The Data Stack Show - Re-Air: Confidently Wrong: Why AI Needs Tools (and So Do We)

Episode Date: December 3, 2025

This episode is a re-air of one of our most popular conversations from this year, featuring insights worth revisiting. Thank you for being part of the Data Stack community. Stay up to date with the latest episodes at datastackshow.com.This week on The Data Stack Show, John and Matt dive into the latest trends in AI, discussing the evolution of GPT models, the role of tools in reducing hallucinations, and the ongoing debate between data warehouses and agent-based approaches. They also explore the complexities of risk-taking in data teams, drawing lessons from Nate Silver’s book on risk and sharing real-world analogies from cybersecurity, football, and political campaigns. Key takeaways include the importance of balancing innovation with practical risk management, the need for clear recommendations from data professionals, the value of reading fiction to understand human behavior in data, and so much more.Highlights from this week’s conversation include:Initial Impressions of GPT-5 (1:41)AI Hallucinations and the Open-Source GPT Model (4:06)Tools and Determinism in AI Agents (6:00)Risks of Tool Reliance in AI (8:05)The Next Big Data Fight: Warehouses vs. Agents (10:21)Real-Time Data Processing Limitations (12:56)Risk in Data and AI: Book Recommendation (17:08)Measurable vs. Perceived Risk in Business (20:10)Security Trade-Offs and Organizational Impact (22:31)The Quest for Certainty and Wicked Learning Environments (27:37)Poker, Process, and Data Team Longevity (29:11)Support Roles and Limits of Data Teams (32:56)Final Thoughts and Takeaways (34:20)The Data Stack Show is a weekly podcast powered by RudderStack, customer data infrastructure that enables you to deliver real-time customer event data everywhere it’s needed to power smarter decisions and better customer experiences. Each week, we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hey everyone, before we dive in, we wanted to take a moment to thank you for listening and being part of our community. Today, we're revisiting one of our most popular episodes in the archives, a conversation full of insights worth hearing again. We hope you enjoy it and remember you can stay up to date with the latest content and subscribe to the show at datastackshow.com. Hi, I'm Eric Dots. And I'm John Wessel. Welcome to The Datastack Show. The Datastack Show is a podcast where we talk about the technical, business, and human challenges involved in data work. Join our casual conversations with innovators and data professionals

to learn about new data technologies and how data teams are run at top companies. Before we dig into today's episode, we want to give a huge thanks to our presenting sponsor, Rutter Sack. They give us the equipment and time to do this show week in, week out, and provide you the valuable content. RudderSack provides customer data infrastructure and is used by the world's most innovative companies

to collect, transform, and deliver their event data wherever it's needed all in real time. You can learn more at rudderstack.com. Welcome back to the Datastack show. We've got Matt, the cynical data guy here with us today. Matt, welcome to the show. Yo, I'm here. Right.

We're going to break from our norm a little bit today. Matt may have a LinkedIn or two to share with us. but we actually have a couple topics we want to cover today. So I'm excited to jump into those. And then I think this is our first bit where we've got a cynical data guy recommendation for a good read. It's a cynical recommendation. Stay tuned. Stay tuned for that at the end.

Okay, so we're going to just launch right into AI stuff today. First topic here. I'm curious when it comes to data or just maybe every day, what are your thoughts? on GPT-5, cynical data guy. So mine are probably a little different because I don't need to code as much with it, but I don't know. I, you know, you

read stuff and people were like, this is the beginning of super intelligence. Reed Hoffman. And then you get others that are like, it's total crap. And to me, it was, you know, it's about the same. It's a little, maybe a little bit better in some ways. It's not as sycophantic, which I appreciate.

But otherwise, I don't know. I do notice that it doesn't take my instructions very well. So I'd like to know how to fix that problem. But other than that, it's not a ton different from what, at least what I use it for. Yeah. You mentioned before the show that I thought it was a fascinating response to the sycophantic, like nature, if you will, and that like some people, and I don't want to like put words in

their mouth. Maybe this wasn't the reason they missed it. But it seemed like some people missed 4-0 because of the, like, personality and, like, kind of style because they did seemingly make some stylistic choices for the default between five and four oh yeah any hot takes are interesting experiences when it comes to that that stylistic change i mean i think probably the biggest ones are the people who love to use it for their like you know the like a i girlfriend or boyfriend or you know the one you know there's a whole group of people that really love the fact that it will tell them just how amazing they they are i saw an art

article the other day about a guy who was having a conversation with 4-0 and was convinced he had figured out like a whole new realm of mathematics. Wow. Because 4-0 told him that he's just asking questions others aren't comfortable with and stuff like that. What was the reality, though? Oh, the reality was it was total crap. Okay.

You know, it was like it was just, I forget what it had to do, but it was it was one of these like branching off of pie or something like that. And so he'd come up with this. He'd even given it a new name for what this thing was. And then he went and searched for something on Google. And Jim and I was like, this is an example of an LLM that tells you this is really brilliant when really there's nothing there.

That kind of burst his bubble. Yeah, it on that. That happens. Okay, so I do have one, I do have one LinkedIn submission for us, if you don't mind, because it's related to this topic. Sure, go ahead.

All right. So this is on the GPTOSS model. So if you're not following, so this is like the subheadline stuff that kind of happened, you know, right before the 5-G-T-5 release around the open-source model. So here's the bits of the post.

It's a longer post. I'm going to edit it a little bit. All right. So I'm obsessed with GPT-O-S-S, a model that hallucinates over 91% of the time. So 120 billion variant, still nearly 80, the larger variant, which is the 120 billion, still nearly 80%. We're talking about a model so unreliable, the instinct would lose to a magic eight ball in a geography quiz.

And that's exactly the point. So he goes on, and this is what I think is really interesting. And he says, and the guy like poster here, it says, a model that hallucinates 91% of the time, how can that possibly be safe? And he says, it's not. And then this is like the really interesting part. if you deploy either of these models, you know, open source for GP5, you'd get fired faster than a recruiter slides into your DMs right after it suggests users eat a rock a day like geology. It's like geologists recommend.

But the open, and then like the open AI team knew this and made it a feature not a bug. But they built models that reached for tools instead of hallucinating facts. So then he goes on to like start, cite some stats on how well this thing performs when you give a tool. So I thought that was really interesting, and I don't know enough technically to know if there's an intentional trade-off thing here. But I thought it was fascinating, and I didn't realize until I read this, that apparently in this like GPT open source model, that it's really heavily reliant on tools to get, you know, to make it useful. So, yeah, what are your initial thoughts or reactions to that?

Clearly, this is the beginning of Skynet and go out and learn on its own. have this. The tools is a big thing. Obviously, you know, even the place I work at, it's a large part of how we, we have our agents work is there's a set of tools they can go and they can call on. I think it also, it also kind of, you know, makes me have a little bit of a side laugh from all the people who are like, LLMs can do everything. And it's like, yeah, you still need some deterministic stuff in control. And a lot of ways, it's kind of more of like, you know, I think there are forms of this where the LLM is not the primary hub of what's making decisions, but there's probably some deterministic kind of app or something or whatever you want to call it,

like controlling it. And it is more of like the interface or the translation for a lot of things. And I think that's probably, especially for these things where you want to be able to say, oh, we're going to have it and it's going to be able to do all these different things. well, yeah, you're going to have to use tools for that. And the tools are deterministic in a lot of ways. Yeah. Again, this is a long post, but at the end of the post, he says,

what if we made a model that's confident, fast, and wrong about everything unless you give it a calculator? And essentially says, they nailed this. This is like fast, a tool-first model that you don't need to run in the data center. Really, really, and I don't have a lot of first-hand experience yet with this model, but really interesting. approach here because like when you're when I'm like watching gpt 5 or these other like like

the content the content coming out around them it's all like we've reduced hallucinations by x% like there's all this like interesting work being done on the commercialized products and then it's interesting to the open source like we're going to swing the exact opposite way coming from you know the same companies right so i thought that was because a lot of the open AI 5 announcement was around less hallucinations was like part of the pitch. Right. So it was fascinating. I think it's also going to expose you a little bit to like, okay, so you're, you know,

what if there's a bug in one of your tools or what if the tools down is the one thing you can say about some of these large foundation models is at least they can pull from a, from their own like quote unquote knowledge set, you know, from their training data. If you don't have that, you're going to still, you could run into some problems if you get some silent failures in the background. Yeah. We're always, like, having this human analogy thing, right? Where they comes into play with agents, like, people are like, oh, think about it like

a junior employee. Like, I've heard that 100 times as you have to. Like, think about it as an intern. Think about it as a specialized researcher. Supplying that here is scary. Think about it as somebody that has all access to all your systems and super powerful tools. But in and of itself, can't do anything outside of the tools. It feels a little scary.

Oh, it's a 23-year-old at McKenzie. Good. Good to know. Okay. Oh, man. All right. So, this is the Datastack show. I got to ask this question related to, you know, again, we've had this, these latest models come out. How do you think this relates to data?

How do you think it relates to the model that we've been under, which is some form of, if you're a data lake person or a data warehouse person, some form of like, hey, let's get all the data in one spot or at least kind of accessible from one spot versus the other paradigm of like, oh, like, MCP and tools, like, that's the future. Like, let's just have the AI things reach out to where the data lives and it's home. And, like, it's responsible for, like, gathering everything and it can do all the collection and analysis.

And, like, we don't have to worry about all this other data stuff. Oh, is that a question for me? Yeah, I guess I'm curious. I'm happy to react to it as well. but I'm curious, from what you've seen, what do you think? Do you think there's enough out there to think that we're moving in the one direction of like essentially like, eh, tools, MCP, like, data's just going to live in home systems and, like, the AI can, like, take care of it?

Or do you think there's still a compelling kind of warehouse, lake house, you know, component to people's stacks? I can reject your premise and say, this is going to be going to be the next big data fight over the coming years. This will be Python versus R for AI.

Because my bet is, and I know from us talking that I think you have a similar view, it's going to depend on your use case. It's going to depend on how much data you have. It's going to depend on how you're going to use it. There's some situations where I think it makes a ton of sense to be like, leave it where it

is, have an age and go get it. You know, give it tools and let it go. And there's other ones where it's like, no, we really need to get this. Everything needs to be in one spot for a variety of reasons. And of course, we will have no subtlety on this and we'll have team warehouse and we'll have team agents and they will just battle on LinkedIn about this. Yeah, I think I, yeah, we talked about this for the show. I think I agree on some level here, but I like to think about it like, who are these actual

people. And I think there's like data people who are like most comfortable with SQL. The warehouses, their happy place or the lakehouse of their happy place, like they're going to tend to opt for that solution. And there's going to be pros and cons. The one I can think of right now is with given technology and AI included, I don't know of any like practical way to do a good job of taking millions of records for multiple systems, all like in flight, like in memory doing like complex analysis and transformations and applying all this business logic and getting something useful. I haven't seen that.

If that's out there, man, that'd be cool to see. But I have not seen that. And I think there's some just practical, like, we were talking about recently around like context windows. Can't do it all in context. I know that much. Maybe there's some clever like vectorization rag stuff. You could do like end memory. But that still seems like fairly out of reach

given that. That also feels like even if you pulled all that stuff and threw it in a memory. That's going to get very expensive, very quickly. Yeah, right, right. And the blessing occurs of AI is people are pushing hard to get on the democratization of it, which is great on a lot of levels.

But, like, say you did get all that working with some, like, magical, cool, and memory, you know, vectorization stuff. And then you, like, let a bunch of people loose on it. Like, say it works, that's going to be crazy expensive. And nobody will have a quantitative idea. like I just asked the question like I didn't know that was going to give a thousand dollars to answer that question right well I think that's also

you get because it gets caught up in that whole we want real time we want real time we want real time and if you want real time the idea of agents and oh we can just pull it whenever we need it and it's the most recent it'll be great and wonderful we'll fool you partially because as you said if you're pulling millions of records and then trying to somehow stick them together and do something with them from different systems real time is not going to be real time at that point either. Right, right. It's going to take a long time for that to process, relatively speaking.

Right. And then you'll get the thing. It's supposed to be real time. What's happening here? Why did this take three minutes to run? Because you pulled 12 billion records from three different systems, and they had to be joined together and do all this other stuff with them. Yeah.

That's funny. Yeah. So I like to think of the data persona. Then I'd think of like a developer persona who, like, if they need data, like, maybe they, maybe even historically they reach for, like, Python and just hit an API. Like, that's where they're most comfortable, or whatever language

they like. If that's my persona, then I think, like, oh, man, like, MCP's so cool. And, like, I'm just going to, like, anytime I have to do any analysis, like, I'm going to reach for, like, AI tool, MCP, if it gets kind of complicated, I'll just have it dump out Python and I'll run the

Python again, you know, if I need whatever it was. I think that's, I think that's a valid way to do it. from an analysis standpoint and I'm imagining like a more technical team that like yeah like we don't we haven't hire any analyst yet or something and they're kind of

that's a persona and then the third that's interesting to me is the is kind of almost like an integrations what used to be an integrations engineer like a data engineer like that persona like they're mainly concerned with like moving data around as like how are they going to feel about this problem where like

they're very familiar with APIs moving data that way they're also very familiar with databases in that way, like, what are they going to reach for? So I think that's going to be a really interesting, like, evolution. I'm going to point out you just defined the two sides of my LinkedIn war right there. Yeah. Yeah. And you described why you're going to have it, because you're going to have one side that's like,

I like databases. This is what I'm used to. Therefore, we should do it. You're going to have another side that's like APIs are all you need. Why are you doing anything else? And they're going to just talk past each other. and greater escalating posts and conferences and talks and stuff like that. Yeah.

That's how you know there's an escalation. It starts with social media and post. And then the ultimate escalation is like literally separate million-dollar sponsored conferences with like essentially opposing views. Where they just take shots at each other just because. Right. Right. Oh, man. We're going to take a quick break from the episode to talk about our sponsor, Rudderstack.

Now, I could say a bunch of nice things as if I found a fancy new tool, but John has been implementing Rudder Stack for over half a decade. John, you work with customer event data every day and you know how hard it can be to make sure that data is clean and then to stream it everywhere it needs to go. Yeah, Eric, as you know, customer data can get messy. And if you've ever seen a tag manager, you know how messy it can get. So RudderStack has really been one my team's secret weapons. We can collect and standardized data from anywhere, web, mobile, even server side, and then send it to our downstream tools. Now, rumor has it that you have implemented the longest running production instance of Rudder Stack at six years in going.

Yes, I can confirm that. And one of the reasons we picked Rudderstack was that it does not store the data and we can live stream data to our downstream tools. one of the things about the implementation that has been so common over all the years and with so many rudder stack customers is that it wasn't a wholesale replacement of your stack it fit right into your existing tool set yeah and even with technical tools eric things like kofka or pub sub but you don't have to have all that complicated customer data infrastructure well if you need to stream clean customer data to your entire stack including your data infrastructure tools head over to rudderstack.com to learn more All right. So I want to leave plenty of time for this. Let's talk. I want to talk risk now, which I always think of the fun topic when it comes to some of this data and AI stuff. But you specifically read a really interesting book. And this isn't actually risk. Like risk getting people, oh, like security and PI, like privacy and stuff. Not that kind of risk. So I want to you to you up for your cynical data guy recommendation here on a book that you read recently. Yes. So in case you guys don't know, twice a year I put out on my own stuff, basically, what did I read and what are some of the highlights of it? My big highlight from the first half of this year was I read On the Edge by Nate Silver. So if you've never heard of Nate Silver, he's a guy who started with like kind of baseball stats and predictions and stuff like that.

He's probably most well known for at this point when he moved over to do election predictions. And that was he was the founder of 538 that did all of the. the like, you know, predictions of who is going to win the elections, who's going to win the Senate, all those types of things. He is also a, like, semi-pro professional poker player. He used to be, he used to make his living this way for a short period, too. So this is his book on basically looking at the world of risk-taking and how people kind of quantify it, how they work through it, how they live with it, specifically through

the lens of professional poker players who have a very high risk tolerance, but also, like, pride themselves on being very good at quantitative. unifying risk and like, you know, and chances in everything. So I thought that was, it's an interesting book in that sense. If you like poker, you will like some of the, a lot of the stories that come out of it. One of my biggest takeaways from this, and this is why I wanted to kind of give it as a recommendation is one of the things he talks about is how people don't take enough risks in their own careers and jobs.

And that's something that I feel like in my time I have seen in the data world of you have these people who, in theory, are supposed to be helping businesses make better decisions, quantify risk, and for some reasons, you know, we get into what we think they might are, I have noticed a lot of data people are extremely risk adverse to the point of where like they don't like to recommend things that have risk to them. They don't like to do stuff in their own lives that they feel have risk associated with them. And I think it's one of these things where it's hurting the individuals and I think it's also hurting like data teams and companies too that we're going through this. So this is kind of like as part of this book

recommendation, it's kind of my pitch to people to like take some more risk. You cannot live a risk free life. And you're probably quantifying risk wrong anyways in your attempts to do so. So yeah. So that's kind of the overview I would have from it. Like I said, it's a good book in that sense that you get to, you know, it's a narrative that you get through it. But I think for a lot of people, it's this idea of like, you're actually being riskier than you think in your attempts to minimize risk, if that makes sense. Yeah, I'd like to drill in on that point, because this is something that I've sought, I've thought some about not in a little while. I've read some of Nate Silver's other books and read some other, you know, other books that kind of

touched this topic. And I think, I think it's really interesting to look at it from the, and I'll give an example in a minute, to look at it from the business person. of the measurable versus unmeasurable risk perspective, and then like the perceived risk versus like actual risk. Like I think there's probably more accesses than that. But I think the most interesting one to me is actually how companies treat cybersecurity and security. Yeah.

I think that's a really fascinating one, especially like there's one that I've interacted with, a company of interacted with, that the, in my opinion, the actual biggest risk for the company was the company sailing, doing, essentially imploding, doing to such high sauce of ever getting anything done. Yeah. From like layers of, and I've seen this happen, like when smaller companies like fall to the trap of hiring tons of employees that are used to being a multi-billion dollar companies and start running the $20 million company like a multi-billion dollar company.

yeah like doesn't typically go well so from an outsider like okay the risk here in my opinion is like you don't land clients like in the business and the business shuts like you don't lane clients you can't move fast enough to like satisfy the needs of your existing clients and your business shuts down like that is actually the biggest risk yeah but a lot of the conversations internally are all about like like minutia around like security like very specific security protocols and this, that, and the other, because they had a cyber event, like, several years ago that was a big deal and, like,

and it caused a lot of disruption for the company. So it's so interesting how these pendulums can swing, so you got $20 million of your company, like rough numbers, like, three years ago that was just operating wildly, probably wildly insecure, like, not thinking about it at all. And, like, but maybe, like, you know, a little bit better,

go-to-market motion, and a little bit better speed and getting things done for customers, right? And then, like, you fire a layer of management, you fire some people, this was a major cybersecurity thing, this was a black guy for the whole organization, lots of drama, and you fire a bunch of people. Then you bring in, like, the risk-free team.

You know, like, you bring in, like, oh, they worked at, like, X Fortune 500 company and Y Fortune, like, they were going to eliminate all the risk. And they do the job that you hired them to do in a very real sense and, like, eliminate all the risk. But what you don't realize is it can have, happen as you just like essentially break your go-to-market machine, you break your service because you used to be like fast and reflective and now everything's like layers of ticketing

systems and like and then like, like, you know, it takes three months to ongoing or a client because of the security protocols. So like you break all that. It's a sense of more organized and a sense it's more secure for sure, but you break the thing that like your customers loved about you. And then like you can accidentally essentially kill the whole company. So like, great you've got this like locked up secure process driven thing that's going to die yeah yeah no it's it's an idea of also not being able to tell the difference of like when risk is okay and when risk isn't okay because it's like you know you get through you know you get into situations where it's like oh man if we do this we might you know as a company it might hurt us or something like that it's like yeah

we are already slowly dying like it does not matter at this point whether we hold it off for six months or something like that. Like you need to do something different there versus when you're in other situations where it's like, well, no, we don't need to take as wild of a swing. But you're always going to take on some level of risk. You cannot go if you're not taking on some level of risk. And that's like where I've seen that with, you know, you get like data teams that have analysts on them or, you know, when you have engineers recast as analysts, which is usually

not a good role for them anyways. Right. And there's this complete reluctance and refusal to make a. recommendation because the recommendation could be wrong or there could be this or there's pros and cons to each choice and so they try to hide behind it's like well here's what the data says right and all you're basically doing is showing a bunch of information but you're not telling people what makes sense to do right and now you get into a situation where they're like well but I don't want to you know like oh but I could be wrong I don't want to be wrong like this sense of like

I'm going to lose something from doing that one of the reality is that everyone's just getting pissed at you because all they feel like you're doing is compiling spreadsheets and hand in charts and handing it. Right, right. Like, you're not of any value if you're not actually pushing something forward. Yeah. Well, but the problem is the incentives. Go back to the security thing.

If I'm like the spirit officer or person that got tasked for security and it's only like my part-time job, which is like a lot of companies that would be in this like market space, like, if I have a major security incident on my watch, I'm held responsible, bad things happen to me. Maybe I get fired. Maybe I get demoted. Like, whatever. Yeah. Really bad things happen. If I'm, let's flip it the other way, if I'm like going to have a massive security budget, spend tons of money on it every year, like lock everything down super tight, get in the way of everybody working, and then just say like, well, it's in the name of security. And then like, and then I'm a typical, say, I'm the like typical CEO. Like, I don't know who's

right. Like, I don't know. It's like less security. We'd still be fine. Like, how am I supposed to know. Right. And in one sense, like, how is anybody supposed to know? Because, like, cybercriminals are getting better and more crafty every day. Like, there's new attack vectors. Like, there is a real sense where this is one of my favorite topics when it comes to risk because, like, it is nearly impossible or it is impossible to fully quantify, like, hey, what's my cyber exposure, you know, at my company? It's like, well, do you have people that work there? Well, you have exposure. Good people work there. People are the biggest weakness you're going to have.

Yeah, and then, like, and then obviously there's some really great tools out there and layering, like, in AI solutions, right, and all sorts of things from, like, your inbound communication, from your network perspective, from your, you know, desktop, laptop, whatever. So there's tons of, like, good solutions in the space and people that are good at implementing it and such.

But, like, to me, I think it's a fascinating space because the people that, like, are able to nail the, the, hey, let people still do their jobs, part of it, are the ones that really can take on so much of the market, but can balance it reasonably with risk. And it's not in either or. There are plenty of, like, I think, secure solutions out there that don't necessarily have to, like, make people's world impossible. But there's at least occasional tradeoffs and occasional small tradeoff that I think, at least for a while, like maybe this changes, like, the, like, people that can, like, successfully quantify, like, hey, this tradeoff makes sense.

The, you know, we're going to do it. Like, that's valuable and very hard to quantify. And therefore, like, because it's hard to quantify, like, it's easier to opt toward, like, well, just to be saved, dot, right? Yeah, I think you're also getting towards the two things that I see from that are there's this quest for certainty and, like, there is no certainty. Right. Like, you are always bearing a certain level of risk one way or the other.

Right. Cannot have certainty. You can have clarity on what your strategy is going to be and the risks you're willing to take, but you cannot have certainty on it. And to kind of go with that, like, when you're in that position as a cybersecurity person, and I think this, I would say this also applies to, like, a lot of data teams, you're in kind of this wicked learning environment of, like, cause and effect are not always going to be coupled. Even more so, you can do everything right, quote unquote, right, and it could still not go well. Yeah. Right. You're a security person, you can find the perfect balance, and you still have a breach.

And now it's your fault. And that's the like, and that's the, and I really feel for security people on teams on this, like, because you could have a security breach. And it's literally like a one in a million like thing that happened where it was like an immediate exploit of a bug nobody knew about. And they got into, like, this thing. And, like, you, you would always historically patched your firewall every week. Like, you could be, like, on it. And then there's this, like, breach. It's, like, literally not your fault.

And the exact same thing could happen to, like, somebody that's completely lax, like, doesn't know what they're doing. And there's no, like, I mean, as a technical person, you could kind of probably suss that out. But, like, downstream to, like, customers of customers, like, nobody cares. Right. Like, it happened, you know? Yeah. And to bring it back to kind of the book. this is one of the things that, like, if you're going to be a good poker player, you have to learn to do, which is, can I quantify the risk based on the knowledge I have? And am I okay with the fact that, like, you know, yeah, I've got, I should win this hand 75% of the time. That still means one out of four times I'm going to lose. And it's not about the result necessarily. It's about the process, you know? And I think for like a lot of data teams, like I've written about this before, the idea of like, you've got like two years if you're like a new data team, you know? Yeah.

And there is a chance that you will do everything right. You'll work to build the right culture, get the right foundation, and you will not get a project that will actually get you what you need. And you're going to be out in two years. And how are you going to handle that? Is it something that you can look at it and say, you know what? I know this will work and I was just unlucky in this situation. Or are you going to like overreact and be like, okay, I don't care about any of that.

We just need to get the things to the people right now as fast as we can. and we will just bubblegum and duct tape it for as long as we have to. Yeah. Well, since we're coming up on fall, I think we've talked about this before, like it's that football analogy, right?

You've ever as a head football coach to the team and you've got, I don't know, maybe you have a year nowadays, maybe you have two years depending on, it's like, it's a similar thing where we're like, okay,

every, you know, recruiting's broken, like I don't have the talent I need. I've got a number of coaches that like I need to fire, but I have to keep them for a certain amount of time because somebody, because my boss told me to keep, like,

you could have so many variables. Because they just fired the previous head coach and they're going to be paying him for the next four years, so they don't want to do that with any other. Yeah, they don't have money to, yeah, you don't have money to recruit the coaches you need. And I think people end up in the data equivalent of that. Yeah.

And part of the people that are successful, to be quite honest, are the people that suss out the situation ahead of time and don't take the job, honestly. That's part of it. And then the other part of it is the people that in the context can develop the skills, tools, and processes to be successful, realize that, like, in two years, like, all right, I was essentially set up for failure.

Like, I couldn't have been successful. But I can go somewhere else. And I learned what I needed to, refine what I needed to. And I can be successful somewhere else. Both of those are options. Yeah. And sometimes it's going to be a thing because the football analogy is my favorite one for that. Because it's like, what's one of the most important things if you're going to be successful

in the long term as a football coach? It's like, you've got to have the right culture in place that's going to sustain you. you know what doesn't win right away having the right culture in place and so you get this thing of this weird balance between that and for you can look at really good coaches and it's a little bit of like a the pieces had to fall together and they didn't fall together in the first time but they fell together in the second time or they were there for the first time but not the second time they did it and like they're still good coaches there's still great coaches they still have it there but there was that one situation where they didn't do it you know you can even look at like

It was the former head coach like the Carolina Panthers, Matt Ruhle, right? Very successful before. He's been successful since. There were things that got in the way from him to be successful, but it wasn't like he necessarily was the one who completely screwed it all up or something.

You can still see some of the same good qualities there. But it didn't work out and he didn't have time and there's a bunch of things that go into that. And so I think that's with a lot of the data teams you get that too. And kind of like what you said, you got to be willing to have that idea of like, I may do everything right in two years, it won't work.

Right. And even in a situation where you're like, this is a slam dunk, it doesn't always work. Yeah. And can you handle that? And are you okay with that? And can you kind of be like, can you tell the difference between here's what I need to change? And here's what I, here's what it just didn't work out this time. Yeah.

And for data teams, like, you're in a support role, right? It's a support role. And like, imagine you were like working on a political campaign and like, and you're like helping to try to get somebody elected. It's like, okay. like cool like I can nail it as the data team here but like if we lose like we lose and it's not really my fault like I can contribute to winning by whatever data teams with political organizations do not super familiar with the space but like I can't really affect winning by any like direct contribution even if I'm over data for the whole like thing I mean I can affect it to some extent but like if there's there can be wins against me where like literally this won't happen regardless of of how hard I work, how good I am, you know, what we do. You can have the best understanding of the electorate. You can have the best targeting that you've got out there.

You can know where all the persuadables are. If your candidate is a terrible speaker, or if their policies are just not popular in that cycle, it doesn't matter at that point. Yeah, exactly. Right. Can you tell I actually worked on a campaign? Yeah, I brought that up. When I was like halfway through this, it was like, oh, yeah, I worked on this.

Like, I'm glad he's able to speak to it. Awesome, man. We can go forever. I think we're almost at the buzzer. Any other little tidbits maybe from your kind of six-month summary year? Any learnings or tidbits through your six-month reading and learning summary? I will. So this is always my plug to tell people to read more fiction because you will learn more,

especially if you want to be a leader in any space. Like, you have to know people. And you're not going to learn that from a textbook. You've got to learn it from actual people and fiction and novels and things. things like that are a very good way of doing that. So it's my, every time I talk about this, it's always one of my plugs I put in there. So I think that's always a good one. Nice.

So yeah. So there you go. On the edge by Nate Silver. And read some fiction. Get away from all of the pseudoscience crap out there. Yeah. All right. Awesome.

Thanks for coming on, Matt. And we will catch you next time. The Datastack show is brought to you by Rudder Stack. Learn more at rudder sack.com.