#102 Data is a road trip, not a destination

The Measure Pod
The Measure Pod
#102 Data is a road trip, not a destination

In this week’s episode of The Measure Pod, Dan and Bhav discuss the concept of treating data as a journey, not just a destination. They explore the reasons why people may view data as a destination and the pitfalls of having this mindset.

Show note links:

🎥 The podcast is now available in vodcast (video) format! Watch the episode below, or over on YouTube.

Let us know what you think and fill out the Feedback Form, or email podcast@measurelab.co.uk to drop Dan, Dara and Bhav a message directly.

Follow Measurelab on LinkedIn and on Twitter/X, and join the CRAP Talks Slack community.

Find out when the next CRAP Talks event is happening on LinkedIn.

Music composed by Confidential – check out their lo-fi beats on Spotify.

Master Google Analytics 4 with Daniel Perry-Reed on the next GA4 Immersion 6-week cohort training course. Charity and group discounts available!

Quotes of the Episode:

  1. “…I can’t imagine learning and doing like a professional or personal development to a point where you are good at something and then this machine comes along and basically is trained on the stuff that you’ve learned and does a similar thing.” – Dan
  2. “…Most likely, nine times out of 10, a new piece of technology will not solve your problems. It is not a solution to something. Technology won’t solve them. You will.” – Dan


*Please note the following transcript is AI generated.

Intro | Topic | Outro


[00:00:00] Dan: Welcome to The Measure Pod with me, Daniel Perry Reed. I am the Principal Analytics Consultant and Trainer at Measurelab. And joining me as always is Bhavik Patel, Director of Experimentation and Analytics at LeanConvert. This is episode 102, and we recorded this on the 28th of June, 2024.

[00:00:30] Dan: Now just a quick admin update. We’re back. If you’ve been listening to us before, we’re back with a new set of episodes. Our plan going forward is to release them every fortnight. So if you’re used to a weekly cadence sorry but we’re going to go fortnightly and we’re just going to keep going. We’ve got guests booked out.

[00:00:44] Dan: We’ve got conversations lined up. We’ve got some interesting stuff between myself and Bhav. We’re not going to have any more breaks. So gone are the days of having a couple of episodes, then. What turns out to be a couple of months off, but we’re going to go fortnightly. So make sure to subscribe on any feed that you happen to be listening to us on.

[00:00:59] Dan: And of course, check us out on YouTube over on the Measurelab account. If you want to see our lovely faces, but enough of that bath, we’ve just recorded our first episode back. And yeah, I thought it was fascinating. We actually talked through one of your blog posts. Data is a. Journey, not a destination. What did you think? 

[00:01:18] Bhav: Yeah, I think, I think in the spirit of the blog post, we actually took a journey to get to that topic of discussion. So I think it was a nice episode. I, I, i a hundred percent stand by the metaphor of, of treating, you know, your data, data as a, as, as a, as a, as a road trip, and not as this destination that, you know, it’s a mad rush to get to.

[00:01:39] Bhav: And I think we know, as we were talking about it, we were exploring some of the, some of the. the reasons why people might see it as a destination. And actually the pitfalls of seeing a destination means that, you know, you stop and appreciate the journey that you’re on. So hopefully anyone who’s listening will enjoy it.

[00:01:56] Bhav: And, and, and, you know, it will probably, I hope it will resonate with everyone as well and get people to shift away from this mindset of, you know, we need to be this super tech, smart, you know, AI advanced organisation and just appreciate what it is. You know, it’s, it’s, it’s, it’s, It’s only as good as, you know, the people using it and it doesn’t need to be AI level, it just needs to be valuable. 

[00:02:22] Dan: And of course, content warning for any data or analytics engineers out there. You know what we mean when we get there. And of course we’ll be if that is you and you have a thought about what we talk about, then please get in touch. There is a form to communicate with me about in the show notes.

[00:02:34] Dan: We’d have you on. I think we’ve set ourselves the task of having an episode with a bunch of analytics and data engineers. So. Please feel free to get in touch. Beth, what’s going on? Anything to plug what’s new with you? 

[00:02:45] Bhav: Yes. If you’re listening to this episode before the 16th of July, we have got CRAP talks, 26, 25, I’ve, I’ve actually lost track coming up. So please do come along. It’s going to be a fantastic evening. We’ve got a [00:03:00] couple of speakers already booked in who are going to be great. And as per usual, the networking aspect of it is a big, big part of why our attendees keep coming. So come along. That’s, that’s, that’s, that’s the main thing I wanted to plug.

[00:03:11] Dan: And that’s the London CRAP, right? That is the London CRAP Talks event sorry yes, that’s correct. Awesome. I think in terms of recording, we’ve just missed to plug the Manchester CRAP, but yeah, that’s it. It was, it goes on, make sure you’re, you, you follow them on meetup just to catch the next one as well.

[00:03:26] Dan: And for me, I’m actually running a bit of a survey at the moment. So my plan is there’s a lot of thinking I’ve been doing over the last couple of weeks around the world of analytics as it’s changing my role in that as well which is maybe a conversation for another time and a future topic maybe.

[00:03:41] Dan: But I really just want to hear from anyone that works in the world of analytics. Please go to the show notes and I will allow you to pause this episode and go do this. I allow you to do that. But there’s a quick feedback form or sorry, there’s a quick survey that I’d love for you to fill in.

[00:03:54] Dan: And I just want to get an idea of what we define analytics as. So what skills do analytics people need and what is analytics today? Now we’ve all got a slightly different perception. We’ve already had a handful of, we’ve already had a bunch of responses and they’re all really fascinating. And there’s some really interesting themes coming out of that.

[00:04:11] Dan: But my, my intention is to publish this at some point in the future, but I want to give it a couple of weeks so that anyone listening to this and the other people could contribute their thoughts. So if you’re in the world of analytics, please jump into the show notes. There’s a quick form. If you follow me on the socials, you’ll see it on LinkedIn and other things as well.

[00:04:25] Dan: I’m sure I’ll be talking about it loads more over the next couple of weeks. Last thing to mention is as well as the links to everything we discuss and of course, my plug of that feedback, that survey and the CRAP talks meet up you’ll find ways to connect with me and Bab or LinkedIn, et cetera, in the show notes, as well as the companies we work for lean convert and measure lab, be sure to check them out, follow them or do whatever you need to do. Everything is over there. So the last thing to say is let’s get to it. Enjoy the show.


[00:04:52] Dan: So we’re back, we’re back recording an episode, Bhav welcome back. Good to see you. It’s actually been a minute since I’ve seen you and I’m looking forward to seeing more of you at CRAP next week. Or whenever this comes out, it might have been in the past. So Bhav, what’s been going on? It’s been, I dread to think how many weeks slash maybe even months now since we recorded the last episode, but we’re back into the new season or the new format, the new flow, have you been, what you’ve been up to, what’s new with you? 

[00:05:15] Bhav: Oh, I don’t know. Hi, Dan nice to see you as well. I’ve missed you. I’ve missed our conversation. So I’m looking forward to, to talking again more on, more on these podcasts, having guests and meeting people again through Crap Talks. It has been a while. Whatever I’ve been up to, mostly work. I’ve been trying to keep my head down. Crack on with some work solving some, some really interesting client problems, which has been fun.

[00:05:37] Bhav: Been spending a lot of time with the family, with the kids. That’s always that’s, you know, that’s a big, that’s a big part of my life. And I’ve been trying, I’ve been trying to write more and I, whilst I end up writing, I never ended up publishing it. So what you see on like socials is kind of like snippets of kind of like bigger thoughts that I just haven’t had time to like fully articulate into proper blog posts.

[00:05:57] Bhav: So sometimes I feel like it’s, it’s just a snippet of all the things I want to say. How about you? What have you been up to? 

[00:06:03] Dan: Well, I, I’ve been obviously reading the stuff you share where, where it pops up in my feed, but actually the, the, the conversation of the day of this episode is going to be on a blog post you shared, which all links will be in the show notes if anyone wants to go in and read the full article.

[00:06:15] Dan: But obviously you’re using ChatGPT to rewrite it, to sound really intelligent and summarise it, right? That’s what you’re doing. 

[00:06:22] Bhav: You, you know, you’re going to like you poke the bear here. You know, I don’t do that. I really don’t like using ChatGPT. So a few a few, a few years back, I actually joined a writing club for and within this writing club the way it works is you write, you write some posts or you write some content and then you share it to a community of people, proper writers, who will then critique it, give you feedback, give you suggestions on how to make it more punchy. 

[00:06:46] Bhav: So I, I, I actually learned how to write. I spent most of us, and I mean, I guess there’s never really a, you know, like you never like graduate from learning how to write, but I’ve spent a lot of time learning how to write what you see on, on, on my blog posts, on, on LinkedIn, everything from sentence structure, complexity of sentences.

[00:07:03] Bhav: I aim for, there’s something called a, a reader score on an app called the Hemingway app. When I finished, I Initially when I was writing, I would write my post and put it onto the Hemingway app and it would score it and what you’re aiming for is you’re aiming for a reading score about six, between six to eight.

[00:07:18] Bhav: And, and the reading school represents grade level. So you wanna write a, a sixth grade, seventh grade, eighth grade level. And I was writing at what was more complex university level. So it wasn’t, and that’s, that’s not a, that’s not a a, a brag, a a brag, sorry. That is, it’s too complex. And, and it was, I was using long sentences.

[00:07:37] Bhav: I wasn’t pausing, I wasn’t breaking, and there were a lot of words stuffed in that I didn’t need to. So through this writing community, I learned how to write for impact. And what you see on things like LinkedIn and on my blog posts, that’s a manifestation of sort of like a few years of continuously writing, learning how to write and retraining my brain in terms of how I write about this.

[00:07:58] Bhav: So it’s not ChatGPT. Although I’m convinced, I’m convinced LinkedIn probably thinks I’m using ChatGPT and it’s It’s, the algorithm is, is kind of like down, marking my scores down because it thinks it’s writing, I’m writing at chat GPT level. But no, I do use it. Like, I mean, I’m not, I’m not saying I don’t use chat GPT, but I use it more for just like, Hey, let’s just correct the grammar if I’ve made a spelling mistake or something like that.

[00:08:23] Dan: Yeah amazing. I, I knew, I knew that, and I just wanted to poke the bear. So thanks for humouring me. But yeah, for sure. I mean, it must be so as someone that really struggles writing and spelling and grammar has never been my forte and let alone kind of like reading is actually a struggle.

[00:08:37] Dan: But as well because of all that stuff. So I think it’s all tied together. I can’t imagine learning and doing like a professional or personal development to a point where you are good at something and then this machine comes along and basically. Is trained on the stuff that you’ve learned and does a similar thing.

[00:08:53] Dan: So not saying that it’s the same from a reader’s perspective, but from the machine perspective, it identifies it as similar and then you might get flagged, it must be infuriating to say the least. 

[00:09:03] Bhav: Yeah. I mean, that’s my assumption. Of course, that’s, you know, me just being sensitive about the fact that I don’t seem to get the same reach on my content as I used to.

[00:09:10] Bhav: For, for what it’s worth, I think. Everyone should learn how to write and I don’t mean in the like a very like they go and spend like 50 hours a month learning how to write but just writing. It’s such a route It’s such a great exercise in Formulating your thoughts formulating your ideas I know i’ve mentioned this to you before one of my life hacks and I said I probably wouldn’t share it But I think I will when i’m speaking publicly like conferences or things like that Whilst it sounds like i’m giving a really good talk and and my What I’m saying is really flowing.

[00:09:40] Bhav: Actually, I don’t think it’s natural. I’m a really bad speaker. I really struggle to speak When I’m, you know, when I’m talking about Topics that I’ve not really written about before so what I do is I write a lot and I think a lot about what I’m writing about. And that really helps me create the sentences and I use them like a go when I’m talking about topics I’ve studied a lot.

[00:10:05] Bhav: So it’s not like I’m, this is, you know, like a conscious stream of what you’re hearing. A lot of it is stuff that I’ve kind of like drummed in, practised, rehearsed over and over again through the act of writing, reading, and thinking. So it comes out as a very coherent sentence. But otherwise it’s, I’m a terrible public speaker, I’m surprised you guys asked me to join the podcast. 

[00:10:26] Dan: Well, that’s it. Maybe you’ve rehearsed it so well that we think that you’re a great public speaker and, and for, for, for what it’s worth. However you get there, the destination is the same. It’s a good talk. I’m a good public speaker. And hopefully our listeners agree that, you know, you’re good to have on the podcast so much so that we kicked our, no, I’m just kidding off.

[00:10:41] Dan: We miss you, Darren, if you’re listening. I miss you too, Darren. This, I tell you what’s fascinating. What have I been up to? I learned something about myself during this break. And it’s, it’s fascinating. I’ve been talking to everyone I possibly can about it. So have you heard of a thing called aphantasia?

[00:10:55] Dan: No, it’s weird. It’s it’s it’s I’m new to this. So I was running [00:11:00] a. Training session via brightness, you know, I do the Google analytics training there and afterwards there’s drinks vouchers and we will hang out at the bar. And I was meeting and chatting with a bunch of other training people and also trainers all sorts of different courses, and then we randomly got onto this conversation with a couple of other trainers about like teaching styles, methodologies and stuff like that, and using slides and aids to kind of explain complex situations, and I went through what I do using Google sheets and sort of drawing stuff as well.

[00:11:25] Dan: And it, and it transpired that this trainer has this thing called aphantasia. Now aphantasia is a spectrum like anything but essentially it’s saying that you have no or very little visual memory. So if someone says to you you know, close your eyes, imagine you’re on a beach, you know on the sand in your, in your toes, waves lapping up against the rocks and stuff.

[00:11:42] Dan: There’s no pictures going on. I’m not a visual thinker. Like I don’t have a visual memory. So it, what it also. Effects is things like if I’m not looking at you, Bev, and someone asked me to describe what you look like, I’ll be like dark hair and glasses. That’s as far as I can get without looking at you.

[00:11:57] Dan: I actually have no image of you to refer back. So I can learn characteristics and features, but I’m not, I’m not looking at a picture of you in my brain. And so we kind of transpired and then I’ve been talking to everyone about this and it’s actually affected a lot of stuff. What I’m kind of like, maybe in hindsight, going back and applying and attributing to this aphantasia, but it’s the complete opposite.

[00:12:16] Dan: I think of what you just said, which is, I don’t rehearse anything and I think and speak and draw out loud. So because I’m, I can’t form stuff visually in my brain. So if I, if I’m working something out, if I’m thinking something through it, it’s, It’s all like in front of me. I have to draw it down because I can’t map it out in my brain visually.

[00:12:35] Dan: And so for me, like when I do a talk like the last measure camp last September I deliberately didn’t do any preparation before I started talking because often when I start talking, it happens and the less preparation I do, the more or the more preparation I do in this, I’m really. Preparing like running a training course, for example, I end up thinking like I’m building a script and I end up getting, I don’t allow myself to deviate from that and I’m almost like reading out a rehearsed script, which actually is kind of detrimental to the way my brain flows and works and react. So, yeah, anyway, I’m the complete opposite. I do the opposite of what you just said but. 

[00:13:10] Bhav: That is fascinating because I am, I am not like that. Like I won’t memorise the script. But when I’m, and I, I, I will prep, even if I’m doing prep to the last minute. So for example I was speaking at a conference last week.

[00:13:22] Bhav: So no, two weeks ago David Mannheim, who those of you guys that know, so he’s a past guest he was supposed to be speaking at a conference that we both are. Unfortunately, he had some health issues and he had appendicitis. Hopefully you’re feeling better, David. But he asked me to step in at the last minute.

[00:13:37] Bhav: So I did and I built the talk the night before effectively and Even though it was last minute preparation I rehearsed in my head as I was building each slide what I wanted to say and I even had every single joke planned out So a lot of my jokes, for these in that situation Were all mapped, I knew exactly what joke I was going to say, what personalisation joke I was going to make, what joke I was going to make about product management, everything like that, was roughly mapped out in my head, so that when I got to the slide, I don’t have the script, but I know roughly the start, the end the start, the middle and the end of the joke. So it’s really here, I’ve never heard of that. 

[00:14:14] Dan: Yeah, but I, well, before, before I found out, I think the, the simple test that was given to me is close your eyes and think of an apple now describe what you see, and then my, I think my response was some, my response was something like, what do you mean, what do I see?

[00:14:29] Dan: I, I can describe an apple. I know an apple looks like I’m like, it’s the way, okay. For our audience and for you, Bev, as well, the way I have explained it to my colleagues at work is that imagine it like a cloud database, right. And I can recall information at will. So I can recall information of what an apple is like, and, you know, the descriptions around it, or the various.

[00:14:50] Dan: The variety of what can happen with apples and stuff, but, but there’s no image of it. Like it’s not, it’s not a dashboard. I’m not looking at stuff. I’m just recalling information as I need it. So that’s the way I’ve described it to my colleagues.

[00:15:00] Bhav: I mean, this is fascinating. We’ll definitely have to talk about this some more next time we’re having a beer. Yeah because when you say, if you said to me like pink lady apple, I can visualise where it is in the supermarket I can see the little sticker on it. I can see myself picking up. I can, I can see the shine on it. You know, like it’s, this is, it’s just, I’m, I’m blown away right now. I’m like, I’ve never heard about this. So it’s really interesting. 

[00:15:20] Dan: So this is fun, but let’s talk about what we’re here to talk about. Maybe we can come back to this another time. So we are here to talk about writing and that our approach is actually one of the blogs that you’ve written now for our listeners. If you’re listening and you’re still listening at this point you don’t have to.

[00:15:36] Dan: to have read the blog from the bath, but if you want to, it’s a couple of minutes, maybe three, five minutes read, and it will be linked to in the description. It’s a really fascinating discussion and it’s the, the title kind of does a lot of the explanation of what you go into, but it’s called data is a road trip, not a destination.

[00:15:51] Dan: And I think maybe most of us can relate to this or can identify a scenario or a previous experience to where this has actually been the cause of a problem. Or maybe in hindsight, it could have been different. For those that haven’t read it, give us a bit of an overview, a quick summary. Why, and actually, why did you write this? What, what drove you to write this down and as a piece of content? 

[00:16:12] Bhav: I think I was doing some client work and it came to, it occurred to me that when people think about data, especially like, and I don’t mean the data people, I think the data people, Instinctively know that it’s not a wham bam. Thank you, ma’am.

[00:16:26] Bhav: You know the type of situation . This is a journey like we’re always going to go on and but yeah when it’s pitched to leadership around Hey, we need investment in data. It’s kind of pitched that this is what we want to get to And that pitch and that destination is what usually drives the funding and things like that.

[00:16:46] Bhav: And then you see frustration within organisations with like, well, why haven’t we got there yet? Like, why is this taking so long? I know in the past, when I’ve asked data engineering for you know, to help me do some data modelling so that I can have an a table or, you know, like a user table or something like that.

[00:17:04] Bhav: It’s taken months, even, you know, like even a year or something to even get them to prioritise it. And it’s always because they’re doing something else and, you know, blah, blah, blah, all of these things. And it’s, and it occurred to me that. When we think about data and the way we talk about data, it’s always pitched as, you know, this is a place we want to get to, this data utopia, and, and it’s not, right?

[00:17:26] Bhav: And I, you know, there isn’t a final destination for data. It’s, it’s a journey we go on, and I started thinking about sort of like what would be what are the milestones you have to hit and for some reason in my brain that those milestones became checkpoints and I started thinking about it actually it’s kind of like a road trip that we go on you know so you could and the other thing is like when you pitch as a destination you have to like everyone needs to come with you you don’t right you you You start off on the road trip as, you know, maybe two, three, four people or however many people, you get to your first destination, there you might need some support from data engineering, then you move on and you might need some support from someone else, and you keep going to these new destinations, and each destination brings new sites, it brings new culture, it brings new people, and And, and you can stay somewhere.

[00:18:13] Bhav: So, you know, let’s say for example, you get to a point where you’re like, you know, I really like the view from here. It gives us everything we need. We don’t need to keep chasing that final destination. We can take a break here for a little while. You know, let’s take a, let’s take a day to break and we can sit here for like a month or two months.

[00:18:28] Bhav: We’re getting everything we need. And only when we realise that, you know, we’ve soaked up enough of this culture and we’d really love to see what, you know, what’s next and, you know, move to a different country. You kind of think, okay, well, what does the next phase look like? And that, and, and in many ways, this.

[00:18:41] Bhav: journey represents the maturity of an organisation in terms of its pursuit of data excellence. But when we, when we pitch it as a destination, you know, we’re, we’re forgetting that it’s going to be a long road. You know, we don’t need to take everyone with us right from the word go. So Sorry, that’s not much, that’s not quite an elevator pitch, but it’s, it’s really around this idea that you’re going on a journey, you know, you, you might start off with a handful of people, you meet different people along the way, you can stop at wherever you want to go, you can go back if you need to, right?

[00:19:11] Bhav: And say, actually, you know, we were okay here, we really liked, and then you, you know, you keep moving forward if you, if you really want to. So that was the premise of it. And each stop is a different place. So I think in my post, I wrote about the first stop being Measurement Strategy Village. And Measurement Strategy Village is where you start to plan, like, what is, you know, what do you want the data to do?

[00:19:34] Bhav: What are the metrics you need? What are the measurement frameworks you need to put in place? What does success look like? Things like that. So it’s, yeah, each one of these, each, each destination on the road trip represents a milestone in your otherwise, you know, long data journey that you’re going to go on.

[00:19:52] Dan: And this is, this is the immediate thing that I related to and I think there’s one part specifically of that that I want to drill into because I think I, this is what I’ve been thinking about more and more so recently, which is the idea if you might enjoy the view, you might stay there or go back a little bit.

[00:20:06] Dan: You might go on a road trip and you might be like, well, actually. This is better than the destination or the destination is too complex. I’m bringing it back to the data world now, but often we, there, there are infinite numbers of maturity frameworks and assessments and scales that you can be assessed on.

[00:20:22] Dan: And I want to shout out we recently hired a guy called Mark McKenzie at Measurelab, and he’s written this really incredible book called your data is fucked for marketers. So it’s a really good book. I’ll stick a link in the description. I don’t get any commission from it. But it’s a really good book.

[00:20:35] Dan: If you’re in marketing and you’re in data, it’s a really fascinating book. But one thing that I was trying to Mark about which is something again, that I, I might not disagree with, but I think is maybe a slight improvement is that the maturity framework that is created in there is numbered. And I found it.

[00:20:48] Dan: Numbering something is quite not problematic, but it makes us think, Oh, you don’t want to be level five, but you’re a level three. You need to be level four, four is bigger than three. And actually it, this, this process, whatever frameworks you’re creating, as soon as they become numbered, it’s like an expectation or an assumption that you want to be a higher number.

[00:21:07] Dan: And actually this is something that I’ve been again, chatting with Mark about. And what I identified in that post from you is that it actually doesn’t matter. You can. You can stay somewhere that actually might be better for you in the long run and enjoy that view for a long time. Maybe in the future, things change and you go forward, maybe you go backwards, but it’s not really back.

[00:21:27] Dan: It’s just changing. And I think it’s about identifying what works and what that doesn’t. What the value is I suppose and not always chasing something next if you’re always chasing the next thing You’re never enjoying what you are and you’re always thinking about like, oh, but we’re not here yet But we are here and that was better than where we were before But we’re always thinking what we don’t have rather than what we do have. 

[00:21:47] Bhav: Yeah, and I couldn’t agree with you more. I think that I don’t know where it comes from this pressure to be AI driven and, you know, like having the latest tech stack and, and, and what have you. And, and again, a lot of this was driven by some work I was doing, you know, for some clients and, and you know, they’re, they’re really nice clients, but they’re not ready for air.

[00:22:06] Bhav: You know, you’d think that, you know, they’re a very profitable business, but they’re not ready for things like ai. So right now, we started off by doing this measurement framework. And again, all, a lot of this was triggered by a lot of this work that I’m doing and this, and it’s, and it was, and it really got me thinking about.

[00:22:20] Bhav: Everyone has their own journey, but one isn’t better than the other, right? Like I don’t, you know, like you don’t need to have Google level of data maturity. You know, you can be where you are if you’re profitable and you’re, you know, getting the insights you need. If you then reach a point where like, I’m not quite getting what I need, then you might consider the next destination on your journey.

[00:22:38] Bhav: And I really liked your point about, um, about numbering things. Cause you’re, you are right when, you know, we see these maturity curves and the way that they’re described is. You know, 5 is bigger than 4, 4 is bigger than 3, 3 is bigger than 2, and 2 is bigger than 1. And, and, in sort of like the data world, we would classify those types of numbers as continuous numbers.

[00:22:59] Bhav: In the sense that there is an element of measurability you can do calculations off it. Like, you know, like one is bigger than the other. But actually it’s not, right? It’s like, They’re categorical variables. Like, you know, being five foot five versus five foot eight doesn’t mean anything. One is taller than the other, but doesn’t mean like, you know, it’s, it’s better or worse than anything like that.

[00:23:21] Bhav: And this is, excuse me, and this kind of view that everyone has to get to five is kind of, I think it’s perpetuated a lot by leaders wanting to show that their, their organisation is like, like, you know, like really moving forward. And, you know, we’re really data driven and, you know, we’re really tech first and all of these kinds of things.

[00:23:43] Bhav: And actually you don’t need to be, it’s like, if you’re doing well with what you have, Hey, let’s enjoy the view in this, in this place that we’re at right now. And only when we’ve maximised. everything we can. And I say this about technology. Technology is one of my biggest gripes. When I see people chasing newer and newer tech stack, I was like, oh, this, we need to do this platform.

[00:24:01] Bhav: We need to move to this platform. We need to upgrade our data warehouse. It’s like, well, are you getting like, like 90%, 100 percent value out of your platforms that you have now? If the answer is no, why do you think a new tech platform is the solution? I think maybe we, you and I have spoken about this in the past before as well, but you know, when, when, when you see companies saying, okay, we need to move to.

[00:24:22] Bhav: To, to this product analysis tool, or this thing, or something like that, it’s like, well, if you’re not getting the support with the technology you have now, if people aren’t using the technology you have now, if you’re not utilising its full functionality with what you have now, why do you think that moving to something bigger, better, shinier, is going to make any difference, right?

[00:24:42] Bhav: What you have isn’t a tech problem, you have a culture problem. You know, allocate those resources, get usage up to its maximum level, squeeze everything you can in terms of feature usage of the platform you have. And then when you reach that point, you’re kind of reaching the end of like, Hey, you know what?

[00:24:59] Bhav: We spent a long time at this destination. We’ve seen everything there is to see. We want to move on and we want to see what else the world has to offer. Then you’re ready to move on. In my opinion. 

[00:25:09] Dan: Now for our long term listens, it’s no secret that I like to run training courses all around Google analytics, Tag manager, and everything in between.

[00:25:15] Dan: Check out a full list of courses over at measurelab.co.uk/training to see all the courses and workshops that we have available. Everything from. Learning Google Analytics 4 to Google Tag Manager, data visualisation, and short for workshops to cover small specific areas of interest, such as user provided data, generative AI for data analysis, as well as lots of other stuff that’s measurelab.co.uk/training for all the details, or you can click the link in the show notes, or if you’re watching this, scan this QR code now.

[00:25:45] Dan: I still religiously refer back to Avinash’s 90, 10 rule with this kind of stuff and saying that you know, with, with every, with, when you’re investing in, let’s say just marketing, for example, if you’re investing in marketing, I think the technology is the 10 and then the people and the process is the 90.

[00:26:01] Dan: So if you’re not currently doing that, spending more on technology is not going to solve your problem. Having more, we have this conversation at work all the time around, should we go to GA 360, for example, and I was just like, well, I don’t know. Okay. If you’re happy to spend anywhere from three to 10 grand a month on technology, what are you going to do differently?

[00:26:17] Dan: Like, how are you going to make better decisions to earn more than that much money back to make it a kind of profit centre, rather than just a cost. Technology won’t solve anything. It just ups limits and expands features. Like it’s not going to make you money. And actually what you do with it, it’s going to make money.

[00:26:32] Dan: I’d rather. I’d rather have, you know server logs and make a good decision, which earns you your 10 X return on this thing than introducing Adobe analytics or another piece of software, or, you know, the amount of times we’ve introduced heat mapping or, or scroll tracking. And it’s just like, Nope, that’s just data.

[00:26:48] Dan: It’s just sitting there. It’s just costing you, you know, page load time and the licence for this tool. doing anything for you. 

[00:26:56] Bhav: I’m gonna, by the way, I’m gonna steal that.You said something there, which I love, and I’m gonna steal it. I’m gonna write a blog post about it. I might even give you some credit for it, but I’m gonna pass it off as an original thought.

[00:27:07] Bhav: You said, if you’re gonna spend five, ten grand. With this new technology, what are you going to do differently? I think that is such a powerful question to ask. 

[00:27:16] Dan: You know, at the beginning we were talking about this Aphantasia thing and how I speak out loud and I process information by speaking. I think that I immediately forgot that I just said that.

[00:27:25] Dan: And so it sounds really intelligent, actually, in hindsight. But it also means that I can enjoy listening to this episode back because I forgot everything that I said. So I like, you know, that’s always a fun thing for me. So just kind of attributing it back to this Aphantasia thing. 

[00:27:37] Bhav: Oh, I’ve written it down. So you’ve done, if you don’t hear it back on this episode, you’re going to see it regurgitated on LinkedIn. 

[00:27:44] Dan: Well, let’s let’s, let’s, let’s, let’s I have one question prepared before this episode, only one. So here it is. How can you identify if you are working with or in a destination mindset? And, and then how I suppose the continuation of that question is how then do you work towards adjusting it to a journey, a mindset rather than destination? 

[00:28:05] Bhav: Oh, that’s a good question. I like questions that haven’t been prepped in advance because it means I have to, I’m forced to think on the spot.

[00:28:12] Bhav: And then that skill I tap into memorised sentences and, and like thoughts. I’m going to take a sec. We’ll probably cut this into a very short time period. I, I, you know, I, I think I know how I’m going to answer this question. I’m going to say if you’re a destination driven organisation versus a road trip organisation when your engineering team or your data team are too rigid.

[00:28:42] Bhav: If you’re rigid in your, in your approach, if you have something that’s 12 months down the line and you have no way of getting things that are important and prioritised. I’m not saying that obviously you should prioritise everything that comes in, but if there is no flex, if there is [00:29:00] no ability to say actually, you know, we’ve got this new challenge that’s just come up.

[00:29:04] Bhav: How do we adapt to this challenge? If there is no movement in your ability to adapt, I would say you’re a destination driven organisation and you should consider being a bit more road trip driven. Now I know this is kind of like flies in the face of like software engineering principles and what have you because, you know, people will just, you know, what’s the point of having a road, you know, like a We’re going to get mixed up in analogies here, like a roadmap, right?

[00:29:29] Bhav: If you keep allowing yourself to pivot and change direction, you’ll never get to where you want to get to anyway. So the idea isn’t that you are completely flexible, but if there is no movement, there is no flex, you know, you’ve got your next six to 12 months planned out to the point where actually adding anything onto a data Into a data into your data backlog is impossible.

[00:29:52] Bhav: I would say you’re a destination driven one. The second one I would say is I think it goes back to maybe that if you’re if you’re not squeezing everything you can out of your platform and you’re constantly changing and technology and you know you’re constantly looking at new shiny toys.

[00:30:13] Bhav: I think in this instance what you have is you are still chasing a destination because you’re trying to get to somewhere and you’re like not giving the technology you have a chance to to be used to the, to its max capacity. And you’re like, Oh, let’s try this. Didn’t work. Let’s move on. Try this.

[00:30:28] Bhav: Didn’t work. Let’s move on. Try this. That didn’t work. I think this is a bit too destination driven as well, because you’re not stopping to take the sights and you’re like allowing, allowing your. program, your tool, your technology, your people to mature. Like you need to spend some time in a destination and then before you move on.

[00:30:46] Bhav: So I don’t destination in a checkpoint before you move on. So that was my unprepped answer. And I hate unprepped answers, but I think I love it. Now I know this. 

[00:30:57] Dan: Now I know that every episode is going to have an element to it. We’re not going to put it in that shared doc we have beforehand. But I think that’s a great answer.

[00:31:03] Dan: And I’m going to say this to the camera, you can quote me on this. Most likely nine times out of 10, a new piece of technology will not solve your problems. Like it is not a solution to something. Technology won’t solve them. You will do something with it. And I think that’s the key thing.

[00:31:18] Dan: So there we go. That will probably be one of the quotes that I’ll put on the show notes page probably. So I think this is a really fascinating subject, this idea that you know, okay, let’s, let’s see if we can put it into context around the world of analytics where we work. So Obviously, we can’t share anything specific in terms of clients we’re working with and stuff.

[00:31:35] Dan: But are there any specific examples that you could maybe anonymize or generalise around that actually identifies this or maybe a challenge that came up because they’re a destination mindset?

[00:31:48] Bhav: I don’t think this is based, this, this idea of this road, road trip, it came about through clients who were in early stages as opposed to they’ve [00:32:00] been this destination mindset. And I realised that when I was, because I was doing some work and I was prepping for this, we, we, we were doing a measurement framework workshop.

[00:32:07] Bhav: So we’ve, we, we went to see them in Croatia. And as I was prepping for it, it was really nice because these clients are, they’re absolutely wonderful. And they’re really open to the idea. They want to be data driven, but they’re You know, it’s, it’s kind of like, what does that look like?

[00:32:23] Bhav: How do we become data driven? So we started off with the idea that like, Hey, look, let’s build a measurement framework to help you get started. They don’t really employ proper, they don’t have proper data, a data platform, like a Looker Studio or Power BI or anything like that. So right now they have a database that it’s a SQL database.

[00:32:44] Bhav: They have GA. They are full stories and they use everything else in Google Sheets. So there isn’t a BI platform, right? And then even in terms of the data, I was talking to one, I was talking to the founder and the CEO of the company. And he really wants people to understand why they’re making decisions.

[00:33:02] Bhav: And it was really, it was a really, really interesting conversation, probably one of the best conversations I’ve ever had with the CEO. Because sometimes what happens is he was in situations where people would be coming to him and talking to him about, Hey, we did this campaign and it went really well.

[00:33:15] Bhav: And he would ask, well, how do you know it did well? And they didn’t really have, you know, they would say, well, the sales went up, like, well, did they go up because of the campaign or did they go up just because they went up? And It was through that conversation I realised that, you know, we, what I’ve got here is I’ve got this ripe opportunity to work with a CEO who understands that there is somewhere he wants to get to, but he’s not quite sure what that would look like.

[00:33:37] Bhav: And actually trying to present him a full end to end diagram of what their data infrastructure would look like from data sources to data warehouse, to BI platforms, to reporting layers, and, and then outputs. This was never going to be something that I was going to be able to get buy in from. So instead we, I took this approach was like, okay, well, let’s start off with [00:34:00] the first thing.

[00:34:00] Bhav: Let’s define what a good measurement would look like for your company. And I look back to companies I’ve worked in and It was this, and I realised that there was, when I was working with the data, and I won’t lie, it’s the data engineering team that has triggered this. Because getting time with data engineering is one of the hardest things that analysts ever have to deal with.

[00:34:25] Bhav: Because for some strange reason, data engineers are working on everything other than the needs of the analysts, who are the primary consumers of that data. And it blew my mind. It’s like, hang on, like me and my team are the primary consumers of your everything we do is with your data. No one else uses this data.

[00:34:42] Bhav: So what is it that you’re working on that is so much more important that the, you know, that, that, that. That is taking precedence over your, the needs of your primary stakeholders. Like, could you imagine you as an analyst, me as an analyst working with us, like, let’s say the CMO comes up and says, hey Dan, I really need some you know, we’ve, we’ve just launched these massive campaigns.

[00:35:04] Bhav: We’re about to pump more money into it than we’ve ever pumped into it before. We really need some help for some analysis. You know, like, oh yeah, sorry, I can’t do it. I’m, you know, I’ve got this roadmap that I’m working on and it’s, it’s, it’s some stuff that it’s going to be around, like, you know, building out great reports for everyone and we’re going to democratise reporting and it’s like, okay, but yes, that’s important, but I’m your stakeholder and I really need support to be able to make sure that we spend this money wisely and That realisation really like, that’s when I knew I was working in rigid organisations and that were destination focused.

[00:35:40] Dan: I think there’s a really good example that I can come up with for what kind of ties into this. And I had a lot of conversations recently, so there’s a big one. In my world, in the marketing analytics world, there’s been a big shift with the move to GA4. Now you don’t have to like it or love it or hate it.

[00:35:55] Dan: You don’t have to be using it, but it has shifted things because it’s Google, it’s big and well adopted. And one of the big things that shifted is the idea of first party data is happening in the world of marketing in general but storing your, your analytics data in BigQuery with an event schema, I know revolutionary, right?

[00:36:10] Dan: But Google Analytics 4 has introduced that from, to, to, to many new people. And a lot of the solutions that we were used to experiencing, like building dashboards in Looker Studio for free, are now, Oh, we have to use BigQuery. We have to use BigQuery. This is the thing that comes up time and time again.

[00:36:25] Dan: And I think what this does is this as an industry. As a marketing industry, I think that it’s still relatively new to introduce the idea of databases, data warehouses, data lakes, you know, the whole cloud infrastructure outside of the kind of engineering or analytics teams. And so I think what’s happening now is they’re like, okay, well, you know, here’s the destination.

[00:36:45] Dan: We need a data warehouse. But that’s not, that’s not what you want. That’s a, that’s a, that’s a means to do something. That’s a means to an end. It’s a tool. But actually people are coming and saying, we need a, we need a data warehouse. And that’s just like, well, that is like saying I need a, I don’t know.

[00:36:59] Dan: I need a car. It’s like, yeah, but if I gave you any car, you’re probably going to be like, Oh, not that car. I wanted a blue one, or I wanted one with four wheels. Why did you only give me one with three, you know, like, so there’s lots of stuff there where it’s it’s how long is a piece of string, you know, I need a data warehouse.

[00:37:13] Dan: And I think that the journey there is actually really important because, you know, storing your own data, governing it, joining it using and activating first by day is really important. But right now, if you’ve just got. A tagging tag manager to power Google ads. And you’re just, that’s it. Maybe it’s too much for you.

[00:37:29] Dan: Maybe there’s a journey there, like the road trip. And actually maybe you don’t get there. Maybe you sit there with a tool like super metrics or funnel and just push it into a dashboard and be happy. Maybe you don’t need to own your own data infrastructure and pay for that. You know, Oh, wait, I have to pay for this.

[00:37:43] Dan: This is new, you know, oh, and how much is the maintenance of this? How many people do I need to hire? You know, it’s a journey. And actually that’s part of that journey. Whereas people often come to us and say that we need it. And it’s because they’ve been told, or they’ve heard this word, or they know that, you know, Google was shouting about first party data.

[00:37:59] Dan: So yeah, it’s just another example in our world where we’re coming at this at the moment and we’re seeing more and more of these conversations and it’s really hard actually to. Especially in a, in a new business call, when we’re speaking to people that we don’t have an engagement with or a relationship with, or we don’t know very well, ultimately is what I’m saying there, it’s really hard to say, that’s not what you need.

[00:38:18] Dan: You need to go on a journey and we can help you with that. But actually I can’t tell you what that looks like just yet, because I need to figure it out with you. You know, we need to figure this out. So, you know, it’s a really hard sell, actually this idea of the journey because the destination is, is tangible.

[00:38:33] Dan: It’s fixed, or at least in their mind, it’s fixed. It’s a product. It’s a thing. Whereas actually selling a journey is bloody impossible. Well, it’s not just really hard. 

[00:38:42] Bhav: No, and you’re, and you’re right. But I think, and again, it comes down to like these, like going, you know, going back to this maturity framework of like one, two, three, four, five.

[00:38:51] Bhav: When you’ve, when you’ve got your eyes set on five. Right. You miss, you miss all the pleasures of two and three and four, you know, like going on that journey to, you know, if you’re a one and you’re set on five, right, you never really appreciate how hard it was to get to two. And that actually just maybe just spending a bit of time at two is, is, is, it’s probably going to be enough and most people get what they need at two, maybe even at three.

[00:39:18] Bhav: And You know, you mentioned how long is a piece of string? Like, you know, I need a data warehouse, you know, you’re so right. And again, I swear to God, I was having like PTSD moments there when you were talking about like building a data warehouse because I’ve, you know, I’ve, I’ve seen it you know, companies I’ve worked out where they, this is like, okay, the engineering team is working on and I feel like this is becoming a data engineering bashing episodes and maybe I just need to get it off my chest to make me feel better.

[00:39:44] Bhav: But data engineering teams are chasing this ball. Perfect data warehouse situation at the detriment of everything else. I don’t, you know, I never specifically named drop any of my previous companies, but you know, cause I don’t, they were all great in many ways. They will have some [00:40:00] problems in many ways, but I’m going to, you know, I talk about Hopin for, for example, when I was at Hopin the data engineering team work, right.

[00:40:05] Bhav: You know, they were really helpful and, you know, they were building reports, but at no point did they recognize that we need to do the analytics right now. To be able to understand how the company is growing because everything was happening so fast and they were so they were chasing Apis and they were chasing like hey We need to get this data into the front end so our customers can use it and blah blah blah all these type of things and I remember all of the difficult conversations because it’s not been that long right with the VP of engineering, the, you know, the, the, the engineering directors.

[00:40:39] Bhav: And they were constantly pushing back on me, you know, like, Bob, like, you know, we, we, this isn’t something we can do right now. We can do it next quarter and of course, and, and, and maybe it’s my fault. You know, I, I saw some red flags and I didn’t, and I, whilst I did flag them, I thought they were, you know, We probably should have, I probably should have pushed harder to our CEO and, and you know, the MDs and all of the, you know, the powers that be to really say, look, forget about getting this data, which is nice to have into the, into the front of the platform where customers can use it.

[00:41:11] Bhav: They already have something there, right? Like, like we’re not a data product, right? You know, we don’t sell it’s, you know, like Fitbit and MyFitnessPal and Strava. These are technically data products. You inject data into them through when, you know, when customers are like going for a run or they’re eating the food and they’re entering all their calories and all that stuff.

[00:41:31] Bhav: These are data products. Hopin was not a data product, yet there were parts of the platform which gave data to a customer, but we treated the entire thing as a data product. And that came at the detriment of me and my team not being able to get what we needed to be able to deliver. do analysis. And whilst the engineer is really great, if you ask them like, Hey, look, I just need this small thing, they would do it.

[00:41:52] Bhav: But we should have been focusing on, like, how do we make analytics analysis reporting? The thing that everyone gets hold of rather than focusing so heavily on this one niche aspect where organisers can see how many people turned up to their to, to the, to their conference and how many people sent messages and things like that.

[00:42:11] Bhav: Like, yeah, it’s nice to have, but they don’t need it that badly. So, yeah, when you, when you’re talking about kind of like how long is this piece of string yeah, it’s, I reckon if I could go back in time and ask all of the data engineering teams, like, what are you working towards? I don’t think I’d get one. I don’t think I’d get a straight answer. 

[00:42:30] Dan: Well, I think there we go. We’ve teed up another episode where I think we need to invite some engineers on and have this conversation again and get their perspective or, or opinion or situation, actually, I think that would be really interesting. So let’s mark that one down in the sheet and let’s get that in the calendar.

[00:42:44] Dan: All right, well, I think, I think we’re at a good point to wrap up our first episode. I just wanted to get a really nice little chat with you in as our first kind of like new series of podcasts in we do have guests lined up and we’ve got some amazing people that we’ve been talking to and we’re [00:43:00] going to be getting them in but I thought I’d do something slightly different.

[00:43:02] Dan: And maybe even put you on the spot again but just to wind us down and wrap us up a little bit. Is there anything that you have read, seen, or listened to recently that you found interesting and would like to plug or share?

[00:43:20] Bhav: I’ve written something that I was, it’s not quite work related, but it was, it was a bit of fun. And it was, it’s called the. I can’t remember exactly how many there were, but I think it was like the top 20 movies and TV shows that every analyst and data person should watch. So, if you do get a chance, I’m sure we’ll put it into the show notes, but I highly recommend this IMDB list of movies I’ve pulled together.

[00:43:43] Bhav: And it’s, I genuinely think that every analyst, data person in the world should watch this collection, like 20, 25, movies and TV shows. So that’s my plug read. Read this blog post and watch the movies. It’s a fun one as well. 

[00:43:59] Dan: Amazing I liked that one. I thought it was a really, really cool little post. And I liked how Tetris was up there in the top five. And I really liked that film. I thought it was a really good film. And I suppose it’s not fair if I don’t plug as well. I am going to share a blog post also. But this is a theme that is coming up time and time again nowadays, which is well, first of all, I’ll plug the blog post.

[00:44:19] Dan: This is a blog post by Dana Tommaso. This is a blog post by Dana D Tommaso and it’s titled marketing analytics data.

[00:44:40] Dan: Is wrong, can it be fixed? And it was such a clickbaity hooky title. I had to read it, but the fundamental aspect of this is not just, it does go into the reasons of like, you know, cookie consent and ad blockers and browsers. Yes, of course. Reasons why data is non complete and difficult and not perfect. But what I found really fascinating about this post is right at the end, or towards the end, it goes through actionable things of how can we adjust things to use partial data?

[00:45:06] Dan: So advice on changing. Fixed numbers to percentages in your dashboards and using the same data, but looking at when you’re looking at, for example, from Google analytics, total users, total sessions, and then you’ve got the percent change year on year, switch it round, have the percent change year on year as the big number, and then have the actual number underneath as a smaller number.

[00:45:24] Dan: And these tactical, tangible ways of working with partial data that I thought were absolutely fascinating. And it’s been something I’ve been thinking about for a long time. And I just love seeing written down and people thinking about the same stuff. So. Big plug there, obviously all these links will be in the show notes.

[00:45:40] Dan: One last question for you, Bhav. I haven’t really figured out how to phrase this question, but I thought it’s something that maybe we could ask our guests as well. What’s something that maybe the people listening and people in the industry probably don’t know about you?

[00:45:56] Bhav: Okay, this is a fun one because it’s a recent hobby I’ve taken up. [00:46:00] So, back in January I had this old watch that my wife got me when we first met, when we were at university together, so we’re going back about 20 odd years or something like that. I’m very old. And I’ve had it for, you know, it was a really nerdy watch.

[00:46:11] Bhav: Like, we were both young, both working part time and it had the, the, on the dial instead of saying 4, it had like the square root of 144. Things like, you know, really like maths, there was a real mathematical nerd watch that I loved because I was doing my master’s degree at the time.

[00:46:27] Bhav: Anyway, fast forward 20 years and I still have the watch, but it wasn’t working. The strap was broken and it was just starting to, you know, it seemed better days. So what I did was I ordered a watch repair kit off of Amazon and I ordered some straps and I fixed it and I put a new battery into it.

[00:46:43] Bhav: It was really nice and I really enjoyed it. I found it very therapeutic. So I started doing it more and more. I started trying to get hold of it, like broken or not so broken. I’m not a horologist, but I started getting watches that just needed a battery, a strap, a bit of polish and a clean. And this was six months ago or seven months ago.

[00:47:01] Bhav: And I now have 400 watches. I only had one. I now have about 400 watches. They’re not like tags or Omegas. I’ve been learning about watches like watches and movements, and I’ve been studying the history of watches, and I now have a very respectable collection of watches. They’re not, you know, none of them are very expensive.

[00:47:20] Bhav: I can’t afford like five grand, one grand. You know, 10 grand watches but they’re really, and some of my favourite ones are like a couple of retro Casio. So I’m really enjoying the retro Casio range. I’ve also got my hands on a couple of hybrid watches, which still use the classical movement, but they connect to your phone via Bluetooth.

[00:47:39] Bhav: And then they use the chronograph to track your steps. And because they’re still working on a battery. It’s not like you’re my smart watch which I have to charge every day and it gets really annoying. And I’m wearing it right now. Actually, it’s a really nice watch but yeah, so something you don’t know about me is i’m i’m not a I wouldn’t say i’m a watch repairer i’m a Watch interior designer.

[00:48:00] Dan: A hobbyist. More than a hobby. Jesus. You, you, you, you caught me off guard with 400 watches. Jesus where, where, just from a practical aspect, where the hell do you keep 400 watches? 

[00:48:10] Bhav: I mean, I mean, if you think about watches, it’s quite small, right? 

[00:48:12] Dan: But 400 of anything. 

[00:48:15] Bhav: Yeah. I mean, I buy them off eBay and they’re usually sold as not working or something like that. And, and to be fair, like, you know, 60% of them are working, 40% of ’em aren’t working or something like that. And, but I have reached a point where my wife’s like, can you get rid of them now? So I’ve, I’ve, I’ve, I’ve, I’ve, I’ve created a small little vintage store.

[00:48:33] Bhav: So the ideal around it is actually most of these watches, there is a philosophical element of it. The watches will end up in just like a, a, a, a dumpy yard somewhere or thrown away. And they’re perfectly good watches. Most people don’t realise that actually your watches are perfectly good. They just need to clean, a new battery, a bit of love, and it’s great.

[00:48:49] Bhav: And so I’ve been selling them for like really cheap, like 10, 15 pounds on, on Vinted, and yeah, 

[00:48:55] Dan: Are you happy for us to stick a link to that in the show notes? 

[00:48:57] Bhav: Yeah, I’ve, I’ve also got a very respectable following on Instagram. So I don’t really have a social media account, like on Instagram, TikTok, Facebook, things like that but I created a watch repair account and I’m up to nearly 700 followers now. 

[00:49:10] Dan: Amazing. All right well, look, if you’re interested, if this is tickling your fancy, then I’ll put the links to these in the show notes. And I suppose, you know, support Bhav, but also stop watches going to landfill and buy sort of restored watches, I suppose. 

[00:49:22] Bhav: Or repair or restore your own watches. 

[00:49:25] Dan: The same question for me probably, probably sewing. I’ve always been involved in sewing I did sewing at or textiles as it’s called at college. It’s been part of my life. My mom and my dad were both in the rag trade and involved in sewing You know, as I was growing up and I’ve got myself a vintage bright orange old school sewing machine that I’ve been making all sorts of bits on, and I haven’t been doing it actively over the last couple of years, but I’ve made everything from aprons to bags to cushion covers.

[00:49:56] Dan: I made me and my wife Christmas stockings for Christmas and [00:50:00] some cushion covers, but I just love it. I love the act of it’s quite scientific. It is a thing that you can get wrong or right. I like that aspect. I think both of us coming from a mathematical background is similar in that way.

[00:50:11] Dan: And you have something to show for it at the end that people can appreciate, which is often something you don’t get in this kind of line of work with data. So yeah, sewing sewing machines, making stuff. 

[00:50:20] Bhav: I love that. I think that’s such a great hobby and it’s one of those ones that’s so practical as well. And as you said, you can make gifts out of it. And it’s so personalised. So I think it, you know, in a world of like fast commerce and Amazon and things like that, actually it’s nice to have a you know, I’m going to start giving my watches as gifts. You’ve just sparked an idea. I never really thought about giving like the, I’ve just, they just sit in my drawer here and I don’t do anything with them, but actually they make wonderful gifts if you revive them and like. 

[00:50:51] Dan: Maybe you could even design a little sort of packaging or like a wrap or some some printed kind of parcel Paper or something like that that just makes it a little kind of like gift, you know, it’s like a Bhav special. 

[00:51:02] Bhav: Oh, I like that. All right, you’ve inspired me to think about my you know, because I do need to get rid of them at some point because it’s my wife’s like no more, you’re not ordering anymore now. 

[00:51:14] Dan: Exactly. You need to get rid of them so you can buy some more, right? All right thanks for humouring me lovely chatting with you Bhav. And for all of our listeners, we’ll be back every two weeks now So I hopefully Did a decent enough job introducing it in the intro, which spoiler we haven’t recorded yet, but we, the plan is to go on to a fortnightly cadence and no breaks, so we’ll see you in a couple of weeks.


[00:51:34] Dan: That’s it for this week. Thank you for listening. We’ll be back soon with another episode of The Measure Pod. You can subscribe on whatever platform you’re listening to this on to make sure you never miss an episode. You can also leave us a review if you can on any of these platforms. We’re also over on YouTube if you want to see our lovely faces and our lovely guest faces while we do this as well, make sure to subscribe to the Measurelab channel to make sure you never miss an episode as they come out. If you’ll leave us review, that’ll be hugely appreciated. You can do that on most of the podcast applications or there is a form in the show notes, you can leave feedback directly to me and Bhav. Thank you for listening and we’ll see you on the next one.

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