Loading summary
Transcript149 lines
- [00:00]
Val Croll
Foreign.
- [00:06]
Rob Collie
Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.
- [00:15]
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. 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 that we think are simple can actually be pretty complicated to hand over to the machine. 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?
- [01:25]
Julie Hoyer
If I had a nickel for every time I heard AI right y what I thought.
- [01:32]
Tim Wilson
And Val Croll, 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?
- [01:42]
Val Croll
Yeah, we're just starting to dabble, really. Just starting to just get our toes. Get our toes at. No, it's top to bottom. Top to bottom here at fin.
- [01:50]
Tim Wilson
The fundamental grounding of the primary offering. Yeah, 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 expl 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 Cowley podcast and he's the author of an upcoming book, Fair Game. Customizing AI to youo Business Is Easier Than youn 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 Fair Gamebook 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.
- [03:01]
Rob Collie
Thank you so much. I've enjoyed this already.
- [03:04]
Tim Wilson
This has been great. 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. 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 the Birth of Power bi, which is inarguably 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 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 helping organizations figure out useful paths forward with AI?
- [03:57]
Rob Collie
I would love to. So I didn't like most people who love data. I didn't know that I loved data. I didn't grow up saying I wanted 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.
- [04:23]
Michael
Right.
- [04:23]
Rob Collie
Yuck. But it actually is about 1 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, 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 early 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 reorged 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, the people who really kind of make the world go round. Like the Excel power users that 1 in 16 crowd back then and 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 the corporate IT zone, right? And I learned the old style of BI through that, 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 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 loved 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 was just sort of expecting, 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. 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, right? 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. So that's the first part of that journey.
- [08:10]
Tim Wilson
And then I mean, the link you make in the book is, and maybe this 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 the leaders, crafters, developers, knowledge workers, and why that's an AI thing?
- [08:39]
Rob Collie
Sure, yeah, absolutely.
- [08:41]
Val Croll
Yeah.
- [08:42]
Rob Collie
And first, let's just bridge one tiny little gap, which is like, I didn't set out to write a book about AI, so I'm a CEO of a data company, 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. 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 of my own, sort of like journey down the rabbit hole to figure out what the future of our company should be. This was about, this was about survival. Like what, you know, like I had to do this and it. 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. It's like right in our wheelhouse. And the difference of sort of like feeling like AI was opaque and inaccessible, going from that to going, oh, that's it. That feeling of like, oh, that's it. 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 10 plus years ago. Me feeling like I understood something and knowing that there wasn't anything all that necessarily special about me. The rest of the world can do this too. I felt like I owed that book. And I felt the same sort of vibes again, but at a greater magnitude this time because everyone's worried about AI. But yeah. So back to your question. I've been calling these people the data gene crowd forever, but what they really are, and I think present company probably is it's we. Right? It's not they. 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 there's the technologists that want to be locked in a closet and have notes passed under the door of what to build. And that's a lot of tech and you need those people. But that's a very inefficient way to work. For most business solutions, 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, the people who picked up Excel and smiled, right, like when you sit down, when people like us sit down with something like Claude code, for the first time, I sort of 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 ooh, ratio 25:80. So for every, if you have. 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, 1 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. This is going to be the new spreadsheets. Application development is now within range for this crafter crowd. We're just in the earliest stages of the world waking up to this. Most crafters haven't yet discovered that they can do this, but at steady state, we're going to have about the same number of people building software as we had building spreadsheets.
- [13:22]
Tim Wilson
Michael, how does your team share AI generated analyses today, professionally we say, hold
- [13:29]
Michael
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.
- [13:36]
Tim Wilson
That's, that's elegant. That's clearly scalable. I mean actually that's horrifying.
- [13:40]
Michael
Why thank you. It's called innovation.
- [13:44]
Tim Wilson
That's the type of innovation that ask why is trying to solve with Prism. Your AI work shouldn't be trapped in one person's cod. Innovation.
- [13:53]
Michael
I have started to realize my private chat history is not a data strategy. It's kind of a junk drawer with confidence.
- [14:01]
Tim Wilson
Well, Prism helps preserve context and memory across users. You know, like metric definitions and source of truth tables, business rules, prior analyses.
- [14:10]
Michael
So when I teach it, it qualifies lead, excludes internal traffic, spam forms, and that one campaign we don't talk about.
- [14:17]
Tim Wilson
The team can build from that shared context instead of starting over entirely.
- [14:22]
Michael
Beautiful. Less Ask Michael what he meant, more the system actually remembers and with Prism,
- [14:28]
Tim Wilson
analyses are organized, traceable, auditable, and reusable with your other data sets.
- [14:33]
Michael
I like it. So my best AI work becomes company knowledge, not a screenshot named Is this important? Maybe png?
- [14:41]
Tim Wilson
Exactly.
- [14:42]
Michael
I guess it's time to stop being a data trash panda when it comes to AI.
- [14:46]
Tim Wilson
Definitely no pandas in this context. Go to Ask Y AI and sign up for the waitlist. Use code APH to jump to the top of that list.
- [14:56]
Michael
That's ask the letter Y AI Code APH because check my Claude history is not a collaboration plan. Tim One of the trickiest parts of measurement is user data gaps, right?
- [15:13]
Tim Wilson
Someone gives you an email in one session, comes back later from another device or converts offline, and suddenly the customer journey gets hard to connect. Exactly.
- [15:22]
Michael
State Enricher is a state power up that stores selected data from incoming requests and uses it later to enrich future events when information is missing.
- [15:33]
Tim Wilson
So once a user has been identified, Enricher can help recognize them in later events and add previously collected details. All while aligning with consent settings and handling personal data securely.
- [15:44]
Michael
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 Enricher to work with cookies, stape store, or both. And it supports uploads of historical data from systems like Shopify, Klaviyo or your CRM.
- [16:05]
Tim Wilson
So if your tracking has gaps and whose doesn't, Stape Enricher can help create more complete, consistent event data.
- [16:12]
Michael
Yeah. Go to Stape IO to learn more about Stape Enricher. Improve event match, quality, attribution and visibility into the full customer journey.
- [16:22]
Tim Wilson
That's stay pretty more complete data with consent and privacy in mind.
- [16:28]
Val Croll
Besides, like the just the the usual fud, what do you think are some of the the barriers to crafters?
- [16:36]
Julie Hoyer
Because like I remember where I was
- [16:38]
Val Croll
the first time I saw a pivot table and it was like loomy back in my chair. It was high school. But of like what what is holding what do you think are some of the major barriers that are holding us the crafter crew back?
- [16:50]
Rob Collie
Well here's one quick funny story before we 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 really even knowing what it was. Just like, fake it. Yeah, no, that's what all the cool kids were saying. So I would say that, right?
- [17:15]
Tim Wilson
And then one day I did that with mcp, just sprinkle it.
- [17:19]
Rob Collie
So for you to see a pivot table in high school, that, that's awesome. That is really, really cool. So the thing about Excel was that, you know, is that it was so. I mean, I think that 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, sort of like 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. Excel is an application development platform. A spreadsheet is an application. There's something intimidating about it. I personally, and I say this in the book, I don't pick up new tech readily. I'm not an enthusiastic, 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 Claude code.
- [18:38]
Julie Hoyer
Right.
- [18:39]
Rob Collie
Like, at least it used to be harder than it is now. It's a lot easier, 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 feels opaque to you. I think there's a sense of loss of control that is terrifying. And I'm seeing this a little bit with our own team. Not as much anymore, but in the early going they're like, well, we derive such confidence from having touched every last single piece of logic and artisanally crafted it. 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.
- [19:37]
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. I can put slicers or I can put data validation and put dropdowns in, and 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 somebody with the data gene saying, I'm crafting an experience. 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 more than just the chat. You got to build agents, you got to do all this other stuff. But there are people saying that, that feels like 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 use the chat to help me build a data product, let me use the chat to help me create something. That's where that parallel I started to see because that light bulb hadn't gone on that, oh, I can create a deliverable for user. And I've now got a broader set of things at my fingertips. Just like going from I gotta hack around in 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.
- [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, I think it's the, I think it's a, 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, like over, like a, like a, a void. You know, those are the places that get us. And there's two big voids here. One of them is that we sit down and we use the off the shelf AI systems. This is a recurring theme in the book is that the off the shelf AI systems are amazing for personal use, they're amazing for individual use. They know everything about 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 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 have to search the web.
- [23:07]
Tim Wilson
You've got the data Gene. It was family night.
- [23:12]
Rob Collie
Okay, look, there's a pile driver driving posts into the lake 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 time between the two arrive and using that to calculate the distance to how far away the pile driver is. And yes, this is something that the whole family is aware is going on. Like dad's calculating this. He's like 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. But if it's about my business. In the book I show an example of this. I just have it write a proposal. Write me a proposal for this project. It goes and tries to do it, but it knows nothing about us. It just gives the lowest common denominator of a proposal and it is completely worthless. It is awful. There's that gap between business use and personal use which is like again it's this void, right? Like it's not you have to jump it all at once.
- [24:33]
Tim Wilson
Well, 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'm like, I've been terrified that I'm going to put something in that's going to be a problem, but if I just ask you to do something without it. So how do I put enough stuff in? And that feels like the other connecting the dots.
- [24:50]
Rob Collie
Which again, I mean, yes, yes, so
- [24:52]
Tim Wilson
there's that sycophantic Tim. So maybe I am just an AI agent. I'm like your book breaks out the be 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, but don't, don't give it any of your information.
- [25:07]
Rob Collie
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 of. 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 which version of the API you call. You can call ones that are actually more protected and that they don't train on your data. You can handle that 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 thing you just did one of these the other night, I think Tim. Right. So the first thing I have people do if they've never used Claude code or Codex or whatever is just build a website on their computer that's just a survey or a quiz that gives a score or whatever. Just get used to the idea of building anything. No AI agent stuff. Just start to get more comfortable with building free form software and then we start to layer in in the next exercise like ok, now we're going to add some LLM to it and see where it goes from there.
- [27:01]
Tim Wilson
I'll throw in Now 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.
- [27:16]
Julie Hoyer
I was like three 20 plus.
- [27:18]
Tim Wilson
I was like no, three kids who are all adults about like it's like just family lore stuff which was like a prompt but. And it was fun standalone. I did wind up Having to throw it on netlify. So it was like I had 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 a link to it in the show notes. And anybody who's just gonna say terribly, yeah, why not?
- [27:51]
Rob Collie
How well do you know Tim Wilson's marriage?
- [27:53]
Tim Wilson
Yeah, I mean it's gonna be referred, it's gonna refer to mom and dad and that's gonna, you're gonna have.
- [27:59]
Julie Hoyer
I'm just gonna have to be okay with it.
- [28:01]
Tim Wilson
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 cowork, even though I've done stuff with gyms, even though 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 push stuff into production to hit the masses. The developers now are still needed for their skills.
- [28:40]
Rob Collie
Yeah. So we P3, we've hired this year our first two full stack developers ever. And it's kind of funny, 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 honestly what it probably would have required a team of 10 developers to do before. So I call this sort of like the migration to the suburbs for developers. 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 developers to do the same sorts of things anymore. At the same time though, the ROI on hiring a developer like in a smaller company or in a department level of an enterprise or whatever is going to be higher than it used to be. So these people are going to, are going to have other jobs they can plug into. They're just going to be in different places. And so like the, the dev team of two that we have here, we don't need to go too deep in it, but just to underline it, we've built things that put us probably about a year ahead of Microsoft in terms of offerings that Microsoft will have. We're able to do things for our clients today with AI around Power BI and things like that that Microsoft is absolutely building, but they're not there yet. And so it gives us an opportunity to fill some gaps there in the meantime. And that is just absolutely bananas. A year ago me would never have guessed that that was a possibility.
- [31:02]
Tim Wilson
You would have said the tool, it's not doable. Even with our DAX amazingness, we can't do that. We'll have to wait until Microsoft rolls that out or you find an alternative way to come at it.
- [31:14]
Val Croll
It.
- [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 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, like IQ measurements. There's a disciplined patient in a developer combined with an aggressive modularization instinct that isn't really present in most crafters. It's not present in me. 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. Even just knowing what to build and figuring out what the roadmap should look like would have just taken us too long. 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 a broad brush here, most developers aren't going to want to be the customer facing interface that does all that conversation. 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. Crafters thrive on interacting with the stakeholders. Right. Like that's what that's the world that they came from, they want to be right there up, 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 do talk a lot in the book about trying to paint pictures of where those boundaries are, but they're incredibly fluid, you know, like it's like you can't paint a precise picture.
- [33:32]
Julie Hoyer
I was going to ask too 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 in search discovery. Like we always talk Tim and Val about like our titles were 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 as 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?
- [34:49]
Rob Collie
So I think I should very much clarify that I'm using a very power BI centric lens for this because our company's all, all been all about power bi. We've long said that we will use another tool if something came along that we like better. But we've been a Microsoft stack company and it's not because of Microsoft patriotism. I literally formed a company around this.
- [35:17]
Tim Wilson
Army is going to be coming for this. The comments are going to be
- [35:22]
Rob Collie
ready for that. When we talk briefly about semantic models, I'm really ready
- [35:30]
Tim Wilson
people too. But that's only going to be like three people, so.
- [35:32]
Rob Collie
I know. Yeah, yeah. I mean you just stiff arm them shots, fire the, 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, they, they reported up through, you know, some non IT function. You know, they're the, they're the shadow IT folks. And so the role you're describing, sort of like the business analyst that interacts with the business and sort of figures out what the needs are, the people who do that, but then also go and like build the spreadsheet that addresses it or in our world, then went and discovered Power BI and started doing the ETL and started doing the data modeling and writing the dax. That's my personal center of gravity for all of this. So at our company, we talk about it as the decathlete model. So 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 100 meters. And so this team of 10 rolls in there, there on the engagement, and you pay a tremendous amount of communication cost between those 10 people. Most of the elapsed time of the project is actually the communication between the members of the team. It's like 99% of the, of the weight of the project is communication cost and not implementation. And so our whole business model from the beginning was built around this idea that. No, no, like you, 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 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 at ground0 for
- [37:43]
Julie Hoyer
it,
- [37:45]
Rob Collie
I think more on the business analyst side than on the engineering side. 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. We were invading their ETL world, invading their data modeling world, tearing down that ivory tower.
- [38:12]
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 column and it just makes our skin crawl with the structure of it. And they manually date it and they put some hacky formulas and send some sort of garbage over the wall to the marketer. And then an analyst gets in or somebody with a 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 that's where we've crossed over and we're like, wait a minute, are we developers? Like, no, you're still not a developer. 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'm just going to build myself a little sandbox. And all of a sudden that became an entire position or a total role of where the analyst just kind of gets better. And the point, I think that again the 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, our arsenal.
- [40:13]
Val Croll
It seems like one of the, the first steps to someone kind of seeing for, for these crafters have their eyes open, is to get out of chat 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? Do you have any advice for taking that leap? Which feels like another step Yeah, I
- [40:53]
Rob Collie
mean, what's really amazing is just how much Smarter Even the LLMs seem to get when you give them something to go on. So the first example that I talk about in the book is this, the concept of what I call a handbook book. So if you, if you had a new hire, and one of the metaphors I use is 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 don't care about the history of the Roman Empire most of the time in your business, right? You don't care about the ground speed of sound underneath Mottlake Seattle most of the time in your business, right? But you do have policies and a brand framework and workflows and things like that. And so like if you were going to hand a new hire some sort of handbook, an instruction manual for their job, it would be written in English, like if you're in the United States anyway, right? 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've, again, now you're writing custom code, right? So you have, you've had to take control and you're not, you're not using the ChatGPT 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, but it's not that hard. It really isn't. And your software, the first thing it does when it, when your software wakes up, someone asks it a question or something like that. Let's say you're building your own chatbot bot. The first time the chatbot 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 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 your corner of the world here? And each of these agents you build like this, they become specialists in that one role, right? I talk about like in the book, you don't. You're never going to build a super being that knows everything about your business because everything about your business can't fit, believe it or not, it can't fit into the LLM's memory. It will overload it. So you have to give it narrow lanes. And you know, so like, if you have like, you know, you can imagine agents with five different roles. Like, I've got one that helps me write marketing copies. I created an editor to help me with my book. And they have different instructions, right? You know, one of them 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, 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 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. You give it the ability to pull information that it wants because you can't, sometimes 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. The tool is just something. It literally just sends a chat request to the tool, the LLM does, and then the tool goes and performs the web search and returns the results back to the LLM. You can do that with your own internal database. You can do that with your own internal knowledge bases, which is crazy.
- [46:13]
Julie Hoyer
So for a very specific example, it immediately when I think of like AI and use of analytics, you care about every tool. Putting an AI chatbot or AI agent on top of their BI tool. Right. And it's supposed to like be so great and then everyone's gotta have one experience with it. Yeah. And as most people that actually go in and use them, they don't know anything about your business and they're. 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, in what you were just talking about, what, 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 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?
- [47:28]
Rob Collie
I think they're both kind of the same 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 an element on the periodic table. It is exactly the same for you as it is for me, as it is for Satya Nadella at Microsoft. It's the exact same LLM and, 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, know. So let's keep in mind for a moment that most AI agents, if we fast forward like two years and we see what the future looks like, most AI agents that are deployed at companies aren't going to be about analytics. You know, like a chatbot that handles the first level of interaction with customer service, for instance, it doesn't need to be performing strategic queries against your data warehouse. It really just needs to have a flowchart in it and also some instructions on how to talk to people and what not to say and all that kind of stuff. And 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 just want to level set that. What's kind of neat though, though, is that even those tactical things that have nothing to do with analytics, they still because they operate on information that needs to be very carefully controlled and fed at the right time. The types of discipline that we've developed as analytics people, as data people, are the right mindset for making those things work properly. 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, something like Claude 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. And the handbook stuff. Stuff is not part of what you're hearing when people talk about semantic models or semantic layers. Handbooks are not really part of that. Semantic layers are a decoder ring for your structured data. And now we're back in the analytics and data crowd. We're back in our home neighborhood now. Yes. And so I think this isn't even really controversial, honestly, because so we were talking about the tableau crowd coming. For me, 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 model and semantic layers. And I can give you the technical reason why, but I can also give you the market reason why. So let's take tableau as an example. And I love this. This just tickles me to death. So the best thing about Power bi, from a technical standpoint, the best 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, oh, it's so cool that you have a semantic model and this star schema stuff. And all like, like it makes a difference in practice. But that's not why it was one. And Tableau long neglected the idea of semantic models. They were all about each time you need a new dashboard, you go write a bunch of new SQL queries 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 they, they ipo, they sold to Salesforce. I mean, those people made gobs and gobs and gobs of money. So it was like, you know, you know, I'd start arguing with them about like, well, you all don't have a semantic layer. And they go, well, I have more money than you. You know, like, God, 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 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 has been talked about on the show before. Open Semantic Interchange. On the Tableau website. A year ago, less than a year ago, there was an article says the agentic future demands an open semantic layer. Everyone now, every data company that wasn't in on semantic layers is now in on semantic layers. Every single one of them has something they either 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
- [53:54]
Tim Wilson
as a company builds a semantically, I think, and this was when we had Cindy Housen on to talk about it, there was something she said that I really like. So she was kind of in the OSI heavy camp. Our last episode we had Jacob Matson. We talked about maybe we don't need one at all. So we kind of covered. I think my, my one question is like, how does the semantic layer not get held up as the monolithic 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 powerplay cubes and sss, they were contained, but that's where I get nervous about semantic layers is it becomes an excuse and people like we have to build the entirety of the story of our data.
- [54:51]
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, 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. Well, 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, 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 that kind of become, they become their own thing, they become like a goal in and of themselves. Those are danger. You do not want that. Right? So one of the things we've been, you know, one of the, one of our philosophies at our company, we call it Faucets first. First we start with the faucet and we build backwards from there. Okay? So yes, we, 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, and, and then when things don't work, you're going to build a lot more hours to fix it. Right? But if you go, if you go faucets first and 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 data model before we build our first dashboard. The whole point is to get to a 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. Right? 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, Right? 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. But it's even less optimal in the tableau world, where it requires a week's worth of SQL work before you get the first dot on the chart. 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 the 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. Like, this is why the decoder ring analogy, I think, is so apt. Semantic layer is just a very nerdy way to say decoder ring. Like, we wrote down what things mean. We wrote down how to translate our company's structured data into meaning that matters to the business. Like, what is gross profit defined as? There's a lot of nuance to that that's 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 semantic 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. And let's just, you know, let's. Let's just party on this thing forever and never test it and never.
- [58:53]
Val Croll
Right.
- [58:53]
Rob Collie
No, that's not the way. Right.
- [58:56]
Julie Hoyer
And I was gonna say too, like, I, I love what you're saying. And I, I just feel like people are finally waking up to this idea 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 semantic 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 in 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 I 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, 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 love like that finally clicked for me in the conversation we've been having. But I'm curious your thoughts on AI being scrutinized for value. And this idea of the data itself is not the value to the business.
- [60:47]
Rob Collie
AI should be scrutinized for value. There's a lot of pressure to just go do AI because everyone can sense the uncertainty of inaction, right? If we're not doing anything about AI, we are falling behind, right, as a business. But 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, 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? 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 was ever under pressure to do bi. Like unfortunately, right? They, they, they, you know, like boards never came to their, you know, C suites and said, you should be doing bi.
- [62:17]
Julie Hoyer
Like they, well I feel like personalization was a little bit like that Val. Like we heard that from a lot of clients, right? Like our goal is to do personalization.
- [62:24]
Val Croll
You're like. But to what end?
- [62:26]
Rob Collie
Like digital transformation. So like I just think that 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, they're so much friendlier, they're so much more user friendly. People actually get value out of them. And even the people who built the semantic models who know 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 about saving me time. 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 hallucinate. It gets real answers from real systems and comes back and I can send my assistant back to do more if I want 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 something yourself. We've got this framework 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 the toe into it that does add value and it's right in our backyard. So I wanted to make sure we, I got that little, you know, career
- [64:23]
Val Croll
advice to land the plane for kickoff for show. For show too.
- [64:28]
Tim Wilson
Yeah. All right, well that, yeah, there's, there's. I have so many more questions, 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 from reading the book, lots of light bulbs have gone 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 last call, something that something or maybe a couple somethings that might be of interest or amusing or entertaining or useful to our audience. And Rob, you're our guest. So do you have a last last call or two?
- [65:17]
Rob Collie
I do, I have one that's relevant and then one that's kind of just funny 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 role he's calling the product engineer. We've had software engineers, software developers, all that kind of stuff. We've also had product managers who were like the designers of it and all that kind of stuff. And he makes a very, I think eloquent case case for I think where we're all going to land with this product engineer role. And so I think it's a worthwhile read and it's not very long and I think it lands the concept pretty cleanly.
- [65:56]
Tim Wilson
Then what's the relevant one now?
- [65:57]
Rob Collie
Well, so you know, you said it could be any link to anything that we've ever liked, right? That's true.
- [66:03]
Julie Hoyer
Right.
- [66:04]
Rob Collie
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 Overtake Took Me. And it is such a. An analytical dispassionate analysis that uses game theory and like and assumes, assumes optimal behavior on the part of the 12 year olds. You know, 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, and they, 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 you know, and like, 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 that would take him.
- [66:56]
Val Croll
I love it.
- [66:58]
Rob Collie
It's so funny.
- [66:59]
Tim Wilson
Oh my God, that's amazing.
- [67:02]
Val Croll
That's really funny.
- [67:03]
Tim Wilson
Julie, what's your last call?
- [67:05]
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 More Alike. And it sparked my interest right away because I feel like multiple times on the show I've said, but what about groupthink and the cyclical 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 gonna converge somewhere anyways. This was very fulfilling for me to see that there's now been research that yes, it'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 the 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 strict stylistic constraints and make sure that you can extract your distinct 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 echo chamber. So it's an interesting read.
- [68:37]
Val Croll
Yeah.
- [68:37]
Michael
Wow.
- [68:38]
Rob Collie
Not everyone's trying to sound the same, but a lot of people are.
- [68:43]
Julie Hoyer
You can read something now and you're like, oh yeah, that sounds like AI. It's pretty.
- [68:48]
Rob Collie
Yeah, pretty quick. Tough thing to resist.
- [68:53]
Tim Wilson
Val, what's your last call?
- [68:56]
Val Croll
So there's a AI tie in for this one but it is very self serving because it comes from some of my coworkers at Finn.
- [69:05]
Michael
But.
- [69:05]
Val Croll
But my thing is 30% of my last calls come from 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 Finn, thinking about how you create the front door to. 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. 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, like some of the research that I did when I was at further with the consent management banners, you know, 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?
- [70:18]
Tim Wilson
So I'm going to kick it old school with a ebook. It's on a GitHub IE site called Models Demystified, 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 R, A lot of the models are kind of R is 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, 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 looks like a good way to do it.
- [71:11]
Val Croll
So that's how Tim touches grass, that's what you're saying?
- [71:14]
Tim Wilson
Yeah, that's what. I'm not measuring the speed of sound waves through different materials.
- [71:22]
Rob Collie
So you know, but you, you can, that's the important bit. And can I just, can I just throw one last little plug in here and you could edit it out if you, if it doesn't fit. I just want to encourage people to go to Fair Gamebook AI and pre order 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, it's 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 writing that book. My job was to help you feel better about it, like actually turn it into excitement. So I genuinely hope that helps people.
- [72:15]
Tim Wilson
Well, and I will say, so you took it from me because I was definitely going to plug the book again. So I will further endorse that because I did find it to be extremely useful. We had a proof of it, so it was a super early version. So I mean it was a proof so we could not copy anything from it, we could not highlight anything in it. So the notes had to be old school school retyping brilliant things.
- [72:44]
Rob Collie
And you know what, Tim? You sit down, you sit down with Claude code and it's wide open. You just, you can do Whatever. There's no protection anymore.
- [72:54]
Tim Wilson
Well, okay. I'm not gonna say maybe there was a way that I can throw the whole thing at AI and have it read it.
- [72:59]
Rob Collie
But even when I'm making those clauses tell me, hey, you know, Rob, this isn't going to actually protect you, you know? Yeah, that's Fairfax Point.
- [73:07]
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 in itself really useful, and this discussion, I think, was extremely useful. So thank you for taking the time to come on.
- [73:21]
Rob Collie
Likewise. And really, I think y'. All, I mean, again, I hadn't met y' all before now. I, 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, you know, the invitation.
- [73:33]
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.
- [73:42]
Rob Collie
Oh, I'm on it.
- [73:43]
Tim Wilson
Make. Make Michael proud.
- [73:44]
Rob Collie
How many reviews you want? I got. I got.
- [73:53]
Tim Wilson
So we. So if you listen on a platform that allows leaving reviews listeners, we would love to get reviews and ratings. That, 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.
- [74:13]
Julie Hoyer
We'll.
- [74:13]
Tim Wilson
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 measureslack. You can just email us at contactnalytics. 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 COD code, no matter what you do. For myself, for Julie, for Val, keep analyzing.
- [74:46]
Rob Collie
Thanks for listening. Let's keep the conversation going with your comments, suggestions and questions. On Twitter @NalyticsHour, on the web at AnalyticsHour IO, our LinkedIn group, and the MeasuredChat Slack group. Music for the podcast by Josh Crowhurst.
- [75:03]
Tim Wilson
Those smart guys wanted to fit in, so they made up a term called analytics. Analytics don't work.
- [75:10]
Rob Collie
Do the analytics say, go for it no matter who's going for it. So if you and I were on the field, feel the analytics say, go for it. It's the stupidest, laziest, lamest thing I've ever heard.
- [75:20]
Julie Hoyer
For reasoning in competition it's all semantics.
- [75:25]
Tim Wilson
Yeah, it is indeed.
- [75:29]
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 okay.
- [75:33]
Tim Wilson
I'm not gonna.
- [75:34]
Rob Collie
I'm not gonna ruin it.
- [75:36]
Tim Wilson
I mean, I will try. I mean, it's not like we can't, like, touch on it at all.
- [75:40]
Michael
All.
- [75:40]
Tim Wilson
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 gonna be on.
- [75:51]
Rob Collie
On a short leash. A shot clock.
- [75:53]
Val Croll
Okay.
- [75:53]
Tim Wilson
All right. I got it.
- [75:55]
Julie Hoyer
All right.
- [75:55]
Tim Wilson
Yeah.
- [75:56]
Michael
All right.
- [75:58]
Tim Wilson
I mean, we're gonna. We're gonna barely scratch the surface of everything I think we would want to talk about, and that's just a sign of a good show.
- [76:04]
Val Croll
So.
- [76:05]
Tim Wilson
Unless my Hellwing is running the show, in which case it's because he has lost control and did a terrible job. And, Tony, feel free to put that in the outtake.
- [76:14]
Julie Hoyer
We always bully him into one more question, you know,
- [76:30]
Val Croll
Rock flag and data, Genes to crafters.