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

00:00:13 | Tim Wilson: Hi everyone, welcome to the Analytics Power Hour. This is episode 301. And hey, here’s a fun little fact. Did you know we had the founder of X.AI on this very show? But it’s not what you think. The date was August 30, 2016. The show was episode number 044. And the topic was artificial intelligence with Dennis Mortensen. So at the time, X.AI was a company that was just trying to solve multi-party meeting scheduling via email. And we talked a lot about like highly specialized agents. And it was this wild conversation about how these simple things

00:00:56 | Tim Wilson: that we think are simple can actually be pretty complicated to hand over to the machine.

00:01:02 | Tim Wilson: But that was 2016. It was a totally different world. And it feels like that was kind of an ancient ancestor of the AI world we’re living in today. And we are going to talk a lot about AI on this episode. And I’m going to do that with my co-host. Julie, Hoyer, you’re from further. Is AI something that comes up at all in your day-to-day?

00:01:24 | Julie Hoyer: If I had a nickel for every…, I would have heard AI, right?

00:01:29 | Tim Wilson: Yep.

00:01:31 | Tim Wilson: That’s what I thought. And Val Kroll, you’re from Fin, the company formerly known as Intercom. Is AI something that your company, I mean, are they doing anything at all with AI?

00:01:41 | Val Kroll: Yeah, we’re just starting to dabble, really. Just starting to just get our toes, get our toes up. No, it’s top to bottom. Top to bottom here at Fin.

00:01:49 | Tim Wilson: The fundamental grounding of the primary offering.

00:01:53 | Val Kroll: Yeah.

00:01:53 | Tim Wilson: And I’m Tim Wilson, or maybe I’m not. And this is just an AI agent that Tim built to record this episode on his behalf. Who knows? So sure, all three of us have been exploring and learning and using AI, but none of us have dived in deeply enough to write a really useful book about it. And as far as I know, none of us were product leaders at Microsoft focused on the creation and build out of Power BI. But our guest has done both. Rob Collie is the CEO of P3 Adaptive, which does custom AI and data work for mid-market and Fortune 1000 companies. He’s a former Microsoft engineer on the Excel team and a founding engineer on what became Power BI. He’s the host of the raw data with Rob Collie podcast. And he’s the author of an upcoming book, Fair Game, Customizing AI to Your Business is Easier Than You Think. It’s not publishing until August 15th, but we got a preview. It’s amazing. And it’s available for pre-order now wherever books are sold, including fairgamebook.ai, which gives instant access to the first few chapters and a few other perks. And today, Rob is our guest. So welcome to the show, Rob. Thank you so much. I’ve enjoyed this already. All right, so we don’t usually kick off the show with a, hey, give us your professional history. But one of the reasons we really wanted to have you on specifically is that you’re not just kind of any old run-of-the-mill AI thought leader, you know, half the people who have LinkedIn accounts right now. You actually kind of came from our people. And I mentioned in the intro, but you had a formative part of your career working at Microsoft, including being part of like the birth of Power BI, which is inarguably like one of the premier, most successful BI platforms out there. So I think that actually may be a good place to start. So can you sort of talk about your role, you know, from Microsoft through kind of connect the dots for us as to how that set you on the trajectory to where you are today and like like helping organizations figure out like useful paths forward with AI? I would love to.

00:03:57 | Rob Collie: So I didn’t, like most people who love data, I didn’t know that I love data. I didn’t grow up saying I want to be a data professional, whatever. I’ve talked for years and years about how some of us just sort of carry this thing I call the data gene and we eventually discovered that we have it. Like when we collide with Excel or whatever for the first time, most people bounce off like in terror. They don’t like Excel, right? Yuck. But like it actually is about one out of 16 people who collides with something like Excel and sticks and goes, ooh, that was kind of cool, you know, or their first collision with SQL. It’s usually Excel. That’s the first sticking point, but not always. And so I discovered that I had the data gene in my course of working at Microsoft. In fact, at one point earlier in my Microsoft career, I said to someone that I would never go to work on something like a product like Excel. Never. It was like beneath me. But then I went and I ended up kind of getting re-organized into Excel and discovering, oh my God, this is totally where I needed to be. And so I got to know a few different really important audiences. I got to know the people who really kind of make the world go around, like the Excel power users, that one in 16 crowd. Back then, even really to this day, they kind of make the world go around in a very kind of almost like thankless way. I got to know those people and really, really, really develop an affinity for them in a way because I’m kind of one of them and I was building products for them. And then at one point, I also got grabbed to be in charge of the BI investments in Excel. And so that was being dragged into like the corporate IT zone, right? And I learned the old style of BI through that experience. And then later, I was recruited to join the Power BI team when it was still called Project Gemini Super Secret Project. And the contrast between how the old style of BI, most of us who are working in data or working in business intelligence, were never part of the old traditional style BI because it was so insular, it was so arcane. It was a very tiny priesthood that was involved in this stuff. And I couldn’t even learn how to do it. I was working on it and I couldn’t learn how to write the formulas in these old systems. So helping to build the Power BI, which was aimed at that original Excel crowd, those people that I like so much, the data gene crowd and giving them industrial strength tools, I never thought that was going to work. I love the project. It was exciting. It was definitely the place that I needed to work at Microsoft when they came to pitch me. But like everything else, I’ve just realized, this is probably going to suck. Or maybe it’ll take three versions to get it right. And then I was shocked at how good version one was. I mean, it was really fucking good. And I knew the world was going to change, but I also knew that consulting companies of the time were not going to want to work this way. They were not going to want to move at that pace.

00:07:24 | Rob Collie: They were not going to want to work with clients of different sizes and pick up smaller, faster projects, which is what the world really needed. And that’s what led me to start the company that I did. I wanted to go fill that niche. And guess what? I got to hire those types of people that I liked so much into these new consulting roles. They became consultants. And that’s what our company is largely staffed by, not 100%, but largely staffed by that data gene crowd that grew up in the business more so than in IT. And so it’s sort of like I got to create a home for some of my favorite people at the same time. It was really kind of hard to pass up.

00:08:05 | Tim Wilson: So that’s the first part of that journey. And then, I mean, the link you make in the book

00:08:14 | Tim Wilson: is that those are the people that you say they’re kind of crafters and why crafters specifically are uniquely suited to do stuff with AI. So maybe that’s my follow on, is can you talk sort of about like the leaders, crafters, developers, knowledge workers, and why that’s an AI thing?

00:08:42 | Rob Collie Sure. Yeah, absolutely. And first, let’s just bridge one tiny little gap, which is like when I, I didn’t set out to write a book about AI. So, you know, I’m a CEO of a data company, you know, 50 plus person data company, and I see AI coming. And it was leading to this sense of dread, which I think we can all kind of relate to, right? Like AI is really good at writing code, you know, it’s actually really good at doing a lot of the things that data professionals do for a living. I went on a research project

00:09:13 | Rob Collie: with my own sort of like journey down the rabbit hole to figure out what the future of our company should be. This was about survival. Like what, you know, I had to do this. And what I found down that rabbit hole was far different than what I expected. And far more encouraging and far more like, oh, we can do this sort of thing. And far more, oh, this is actually a data problem. Like it’s like right in our wheelhouse. And the difference of sort of like feeling like AI was like opaque and inaccessible, going from that to going, oh, that’s it. That feeling of like, oh, that’s it. Like that feeling is what compelled me to write a book. Because when I found myself in possession of that feeling, which by the way is exactly what led me to write the first Power BI books that I wrote, you know, 10 plus years ago, like me feeling like I understood something and knowing that there wasn’t anything all that necessarily special about me, like the rest of the world can do this too. Like I felt like I owed that book. And I felt the same sort of the same sort of vibes again, but like at a greater magnitude this time, because like everyone’s worried about AI. But yeah, so back to your question.

00:10:32 | Rob Collie: You know, I’ve been calling these people the data gene crowd forever. But you know, what they really are, and I think present company probably is, you know, it’s we, right? It’s not they. You know, what we really are are people who are willing to think systematically about business problems, while also remaining close to the business and understanding that, you know, there’s the technologists that kind of like want to be like locked in a closet and have like notes passed under the door of what to build, right? And that’s that’s a lot of tech, right? And it’s but it’s a very and you need those people. But that’s a very inefficient way to work for most business solutions. Like being immersed in the business, while at the same time having some tech skills, enables a completely different style of work, a completely different tempo and a completely different quality of output. But with the with with AI coming along and the ability for people like us, people who picked up Excel and smiled, right? Like, when you sit down with people like us sit down with something like Claude Code for the first time, we sort of like get over our fear of trying something new. It’s that same feeling like captivating feeling like, Oh my God. And so I needed to come up with a new name for these people. It’s no longer the data gene crowd. I needed a noun for these people in the book. So I had to come up with something and crafter is where I landed. So that leaders, crafters and developers and ratio to 580. And so for every, if you have a if it just works out that way, if you had a population of 87 people who work in an office, you know, like two of them are developers, five of them are crafters, and 80 of them are knowledge workers of various, various flavors. So like only so five relative to 80, like that’s how many people again, one to 16 are willing to engage with something like Excel. And what I’m seeing is that that same data gene crowd, plus the ability to use coding agents, AI coding agents, to build custom software. I mean, this is like going to be the new spreadsheets. You know, we application development is now within range for this crafter crowd. And we’re just in the earliest stages of the world waking up to this, like most crafters haven’t yet discovered that they can do this. But we’re at steady state, we’re going to have about the same number of people building software as we had building spreadsheets.

00:13:17 | Tim Wilson: Michael, how does your team share AI generated analyses

00:13:26 | Michael Helbling: today professionally? We say, hold on, I think I asked Claude this last week and then sort of disappear into a chat thread like a raccoon in an air duct. Oh, nice. That’s that’s elegant. That’s

00:13:36 | Tim Wilson: clearly scalable. I mean, actually, that’s horrifying. Why thank you. It’s called innovation. That’s the type of innovation that ask why is trying to solve with Prism. Your AI work shouldn’t

00:13:49 | Michael Helbling: be trapped in one person’s called innovation. I have started to realize my private chat history is not a data strategy. It’s kind of a joke drawer with confidence. Well, Prism helps preserve context

00:14:03 | Tim Wilson: and memory across users, you know, like metric definitions and source of truth tables, business

00:14:08 | Michael Helbling: rules, prior analyses. So when I teach it, a qualified lead excludes internal traffic, spam forums and that one campaign we don’t talk about, the team can build from that shared context instead of starting over entirely. Beautiful. Let’s ask Michael what he meant, more the system actually

00:14:26 | Tim Wilson: remembers. And with Prism, analyses are organized, traceable, auditable and reusable with your other

00:14:32 | Michael Helbling: data sets. I like it. So my best AI work becomes company knowledge, not a screenshot named. Is this important? Maybe.png. Exactly. I guess it’s time to stop being a data trash panda when it comes

00:14:45 | Tim Wilson: to AI. Definitely. No pandas in this context. Go to ask dash y.ai and sign up for the wait list.

00:14:52 | Michael Helbling: Use code APH to jump to the top of that list. That’s ask dash the letter y.ai code APH. Because check my cloud history is not a collaboration plan. Tim, one of the trickiest parts of measurement is user data gaps. Right. Someone gives you an email in one session,

00:15:15 | Tim Wilson: comes back later from another device or converts offline and suddenly the customer journey gets

00:15:20 | Michael Helbling: hard to connect. Exactly. Staped and richer is a state power up that stores selected data from incoming requests and uses it later to enrich future events when information is missing.

00:15:32 | Tim Wilson: So once a user has been identified, and richer can help recognize them in later events and add previously collected details, all while aligning with consent settings and handling personal data

00:15:42 | Michael Helbling: securely. Yeah, it’s especially useful for cross device journeys, offline conversions, longer buying cycles and connecting CRM outcomes back to earlier website activity. You can set up and richer to work with cookies, state store or both. And it supports uploads of historical data from systems like Shopify, Clavio or your CRM. So if you’re tracking has gaps and who’s doesn’t, Staped and richer can help create more complete consistent event data. Yeah, go to state.io to learn more about Staped and richer, improve event match quality, attribution and visibility into the full customer journey. That’s Staped.io, more complete data with consent and privacy in mind.

00:16:28 | Val Kroll: Besides like the just the usual FUD, what do you think are some of the barriers to crafters? Because like I remember where I was the first time I saw a pivot table and it was like, blew me back in my chair. I was high school, but of like, what is holding, what do you think are some of the major barriers that are holding up the crafter crew back?

00:16:52 | Rob Collie: Well, here’s one quick funny story before I answer your question. I worked on the Excel team for a full year before I knew what pivot tables were used for. And I love that. I would fake it be like, yeah, you could throw this into a chart, you could throw it into a pivot, you know, I would just say things like that without ever you really even knowing what it was. Just like, fake it. No, that’s what all the cool kids were saying. So I would say that, right? And then one day, so for you to see a pivot table in high school, that’s awesome. That is really, really cool. So the thing about Excel is that, you know, is that it was so, I mean, I think the most people are even even people who end up liking Excel are scared of it at first. You know, thinking back, I think I was, you know, and, and even when I worked at Microsoft, like every couple of years, people would move around between teams and office, you know, it’s really an open season recruiting. I tried to get people to come to work on Excel and they, they wouldn’t want to. They would want to go work on outlook and word and PowerPoint because they already understood those things. But they could sense that Excel was deeper, you know, Excel is an application development platform, you know, it’s a spreadsheet is an application, you know. And so, like, there’s something intimidating about it. I personally, and I say this in the book, I don’t, I don’t pick up new tech readily. I don’t, I’m not an enthusiastic, like, ooh, shiny, go grab it off the shelf kind of person, like I kind of have to force myself, you know. And it’s really just sort of like not even knowing where to start. And like, how do you even get it installed? Like, even just going and installing something like like cloud code, right? Like, at least it used to be harder than it is now, it’s a lot easier.

00:18:43 | Rob Collie: But and not understanding the nature of the system you’re working with, like, it just feels like magic. Like, so you’re putting so much trust in something else that’s that’s that feels opaque to you. I think there’s a sort of like a like a sense of loss of control that that is that is terrifying. And I’m seeing this a little bit with our own team, like, you know, not as much anymore, but in the early going, they’re like, we derive such confidence from having touched every last single piece of logic, you know, and like artistinally crafted it, right? Like, like, how can we delegate parts of that to another system, you know? So there’s a number of things like that. But for me, honestly, it was just kind of getting off the starting line. I just had to sit down and build something.

00:19:39 | Tim Wilson: I have a little bit of a theory, and it bridges those worlds. And it’s interesting when you say, you know, PowerPoint and Word, and Outlook and Excel. And I don’t think I’d really thought about it. If I’m doing something in PowerPoint, I’m making slides, I know I am constrained to this thing of slides and Word, same thing with Excel, I feel like the people with the data gene, the crafters, somewhere along the way, they go from it’s a table where I’m doing formulas to Oh, wait a minute, I can actually build an interactive app, you know, I can put slicers or, you know, I can put data validation and put drop downs in and I can, I can, I can kind of build a data product, which I think is one of the reasons Power BI is successful is because there’s the progression through up to Power BI. And I think the same thing happens with any BI platform is you have that somebody with the data gene saying I’m building, I’m crafting an experience. And to me, what I think might be happening, and this was purely from actually reading the book that this little light bulb went on, we are all introduced to this new world of AI through the chat interface. And there are plenty of people out there saying it’s not, it’s more than just the chat, you got to build agents, you got to do all this other stuff. But there are people saying that it feels like it’s, it’s this other thing far away. But really where it’s close is saying, okay, if you just step out of the chat and say, let me, let me use the chat to help me build a data product, let me use the chat to help me, like, create something like that’s where that, that parallel. Okay, yeah. You know, I started to, to see because it just that light bulb hadn’t gone on that, oh, I can create a deliverable for my audience, for my user. And I’ve now got a broader set of things in my fingertips, just like going from, I got to hack around and excel to build an interactive dashboard, I go to Power BI, I’ve now got a deeper, a lot of these things, I don’t have to come up with the indirect formula in a data validation dropdown to make this filter work. It’s just natively there.

00:21:52 | Rob Collie: I think you just nailed it. I think you nailed it. And I didn’t, and I’m not saying that like, that’s exactly what I was thinking. I’m saying that like, I think you just gave an answer that I didn’t have. But I think it’s the, I think it’s a really good point. So there’s a, there’s a mixture of kind of like gaps you have to, you have to hop, right? Like, you know, smooth climbing curves are one thing, smooth climbing learning curves are one thing, but having to like jump from point to point, like over like a, like a void, you know, those are the places that get us. And there’s  two big voids here. One of them is that, you know, we, we sit down and we use the off the shelf AI systems. This is a recurring theme in the book is that the, the off the shelf AI systems are amazing for like personal use. They’re amazing for individual use. They know everything about like all of human history. It’s insane. I was using Claude the other day and it was telling me about the geologic construction of the ground underneath my house here in Seattle. It, without even having to search the web, it didn’t have to search the web. It knew what the geographic makeup and so we were calculating the speed of sound in the ground near my house and it didn’t

00:23:05 | Val Kroll: have to search the web. You’ve got the data gene. It was family night.

00:23:12 | Rob Collie: Okay. Look, there’s a power driver driving, driving a post into the lake near our, near our house and the sound arrives in a two syllable thump. You get a thump through the ground and then you get the clank through the air. It’s one thing, but because the speed of sound in the ground is faster, the ground wave gets here and I was measuring the delta between the, the time between the two arrive and using that to calculate the distance to how far away the pile driver is. And yes, there’s something that the whole family is aware is going on. Like dad’s calculating this is like, it’s two, it’s, it’s 200 meters plus or minus 15. But anyway, so like, like, but to think about it, like that is publicly available knowledge that’s going into this LLM. You know, but like if it’s about my business, and in the book, I show an example of this, like I just have it like write a proposal, write me a proposal for this project. It goes and tries to do it, but it knows nothing about us. It, it just gives the lowest common denominator like a proposal and it is completely worthless. It is awful. And so there’s that, there’s that gap between business use and personal use, which is like, again, it’s, it’s, it’s this void, right? Like it’s, it’s not a, you have to jump it all at once.

00:24:32 | Tim Wilson: Well, it’s the same, it’s the same void of saying, I know I’m not supposed to put private data into this if I’m using a public or I, I’m like, I’ve been terrified that I’m going to put something in this going to be a problem. But if I just ask her to do something without it, so how do I put enough stuff in? And it’s, that feels like the other connecting the dots, which again, I mean, yes, yes. So there’s that sycophantic Tim, so maybe I am just an AI agent. I’m like, your book breaks out the different pieces and how to think of them. But that feels like it’s again, it’s like, you’re using chat GPT and somebody’s like,

00:25:04 | Rob Collie: but don’t, don’t give it any of your information. And so then this is the second void is that you have to get comfortable building real, regular software, like stuff that’s written in Python or whatever, which is not what data people have traditionally done. Like we’ve worked in ETL, we’ve worked in formulas, we’ve worked in data pipelines and things of that sort. But until you can write your own custom code, you can’t build the rest of the system around the LLM to customize it. If you’re in charge of that software, then you’re also in charge of the storage and you’re in charge of like, which version of the API you call that is you can call ones that are that are actually more protected and that aren’t that they don’t train on your on your data. So you can you can candle that sort of sensitivity. But you have to go from being a non software developer to being a software developer in order to get this customized experience where you can control the flow of information and you can give context about your business at the right time to the LLM. So you have to sort of jump both of these voids at the same time to get started. And this is why like in the appendices of the book, the first

00:26:20 | Tim Wilson: thing you just did one of these the other night, I think, Tim, right? So the first thing I have

00:26:28 | Rob Collie: people do if they’ve never used cloud code or codex or whatever, like it’s just build a website on their computer. That’s just like a survey or a quiz that gives a score or whatever, just get used to the idea of building anything. No, no AI agent stuff, just get just start to get more comfortable with building free form software. And then we start to layer in and the next exercise, like, okay, now we’re going to add some LLM to it and see, you know, where it

00:26:58 | Tim Wilson: goes from there. So I’ll throw in now. This is I may regret this, but for shits and giggles, what I wound up doing was a little 15 question multiple choice quiz about my wife’s and my relationship and about us that I sent to my three 20 plus kids, 20 adult children. I was like three 20 plus. No, three 23 kids who are all adults about like, it’s like just family lower stuff, which was like a prompt. But it was fun standalone. I did wind up having to throw it on Netlify. So it was like, I would like one part of my like, Oh, I already have a Netlify account, I’ll throw it there. But it’s now, of course, gone to where now my parents have taken it. I mean, my kids do the best, but they’ve also had a few like, what you did? I mean, so, you know what, I’ll throw it into it in the show notes and anybody terribly. Yeah. Why not? How well do you know, Tim Wilson’s marriage? Yeah, I mean, it’s going to be referred, it’s going to refer to mom and dad. And that’s going to, you’re going to have to recognize that. Okay, I’m just going to have to be okay with it. Anyway, it was a great episode. But just, I mean, throw in that, that was like the little unlock was even though I’ve done stuff with co-work, even though I’ve done stuff with gyms, even though I’ve, I feel like I’ve done a lot, there was a piece by the time I got to the end of the book of saying, Oh, no, no, no, there’s a software component where what I’m developing with limitations, right? Because you also talk about where you still need developers. This is not now you’re turning the people with the data gene, the crafters into full blown,

00:28:32 | Tim Wilson: push stuff into production to hit the masses. The developers now are still needed for their

00:28:39 | Rob Collie: skills. Yeah. So, you know, we P three, we’ve hired this year, our first two full stack developers ever. And it’s kind of funny, like, in fact, I was even in Fortune magazine at one point, talking about this, because, like, big tech is laying off developers, because, because AI is so good at writing code, in fact, it’s better writing code than anything else. At the same time, though, while it’s expanding the capacity of an individual developer to do so much more, that makes their ROI pencil out much differently. And so, like, you know, in order to achieve what we’re achieving today with two developers who are all in on using AI to write code, we’re getting the results of, like, honestly, like, what are probably required a team of 10

00:29:32 | Tim Wilson: developers to do before. So, I call this sort of like the migration to the suburbs

00:29:40 | Rob Collie: for developers. Like, so, like, like, there’s been a concentration of developer talent in big tech firms, because they build huge amounts of technology and they require engineering teams of, like, 30 developers at a time. And I don’t think they’re going to require 30 developers to do

00:29:56 | Tim Wilson: the same sorts of things anymore. At the same time, though, the ROI on hiring a developer,

00:30:04 | Rob Collie: like, in a smaller company, or a department level of an enterprise or whatever, is going to be higher than it used to be. So these people are going to have other jobs they can plug into. They’re just going to be in different places. And, you know, so, like, you know, the dev team of two that we have here, we don’t need to go too deep. And just to sort of underline it, like, where we’ve built things that put us at probably about a year ahead of Microsoft

00:30:33 | Tim Wilson: in terms of offerings that Microsoft will have. It’s like, we’re able to do things for our clients

00:30:39 | Rob Collie: today with AI around Power BI and things like that, that, you know, Microsoft is absolutely

00:30:45 | Tim Wilson: building. But they’re not there yet. And so, you know, it gives us an opportunity to fill some

00:30:52 | Rob Collie: gaps there in the meantime. And, like, that is just absolutely bananas. A year ago, me, would never have guessed that that was a possibility.

00:31:02 | Tim Wilson: You would have said the tool that’s not doable, even with our DAX amazingness, we can’t do that. We’ll have to wait until Microsoft rolls that out. I mean, or you’d find an alternative way to come at it.

00:31:14 | Rob Collie: No, I’m thinking it’s more like I just hadn’t realized how valuable developers were going to be. Like, like, there’s a difference between what a crafter, like, so I think of myself, I’m 100% a crafter. I’m not a developer. And I talk about there’s a form of discipline that’s present in a developer. These are mostly personality traits as opposed to, like, intellectual IQ measurements. There’s a disciplined patience in a developer, combined with, like, an aggressive modularization instinct that isn’t really present in most crafters. It’s not present in me. And so, the things that our dev team of two have built are things that our crafter team would not have been able to build. They’re just levels of complexity. And even just knowing what to build and figuring out, like, what the roadmap should look like would have just taken us too long. The developer brain is still really useful. And so, one of the things we’re working on on our team is figuring out how to hybridize developers and crafters to give the best possible results to our clients. Because most developers, and I’m painting with the broad brush, are most developers aren’t going to want to be the customer-facing interface that does all that conversation. Like, they want to be writing code. Even if they’re capable of having those conversations, they love writing code, you know? And meetings take them away from that.

00:32:53 | Tim Wilson: But crafters thrive on interacting with the stakeholders, right? Like, that’s the world

00:32:59 | Rob Collie: that they came from. They want to be right there, you know, like, almost like pressed up against the audience that they’re helping. And so, but there are certain kinds of code, certain kinds of platforms, et cetera, that, like, you really, you’re still going to want developers to do those things, even though we’re building solutions at the crafter level. And I

00:33:19 | Tim Wilson: do talk a lot in the book about trying to paint pictures of where those boundaries are,

00:33:26 | Rob Collie: but they’re incredibly fluid, you know? Like, it’s like, you can’t paint a precise picture.

00:33:32 | Julie Hoyer: I was going to ask you to clarify, like, when we’re talking about crafters, are we, in my brain, and I feel like maybe some of this wording comes from, like, working at further and search discovery, like, we always talk to him and about, like, our titles, or maybe a little different than somehow, like, the industry talked about them. But in my head, right, crafters, the way you mentioned it, Rob, to me, I would put in, like, this engineering group that’s more like building the back end of, like, a BI tool, and, like, surfacing the data type engineering role, whereas I came up through more of, like, the classic, like, analytics role. We kind of talk about them as, like, functional analysts, very tied to, like, the business. I could, like, build a dashboard, of course, but I wasn’t doing, like, the ETLs in the background. I was doing more of the, like, business, what are you asking? What is your problem? What’s your hypothesis? Let me actually go manipulate the data, build something in R, right? Like, a model, do an analysis, and then bring it back to you. In your framing is a crafter, both of those things, because when you say, crafters need to become software developers, and, like, the way of using AI, I’m just curious, are you talking more what I’m saying are engineers, or are you including this, like, functional analyst group as well? So I think I should very much clarify that I’m using

00:34:52 | Rob Collie: a very Power BI-centric lens for this, because, you know, our company’s all been all about Power BI. You know, we’ve long said that we will use another tool if something came along that we like better. But so, but we’ve been, we’ve been a Microsoft stack company, and it’s not because of Microsoft patriotism. Like, I literally formed a company around this thing that I saw.

00:35:16 | Tim Wilson: The Table of Army is going to be coming for this. The comments are going to be…

00:35:20 | Rob Collie: Well, I am, I am ready. I am ready for that. When we talk briefly about semantic models, I’m really ready. So… And the Domo people too, but that’s only going to be like three people. So… I know. Yeah. I mean, you just defarm them. The people that I’m talking about have kind of always sat on the boundary between the business and IT, but they’ve lived mostly in business. So they, you know, like they reported up through, you know, some non-IT function. You know, they’re the shadow IT folks. And so, you know, the role you’re describing is sort of like the business analyst that does all the, interacts with the business and sort of figures out what the needs are. The people who do that,

00:36:08 | Tim Wilson: but then also go and like build the spreadsheet, you know, that addresses it, or in our world,

00:36:15 | Rob Collie: then went and discovered Power BI and started doing the ETL and started, in sort of doing the data modeling and writing the DAX. That’s the, you know, that’s the, that’s my personal like, like center of gravity for all of this. So at our company, we talk about it as like the decathlete model. So like most consulting firms, the way they’ve been constructed over the years is they have specialists. They’ve got someone who throws the javelin. They’ve got someone who runs the hundred meters. They’ve got, right. And so this team of 10 rolls in there on the engagement and you pay a tremendous amount of communication costs between those 10 people. Moest of the elapsed time of the project is actually the communication between the members of the team. It’s like 99% of the weight of the project is communication costs and not implementation. And so our whole business model from the beginning was built around this idea that, no, no, like you, you need to be the decathlete. You’re the one that talks to the customer. You’re the one that understands their needs. You’re the one that builds the ETL. You’re the one that builds the data model. You’re the one that builds the dashboards and all that community, your bandwidth within your own head is just so much faster than the bandwidth between people. And by the way, also less error prone. And so at least again, speaking through the lens of my company, the company that we built, this is the persona that’s kind of like, like at ground zero for it. And so I think more on the business analyst side than on the quote unquote engineering side. But, but in the Power BI world, a lot of those business analyst types acquired these skills that had traditionally been the purview of the back office IT types, right? Like we were invading their ETL world and invading their data modeling world, right? Like, you know, tearing down that ivory tower.

00:38:11 | Tim Wilson: I feel like it’s the media agency that would say, oh, we manually update this spreadsheet and we put our dates across in the columns and it just makes our skin crawl with the structure of it. And they manually date it and they put some hacky formulas and since some sort of garbage over the wall to the marketer. And then an analyst gets in and or somebody with the data gene gets in and says, this is error prone. It’s not really giving the marketer what they’re trying to ask. So I’m going to figure out a way to maybe it’s VBA, you know, maybe it’s Google, maybe it’s Apps Script, because we’re doing in Google Sheets. I’m going to figure out a way that we streamline the process and deliver something that is more aggregated, more useful. Oh, and I’m going to put a layer of interactivity on it. And I’m not going to be able to stop. And then suddenly I’m the person who the digital marketers come to and they say, can you make one of those spreadsheets? And like, that’s where we’ve crossed over and we’re like, wait a minute, are we are we developers? Like, no, you’re still not a developer. You’re a, but I also feel like it’s where analytics engineers arose. That is a whole field that came out of analysts who said, damn it, I can’t keep waiting for the engineering team. Every time I want to change the ETL, I got, I’m just going to build myself a little sandbox. And all of a sudden that became an entire position or a total role of

00:39:37 | Tim Wilson: where the analyst just kind of gets better. And the point I think that, that like in the

00:39:44 | Tim Wilson: light bulb that went on for me was like, oh, wow, I would never consider building a front end. I would never consider something that limits the rules that is actually software. But guess what? Those same people with a little bit more knowledge and a little bit of practice can actually extend it with a boundary of where does it cross over to a developer, but they can actually, they have this other thing that we’ve never even thought of as being in our arsenal.

00:40:13 | Val Kroll: It seems like one of the, the first steps to someone kind of seeing for these crafters have their eyes open is to get out of chat, um, to start building. And I think some of those examples you guys talked about was great, but something you touched upon earlier Robin obviously got into it deeply in the book is thinking about how do you give it the context about your business that is required in order for it to actually be the partner for you that it needs versus just like the generic off the shelf, like how would an individual contributor think about going about that in a way that’s productive for them in role? Like do you have any advice for taking that leap, which feels like another step? Yeah, I mean, the, what’s really amazing is just

00:40:55 | Rob Collie: how much smarter even the LLM seem to get when you give them something to go on. So the first example that I talk about in the book is this concept of what I call a handbook. Um, so if you, if you had a new hire, uh, and one of the metaphors I use is, um, an LLM is basically something that has a, especially the frontier new LLMs, they basically have PhDs in every human subject, every, everything that you could get a PhD in the LLM has that level of knowledge and even ability to reason to a certain extent in all of those subjects, but it shows up every day knowing nothing about your business. And if you thought of it as a person for a moment that showed up with all of that skill, but knowing nothing about your business, they would be relatively useless to you. You know, like you don’t care about the history of the Roman empire, uh, most of the time in your business, right? You don’t care about the ground speed of sound underneath Moett Lake Seattle most of the time in your business, right? Um, but you do have policies and a brand framework and workflows and things like that. And so like, if you were going to hand, um, um, a new hire, some sort of handbook and instruction manual for their job, uh, it would be written in English, like if, if you’re in the United States anyway,

00:42:17 | Tim Wilson: right? Like, you know, it’d be written in natural human language. That same sort of document can be handed to an LLM every time it wakes up. And if you, if you, again, now you’re writing custom

00:42:30 | Rob Collie: code, right? So you have, you’ve had to take control and you’re not, you’re not using the chat GPT built in interface or the off the shelf interface anymore. Like you’re sort of like taking control of it. You’re building your own software. Um, but it’s not that hard. It really isn’t. And your, your software, the first thing it does when it, when your software wakes up and someone asks it a question or something like that, let’s say you’re building your own chat bot, the first time the chat bot wakes up every time it goes and grabs that handbook and feeds the handbook to the LLM saying, here’s what you’re doing for us today. Here’s your personality, here’s the rules that you follow. And that lands in the LLM’s brain before any question from the user does. And suddenly it goes from being this off the shelf useless thing that can’t help you with your business because it doesn’t know your business to being a world beater. Um, and if you just think about that for a moment, just pushing some instructions and some context, just like some information about your business. Who are we? What do we do? Like what’s, what’s your corner of the world here? And each of these agents you build like this, they become specialists in that one role, right? You don’t, you don’t, um, I talk about like in the book, you don’t,

00:43:48 | Tim Wilson: you’re never going to build a super being that knows everything about your business because

00:43:54 | Rob Collie: everything about your business can’t fit, believe it or not, it can’t fit into the LLM’s memory. It will over, it will overload it. So you have to give it narrow lanes. Um, and you know, so like if you have like, you know, but you can imagine agents with five different roles, like I’ve got one that helps me write marketing copy. I created an editor to help me with my book. Um, and they have different instructions, right? You know, one of them is, is instructed how to write like our company. And the other one is instructed not to write anything for me. The other one’s instructed to help hold me to my standard of writing. And when it makes suggestions on fixes, don’t make suggestions that make it sound like boring business voice. Remember that I’m still trying to sound like me when you’re criticizing my work. So like, don’t, for example, don’t, I don’t want my editor to ever say to me, Hey, Rob, you start too many sentences with the word and or but, you know, that’s just how I write, you know, so shut up about that. We’re not going to do it that way, you know. So this idea of handbooks is really like the, the first place to start. But then over time, you start to give these agents, and again, you’re just giving the LLM sort of a menu,

00:45:18 | Tim Wilson: you give it the ability to pull information that it wants. Because you can’t sometimes,

00:45:24 | Rob Collie: you don’t, you can’t know in advance what information it needs. So you give the LLM some ability to go and pull information it needs. And by the way, we’ve all seen this in action already. Every time you go to an off the shelf LLM product and you ask it a question, when it switches off and does a web search, it’s doing exactly that. It’s recognizing that it needs more information from elsewhere and triggering something that’s called a tool. And the tool is just, it’s something that literally just sends a chat request to the tool, the LLM does, and then the out that the tool goes and performs the web search and returns the results back to the LLM. And you can do that with your own internal databases. You can do that with your own internal knowledge bases, which is crazy.

00:46:12 | Julie Hoyer: So for a very specific example, it immediately when I think of like AI and use of analytics, you hear about every tool putting in AI chatbot or AI agent on top of their BI tool, right? And it’s supposed to like be so great. And everyone’s got to have had experience with it. Yeah. And as most people that actually go in and use them, they don’t know anything about your business in there. And I have not found a helpful one so far. And so then of course, that gets us into the idea of like, Oh, it needs a semantic layer, it needs context. So I’m kind of like wrapping us into two possible paths here, and you can choose whichever one, but I’d love to talk about them both. One being the whole semantic layer conversation. But two, and what you were just talking about, what advice would you give an analytics practitioner, a crafter of your company has this BI tool, and it has this AI agent? Like, are you saying you can control that AI agent with like these handbooks and things, which it sounds like maybe it leans into semantic layer, or are you saying, don’t use the off the shelf thing, go make your own custom one? Or is this scenario not applicable at all? Option C. I think, I think they’re both kind of the same

00:47:29 | Rob Collie: conversation, honestly. Like, the key thing to recognize about any AI solution, and this is the thing that kind of blew my mind, is that the LLM is sort of like, almost like, it’s just something you pick off the shelf, and it’s almost like, like the, an element on the periodic table, it is exactly the same for you as it is for me, as it is for the, as it is for Satya Nadella at Microsoft, it’s the exact same LLM. And it’s really just like a component. And most of making, not most of, making these things do anything useful for us is all about just regular software, not AI, just regular software. And the regular software’s job is to handle like the user experience, just like it always does, as well as the flow of information to and from the LLM, like giving the LLM access to what it needs to know. So let’s keep in mind for a moment that most AI agents, we fast forward like two years, and we see what the future looks like. Moest AI agents that are deployed at companies aren’t going to be about analytics, you know, like, you know, like a, a chat bot that handles the first level of interaction with customer service, for instance, it doesn’t need to be performing strategic queries against your, you know, your data warehouse, right? Like it really just needs to have a, like a flow chart in it, and, and also some instructions on how to talk to people and what not to say and all that kind of stuff. And that, that sort of falls into the handbook category. So there’s a lot of tactical AI agents that aren’t going to have anything to do with what we think of as analytics. I mean, I just want to level set that. What’s kind of neat, though, is that even those tactical things that have nothing to do with analytics, they still, because they, they operate on information that needs to be very carefully controlled and fed at the right time, that the types of discipline that we’ve developed as analytics people, as data people, are that are the right mindset for making those things work properly, you know? And now that you’ve given us, the world has given us crafter types, the ability to write real software very quickly with the use of an agentic, you know, like something like cloud code, we’ve got everything at our fingertips, that we’re going to be really good at building those sorts of solutions, even though they’re not related to traditional analytics at all. And so I want to make sure that I’m not pigeonholing us data crowd into just the traditional data crowd type AI stuff. So that’s, and the handbook stuff is not, is not part of what you’re hearing when people talk about semantic models or semantic layers. Handbooks are not, not really part of that. Semantic layers are a decoder ring for your structured data. And that’s, now, now we’re back in the analytics and data crowd, you know, we’re back in our, in our, in our home neighborhood now. Yes. And so the, the, I think this isn’t even really controversial, honestly, because so we were talking about, you know, the Tableau crowd coming for me. Okay, so it doesn’t matter what platform you’re associated with, like if you’re a Tableau professional, you’re a Snowflake professional, you’re a Power BI professional. I think it’s really just like a foregone conclusion that you are going to be working with semantic models and semantic layers. And, you know, I can give you the technical reason why, but I can also just give you the market reason why. So let’s take Tableau as an example, and I love this, this just tickles me

00:51:33 | Tim Wilson: to death. So for, for men, the best thing about Power BI, from a technical standpoint, the best

00:51:41 | Rob Collie: thing about Power BI was not the reason why it got adopted. The reason Power BI won the BI wars, and has the largest market share is because it was cheapest and it was integrated with the rest of Microsoft. That’s it. No one was evaluating like, ooh, it’s so cool that you have a semantic model in this star schema stuff and all, like, like, it makes a difference in practice, but that’s

00:52:03 | Tim Wilson: not why it won. And Tableau long neglected the idea of semantic models. It’s, they were all about

00:52:11 | Rob Collie: each time you need a new dashboard, you go write a bunch of new SQL queries to make a big flat, to make a big flat wide rectangle of data that you hide behind the dashboard. That’s the philosophy over and over and over again. And the thing is, it worked, right? I mean, like the IPO, they sold the Salesforce, I mean, those people made gobs and gobs and gobs of money. So it’s like, you know, you know, I’d start arguing with them about like, well, y’all don’t have a semantic layer. And they go, well, I have more money than you, you know, but you’re right. So they eventually invented a semantic layer that sort of was an attempt to sort of add what Power BI had been doing from the beginning, but then they neglected it. A friend of mine at Tableau was in charge of that for a long time, and eventually he just retired in frustration. He just gave up. Not even, not even making this up. Okay. Well, after so many of these companies have neglected semantic layers forever, they all banded together to form this OSI thing, which I think you have has been talked about on the show before. Open semantic interchange. And, and on the Tableau website, a year ago, less than a year ago, there was an article says, the agentic future demands

00:53:25 | Tim Wilson: an open semantic layer. Okay. So everyone now, if they, every data company that wasn’t in on semantic layers is now in on semantic layers. Every single one of them has something that either

00:53:41 | Rob Collie: are sticking with the one they already had, like in Microsoft’s case, or they’re banding together in this open standard and rapidly integrating into that product and trying to get their customers to adopt it. Now, why would they be doing that? As a company builds a semantic layer, I think,

00:53:56 | Tim Wilson: and this was when we had Cindy house and on to talk about it, there was something she said that I really liked. So she was kind of in the OSI heavy camp our last episode, we had Jacob Madsen, we talked about maybe we don’t need one at all. So we’ve kind of covered, I think,

00:54:10 | Tim Wilson: my one question is like, how does the semantic layer not get held up as the monolithic

00:54:19 | Tim Wilson: boogeyman and the monster dev project of saying, yes, yes, yes, yes, we will do glorious things with AI as soon as we build the semantic layer, because we did that with data dictionaries, we’ve done that, I mean, at least in the days of power play cubes and SSS, like they were contained, but like where that’s where I get nervous about semantic layers as it becomes an excuse and people like we have to build the entirety of the story of our data.

00:54:50 | Rob Collie: You could take your concern there that you just shared and you could plug and play replace that with data warehouse, like seven or eight years ago. And that would be something that was a talk and a spiel that I would give everywhere, like the data warehouse. So we got to go build the data warehouse first. And the other thing is data warehouse is never done. And by the way, the other thing about data warehouses is that you would find that you’ve been trying to anticipate all of these future needs, and you anticipated them poorly. So you built plumbing to nowhere, places that faucets were never going to be needed. But then the first time you need a faucet, you go and you look and there’s no pipe, the place you needed the faucet. So you over engineered and under engineered at the same time. And yeah, I agree, like those sorts of monolithic projects

00:55:35 | Tim Wilson: that kind of become, they become their own thing. They become like a goal in and of themselves.

00:55:42 | Rob Collie: Those are danger. You do not want that. So one of the things we’ve been, one of our philosophies

00:55:52 | Tim Wilson: that are our company, we call it faucets first, we start with the faucet and we build backwards

00:55:57 | Rob Collie: from there. So yes, we do need responsible data plumbing. But if you’re building plumbing forward, you’re going to lose. Now, if you’re a consulting company and you’re building plumbing forward, you’re going to win because you’re going to build a lot of hours. And then when things don’t work, you’re going to build a lot more hours to fix it. But if you go faucets first, if you go faucets backwards, you can get everything that you need. So I think semantic models, and the way we approach Power BI is to sit down and go faucets first. We don’t go build a big

00:56:33 | Tim Wilson: data model before we build our first dashboard. The whole point is to get to a

00:56:40 | Rob Collie: first dashboard as quickly as possible so that the customer can go, okay, that’s not right in these three or four different ways. And then we go modify the data model to fit it. The same thing here. If your goal is to build, and okay, so let’s bridge a really important gap here. The reason why a semantic model is suddenly so important today, it was important to BI, but no one cared other than Power BI really. But everyone now cares is because you don’t have what I call the semantic shock absorber of the people that you lock in a room and make them write the queries. So each new dashboard that’s created is really representing new questions that have emerged. The business has new questions, so you can build a new dashboard. This has been inefficient really, even in the Power BI world, this is really not optimal. Okay, but it’s even less optimal than the tab low world where it requires like a week’s worth of SQL work before you get the first dot on the chart, right? If an AI agent forms a new question or a user comes to an AI chatbot with a new question, there’s no time for someone to go write a week’s worth of SQL. And you also don’t want to trust the AI to go write that SQL because it will write it slightly differently each time and it won’t know which column means what in the source data. This is why the decoder ring analogy I think is so apt. Symantec glare is just a very nerdy way to say decoder ring. We wrote down what things mean. We wrote down how to translate our company’s structured data into meaning that matters to

00:58:17 | Tim Wilson: the business. What is gross profit defined as? There’s a lot of nuance to that that’s

00:58:24 | Rob Collie: different for every single business. And so if you look at it through an AI lens and you go, okay, what does this AI agent need to do? And then you build the Symantec model that is required to power that. You’re in a much more focused means of engagement than if you’re trying to like, let’s go model the whole business, just the whole thing. And let’s just party on this thing forever and never test it and never write. No, that’s not the way, right? And I was going to say too,

00:58:56 | Tim Wilson: like I love what you’re saying and I just feel like people are finally waking up to this idea

00:59:05 | Julie Hoyer: and that like surfacing the data alone was not the valuable part. And so like why people could get the budget approved for data warehouse or like projects or the huge Symantec layer projects. I feel like it’s because and Tim, you’ve talked about this a million times, right? Like the promise of the value of like having the thing, having the data and in current conversations with clients, and I’ve seen it more like across the industry and articles, you know, there’s this whole like, well, what’s the actual return on investment for AI? And I just feel like AI has become this thing that really highlights to people that just because your data is there or you have all this data, right? Like even though AI can access it and use all of it that you’ve spent so much time and effort to create and surface, the output of AI is not inherently valuable to you. You know, like it is finally like the mirror to that flawed thinking. And so it’s interesting when you were saying like faucet first design, I do feel like everyone is now afraid to just be like, oh yeah, do another AI POC. They’re like, no, I need to know that my faucet in my business is going to work, not that you can go make plumbing in another room. Like I don’t care about that plumbing in another room. So I just, I really like that finally clicked for me in the conversation we’ve been having, but I’m curious your thoughts on like AI being scrutinized for value and this idea of like the data itself is not the value to the business. AI should be scrutinized for value,

01:00:49 | Rob Collie: you know, there’s a lot of pressure to just go do AI, right? Because everyone, everyone can sense the uncertainty of inaction, right? If we’re not doing anything about AI, we are falling behind, right? As a business. And so, but like what happens is the people who don’t know what to do about AI at all, they don’t know how to apply it, but they’re important. They’re like board members or whatever, they lean on executives and say, hey, you need to be doing AI, right? And the executive doesn’t know. So they turn to someone else and say, you know, you need to be doing AI. And then you end up in these weird, crazy things. We’ve all heard these stories and they’re real about people measuring teams usage of AI tools. And by the way, they’re always the off the shelf tools. So they’re the wrong things for most business uses, right? And making sure that people are using them, because if they’re not using them, then they can’t go back up the chain and say, hey, we are doing something about AI, right? If we’re in a really, really strange place where no one knows what to do with it, but the amount of pressure to do it is I don’t think we’ve ever been in a situation like this. Like BI wasn’t like this, right? Like no one has ever under pressure to do BI, like, unfortunately, right? They, you know, like the boards never came to their, you know, C suites and said, you should be doing BI. Well, I feel like personalization was a little bit

01:02:18 | Julie Hoyer: like that, Val. Like we heard that from a lot of clients, right? Like our goal is to do personalization.

01:02:23 | Tim Wilson: You’re like, but to what end? Like digital transformation. So like, I just think that

01:02:31 | Rob Collie: the semantic model case with an AI chat interface over the top of it is one of the easiest wins for AI adoption that’s actually going to add ROI. Like so for people who have Power BI investments, if they’ve got good semantic models, like we’re, we’re putting in AI chat interfaces. And honestly, they’re so much better than dashboards. There’s so much friendlier, there’s so much more user friendly, people actually get value out of them. And even the people who built the semantic models who know it, the ins and outs, even I use the chat interface to go and do all kinds of things for me to go research things for me that I wouldn’t do otherwise. It’s not even like saving me time. It, it, it, I form better questions and kick my buddy off to go research it for me. And again, it leans on the semantic model. It doesn’t, it doesn’t hallucinate. It gets real answers from real systems and comes back and, and, and, you know, and I can sort of, I can send my assistant back and, you know, to do more if I, if I want, you know, under all this pressure, and again, as data and analytics professionals, it is one of the easiest and most trustworthy and best places to start dipping our toes into AI is to start enabling these sorts of AI chat interfaces that actually work, not the ones that come off the shelf from the vendor. But if you build it, you build something yourself, like we’ve got this framework that we’ve built, again, we have these two developers that have been doing amazing things for us, like our clients freaking love this stuff and we love it. So I think it’s one of those, it’s not the only thing. I do not want to say this is like the future of AI, but it is a great place to dip

01:04:12 | Tim Wilson: the toe into it that does add value. And it’s right in our backyard. So I wanted to make sure we,

01:04:20 | Rob Collie: I got that, that little, you know, career advice. Yeah, it’s a great place to land the plane to kick

01:04:24 | Tim Wilson: off for show two. Yeah. All right. Well, that, yeah, there’s, there, I have so many more questions,

01:04:34 | Tim Wilson: so many more thoughts, but we also up against the clock. So this has been a fantastic discussion. And yeah, I think it’s even in this discussion for me in the book, lots of light bulbs have gone on for me. So this has been enormously useful to me, at least, and it’s all about me. But when we last thing we like to do on the show is go around the, go around the group and everyone share a blast call, something that something or maybe a couple of some things that might be of interest or amusing or entertaining or useful to our audience. And Rob, you’re our guest. So

01:05:14 | Rob Collie: do you have a last call or two? I do. I have one that’s relevant and then one that’s kind of just funny off, off topic. So the one I’ll obviously provide you the link, but an old friend of mine at Microsoft named Uli Homan wrote something on LinkedIn recently, where he talks about this new

01:05:30 | Tim Wilson: role he’s calling the product engineer. We’ve had software engineers, software developers,

01:05:35 | Rob Collie: all that kind of stuff. We’ve also had product managers who like were like the designers of it and all that kind of stuff. And he makes a very, I think, eloquent case for, I think, where we’re all going to land with this product engineer role. And so I think it’s a worthwhile

01:05:50 | Tim Wilson: read and it’s not very, it’s not very long. And I think it lands the concept pretty cleanly.

01:05:55 | Rob Collie: Then what’s the relevant one? No, you said it could be, it could be any link to anything that we’ve ever liked, right? That’s true. Right. So this is an article from 2005 on the McSweeney’s website. It’s titled, a realistic assessment of how many 12 year olds I could beat up before they overtook me. And it is such an analytical, dispassionate analysis that uses game theory and assumes optimal behavior on the part of the 12 year olds. It’s like, no, the 12 year olds aren’t going to get up in a nice line and let me take them one at a time. They’re going to form a circle, right? And they’re going to know that groin kicks are their primary weapon against me. And here’s how big an average 12 year old is and just really just breaks it down and eventually comes up with a very reasoned argument for why this certain number is probably the number

01:06:54 | Tim Wilson: that would take him. I love it. That’s so funny. Oh my God, that’s amazing. That’s really funny. Julie, what’s your last call?

01:07:05 | Julie Hoyer: Mine. Actually, I feel like this ties back to Rob, what you were talking about in your use with AI agents. A colleague recently shared a article called AI May Be Making Us Think and Write Moere Like. And it sparked my interest right away because I feel like multiple times on the show, I’ve said like, but what about like group think and like the cyclical, you know, like self-fulfilling prophecy. I’m like, if AI is then using our AI outputs to then continue modeling, I’m like, it’s all just going to converge somewhere. So anyways, this was very like fulfilling for me to see that there’s now been research that, yes, that’s actually happening. They’ve pretty much said that they’re seeing all of this really come to a point where everyone’s losing their unique voice in writing. And because of that too, what everyone is putting out is becoming more similar. So it is like this idea of the group think happening. And it’s funny because they say that AI solution, Rob, similar to what you did, is they were saying, you need to make sure you have a very clear framework to ask your AI model to enforce like strict stylistic constraints and make sure that you can extract your distinctive voice. So I thought that was funny. It’s like, AI is the answer, even though AI is the problem. And it was this whole, you know, echo chamber.

01:08:34 | Rob Collie: So it’s an interesting read. Wow. That’s not everyone’s starting to sound the same, but a lot

01:08:40 | Julie Hoyer: of people are. You can read something now and you’re like, oh, yeah, that sounds like AI. It’s

01:08:47 | Rob Collie: pretty quick. So I’m going to resist. Val, what’s your last call?

01:08:55 | Val Kroll: So there’s a AI tie-in for this one. But it is very self-serving because it comes from some of my co-workers at SIN. But my thing is, 30% of my last calls come from like UX collective, like this publishing group on Medium. And this could have very well been on there. So it’s a Substack article, all that caveat build up. And it’s called Designing the Front Door. And what I thought was really interesting is it’s like from this engineer and this designer at SIN thinking about how you create the front door to some of the agents that are not the helper agents, the chatbot, the little thing you see in the bottom right hand corner of your screen, right? But they’re calling it like the spotlight messenger. And what I think is really interesting about the article beyond the solution that they landed on is how they use data and experimentation and lots of research to figure out what is the optimal way to bring some of that to bear in a way that doesn’t harm other interactions, whatever. And it just feels like some of the research that I did when I was at further with the consent management banners four years ago and thinking about like, how do you surface this when there is no established pattern? And so it’s an interesting look at it with some fun examples. And it’s a quick read. So it’s a good one. So how about you, Tim?

01:10:17 | Tim Wilson: So I’m going to kick it old school with an ebook. It’s on a github.io site called Moedels Domestified, a practical guide from linear regression to deep learning. So if you want to step away from AI and just kind of understand generalized linear models a little bit deeper or causal modeling, I have not read the whole thing. I’ve kind of sampled it. It’s got kind of a lot of our a lot of the models are kind of ours available and Python. So you can kind of play with either one. But I feel like that maybe when I get overwhelmed by Claude and going back and forth, I it’s a good little thing to dip in to say, maybe I should just give some deeper statistical knowledge and a good old fashioned ebook. It’s a good way to do it. So that’s how Tim

01:11:11 | Tim Wilson: touches grass. That’s what you’re saying. Yeah, that’s what I’m not measuring the speed of sound

01:11:19 | Tim Wilson: waves through different materials. So you know, but you can, but you can’t. That’s the important

01:11:27 | Tim Wilson: thing. And can I just kind of throw one last little plug in here and you could edit it out if

01:11:34 | Rob Collie: you if it doesn’t it doesn’t fit. I just want to encourage people to go to fairgamebook.ai and preorder the book. I mean, I genuinely wrote this to help people writing books doesn’t make enough money to ever be worth the personal sacrifice. Like and so I was really gratified by y’all reading it in advance. And yeah, it’s it’s it’s for our crowd is for leaders. And I think it will actually help you not be so afraid. Like if you’re having some angst about AI, like my I felt like sitting down right in that book, my job was to help you feel better about it. Like I actually turn it into excitement. So I genuinely hope that helps people.

01:12:15 | Tim Wilson: Well, and I will say so you you took it from me because because I was definitely going to plug the book again. So I will I will further endorse that because I did find it to be extremely useful. We had a proof of it. So it was like a super early version. So I mean, it was a proof so we we could not we could not copy anything from it. We could not highlight anything in it. So like the the the notes had to be like old school retyping brilliant things.

01:12:43 | Rob Collie: And you know, you sit down you sit down with cloud code. And it’s wide open. You just

01:12:49 | Tim Wilson: you can do it. There’s no protection anymore. Okay, I’m not going to say maybe there wasn’t

01:12:56 | Tim Wilson: a way to throw the whole thing at AI and have it read it. Even when I’m making those clouds,

01:13:01 | Rob Collie: tell me, hey, you know, Rob, this isn’t going to actually protect you, you know.

01:13:06 | Tim Wilson: All right. Well, Rob, thank you so much for coming on. On the one hand, you’re like promoting a book, but the book is itself really useful. And this discussion, I think was extremely useful. So thank you for taking the time to come on like this. And really, I think y’all I mean,

01:13:22 | Rob Collie: again, I hadn’t met y’all before now. I really think this is a I love your format. I love your approach. I think you’re doing great work here. So I really appreciate your the invitation.

01:13:33 | Tim Wilson: Well, awesome. So what you could do and any of our listeners could do or you could have an agent do is go leave us a review and a rating. Look at that. Look at that segue. Oh, I’m on it. Make Michael proud. How many reviews do you want? So if you listen on a platform that allows leaving reviews, listeners, we would love to get reviews and ratings that helps us out. If anybody, if you’d like a sticker for the show, you can go to analyticshour.io and fill in the little form there. We’ll send you stickers. We also love to hear from our listeners so they can you can you can reach out to us on LinkedIn on the measure slack. You can just email us at contact at analyticshour.io. But regardless of how many faucets you’ve turned on, if the faucets first, if the plumbing’s first, if you’re deep, deep into cloud code already, or if you’re now just inspired to get into cloud code, no matter what you do for myself, for Julie, for Val, keep analyzing.

01:14:45 | Announcer: Thanks for listening. Let’s keep the conversation going with your comments, suggestions and questions on Twitter at at analytics hour on the web at analyticshour.io, our LinkedIn group and the measure chat slack group. Music for the podcast by Josh Grohurst. Those smart guys wanted to fit in so they made up a term called analytics. Analytics don’t work.

01:15:10 | Tim Wilson: 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

01:15:21 | Julie Hoyer: in competition. It’s all semantics. Yeah, it is indeed. It’s a challenge.

01:15:28 | Rob Collie: And I saw your, I saw what you wrote in the doc in response and I’m like, oh, yeah, so that’s

01:15:32 | Tim Wilson: okay, I’m not gonna ruin it. I mean, I will try. I mean, it’s not like we can’t like

01:15:39 | Tim Wilson: touch on it at all. I just think there’s like so much other like gold for us to hit on. As the moderator, I will say, you know what, I’ll allow it, but you’re going to be on a shot clock. Okay, I got it. I mean, we’re going to, we’re going to barely scratch the surface of everything I think we would want to talk about. And that’s just a sign of a good show. So unless Michael Helbling is running the show, in which case it’s because he has lost control

01:16:08 | Tim Wilson: into the terrible job. And Tony, feel free to put that in the outtake. We always bully him

01:16:14 | Julie Hoyer: into one more question, you know?

01:16:29 | Tim Wilson: Rock flag and data genes to crafters.