to actually be better in the people side of things and not just ignore it. [00:28:59] Speaker A: Right. I mean, I think for some people that's definitely getting out of their comfort zone. But I think you're right in that you can't be that mouse hiding away. You need to be. You need to be in touch with someone. And what I usually see with CDPs or any kind of project, it's not about the tool. You need to find someone that resonates where what you're doing, it resonates with them and they can help to be your voice at times. [00:29:28] Speaker B: Absolutely. Yeah. [00:29:30] Speaker A: I don't know. Have you ever watched the TV series for All Mankind? [00:29:34] Speaker B: No. I think it's one in Space, right. [00:29:37] Speaker A: Yeah. It's the alternative history to what happened with the space landing and how science and the space program evolves through time. [00:29:48] Speaker B: Oh, interesting. Never seen it, though. Okay. [00:29:51] Speaker A: It's very good. So there is this one woman. I can't come up with a name, but. And not too many spoilers. But what she does is that at moments where she is deep in thought or needs to solve a problem, she falls back to music. She plays the piano, and nobody knows this about her. So maybe you see the question coming. What role does music play for you? I mean, is there a link between the two other than the parodies? [00:30:20] Speaker B: Yeah, very good point, I think. I mean, and I always talk about this, that my musical journey started much earlier than my data journey. So I'm actually feeling more rooted in music than in data even, because it has been part of my life for a much longer time. [00:30:34] Speaker C: Right. [00:30:34] Speaker A: Yeah. [00:30:34] Speaker B: I started playing the piano when I was 5, and so it has been a big part of me so far. I would say that being a musician, though, taught me a lot that I only in hindsight realized helped me in my data job. [00:30:46] Speaker C: Right. [00:30:47] Speaker B: So, for example, the finding the balance between the analytical side and the creative side a little bit. Because you can look at music very analytically too. [00:30:57] Speaker C: Right. [00:30:57] Speaker B: Like, every note has a certain sound frequency and certain notes together sound better than others together sound. You can break it all down and make it just math and it would still work, right? [00:31:07] Speaker A: Yeah. [00:31:08] Speaker B: But there's art in it and there's something that is dealing with the emotional side of things too, equally as it is with data. Right. If you. You can look at it very analogy, which we usually do. You can also look at it creatively and artistically and say we are creating a whole narrative and a story that's resonating with people emotionally so they actually love the numbers. Right. Typical example. We just talked about Spotify. Spotify Rabbit is a great example. You are making everyone looking forward to their top songs of the year and top artists they listen to. And it's really just basic number squaring. Right. From an analyst point of view, it's not that hard, but you make everyone care about it. It's like something they learn about themselves and they're really eager to learn and it resonates with them. Right. [00:31:51] Speaker A: We should build reports internally to say, hey, this is how many reports you reviewed in the last year. This was your favorite one. Yeah. [00:31:58] Speaker B: This is how many I sent you, and this is how many you actually opened. That's not going to make too many friends There, but yeah, exactly. No, the other side was it because I was part of orchestras and jazz combos and bands and so on. So of course, being a team player and making music together with individual contributions to the bigger outcome is really what I learned there. [00:32:22] Speaker C: Right. [00:32:23] Speaker B: And literally synchronizing rhythm, synchronizing sound with each other, listening and contributing at the same time. All of that, I think had a lot of impact on me as a professional as well, on how I deal with collaboration. [00:32:36] Speaker A: You really thought about this because the metaphor is mind boggling when you relate it to what you're doing right now. Because the music can be very mathematical. There's a relationship, especially in orchestras with the rest of your band, which is kind of the company. But you're also talking about the emotional side and not only for the listener, but also for the player. Because I can imagine it as a, as a musician yourself, there's also a high level of emotions involved in it. So that metaphor is, wow. It's like a mirror into a different side. Two sides of the same coin, I think is the expression. [00:33:14] Speaker B: Yeah, absolutely, yes. I mean, and also you can imagine it's not the first time I've been asked a question. So I have to admit that only after I've been asked this question maybe one to one or two times, I had to really think deep and then actually realizing and reflecting on myself what it actually taught me as my, as a data professional. But I realized that these are the things that I'm intuitively doing that I had to just make more concrete and bring up more explicitly to explain why this is for me, so complimentary. Right. [00:33:45] Speaker A: You're going to, you're going to be the perfect Martech therapy guru here. This is, this is. Yeah, I like it and I, I like the synergy between both sides. So you mentioned that if we're going back to tag. One of the problems that I'm finding now, there's a lot of startups and scale ups and they're being led by very great leaders, young leaders, but there are still a lot of companies out there that are dealing with older incumbent leaders who. So I mean, is it a situation where if we're talking they might have probably they adopted data, they agree and they use it for their daily decision driving. But what about the human part? I mean, is there going to be a kind of a generation gap between the more hardcore old school business types, the extroverts, where. And not necessarily the introvert, more empathetic side of data, Is there going to be a change of the guard when a new generation comes into play and the human side is going to be more prevalent. [00:34:55] Speaker B: Yes, but I think that it's not only driven by humanizing data strategy as one concept. [00:35:02] Speaker C: Right. [00:35:02] Speaker B: I think it's driven by a lot more than that. Like everything that came in waves now, right. For example, the agile transformation, that instead of waterfall we're doing things in products and more iterative sprints and being more agile. That wave hit us already like thinking in products and these kind of things. The other side is thinking in processes and more efficiency driven ones and being able to structure how things are being done in organizations also came hit us in data itself. We went from this high decentralized approach to a more federated approach all over the organization. So that has been hitting us too. And all of that created, I think the need for being more humanized anyway. [00:35:44] Speaker C: Right. [00:35:44] Speaker B: Because you kind of went into more independency across different teams. No matter if you call them product teams or squads or federated domains, like all of them are more actually separated than before and the actual thing that holds them together is human humanity. [00:36:02] Speaker C: Right. [00:36:02] Speaker B: Shared goals, intrinsic need for exchange and knowledge transfer and so on, all of that coming together. So I would almost see it as there's really no space for sticking to the old world and trying to ignore the human aspect. Everyone will recognize that it needs to be done. It's then more about who's doing it in a more intuitive or authentic way than others. But either way that is a problem that needs to be solved. [00:36:29] Speaker A: Right? [00:36:30] Speaker B: So it's almost like leadership style has changed and there's no alternative. It needs to be more human. [00:36:37] Speaker A: Nice now, but has, has the corona period or you know, the work from home return to office, has it had an effect on that? I mean, do people need to be in close proximity or does that not really matter? [00:36:52] Speaker B: Well, I mean it's, it's such a hot topic, right? And there's so many polarizing opinions about if returning to office or staying remotely is good or not. I think my 2 cents on it is really as long as it's rooted in trust. Again that word. [00:37:06] Speaker C: Right. [00:37:07] Speaker B: And you are measuring people by their outcomes and their results rather than how much time they spend where then I feel like intrinsically we are all in a better space. No matter if we decide to be in the office or not, or if we want to remote more. We take care of each other and take care of the relationships then naturally, because we know that each of us are contributing in a certain way and we all have different needs towards where we want to be. So why shouldn't we adapt to it as long as we all work together in the best way possible. [00:37:36] Speaker C: Right? [00:37:37] Speaker A: Yeah. And does that play well into your vision of sustainability that I read about in the book? [00:37:42] Speaker B: Yeah, of course. Less driving and of course. Yeah, of course. That's the thing. I mean, but I also, I am used to home office nowadays, but whenever I get to actually meet colleagues in person again, I'm also very happy. [00:37:57] Speaker C: Right. [00:37:57] Speaker B: So either way, as long as it fits our expectations and our styles are working, I'm really open to anything. [00:38:04] Speaker A: Exactly. No, but I mean, don't be fooled by the rowing machine back there. It's got a nice dust on it. So even though I work from home, I never find time anymore. Okay, so, hey, last question to wrap things up. And again, first of all, thank you very much for your time today. Just before the weekend. What are your views and hopes for 2025? Around. Maybe around data strategy or data in general? [00:38:33] Speaker B: Yeah, I think, and I hope for 2025 that data, AI and business all converge more together into one strategy and that we don't have three separate strategies anymore. [00:38:48] Speaker C: Right. [00:38:48] Speaker B: Like one big business strategy, one big data strategy, one with AI strategy. Because this is how it currently looks like. A lot of people have like just individual strategies for different things and organizations dealing with it. But nowadays AI is being established as this core capability in any organization and that is helping with productivity and with revenue generation and all these kind of things that it's already by nature being closer now made to the business goals of our organization. And at this point in time now everyone realizes too that data is a foundation of AI. [00:39:26] Speaker C: Right. [00:39:26] Speaker B: And that no matter if, if you are training a new model from scratch or just doing some RG or some fine tuning with your organization's data, you need to have good data to do it. Right. [00:39:36] Speaker A: It's exposing the data quality of a lot of companies. [00:39:41] Speaker B: Exactly. So that means data, AI value, they're all connected all of a sudden now, even more than before. So I hope that it can only lead to one strategic effort and not three strategic efforts. [00:39:53] Speaker A: Yeah. [00:39:53] Speaker B: And it converges into like bringing everyone behind it too. [00:39:56] Speaker C: Right. [00:39:57] Speaker B: That finally you break the silos and you have everyone ideally represented in these kind of strategies and they all feel like they're part of one bigger picture, not three bigger pictures. [00:40:06] Speaker A: That would definitely be nice. Yeah. My biggest fear around AI and getting it included into technology is who's going to monitor the outcomes before anything goes out to potentially user end users, be it client or internally. I mean, like you said, the data quality has been exposed now. It forms the basis of the LLMs of anything you do with AI. But the amount of damage that it could potentially do when it goes wrong. Fingers crossed, we'll have a good year next year, but it's definitely ending with a bang with some nice mergers and acquisitions that are slowly being announced now. But again, thanks a lot for your time and yeah, I'm wishing you a superb 2025. And just one last question. Any more ideas for books in the future? Are you going to pick up your musical career? [00:40:59] Speaker B: No, not yet. I don't know where I will basically channel my creative energy next year. I don't have it in me to write another book right now, I would say. But let's see where it goes. I haven't made any of the decision about my music or writing a book very much in advance. Even the book writing was more of a spontaneous choice I made beginning of 2024. So, yeah, I don't know what's coming yet. [00:41:24] Speaker A: Yeah, that's when you start. Wow. [00:41:25] Speaker B: Exactly. Yeah. [00:41:27] Speaker A: But I'm definitely going to have a listen to your music on Spotify and I hope the watchers and listeners do the same. It's been a real pleasure talking to you. [00:41:35] Speaker B: Thank you so much. [00:41:36] Speaker A: Bye. Bye. [00:41:37] Speaker C: By.