0:00:06.2 Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

0:00:14.2 Michael Helbling: Hey everybody, welcome. It’s the Analytics Power Hour, and this is episode 262. Hey, Happy New Year. 2025, that’ll probably be the year of, well, what exactly? There is a pretty steady flow of prognostications every year about the things that will define the coming year, and we’re not completely immune to desire to define the future. I didn’t say that very clearly, but we do wanna define the future. So what will 2025 bring? It’s probably the year of Tim Wilson still being frustrated with people calling stuff the year of.

0:00:49.3 Tim Wilson: That’s… Yeah, yeah, yeah. That’s fair.

0:00:53.8 MH: Probably accurate. Yeah.

0:00:55.9 TW: You could try in with Tim being frustrated with people. You don’t really need to say…

0:01:00.1 MH: Oh, uh, there you go.

0:01:01.5 TW: Further qualifiers.

0:01:01.6 MH: Wow.

0:01:02.6 TW: Not necessary.

0:01:03.0 MH: We still like you. And 2025 probably be the year of Moe still liking Adam Grant and Brene Brown. Hey, Moe.

0:01:11.8 Moe Kiss: Yeah, probably, actually. That’s a very good prediction.

0:01:14.5 TW: Oh, there’s gonna be a huge scandal with one of them between like recording and that coming out.

0:01:19.0 MH: Oh, geez.

0:01:22.1 TW: It’s gonna be…

0:01:23.4 MH: Yeah. That’s… Alright. Well, and I’m Michael Helbling. Well, some attempts at categorizing the future that is coming at us awfully fast is definitely warranted. So what better time than the first episode of 2025? Insert Zager and Evan’s pun here. And to do this right, we wanted to have a guest who has a great track record of observing our industry and seeing where the puck is going. Barr Moses is the co-founder and CEO of Monte Carlo, the data reliability company. As part of her role as CEO, she works closely with data leaders at some of the foremost AI driven organizations like Pepsi, Roche, Fox, American Airlines, hundreds more. She’s a member of the Forbes Technology Council and is a returning guest to the show. Welcome back, Barr.

0:02:04.6 Barr Moses: Thank you so much. I am honored and pleased to be a returning member.

0:02:09.7 MH: No, we’re serious. We love the way that you take such an interest in really having, from your level, a real good clear view of where our industry is and the data industry is going. Before we get started, let’s just get a recap of what’s going on with you and Monte Carlo.

0:02:29.4 BM: Yeah. It’s been a whirlwind couple of years for… Not only for Monte Carlo, but I’d say for the entire data industry. I’m just reflecting last time I was here, this was 2021. Is just kinda coming out of COVID, I think, we were all like getting comfortable behind the camera and feeling comfortable at home. And the world is obviously very different today, but maybe just to kind of give a quick recap. Monte Carlo was founded to solve the problem of what we call data downtime. Periods of time when data is wrong or inaccurate. And five, 10 years ago, that actually didn’t seem important at all. I think people spend some time thinking about quality of data and you guys know this better than I do, but it probably didn’t get the diligence that it deserved back then. You could kind of like skirt around the issue, could probably… It was very common at the time to just have like extra eyes on the data to make sure that a report is accurate. And if it was wrong, you’d kind of be like, ah, shucks, so sorry. And kind of like move on.

0:03:33.7 MK: I also, but sorry to interrupt, but I also think it maybe wasn’t as complex and so like as complexity has grown, that the ability to troubleshoot and dig into the why it’s not reliable is even harder. But sorry to break your stride there.

0:03:50.8 BM: Not at all. No, I think that’s spot on. And maybe just to unpack that a little bit, I think it was less complex because one, the use cases were limited. So today, we call it data products and very fancy names for… But the use case was maybe just revenue reporting to the street. And the… So the use cases were fewer, the timelines were fewer. So you maybe used data like once a quarter to report the numbers. And also there were fewer people working with data. So maybe it’s like a couple of analysts under the finance team. And so you really had a lot more time, less use cases, less complexity in which… And the stakes were lower. And so in all of those instances, like it kind of didn’t really matter if the data was accurate or not. And then there was this big wave of actually people starting to use data. Remember when people would say, oh, we’re data driven, and you kind of like didn’t really believe them. That whole thing… There was a period back in time.

0:04:48.4 TW: It’s still happening.

0:04:48.5 MH: Yeah.

0:04:49.7 BM: Still happening. Totally agree with you. So I think there was this big push, and that’s sort of when Monte Carlo created the category of data observability, which is basically allowing people creating data products, whether those are data engineers, data analysts, data scientists, anyone working with data to make sure that they are actually using trusted, reliable data for that. And sort of kinda like helping when someone’s looking at the data and like, what WTF the data here looks wrong. Helping those people come answer the question of what’s wrong and why. That was sort of kinda like the reason how Monte Carlo was born. Now fast forward today, I can’t believe it’s almost 2025. It’s like four years since. I like to say that I think the data industry a little bit like Taylor Swift, we kind of like reinvent ourselves every year.

0:05:36.8 BM: We need to do like an Eras tour and kind of like go through all of the periods of time of the data industry. And I think the most recent era being swept by generative AI, the implication of that means that bad data is even worse for organizations. And we kind of unpack what that means. But at a very high level, what Monte Carlo does is help organizations, enterprises, make sure that the data that they’re using to power their pipelines, power their dashboards, power their generative AI applications is actually trusted and reliable. And we do that by first and foremost, knowing when there’s something wrong. Knowing if the data is late or inaccurate, but then also being able to answer the question of why is it wrong and how do I actually resolve an issue? I’ll sort of pause there instead of a long answer and a lot more that we can go into. But, whew, it’s been a fun couple of years.

0:06:36.1 MH: Nice.

0:06:36.1 TW: Well, but also, I mean, one, I guess just to clarify, we’re not saying that in 2021 people weren’t using data. I mean, that’s been a… That’s been ramping up for a while. I think also the modern data stack, I’m not sure where that phrase was in the inflated expectations versus… It definitely, I feel like since the last time you were on the modern data stack as a phrase has slid into the trough of disillusionment at least a little bit, which is kind of interesting. I don’t know exactly how that applies to kind of where we’re going from here, but I feel like there was a point where it was like, if we just have all these modules plugged in together with the right layers on top of them, then like all will be good. And it feels like we’re a little past that, that that nirvana, even if we got there, wouldn’t actually necessarily yield the results that were being promised. But…

0:07:37.3 BM: Yeah. I mean, I think, look, putting myself in sort of the shoes of data leaders today, you’re facing a really tough reality because like every 12 to 18 months you’re being thrown at with sort of a new concept. Call it modern data platform, call it generative AI, call it whatever you want you’re sort of expected to be on top of your game and sort of understand the words or trend du jour. But I think if you sort of unpeel that for a second and go back to fundamentals, there are a couple of things that I think remain true regardless and have remained true for the last 10, 15 years, which is first and foremost, like organizations want to use data, and data is a competitive advantage. How you use it and in what ways, like, I think that is undisputable. Like strong companies have strong data practices and use that to their advantage.

0:08:22.1 BM: You can talk about how, for example, you can use it for better decision making internally. That was sort of one of the dominant use cases in the beginning. You can use it to build better data products. Like for example, you can have a better pricing algorithm. And I think today, you can talk more about this, but I think data is the moat for generative AI product innovative solutions. And so regardless of where the hype cycle is, I think one core truth is that data matters to organizations. What we do matters. And so data continues to be a core part for organizations. I think the second sort of fundamental truth that we believe in is like reliable data matters. Like the data’s worthless if you’re working with…

0:09:03.8 MK: Completely.

0:09:04.5 BM: Yeah. This even goes without saying, but like having something that you can trust in is sort of fundamental to your ability to deliver it. And then I think the third thing that’s sort of always remained true is like innovation matters. Like you have to be at the forefront. And so organizations that are doing nothing about generative AI or doing nothing to kind of learn what’s next will be in a difficult position. I’m curious for your takes about that the modern data platform in particular. I think one of the benefits of that was that data leaders were met with many solutions for many problems, but actually were inundated with perhaps too many solutions. And so ended up in a position where they had to make bets on a variety of solutions and ended up with maybe sort of a proliferation of tools. And now, there’s a big movement to actually consolidate that or cut back to what’s necessary. And so if you’re not solving a core fundamental truth, then you probably don’t deserve to live in the modern data stack, if that makes sense.

0:10:08.4 TW: You don’t deserve to live in the modern data stack.

0:10:10.6 BM: I’m sorry.

0:10:12.7 TW: That was [0:10:12.8] ____.

0:10:13.6 MK: I so deeply love when the podcast intersects with things that are like completely churning through my brain at the moment. And it is like this beautiful, like chef kiss because these are all kind of concepts that I’ve been giving a lot of thought to over the break. I wanna dig into what you… You mentioned data can be a moat. Can you say more about that? Especially you said, I think relative to GenAI.

0:10:40.6 BM: Yeah, for sure. I’m happy to. So I think what’s happened to, let’s sort of think about like the last, I wanna call a year or two in generative AI. I’ll actually start by sharing a survey that we did that I thought was really, really funny. We basically interviewed a couple hundred data leaders and asked them what percentage of data leaders are building with generative AI. Can you guess what percentage of data leaders?

0:11:13.5 MK: Oh.

0:11:15.7 MH: Probably all of them are saying that they are at least.

0:11:17.8 TW: Yeah. Yeah.

0:11:18.9 MK: Really?

0:11:19.0 BM: It’s… Yeah. So like, I think like 97% said it. Like not a single person…

0:11:24.8 MK: Stop it.

0:11:25.8 BM: Yeah. That’s… You’re just spot on, Michael.

0:11:30.7 MH: Oh no. We’re all doing it for sure.

0:11:33.0 BM: We’re all doing it. We’re all doing it. Everyone…

0:11:35.8 MH: 2025 is the year of maybe building with AI, maybe.

0:11:40.8 BM: Yeah. Maybe. We’re all doing it. Which I think… How often do you do a survey and get almost a hundred percent response rate. Like for a question? It’s pretty outlier. Second question that we asked was, what percentage of you’re like, do you feel confident in the data that you have? Like do you trust the data that you have that’s running it? What do you think is… What percentage of people trust the data that they’re using for generative AI?

0:12:05.0 MH: That’s 70%.

0:12:07.0 BM: That’s not bad.

0:12:08.1 MH: It was… Was it 70? Okay. ‘Cause usually, the Duke Business School used to do a CMO survey every year and they would ask data questions like that, and there was usually about a 60% gap between how important it is versus how much they trusted it. It was always a very big delta. So yeah.

0:12:26.0 BM: It’s exactly right. So 60% said they don’t trust it. So I think there is, that’s exactly the doubt there. So only one out of three trust and two out of three don’t trust the data. So it’s interesting that everyone is building generative AI, but no one has the core component to actually deliver the said generative AI. I think that speaks more to kind of human nature. And will we wanna be where we are?

0:12:50.0 MK: Can I ask, this concept has been rolling around and I’ve been like digging up old blogs on it, but it just seems to have dropped off. There was a lot of hype. I feel like it was probably two years ago, but I mean, the last four years have blurred together, so it could be anywhere between two to six years, about a metrics layer. And it’s… I feel like I’ve done all this, like had to do all this like mental processing around like how does a metrics layer or a semantics layer differ from like a store schema, data warehouse to like have a reliable data set. But it doesn’t seem like anyone is talking about that right now. And I’m curious to hear your perspective.

0:13:30.1 BM: Wow. This is a… That’s a really good question, Moe. I think there’s… Yeah, I’m curious for your opinions, but I think sort of going back to like sort of the Taylor Swift kind of analogy from before, there is this like, I think there’s this desire to like chase the shiny object right now. And going back to the survey, like if you’re not talking about generative AI, you’re gonna be left behind. And I think there’s a lot that goes into delivering generative AI right now. You can talk about what those things are, and I’ll go back to your Moe question for a second as well. But I think if you’re not on track or have a really strong solid answer to how you’re on track, you’re kind of on the hot seat right now as a data leader. And I think that has just sucked the air out of the room in every single room where there is a data leader or an executive leader.

0:14:24.3 BM: And I’ll explain what I meant by sort of data is the moat. I think the… If you think about like what a data leader needs to do now, basically, like the first thing that’s being asked is like, what models are you using? What foundational models are you using? Like what LLMs are you using, et cetera. Like between like OpenAI and Anthropic, like et cetera. There’s lots of options. The thing is, every single data leader today has access to the latest and greatest model. Everyone has access to that. And so I have access to that, Moe, you have, Michael, you have, everyone here has access to models that’s like supported by 10,000 PhDs and a billion GPUs. And that is true for me and every other company around me. So in that world, how do I create something that’s valuable for my customers?

0:15:15.4 BM: How do I create something that’s unique? Like what is, what is the advantage? Like I can create a product just like you can create a product. And so what’s a distinguishment here? Like why how… If like for example, if I’m a bank, how can I offer a differentiated service if I have access to the exact same model as you do and the exact same ingredients of the generative AI product, if that makes sense. And so I think what we’re learning is that in putting together these generative AI applications, which are today really limited to chatbots, if you will, or sort of a gen tech solution, et cetera, and all of those instances, the way in which companies make those products personalized or differentiated is by marrying… By introducing their enterprise data. Basically corporate data.

0:16:05.1 BM: And so let’s take a practical example. Like let’s say I’m a bank and I wanna build a financial advisor solution. I want to be able to help Tim fill out his taxes. And so I’m gonna be able to do that better if I have data about Tim’s background, his car, his house, whatever it is. And so I can offer you a much better differentiated product if I have reliable data about Tim that I can use. And so that’s the only difference between bank one and bank two, it’s what kind of data do we have to power that product? Yeah. So just to summarize, like we all have access to latest, greatest models, but the only thing that differentiates… Differentiating generative AI products is the data that’s powering them. And so that’s why data is actually your moat in the world of generative AI.

0:16:47.6 TW: I guess counterpoint, like I feel like that is coming from a… That’s coming from a super data-centric perspective. I mean, and I guess this is what terrifies me is that year 2025 could be supercharging this obsession with more and more and more and more, more data. As you throw more data in, then it’s harder to keep it clean. You’ve got more things that can conflict. And so absolutely. And we fought this battle in the past where there’s… You chase all this data because anytime something isn’t seen as valuable, the easy thing to default to is to just to point to some data that’s not clean enough or not clean. It may be clean enough, but it’s never gonna be perfectly clean or data that’s missing. And so that can feed this like horrendously vicious cycle where we completely lose sight of like, what are we trying to do?

0:17:41.5 TW: And oh, what we’re trying to do is get as much data as possible. Like the counterpoint is those banks could differentiate by thinking about with way less data what their customers really value, what they most need. And it’s not an either or, but if there’s a deep understanding of their customer and they value something, it may need very little data. It may be using data in a different way from they already have it. So I think there has to be that balance. I would hope that we get to that point of like, we can’t just be in this arms race for more and more models, more data, more whatever. So okay, Val. Unleash…

0:18:20.4 MK: Okay. So my visceral reaction, my visceral reaction is like, I can absolutely see that some people would use, like, what you’re saying the GenAI hype train to be like, we need more data. I don’t think that’s what Barr is saying, but I will obviously give you the opportunity to speak for yourself because my reaction is, but it’s not about the quantity, it is about the quality. It is not about let’s collect more data, it’s that we have… The last few years has been all about, like, let’s have fucking data lakes, let’s just dump data from backend services into anywhere and it’s created, I mean, I think we’ve said a swamp before, but it’s like, you can’t ask important questions, like, what do my customers value if the data that’s there is a complete trash fire and I don’t think it’s about quantity.

0:19:16.1 TW: You’re drawing, there’s also this distinction of, like, it is so easy to say, I found an error in the data, this field is missing or this field is incorrect. Fix it as opposed to, you just… You just said if your data is a dumpster, a trash fire, there is a gradation of which… So put aside the more, more, more data and bring in the pristine data. That point, it is so easy to find a problem in the data and chase that and extrapolate from that. So, absolutely, we need proper governance, but you can replace either more, more, more data, which they’re absolutely… You can Google for it and find all sorts of articles and say who’s gonna win are the ones who collect all the data, you will find I completely grant you a… The data has to be garbage in garbage out. I mean, that is like a path that may become my next favorite thing to hate on after in god we trust, all others must bring data. It’s so easy to say garbage in garbage out. It’s like, well, people are not pouring garbage in.

0:20:19.8 TW: Yes, there are errors. Yes, there is process breakdown. Yes, there needs to be governance and observability, but it is so easy to say that if we’re not getting value out, oh, it’s a data quality issue and now you can get equally obsessed around over chasing that. So Moe, I feel like you were putting… You were again, putting words in my mouth and like, well, you, it’s not bad at all, but…

0:20:44.1 MK: No, no, no, I just, I think sometimes that like when we’re discussing this concept, there are like extremes and it’s…

0:20:51.5 TW: Says the one who said dumpster fire.

0:20:53.7 MK: Or like it sometimes is interpreted as a binary thing and it’s not like, I do think there is a spectrum. It just often happens that you’re at one end of the spectrum and I’m at the other end. But let me just elaborate what I mean by quality, because I, again, can see a situation where a business goes, we must have perfect data. And that’s not what I’m saying. I’m saying the data has to be meaningful so that you can create connections between different data sources and that the way they relate to each other is consistent so that different areas of the business are not like tripping over themselves, making mistakes, because it’s like fundamentally so unstructured and so… To me, it’s about how all those things connect together. It’s not just about like, is this number accurate to the 99th percent or whatever. It’s… I don’t know. I’m going to just shut up and let Barr talk because I feel like she probably…

0:21:53.2 BM: No, I love this. I’ve been, I love hearing y’all’s thoughts. I’m, yeah, I love it. Well, okay. So a couple of thoughts. One, obviously I’m biased. I have a very data-centric view. I will not, for a minute, pretend that I have nothing but bias. And I think my bias comes from a place of like, yeah, I think data is like the most interesting place to be in in the past five, 10 years and in the next five, 10. I think it’s like the coolest party that everyone wants to be a part of and like they should and, I’ll continue thinking that I have strong, I wake up every day and choose to be part of the data party. And I think it’s where we’re having fun. So yes, I’m a 100% biased and I agree with you. I think data hoarding has been a huge issue, a huge problem. And I think it’s been sort of a strategy that has largely failed. Oh, let’s just collect all the data and like hope that it solves or think that more data is more helpful. It’s actually interesting. I was just sitting down with the founder of a data catalog company a couple of days ago and we were talking about how 95% of the problems that people… 95% of the questions that people have of data have already been answered.

0:22:58.5 BM: And so their challenge is just finding the answer and surfacing it, there’s very, very net new insights being created, if that makes sense. And so really, their challenge is about how do we help company or help people, users, discover the answer versus create a new answer, which is actually mind-blowing if you think about what a small percentage of new insights are generated.

0:23:22.0 BM: It sort of made me a little bit sad for the human race, but also happy that maybe we can solve this. But I think that I digress here. But my point is, I think the point that you’re making, Tim and Moe, is an important point. I don’t think that more data is necessarily better. In fact, I think there are a lot of areas where less is better and more precise answers are better. For a minute, I’m not advocating for that, not at all. I think what I am saying is, most of the… If you look at like chat GPT or kind of things that like anyone has access to that is trained on data that everyone has access to like we can all sort of… It’s funny, people used to say let me Google that for you and I was trying to think what’s the new like let me Perplexity that for you I don’t know it doesn’t doesn’t like roll it off the tongue just as much.

0:24:14.1 MH: Well, let me ask Claude would work you know so.

0:24:16.9 BM: Exactly let me ask what Claude says, but I think the point is like from that perspective everyone has access to that and also everyone can use those models to train their data And so everyone sort of has access to that. But if you have some data about your users, let’s take like I don’t know like a hotel chain that’s trying to create a personalized experience for their users. No one knows as much as they do about, I don’t know, they’re like… How you like to travel, the kind of food you like to eat, the kind of ads that would speak better to you. Not that I’m advocating for like an ad-centric world but my point is like the power today and where I think the leverage lies in is in having things that not everyone has access to. And the reality is everyone has access to the latest and greatest LLM. So that cannot be your moat or your advantage. And by no means means that we have to have too much data or a lot of data. I’m not advocating for that, and I think it’s a very important clarification.

0:25:18.3 BM: I actually will say that oftentimes in the companies at least that I work with, one of the biggest challenges is that they have so much data they don’t even know where to get started. And so a lot of the work is actually saying, let’s try to… You can think of like layers of important data tier one, tier two, tier three. Then think about like what’s the core data sets that we care about making sure that those are really pristine and reliable. So oftentimes like actually starting small is the winning strategy.

0:25:46.6 BM: I find when companies… When we work at the company, company is like, I wanna observe everything wall to wall. I’d be like, whoa, whoa, whoa. Hold on. You’re gonna… That’s gonna be really hard. Tell me why. Are you actually using all of that data?

0:26:00.9 BM: And that strategy often fails. And so I’d much rather start with, what’s a small use case that you actually really are using the data for, and that’s really important for users. Let’s start with making sure that that’s really highly trusted and reliable. So I agree with you is my point here. And I think it’s an important clarification.

0:26:17.8 TW: Moe, are you gonna?

0:26:19.7 MK: No, I am like waiting for the next like rant.

[laughter]

0:26:27.1 BM: We can rant, by the way, I’m happy to rant about garbage in garbage out. I think that is a great rant. I’m happy to like carry the torch on ranting against that, Tim, if you’d like. I don’t know if you wanna share why you wanna rant, I’m happy to share my rant about it. Go for it.

0:26:45.4 MK: So I’m curious, Tim, like when I said that stuff about like connectivity. What, like, what’s your views on that? Because I feel like you can only answer important questions if the data is kind of, I don’t wanna say structured, but I’m thinking about, like, Barr’s comment of the competitive advantage that you have is your data set, like, it’s not the models. So how that all works together then to me becomes the most important bit. And I really like Barr’s concept. Actually, someone in my team did this recently, where they went through of, like, what’s tier one, tier two, tier three. And I think it’s such a great framework to help the business understand the different levels of importance. But, Tim, what’s your thoughts on that connectivity piece?

0:27:39.8 TW: So, one, I mean, there is nuance. I try to not say things like, it all has to be connected, or it’s a dumpster fire, or it’s perfectly pristine. And maybe I fell into it a little bit, and then we chase the more and the more and the more. But, I mean, I would love for there to be a little bit more discipline and nuance. Like as Barr when you said, starting small, that is… There is no pressure, no force in business right now that says, when doing anything with your data, you should go lock yourself in a room with some smart people on a whiteboard, and then come out with a mandate, that it’s an absolute minimalist approach. And then you build from there. Because when you say some… And I feel like I see this, and I see it, I mean, I’m spending too much time on LinkedIn and reading articles, that if someone says, this is data that we uniquely have as a bank or a hotel chain, therefore, they make the leap to we have it. Therefore, we need to feed it in and connect it because that is something unique to us and therefore, it provides competitive advantage.

0:28:55.4 TW: And there’s kind of a… That’s the default position is it’s our unique data, we must use it. And where I see that going wrong is there’s a missed step to say, really, just because we have it uniquely doesn’t mean it’s necessarily valuable. If somebody says, here’s why we think it can be valuable, what’s our minimum viable product? What’s our minimum way to test that it would be valuable? But instead it kind of is like, there’s this tendency to say, it’s ours, put it in the system. Make sure it goes through that it’s pristine. Which when you flip it around to LLMs they’re doing stuff probabilistically, like hallucinations are coming out, all of that’s getting better, but it’s like, even with pristine data going in, it’s going to give kind of inconsistent results.

0:29:52.7 TW: And we’re kind of like, oh, that’s cool. Well, it’s like, well, then I can’t remember who wrote… Might have been Ethan Malik or somebody who pointed out like, yeah, data that’s got noise in it, putting into some… It’s not that if you put pristine data in, you’re gonna get a definitive deterministic answer out. If you put pristine data in you’re gonna get a probabilistic answer out, if you put noisy data in, you’re gonna get probabilistic with a bigger range of uncertainty. And I just I think there’s just thought and nuance to say if you had a bias towards less, and it’s not saying don’t do it, it’s just saying, move with deliberation so that like you figure out something is a tier one.

0:30:38.7 TW: And then you say that’s tier one, it’s a differentiator, lock that in and make sure that it is clean. And when you’re connecting it to something else… So that’s what I was… I guess that was… I was like, I’m not gonna rant about this, I’m gonna have a very nuanced thing to say. And then whoop, here it comes.

0:30:55.5 MK: That was very eloquent. No, that was eloquent. But okay, can I add some color to the situation. I feel like there are some companies that still have a highly centralized model for how they store their data or how it’s built, that sort of stuff, my world is very different to that. Everything’s done completely decentralized. So like in marketing, we have marketing analytics engineers and data scientists creating data sets. And then over in the growth team, there are people creating data sets, and over in teams in education. And even if you start with that, like, let’s do something small, it’s often created in isolation. And the problem is, is like, it’s really hard to answer a cross cutting business question, like, what’s important to our customers or what do our customers value when everything is built in this completely decentralized model because like, if I take my tier one tables and data sets, that will be completely different to another department’s tier one data sets. And you might not be able to answer that question. I agree, just to be clear, I totally agree. I love this idea of like starting with less, but you can only start with less if it is, I don’t know if the right word is like company-wide or like it’s centralized. I feel like there’s this tension in how technology is built in some companies.

0:32:22.7 TW: Can I quickly, unfairly, I’m gonna admit this is unfairly picking on an example that you just threw that if it’s like, what do our customers value? And it’s like, well, I have to have all the data and hook it all together or I could field a study and ask them. There is this story out there of, I’m gonna plug in, I’m gonna launch my internet and I’m gonna say, what do our customers value the most and then through all of this magic, it’s gonna generate it and you say, well, why can’t it? It has to connect all of this stuff? If that’s a fundamental question, then there are alternative techniques that have been around for 50 years, which is usability testing or focus groups or panels for some of that. That’s unfair ’cause you just yanked that out as one example. So I’m gonna acknowledge fair point.

0:33:13.4 MK: It was just a random example, but yes, I agree that there are other research methods that would be more appropriate there. Again, I’m gonna shut up and let Barr speak.

0:33:24.3 BM: No, not at all. I love this. I feel like I’m asking questions that I haven’t thought of in a while, so that’s good. No, I mean, listen to this. My reaction is a couple of things. One is going back to sort of data as being faced with sort of a really tricky part of their journey, I think, and you talked a little bit about sort of what does a great model look like for a team? Is it sort of centralized or decentralized? And I think organizations go back and forth on that, and it also is a little bit of like a function of the environment in which they operate. So we work with highly regulated… Companies who operate in a highly regulated environment. So think like financial services or healthcare or anything like that, and in those instances, they’re actually privy to significant regulations and audits, and in those instances, you really need to have really strong data management and data quality controls in place, and oftentimes that needs to be across your entire data estate, and that is sort of… It’s sort of like a table stake, so you can’t really operate without that. I think that’s very different from like a retailer organisation or retail company or an e-commerce company.

0:34:38.6 BM: So first and foremost, I think this is really dependent on what the environment you’re operating, and also what problem are you trying to solve when, we say data products or generative AI applications It’s very broad. And I think if you really think about what actually is being used, there’s a couple of things. One is creating personalized experience for your customers, but it can also be inwardly looking for a company sort of automating internal operations.

0:35:05.5 BM: So an example, Fortune 500 company that we work with, they have a goal to have their IT organisation, 50% of their IT work needs to be either completely AI automated or AI assisted. That’s sort of their goal. And that’s in terms of internally automating sort of human manual tasks. And so I think it sort of depends on what you’re trying to solve. And I think that that’s sort of what data leaders need to ask themselves today. Maybe sort of one thing that’s coming out of that is I think there’s this sort of blurring line between different people working with data. So in the past, there’s sort of you can really draw the lines, I think more clearly between engineers, data engineers, analyst, data scientist, all of that is becoming a lot harder to distinguish and I think my view is sort of in, the teams that will be building generative AI applications will be a mix of that. So it will include both engineering and data people. I don’t think… I think how does this work? Someone wakes up one day in a company and is like, hey, CTO, go build a generative application. And so like a bunch of engineers like run off and build something. And then someone’s like, hey, CDO, chief data officer, go build a generative application. And then like the data team runs off and like build stuff.

0:36:23.4 BM: And so you end up having data teams trying to build stuff that software engineers should be doing and software engineers trying to build data teams. But at the end of the day, a strong generative AI application or any data product needs a good UI, which should be built by software engineers. You’re not gonna like, that’s not the data team’s job. And it also needs good data pipelines and reliable pipelines. And that doesn’t make sense. You don’t need a front end engineer to build like a data pipeline. And so I think at the end, there will be some convergence of like what the roles are. But right now there’s a lot of people sort of crossing lines and lots of blurry lines in between.

0:37:00.9 MK: And what’s your perspective on data products being more as like a platform product versus… I don’t know. I feel like there’s been… There are many kind of ways you could cut it. Sometimes data products seem to sit more in like a marketing technology space or whatever. But it seems at the moment there is kind of a lot of perspective about it really sitting in that product platform sphere. And product PMs are quite different as well to like a customer facing product manager.

0:37:36.8 BM: Yeah, I mean, I think if you look at like the product… Oh, go for it, Tim.

0:37:36.9 TW: Well, I just want to clarify. So when you say a platform product, are you saying the data product is a platform that then gets kind of in… Winds up serving a bunch of different use cases? Or are you saying just where… Are you saying organizationally or are you saying what the data product is a platform with a bunch of features? What do you mean by…

0:38:02.4 MK: Yeah, when I say platform product, I’m more meaning like the products that you build, I suppose, in house that serve as the platform for internal stakeholders and the tools that you’re building to service your organisation. And I suppose, as I’m saying this out loud, I’m like, I suppose you could have data products that would be doing that. And you could also have customer facing data products. And those things would probably be different. Oh, wow. I really answered my own question there, haven’t I?

0:38:26.3 BM: No, it’s okay. I can elaborate. But I think you did answer part of it. So maybe also just like take a step back for a second. If you think about data products and where they are in the hype cycle, I think there’s sort of… It’s like there’s this hype and then they plateau. And then you’re like, oh, now I can actually make use of this.

0:38:39.0 MK: Yes.

0:38:40.4 BM: And I think that’s where data product is like, oh, now I can actually really use this thing, which is good, I think. I think data products can really mean whatever you want. It can both be… It can be let’s walk through a simple example, like an internal dashboard that the chief marketing officer is using every day. And so it’s basically like a set of dashboards or a set of reports. And then there’s a lot of tables with this… Followed by a particular lineage that feed into that report. And so it can be a combination of user attributes and some different information about those users and also some user behaviour. And it can be a bunch of sort of different third party data sources. And so all of that can be part of a data product. Sort of from… And you can describe that as basically like all the assets that are contributing to said report or dashboard that the CMO is looking at. My point is, you can basically use data products as a way to organise your data assets and to also organise your users and data teams. And so to me, it’s less of a question of is this part of a platform or not? Because that varies, as I mentioned, by the organisation, the size, the maturity of the organisation.

0:39:50.9 BM: For me, it’s more a way for companies to organise what they care about. And so oftentimes if we will work with a data platform team, we’ll say, Hey, what’s the data that you care about? And then they might tell us, Oh, we have a marketing team and that really focuses on our ads business. And the CMO there looks at this dashboard every morning and they are so sensitive to any changes that they have there. And so we wanna make sure that all the data pipelines from ingestion, third-party data sources, through transformation, all the layers through to that report, we want that to be very high quality and accurate. So we wanna make sure that that entire data product is trusted. That’s one way to think about it. Now, the ownership of those assets can be by the data platform itself or it can be by the data analysts that are actually running the reports. Oftentimes, it’s a combination of both. So you might have data analysts looking at the reports, the data platform running the pipelines, the totally separate engineering team that’s owning the data upstream and sort of the different sources. And so oftentimes, it’s actually all of them are contributing to sort of the said data product, if you will.

0:41:01.0 BM: But to me, where data products are most useful is in a way to organise data assets and organise a view of the world for a particular domain, for a particular use case, for a particular business outcome, if that makes sense.

0:41:15.1 TW: Do data product… And this is, I guess, for both of you, data product, product managers, what’s the breadth. Do they go… Do they engage all the way up to the upstream engineering, owning the data creation all the way through to the use case and the need? Or does it… Is there a natural cutoff where they say, this is engineering’s problem, they’re just, they need to be managing the data coming in? Or how broad does that role go? Assuming it, I guess, maybe there’s a precursor question, does that role get defined and exist as you are a data product, product manager for this data product or set of data products? And if so, what’s the scope of that role?

0:42:03.1 MK: Yeah, doesn’t it depend on the organisation? I mean, we’re having lots of conversations at the moment, ’cause like I said, we have a decentralised model, which is quite unique. Because well, it’s not unique, but it creates different layers of accountability. ‘Cause if you have engineers that have a back end service, and they’re pushing that data to you, and then you’re building a data product off it, the question that comes to mind for me is like, who’s accountable? Well, it’s not an easy answer, in that model, I think it’s the responsibility of the team that are in the backend service to make sure that the data is getting pushed correctly out. But then likewise, for the people who are receiving it, they have layers of accountability as well as the people that are using that data. But in a completely different model, where you don’t have that… Like you have a more centralised model, those lines of ownership could be different. And so I think it’s so dependent on the company, and how they’re structured to understand where something starts and ends.

0:43:07.8 MK: I think it’s probably impossible to think that a data product PM would own everything completely end to end. I can’t envisage a world where that would happen, just because there are so many different parts of the bit I don’t know, anyway, I’m not making a lot of sense now.

0:43:32.9 BM: Yeah, yeah. I mean, this is a maybe not what you’d wanna hear. But I think it’s a it depends answer. It depends on the maturity of… I mean, I don’t wanna repeat what Moe said, but I strongly agree with that. It’s hard to draw the lines, I think some of the teams that do this better are those that are able to have like a strong data governance team that can actually sort of clearly sort of lay out what that looks like. The most common model is something like a federated model where you have a centralised data platform, like what you said, Moe, the centralised data platform sort of defines what excellence looks like, what great looks like. And so they might define like, these are the standards for security, quality, reliability, and scalability. And so whenever you’re building a new data pipeline or adding a new data source, you need to make sure that it passes these requirements on each of those elements. And so in that way, the centralised data platform defines what great looks like. And then no matter what team you’re on, this could be the data team serving the marketing team, or finance team, or sort of whatever use case it is, will adhere to the same requirements that the centralised team has defined.

0:44:44.7 BM: So we see a lot of that. I think that’s, again, with generative AI, we will see more of that. Because maybe going back to sort of what we said at the very, very beginning of the call, how we use data 10 years ago was a lot simpler. There were very few use cases and very few people using data. But today, because there’s so many more use cases, so many more people using it, and more in real time, the need for a centralised sort of governance definition is more important. I mean, this is also you kind of see this… I think the sort of LLM or generative AI stack is still being defined. But one of the questions you raised this, Tim, was hallucinations are very real. And when you release a product, and the data is wrong, it could have colossal impact, both on your revenue and your brand. Maybe the example that I like to give them the most is, I don’t know if you all saw this sort of went viral on Twitter, or X, I’m not gonna get used to that thing. But when it went viral on X, someone did this thing on Google, basically, the prompt was something like, what should I do if my cheese is slipping off my pizza? And the answer was like, oh, you should just use organic super glue. And the…

0:46:05.4 MK: Oh, wow.

0:46:07.7 BM: It’s obviously a bad answer. And honestly, I think Google can get away with it because of such strong brand that Google has these days. And so, yeah, I’ll probably continue to use Google, even though they gave me a shit answer about organic super glue for my pizza. But most brands, if I’m an esteemed bank, or an airline, or a media company, I can’t afford to have those kind of answers in front of my users. And so like, actually getting that in order is… Again, Google, can get away with it. But like 99.9% of us cannot.

0:46:41.9 TW: Nice. I wanna switch gears just a little bit and talk about something else that kind of obviously ties in, but also kind of reintroduces a lot of challenges, which is unstructured data. And going into next year, one of the articles I was reading that you’d written, Barr, was kind of like saying, well, it’s gonna be one of the things, could you kind of give a perspective about, okay, so we’re gonna be using a lot more unstructured data, but then doesn’t that… How do how do we then take all the things we’ve just been discussing about how challenging data is? And now, we’re just gonna slam on now a new set of challenges on top of that, they’re gonna kind of redo the whole thing. What do people do about this?

0:47:26.6 BM: Yeah, great question. We should do at some point, like a 2025 will be the year of and see what we come up with. I don’t know if it’ll I guess be…

0:47:30.4 TW: Round robin.

0:47:35.7 BM: Yeah, exactly.

0:47:37.7 TW: Yes, Claude. I’ll ask perplexity. Yes, ChatGPT. Please.

0:47:41.6 BM: Yeah, exactly. Exactly. I mean, honestly, if like, if we could foresee that we probably wouldn’t be in this business. We’d be doing something else if we could be forecasting that. But I think as will 2025 be the year of unstructured data? I don’t know. But I can tell you this for the last 10, 15 years, most of the data work has been done with structured data. And structured data is very easy. It’s like data that’s like in rows, columns, tables that you can analyse in a pretty straightforward way with a schema and and most of like the modern data stack and whatever solutions that we all use and love and day to day has been have been focused on structured data. That being said, if you look at where the growth is, I think there’s like some crazy estimates from Gartner like 90% of the growth in data will come from unstructured data or something like that. Or and just to define when we talk about unstructured data, things like text, images, et cetera…

0:48:36.1 TW: Well, 80% of unstructured data will be generated by an LLM. So no, I’m…

0:48:37.0 BM: It’s turtles all the way.

0:48:37.1 TW: Yes turtles.

0:48:40.0 BM: If you know what I mean. I think the former founder of OpenAI said something like, we’re at the peak data of AI now. We’re at the time we’re like, this is the most data that we have to train. And from now on, we’re gonna have to like rely on synthetic data in order to do that. And that goes back to your question of like hoarding data. But going back to the unstructured point, I think unstructured data is becoming more and more important. And we’re seeing organisations not only start to collect more of that, but also understand how to use it and how to what to do with it. I think this is very early days for this space. And I think we’re still sort of watching and kind of understanding what’s happening. But I think one of the things just to make this really concrete with an example, I think is a cool example. We work with a company that’s a Fortune 500 insurance company. And one of the most important types of data for them, unstructured data is actually customer service conversations.

0:49:38.3 BM: So let’s say I have a policy or something that I’m upset with, and I wanna chat with someone and then have this conversation. And you can analyse that conversation to understand my sentiment. How pissed off am I? Am I like yelling representative? I don’t know, I’m like, get me my manager or whatever it is, or and I’m like, super happy. Thank you so much. That’s what I mean by sentiment. So you can sort of analyse like, what is a conversation like? And basically you can also ask the user for feedback. Sort of scoring that. One of the things that this customer does actually uses LLM to create structure for this unstructured data. What do I mean by that? They basically take a conversation and then score that conversation. So like, zero to 10, this conversation was a seven or an eight or something like that. Now, what’s the problem? The problem is that sometimes LLM hallucinate, and they might give a score that’s, let’s say, larger than 10. What does that mean if a score… If a conversation scored a 12, for example. And so actually, the way in which we were working with this company is allowing them to observe the output of the LLM to make sure that the structured data is within the bounds of what a human would expect to score an unstructured data, which is the customer conversation.

0:50:57.1 BM: And so in that instance, we’re sort of using automation in a way that we maybe hadn’t expected before, in order to add value and to sort of… In this instance, is actually like reduce the cost and improve the experience for the users in this case.

0:51:12.0 TW: But it’s one of those, that brings up the case of, say that it just… That scoring, that model, it just, it shits the bed 10% of the time, but it does way better 60% of the time. And it does about the same as a human, and it’s overall, a little bit cheaper. I think that there are the trade offs. And I mean, maybe this goes back to earlier, or the discussion that if it’s like, well, we’re gonna pull out the one that it said at 12, and say, you got to fix that from happening. That’s one approach, make this never happen. The other option is, it’s gonna happen. So the process needs to be human in the loop or human on the loop, like don’t completely hand this over, so that you can catch the ones because a human would catch it. And there the trade offs are… And you know what, maybe they’re even it’s okay, you’re gonna have a small percentage who are totally pissed off, even if you’re just running humans, ’cause their wait time was too long or something else.

0:52:15.3 TW: Is your goal to have every customer have a delightful experience? Or is it to actually have fewer customers have a horrible experience? It may be a different set of customers that are having a horrible experience. And then probably mode of your connected, you wanna make sure the ones with the highest predicted lifetime value, you’re not saying, great, we have way fewer customers are pissed off. Unfortunately, it tends to skew towards the ones that are the highest lifetime value so.

0:52:44.0 BM: I think that’s… Yeah, I mean, I think that’s spot on. And I think it’s… I mean, one of the questions that I remember sort of thinking through is like what’s worse, like no answer or a bad answer? I’m not sure I can tell you, we’re not creating sort of agents, if you will, in order to say, oh, I don’t know. That’s not how you create them. But oftentimes, like, that actually might be the better answer. I think Tomasz Tunguz, who we sort of collaborated with on predictions for next year, sort of mentioned to us that like what you’d expect is like 75% to 90% accuracy is considered like state of the art for AI. However, what’s often not considered, I mean, on the face of it, 75% to 90% seems really legit and reasonable. But what’s not considered is like, if you have three steps, and each is 75% to 90% of accuracy, the combination of that is actually ultimate accuracy of only 50%, which is, by the way worse than the high school student would score in that sense.

0:53:55.1 BM: And so is 50% acceptable? Probably not. And so what ends up happening is, is actually what I think we were seeing in markets is like, the market actually took this big step back. I think a year ago, there was this huge rush to adopt generative AI and to try to build solutions. But as we were seeing that the accuracy is sort of at those ranges, companies did take a step back and actually are re-evaluating or rethinking where to place their bets or place their chips, if you will. I still find that most companies evaluate a solution with a human thumbs up or thumbs down. Was this answer good or not and allowing users to just mark like, yep, this was great, or no, this kind of sucked. Companies still have that. And I don’t think we’re moving away from that unless there’s sort of big, big change in the near future.

0:54:41.4 MK: I have a totally unrelated random question, Barr. With the companies you’re working with, is the focus of reliability and the work you do quite different depending on whether data’s structured or unstructured? In the use case you just gave, like, it sounded like it was quite different. But what are you seeing across the industry?

0:55:07.8 BM: Yeah, 100%. I think the use cases that we cover vary tremendously based on industry and company. And I think that’s a reflection of the variability in what you can do with the data across the industry. So it can range. The sort of types of products that we work with can be data products that are more like a regulatory environment, where one mistake in the data could actually put you at risk of regulatory fines if you are using data in some incorrect way, or not following what is defined as sort of best practises for data quality, sort of like this blanket statement that’s very high level, but actually, is very important in these environments. That’s like one.

0:55:51.1 BM: The second could be where you have a lot of internal data products, so like a lot of reporting or product organizations that are doing analysis based on cohorts or segmentation of your user base. A third could be data products that are sort of customer facing. So for example, if we have the easiest thing that is like a Netflix recommends your next best view, for example. And then a third… I guess a fifth use case could be a generative AI application. So for example, an agent chat bot that helps you ask questions and answer about your internal process or your internal data. So you can ask really basic questions like, how many customers do we have? And how many customers have renewals in the last few years? Or if I’m in support, I can ask, how many support tickets has this customer submitted in the last year? And in what topics? And what was their CSAT, sort of questions like that.

0:56:51.0 BM: And so each of these can include structured or unstructured data, and each of these can cover very, very different use cases and very different applications of the data. So if anything, I see that there are less homogenous sort of applications of the data, if that makes sense. And I actually anticipate that this will carry through to the generative AI stack. So there’s people create software in a multitude of different ways, in a multitude of different stacks, the same can be said for data. There’s not one single stack that rules at all, there’s not one single type of data that rules at all in order to create data. I think the same will be true for generative AI. There’s not one single stack or one single preferred language of choice, and there’s not one single preferred method, whether it’s structured data or unstructured data. I think that this does very much sort of vary. I will say from my bias point of view is the thing that is common, sort of going back to like the foundation of truth and sort of what is very important is like every organisation needs to have, or needs to rely on their enterprise data to make sure that it’s high quality trusted data so that they can actually leverage and capitalize on that and I think it’s a messy, messy route to get there.

0:58:09.8 BM: Maybe 2025 will be the year of messiness. Sometimes you just gotta like lean into the messiness on our like path, like this random random path to kind of figure it out. But there’s a lot more to figure it out there. But I don’t see us sort of converging on like one single path or use case or even type of data.

0:58:29.1 MH: All right, we’ve gotta start to wrap up. This is so good. And yeah…

[overlapping conversation]

0:58:35.1 TW: Oh, we figured it all out. So we’re good to wrap.

0:58:36.4 MH: Maybe we will do this before…

0:58:37.0 S1: Exactly.

0:58:39.8 MH: 2025 will just be the year of leaning into the mess. And maybe that’s the best we can do right now. Anyway, one thing we love to do is go around the horn, share last call, something that might be interesting to our audience. Barr, you’re our guest. Do you have a last call you wanna share?

0:58:55.4 BM: Sure. So this concept that someone has shared with me recently, which I’ll call sort of watching the avocado, if you will. I don’t know if you have experienced this, but you buy an avocado and it’s like, it’s not ready, not ready, not ready. Boom, you’re too late. It’s already like you can’t eat it anymore. That happens to you. And so I think the idea is like a lot of sort of new technologies and trends are like that. And in this case, sort of this is like generative AI. We’re too early, we’re too early, we’re too early. Boom. You missed the boat.

0:59:30.4 BM: And so I think one of the things that I take away from that is like as data leaders, as sort of data practitioners, how do we keep watching the avocado? We gotta hit the avocado before it’s too ripe. But the timing matters here, especially for a lot of these sort of trends and technologies.

0:59:46.7 MH: Nobody likes bad guacamole. If any listener now uses that when they’re talking somewhere internally, if they use the analogy, please let us know. I wanna… I like that. We gotta watch the avocado. Yeah, that’s awesome. All right, Moe, what about you? What’s your last call?

1:00:06.3 MK: Okay. I’ve been doing lots of thinking about how I make 2025 really great. And I think one of the tensions I’ve found is that I’m naturally inclined to like, wanna go fast and get to the place that I wanna get to. And so this is not anything other than just kind of a personal learning or a personal goal that I’ve set for myself. It is the start of 2025 after all, that I wanna be more intentional about enjoying the journey. And the analogy I have is I love going to the beach, going to the beach with two small humans is really fucking hard. There’s all this shit to pack. You’ve got a card at all down there. Everyone needs sunscreen on like… And so sometimes the bit of getting to the beach is so unpleasant that by the time you get there, you’re all like flustered and hot and you don’t wanna be there and you’re like, Oh, fuck it. Let’s all just go home. So I’m trying to enjoy the journey to get there more. So I went to the beach the other day, it took us an hour to get there. My kids wanted to stop at this playground. They wanted to look at the bird. They wanted to have a snack and I’m like, you know what? That’s okay.

1:01:19.2 MK: I am just going to lean into letting… Enjoying the bit to get there and not focusing so much on kind of the end state. And it’s not just about kids. It’s also about work. ‘Cause like, if you’re constantly trying to like come up with this huge, amazing strategy and deliver this project, but you’re miserable in the months delivering it, that kind of defeats the purpose. So anyway, that’s just my intention for the year that I chair. What about you, Tim?

1:01:44.9 TW: Well, my publisher is gonna hurt me if I don’t plug Analytics the Right Way. So if you’re, depending on when you’re listening to this, it is less, 15 or fewer days from actually being available, but Analytics the Right Way is available for pre-order until January 22nd, in which case it will be available as a print book or an ebook, and the audio-book’s coming out four or five weeks after that. So that does have a section talking about human in the loop versus on the loop versus out of the loop and some of the AI trade-offs, but it is not an AI heavy book at all.

1:02:21.6 TW: So that’s my obligatory self… My log rolling last call. But for fun, I’ve definitely last called stuff from The Pudding before, but one that they recently had, it’s at pudding.cool, but it was Alvin Chang got a data set that looked at a whole bunch of different roles and it was how much they spent of their time sitting versus standing. So it’s kind of one of those like scrolling visualizations. You enter kind of some stuff about your job first. So it can then kind of locate you on it.

1:02:52.7 TW: But it’s just a simple X axis that goes from sitting all the time for work versus standing all the time for work. And then it looks at a whole bunch of different, it varies what the Y axis is as you scroll through it. So it’s kind of just a fun visualization. And it also starts to call out like how tough on bodies a lot of professions are because they’re required to crouch or stand all the time. They can’t take breaks and that sort of thing. But it’s just kind of a fun interactive visualization. So worth checking out to roll X. What about you, Michael? What’s your last call? I mean, it was going to be the book.

1:03:31.2 MH: Tim, I was actually ready to do one on the book for you just in case you didn’t cover it. So good job. We’ll report back to your publisher. You’re doing it. You’re doing what you can do. So actually mine is recently Recast, who I think is some of the best in the game when it comes to media mix models, they’ve started publishing a series of YouTube videos on how to think through the creation of those models. And I think it’s a great watch for anybody who’s engaging with that kind of data. So I’d highly recommend it. And they’ve put a couple out already and then I think there’s some more to come. So that would be my last call. All right. So what is 2025 the year of?

[laughter]

1:04:22.8 MH: I would just have one word. Everybody has to go around and do like a one word. It’s more like a faster. No, nothing.

1:04:31.9 TW: Moderation.

1:04:33.3 MH: I think 2020… Yeah, there you go. I think 2025 is gonna be the year of being thoughtful, keeping with the work, increasing insights, maybe helping with process. That’s none of that’s actually gonna happen, but I just sort of like wish it were. So that’s my take on it.

1:04:53.5 TW: So you use the one word for all of us. You just, you kind of took, we all deferred or…

1:04:58.0 MH: Well, nobody answered, Tim. So I just figured we were not gonna…

1:05:02.0 TW: No, I yielded my one word to you. That’s good. I like it.

1:05:05.0 MH: So I couldn’t think of a better person to help us kick off 2025 with than you, Barr. Thank you so much for coming on the show. It’s been awesome.

1:05:14.7 BM: Absolutely. I hope 2025 will be even better and greater than 2024. And I would probably be remiss if I wouldn’t say that 2025 would be the year of highly reliable data in AI.

1:05:29.1 MH: That’s right. Hey, what’s the saying from your mouth to God’s ears or whatever. We absolutely would want that.

1:05:38.9 BM: Amen.

1:05:39.5 MH: Thank you so much. Awesome. Thank you so much for coming on the show again. And of course, no show would be complete without a huge thank you to Josh Crowhurst, our producer, just getting everything done behind the scenes. As you’ve been listening and thinking about 2025, we’d love to hear from you. Feel free to reach out to us. You can do that via our LinkedIn page or on the Measure Slack chat group, or via email at contact at analyticshour.io. We’d love to hear your thoughts other things that you think are big topics for 2025 in the world of data and analytics. So once again, Barr, it’s a pleasure. Thank you so much for taking the time. We really appreciate having you on the show again. And you’re on track now there we keep talking about the five timers jacket. That’s gonna be a thing. So you’re in the running. There’s only been a few people have done this a couple of times.

1:06:30.1 TW: Are you prepared to have five kids, I guess is the question. We might need to break right now.

1:06:37.0 MH: Yeah, anyway, so of course I think I speak for both of my co-hosts, Tim and Moe, when I say no matter where your data is going, no matter the AI model you’re using, keep analyzing.

1:06:51.0 Announcer: Thanks for listening. Let’s keep the conversation going with your comments, suggestions, and questions on Twitter @analyticshour, on the web at analyticshour.io, our LinkedIn group, and the Measure Chat Slack group. Music for the podcast by Josh Crowhurst.

1:07:10.0 Charles Barkley: So smart guys want to fit in, so they made up a term called analytics. Analytics don’t work.

1:07:14.2 S?: Do the analytics say go for it no matter who’s going for it? So if you and I were on the field, the analytics say go for it. It’s the stupidest, laziest, lamest thing I’ve ever heard for reasoning in competition.

[background conversation]

1:07:31.1 TW: Yeah, my smart speaker decided to weigh in on that.

1:07:38.2 MK: I love it. What did they have to say about that?

1:07:41.6 MH: It’s the perfect little end note to that particular thing.

1:07:46.4 MK: Yeah. Yeah.

1:07:46.5 MH: Tim, I’m coming for you.

1:07:46.8 TW: [1:07:47.3] ____ thumbs up or thumbs down, man.

1:07:49.6 MH: I’m in the background saying, nope, I don’t think I can… Actually it basically said I don’t know now that I think about it. It was like whatever it decided it had heard, which was nothing. Yeah, perfect. Rock flag and lean into the mess!

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