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The Analytics Power Hour
#263: Analytics the Right Way
01:06:18
Every so often, one of the co-hosts of this podcast co-authors a book. And by “every so often” we mean “it’s happened once so far.” Tim, along with (multi-)past guest , just published , and we got to sit them down for a chat about it! From...
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- [00:00]
Dr. Joe Sutherland
Foreign welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.
- [00:14]
Val Croll
Hey everyone, and welcome to the Analytics Power Hour. This is episode 263 and I'm Val Croll from Facts and Feelings. You know, writing can be hard. While I am absolutely just opening the show with some totally off the cuff extemporaneous remarks, it's not hard at all for me to imagine a world where the intro that we do for every episode is carefully written out ahead of time. But that definitely wasn't done here. Nope, I'm totally freestyling and free associating. And that's how this Tim style rambling I'm doing, which just happens to be the topic of writing, is a nice transition to what this episode is all about. It's a first for the Analects Power Hour. And no, I don't mean because it's the first time I've done the show opening.
- [00:58]
Julie Hoyer
It's.
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Val Croll
It's because we've secured an exclusive designation as the official podcast for what is sure to be the most talked about analytics book of 2025. The book, you might ask. Analytics the Right Way. Or the full title, analytics the Right Way A Business Leader's Guide to Putting Data to Productive Use. I'm joined today by Julie Hoyer from Further for this discussion. Julie, are you excited to talk with these book authors?
- [01:25]
Julie Hoyer
Oh my gosh, absolutely. Have been waiting for this all what, month?
- [01:33]
Val Croll
Very nice. I'm also joined by Tim Wilson, my colleague from Facts and Feelings. And he's more of a guest today than a co host because he's one of the co authors of the book. Tim, welcome to the show.
- [01:45]
Tim Wilson
I guess hopefully this is the last time we'll use this little gimmick.
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Val Croll
Maybe we'll stop doing cool. We won't have you on as a guest. How about that? No, never. You'd never. And we're joined by Tim's co author, Dr. Joe Sutherland. In addition to working with corporate executives as a consultant and advisor, Joe founded the center for AI Learning at Emory University, where he also teaches. And as it happens, Julie and I both got to be his students in a way when we worked with him together at Search Discovery. Now Further, Joe has a list of credentials that is, frankly, kind of intimidating. Let's see if I can get through it. He has one political science degree from Washington University in St. Louis and three more, including a couple of doctorates from Columbia. He's a fellow at the Weidenbaum center on the Economy, Government and Public Policy at Washoe. He worked in the Obama White House from 2011 to 2013. Casual. He published academic papers all over the place. He's been on this podcast three times now, believe it or not.
- [02:50]
Tim Wilson
That's an accomplishment.
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Val Croll
Sure is. Yeah. But intimidating. Not really. If you know Joe, he's not scary at all. Today we get to welcome him as our guest. Welcome to the show, Dr. Joe.
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Dr. Joe Sutherland
Thank you very much. It's good to be back. That's the reason we wrote the book, actually, was because Tim dangled the podcast appearance and he said, hey, you'll actually do.
- [03:13]
Tim Wilson
They'll let me on as a guest.
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Val Croll
I love it.
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Tim Wilson
I just need to bring somebody with some real credentials.
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Dr. Joe Sutherland
That's.
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Tim Wilson
That was the.
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Val Croll
Yeah, that's the hook.
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Tim Wilson
Yeah.
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Val Croll
I love it. So excited for this one. So I guess a good place to start would be asking you guys just a little bit about how this book came to be. I know you guys work together at Search Discovery, because I was there to see it, had the privilege to see it, but this didn't come together till a few years later. So I'm curious, kind of how it started. A little bit of the origin story. And what did you guys see that was not out there in the space that you wanted to kind of address with analytics the right way?
- [03:50]
Dr. Joe Sutherland
That is a great question. I actually have a specific memory of when this book, like, hatched in my mind, which is I was, like, on my back patio on the phone with Tim. This is, like, years ago. And I think one of us just goes, we should write a book. It's true. And the truth is, like, I do think we're ideologically aligned in so many ways when it comes to, like, the practice of data and analysis and machine learning and artificial, all these things that you hear about today. And I just knew that by coming together with Tim, something wonderful would be made. And you know where it went to, right, was I get a lot of these customers, clients, or folks, you know, I guess I encounter a lot of them at the center all the time who go, I'm ready for AI. Can I get into it right now? Let me just buy it. Like, you know, like, let's do it. And they never ask the question, like, well, what are you actually trying to achieve? And how do we get there first? And do you even have the data availability? Have you thought through where your investments need to go? And I actually think that the principles behind making our way towards these artificial intelligence projects and capabilities at companies which are truly transformational, the principles are universal. I mean, you can really link them back to Any data or analytics question. And I wanted to give, you know, the corporate executives of the world and any sort of business leader, I wanted to give them a book that would basically say, hey, look, read this and, or give it to your people, have them read it right and you'll get there. That's kind of what I was hoping to get out of it when we started.
- [05:28]
Julie Hoyer
And that's no small like task either. That is a lofty goal.
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Tim Wilson
Well, I mean, I think part of what happened, Joe and I met like he was thinking about like the introduction happened. I remember sitting in Atlanta in a conference room, me thinking, this guy's gonna make me feel stupid. We hit it off and then as we work together, I have some very clear memories of sort of having an expectation than when you bring in a data scientist. And that's kind of what Joe's sort of the role, the branding he was, we were using for him at the time was data scientist. And I had, I'd gone through this journey on my own where I was going to try to become a data scientist like a few years before and kind of realized after a few years, like, no, I can do really useful stuff, but I'm not really going to be ever something that I would consider a data scientist. But I had this expectation that when you talk to a data scientist, they're going to start immediately talking about models and methods and you know, the vast quantities of data and the number of times that that Joe would get brought in and there would be somebody, we want to do an X, we want to do AI, we want to do machine learning, we want to build a model that. And he very consistently would say, wait a minute, like we first have to define the problem, we have to frame the problem. And so having someone who had all the horsepower to do all the go super deep. And I think, Julie, you might have even lived it more than I did. Like, yeah, he can really go deep, super deep on the technical. Was always saying. But the way companies tend to fall down is they skip that clarity on what they're trying to do, what are their ideas. And so we had, while traveling, while just doing catch ups, we had many, many. There are many memories in my mind of Joe and I sitting across from each other at a coffee shop, at a restaurant, at a bar, having these discussions where I was actually learning a lot. He introduced me to the fundamentals of causal inference, which kind of blew my mind. And I was like, oh, this is a very important idea. Not all of the mechanics and the details that go into it. Just the basic ideas behind what you're trying to do and why you're trying to do it is really powerful. So I'd had an idea to write a book eight or nine years ago. This book has very prominent vestiges of that. It is a much, much richer book because there was a lot more depth of thought, a lot more experience, a lot more collaboration on a much broader and deeper set of projects going into it. But it was, it's not a book to say this is going to teach you data science, but it's also not a kind of lofty, hand waving book that is just, you know, get all the data and get all the data super clean. We really wanted to write one, as Joe said, for kind of the business manager, the business leader, the business executive, so that they are positioned to actually get value out of their data, out of their analytics in a productive and efficient way.
- [08:38]
Val Croll
So that's interesting, you both called out the audience kind of in your description there. And I think that that's a really interesting choice because you think, oh, I'm going to write an analytics book. I'm going to write it to my people, to my analytics cohort and professional. How come you guys made that choice? Was that kind of always there from the beginning or did that kind of come together as you were starting to frame out what some of the topics you were going to dive into were good?
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Dr. Joe Sutherland
I mean, there's a lot there. I think one of the points we make in the book is, I mean, we make so many points, right? And I think that they're all like just new mental models for thinking. That was one of the reasons I loved the collaboration with you, Tim, was like, we just developed some really cool new mental models for how to think about the world and how to think about data and analytics and all those exercises that we go through in corporate America. But, you know, a few thoughts. One is, I've realized more over the past few years that there is this zeitgeist in the analytics or IT or technology industry vertical, what have you, where in a lot of ways you feel like you can just purchase insight. Like, you know, and I don't know, I feel like it comes from a variety of forces, right? And we talk about this in the book where it's not like there's some sort of bad actor out there who's trying to convince you to buy their product when it really doesn't create any value at all, right? There is a reason why these things happen, but I just don't get the sense that As a business leader these days, you can always trust everything that comes from your tech or analytics or data folks without understanding sort of the more fundamental concepts. I'd be curious to know your thoughts about that, Tim.
- [10:21]
Tim Wilson
Yeah, I mean we definitely had a lot of discussions about this and I'm in a spot that having in many ways kind of facts and feelings and kind of the drive behind facts and feelings, the consultancy that Val and I and Matty Wishnow started is, it's aligned with that. There is, there are just forces that are naturally happening in the world, in the business world that kind of over index towards collect more data, run fancier models, find more technology, hire more data scientists, push to do more. And it just seemed like to us when we were working with clients that there was kind of, they were trying to start on step four and they'd skipped steps one, two and three. And even if the analyst or the data scientist was trying to go back to steps one, two and three, which is around thinking and this is not, there's not like a six step thing in the book. So that's, that's a metaphorical steps one, two and three. That, that, that's really where the most opportunity to kind of redirect an organization's investment is much more about getting the business owners who are trying to get value out of the data if they get off the hook and get told to just lob it to the analytics team and say, bring me some value. Having grown up in that analytics world and feeling how difficult that is and sort of slowly realizing that, oh, it's because we're not putting enough upfront thought into it. So even though the audience is kind of the, you know, business leaders, we certainly think analysts who, and data scientists who, who read it will hopefully it will help them think differently as well and give them confidence to say, no, no, no, I have to go engage more farther upstream. We have to have clarity of, wait a minute, this is a data, this is, this is an analytics ask. Am I trying to just like objectively and concisely measure the performance of a campaign or am I actually trying to, you know, figure out something to make a decision going forward and giving everyone kind of a little more clarity of language and you know, ways to interact. But it really does go to. A lot of that burden falls on the business with the layer of. I think there's, I think we agreed there were some sort of fundamental, fundamental misconceptions that the industry has. Analysts have it as well. Often business tends to have it as well. More data is better if I have all the data, you'll build me a perfect model. You'll get to an unambiguous truth. So I think there is a level of statistical fluency that they're not super difficult ideas, they're kind of mind blowing. That's the nerd in me. The potential outcomes framework. Boy, give me that second cocktail and get the wrong person in the corner. And they are, they are. Yeah. I talked about counterfactuals like four miles into a seven mile run with my trainer that where was she gonna go? You know, she.
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Dr. Joe Sutherland
So no, no, but just, just to jump in on that, like there are more pieces to this book that I just, I want to communicate to the audience. Number one, my wife actually, my wife reviewed the manuscript and she goes, Joe, this is kind of like your philosophy on life in a treatise, like in a statistics like you know, sort of framework. And I think there are just a lot of really cool. It's not dispensations, like we dispel a lot of the misconceptions that will help you. I almost feel like live your life like better. It's hard to describe. Like, you know, I think back to, you know, you know, I got all these degrees, right? And I only have one doctorate by the way. Just, just. I don't have multiple doctorates. Just one.
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Tim Wilson
Just watch it.
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Dr. Joe Sutherland
No, no, you don't have to re record, it's fine. But the, you know, it's like I always thought like why did I get a degree in statistics and, or at least you know, with a methodological focus and statistics and applications of machine learning, like why did I do that? And it's like I really do think it was to sort of like self soothe and cope with the natural like OCD impulses and anxieties of life that I've experienced my whole, like once I understood the world in probabilities and sort of through the framework of a probabilistic approach, like it made my life better. I took things less personally, like I made better decisions. And I really do believe that the way that we think about the world through this book is actually going to be really helpful to people. So that's one point that I want to make.
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Tim Wilson
I mean I just took up like photography for self soothing. But you know, if you got to go get you know, some variable number of advanced degrees, you know, to each their own.
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Val Croll
That's right.
- [15:41]
Julie Hoyer
Well I'm glad you guys called out too that like this is still valuable for analysts to read especially because now I can't wait to just like buy this and make all the analysts I know read it. I'm so excited about that. So I'm glad you touched on that because that was going to be my follow up question. But the other thing you guys already mentioned that I really wanted to touch on was the misconception. So going through that was one of the opening parts that I love the most is that you guys broke down, like, what are the misconceptions of like, how we got here? Like, why is it the way it is? And so without giving away too much, I didn't know if you guys wanted to like dive into the ones you mentioned.
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Tim Wilson
We can't. I don't think we're. I mean, it's so eloquently put in the book that even if we just kind of off the cuff try to rattle them off, it's. You're still.
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Val Croll
We definitely couldn't do it justice.
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Tim Wilson
I mean, I think I had, and I'll give a little credit to Matt Gershoff on this as well, when years ago at a super week. And he said, you know, these three things that business is about making decisions under conditions of uncertainty. There's a cost to reducing uncertainty and uncertainty can't be eliminated. So I sort of had that. He kind of introduced me to this idea that the goal was not to eliminate uncertainty and there are diminishing returns. And I still think that is like, that is a huge thing. Like, we've lived that where people say, what is the answer? And you have way too many data professionals walking around quoting Deming saying, God we trust, all others must bring data. And they just kind of wield a misunderstanding of that as though you have it. Without data, you're just another person with an opinion f you. I'm like, well, that has perpetuated this huge misconception that data gives you an objective truth. And it just, it's just never perfect data. So even getting truths about the past which aren't that useful, it's never truly perfect. And it certainly says truths about tomorrow, you're just not going to get. And it's like, even though people say, yeah, that totally makes sense, but we just operate where when the analyst says, I don't know, I can't give you a definitive answer. So to me, that's probably like one of my favorite misconceptions is that this gold rush for data is because it's going to let us eliminate or essentially eliminate uncertainty, which is just a fool's errand. But that is what the industry is doing. So there's one of my favorite misconceptions. I don't know. You want to do one, Joe?
- [18:17]
Val Croll
Yeah, your take, Dr. Joe.
- [18:19]
Dr. Joe Sutherland
Let me tell the. The Ye olde economists joke because I actually love this one. And also it does link back to the misconcept. Yeah. So one of my favorite misconceptions just comes with this idea that data are inherently unbiased. And as a trained statistician economist, I could tell you that's just totally false. There's actually a great economist joke that goes as follows. So CEO of a major company, he's hiring for a role. He brings in three folks to interview for the job. A mathematician, a statistician, and an economist. CEO calls the first guy in. He's the mathematician. He says, look, what does two plus two equal? And the mathematician goes, well, it's four, of course. CEO goes, four, Are you sure? He goes, yes, exactly four. That's exactly what the answer is. CEO is not, not pleased. Calls him the statistician. He says, what's two plus two equals four? Statistician will go, on average is four, you know, give or take 10%. CEO is still not pleased. So he calls the economist, and he gives him the same question. And the economist gets up from the chair, he looks around very sneakily, he closes the door, closes the shade, sits right next to the CEO and goes, what do you want it to equal? And I just.
- [19:34]
Val Croll
It's.
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Dr. Joe Sutherland
It's so true. You know, it. Oh, oh, oh, there's a laugh. Who did that? That's cool.
- [19:42]
Val Croll
We've, we've leveled up the production of this since your last time you came on the show.
- [19:48]
Dr. Joe Sutherland
I feel like we just entered the new k, new millennium. Like, but, but I actually, I love that joke because there's this old adage too. It's like, with the nut torturing, the data will confess whatever you want them to confess. Right? That's just the truth about data. So stop thinking about it as something that's inherently unbiased. It's how you deal with it and how you build confidence in your methodology that really lets you get to the right answer.
- [20:14]
Val Croll
I love that. It sounds like you guys have a lot of things that you're packing into this book that you're packaging for these business leaders. Like, how do you. How did you walk them through this? Like, was there an overarching, like, framework that you leveraged? Because I think that I intuited one from, you know, working with you all over the years. But I think it would be helpful if you guys talked that through A little bit. If that was one of the mechanisms that was kind of driving the narrative and how you were packaging it up, sure.
- [20:43]
Tim Wilson
I mean, when it comes to the, the, the outline that was in the book proposal, this is just kind of amuses me. I get sort of irritated with business books that feel like there's way too much wind up where they're like, you're into like the fourth chapter and they're still telling you what they're gonna tell you in the book. We actually did have to add like an additional introductory chapter because we had so much to say. So I'm sure every author says, well, we're not guilty of that. But you know, like, so there is that, that, there's. That part of just from the, the structure of the book is there are a couple chapters up front trying to say a lot of the common ways of behaving are problematic. And let's help you understand, you know, why those are problematic then kind of the, the core of the book, it is kind of a framework trying to keep things as simple as possible, which is, and I've talked about pieces of this on many episodes of the Analytics Power Hour podcast in the past, but that fundamentally, when you're trying to put data to use for an organization, there are kind of three discrete things you can do. You can be trying to measure performance objectively and concisely, which so many organizations really, really struggle to do. Well, they may have a lot of reports and dashboards, but they're not really doing a good job of objectively saying how are we doing relative to our expectations in a meaningful way. There's validating hypotheses that's like the analysis or testing or that's got multiple chapters devoted to it because that's where we're trying to make better decisions going forward. So lots of ways to validate hypotheses. I think the, at least in marketing, there's a lot of talking about if you're doing a B tests on a website, they'll say, what's your hypothesis? Well, everything that we're doing with lots of different techniques, it really should be grounded in validating a hypothesis. And then the third is you have data that is just part of a process. It's enabling some operational process. And those sort of, they fit together interestingly. And we did do a lot of kind of thinking and talking about how to talk about AI. This is not a book that is AI AI, AI AI AI. Because we went in saying AI, it's purpose, it delivers value because it is part of an operational process. So it actually fits in this one area. And so everyone who's, you know, if you're super excited about AI, it's not doing a whole lot. It might be a code assisting or something on, on validating hypothesis to develop some code for a model. But it's not like AI replaces the analyst because those other two, measuring performance and validating hypotheses really are much more about kind of human, human thought.
- [23:43]
Dr. Joe Sutherland
So I 100% agree. It's, you know, 2024, in retrospect, was just, you know, it was the year of agentic AI, right? It was, everybody was very interested. How do, how do we use large language models to replace analysts and replace people? And you know, the truth is, like, it really preys upon the super lazy impulse that I think we have as like a, you know, as a human species in society, right? Which is like, man, if I could just create a machine that could just do my work for me and delegate the work to the machine, like, I could go, I can go golf. You know, while it does, does all the super valuable stuff that I was doing, right? I'll just go golf. And like, the, if you read through the book, you'll, you'll actually, it demonstrates why that is like a super, like, it's just not true. You could never really do that. Actually, it. I'll jump in on. We kind of talk a little bit about this in the book, but there's a guy who got the Nobel Prize back in the 70s. His name was Herbert Simon. And he had this idea that as a society, what we do when we're looking for the answer to make a decision, we sort of just look in our local area and space and talk to our friends. Good examples. Like if you're trying to find your ideal soulmate so you can marry them. Like, most of us don't go and date like the 8 billion people in the world, right? Like, and find the best one. What we do is we go and we ask our friends and we kind of go, somebody who's on the periphery of your social circle or that, you know, growing up, like, we look to find the best possible alternative that's just in the local area where we're looking. And we can do that actually super well because we, we're baked in with these impulses and intuitions. But, you know, machines like to find the best option and to make the best estimate of what might happen in the future if a decision is made, they have to search like the entire space of possible outcomes and opportunities. It's kind of like we often refer to it as boiling the ocean. And it's virtually impossible, right, to be able to make really, really incisive decisions and insights with an approach that boils the ocean. It's actually just not even really feasible within the amount of time that we have available to make those decisions. And so I'm not sure why I got into that, but I thought it was important. Oh, agentic AI, that's what it was. The takeaway there was just, you know, I do think that people with this artificial intelligence revolution happening over assume that we can delegate to the machine. But the truth is you're still going to have to go through the decision making processes that we articulate in the book.
- [26:27]
Tim Wilson
I literally saw a post on LinkedIn that said because there's so much around like the generative bi and oh, the simple questions. And it's like, you know, for instance, if I want to know how many leads came from California last month, I should be able to do a natural language query. And I'm like, that's literally no one is saying I have very simple, straightforward, defined questions and it's spending me so long to get to it. So that's, to me, that's also, it's kind of the, it's this saying, well, these dots, they're close enough to connecting. Let me go ahead and make the leap that I can just, you know, ask if I could just ask the AI to give me, you know, insights. And then it's like a for instance. I'm like, well, well, nobody getting told how many leads there were from California last month is rarely the type of question that takes you anywhere.
- [27:17]
Dr. Joe Sutherland
Let me actually, I do want to dig in on this because like what you're describing, actually I would, I would think of as. It's not even an insight generation using artificial intelligence. What you're describing, the process of doing the query to get the answer is just being replaced, right. By some sort of general generative AI technology. So that actually is consistent within our book. We kind of break out insight generation from operationalization and operational technologies enabling automation. And that example you just gave, I actually would throw it in the bucket of yeah, it is sort of like an operational enablement problem, right. Which is just, oh, we need to get to the query faster. Right. And that to me is consistent with the use of AI.
- [27:58]
Tim Wilson
But yeah, it's fine to do it. It's just a, it's to just paint that as saying, and this is what's going to replace. I'm like, no, like you're, you're actually missing the boat on what should be going in to actually getting real value out of this. If that, if you think that it's following that path is what's, what's going to do it.
- [28:21]
Julie Hoyer
Well, I want to draw back on something too that you guys mentioned. Like you called them decision making frameworks and I'm luckily enough to have worked with you all. And so it's very ingrained in me. But I run across this a lot still where people talk about the value of a, a new product, a new technology. It's like it's going to give us insights. And then you ask them more about it and they say, oh, it's going to give us knowledge. We'll know what's going on. There's value in knowing. And it's like to a point, there's value to knowing. But I'm like, the real value comes when you act on the knowledge. Right. Like, and you guys make very clear distinction about that, especially in this framework. And I think that's how we as a small group here today think about it. But it's still shocking to me that I run into a lot of people that I have to make that argument to and really say, like, I think there's one step farther. And like, so when we talk about value to clients and of different services and things as consultants, like them being able to go take action on what we've, you know, helped them learn, like, that is really the end point. And I'm, I think a lot of people's minds will be very blown and like opened to that by reading this book, which I am so excited about.
- [29:28]
Tim Wilson
Well, and, and I do think what, what happens? And we have, well, trying to explain sort of counterfactuals potential outcomes framework which I mean, I think Joe was like, rein it in, Tim. Rein it in. I know you're excited about this, but.
- [29:43]
Julie Hoyer
Spin off book.
- [29:44]
Tim Wilson
But. But what?
- [29:46]
Val Croll
It looks the right way Part two.
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Dr. Joe Sutherland
Are we doing another book now? Book number two. We just have to. Right now. I remember when we were on the.
- [29:54]
Tim Wilson
Well, I mean, to be fair, there were lots of things where I was.
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Julie Hoyer
Joe just wants to come back a fourth time.
- [29:58]
Tim Wilson
I was the one who was like trying to.
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Dr. Joe Sutherland
If I come back five times, I heard there's a gift.
- [30:02]
Tim Wilson
There's a. You get the jacket. Yeah, there's a jacket.
- [30:04]
Dr. Joe Sutherland
Okay, okay.
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Tim Wilson
But I would often take a crack at saying, I'm going to try to describe this because I am closer to the less deeply, deeply immersed in the mechanics of this Joe would come back and basically in their. Their footnotes in the. But we had fun with the footnotes. But there are lots of times where we're like, if a trained statistician is reading this, we are taking a shortcut. It is not material for what we think people need to know. Joe does have his reputation, you know, so it would be in the footnote and say, look, this is, you know, technically not quite correct, but it's good enough. And that. Which I think goes to a lot of what we were trying to do with the book. But I've seen that again and again when someone says, oh, we're just going to make this change and we're going to see what happens and we'll figure out if it. We'll make a change and then we'll just see if it worked or not. And we sort of walked through an example of saying, well, what if you make a change and this is what the data looks like? Because it usually looks. It's not some abruptive, massive step function that says, look, we changed the button color on this page and revenue jumped way up. I deeply believe that's what, as human beings, we think is going to happen. We're going to do something and it's going to have this abrupt, sudden, immediate impact. And we'll. We'll look at the chart and the chart will kind of go along and it'll have a big jump and go on after and we'll say, see, that's what happened. That doesn't happen. And so helping people understand that, that it's like, no, if you're trying to. If you're going to have an intervention, if you're going to do something and you want to see whether or not it worked or not, you can't just say, let me do it, and then I'll wait and look at it afterwards and we'll have a, you know, we'll just. It's going to be obvious. Like, it's not obvious. And then it gets dumped on the analyst to say, well, figure out the answer anyway. And, well, the easiest way to have figured out the answer would have been to think about how you were going to answer that question before you actually made the change.
- [32:06]
Dr. Joe Sutherland
So, well, it's amazing, right? It's like, think about how you'd want to answer the question before you even try to answer it or get into it, right? But if you don't do it and then you force an analyst who, you know, God forbid, hasn't had the experiences that we've had in the Wild west of data analytics. Like, you might end up having somebody who looks at what happened and you know, you draw the wrong conclusion. Right. That's kind of the risk. Like, oh, when we cut our investment in sales professionals in the Southeast, like our, our efficiency went way up. Well, let's just cut more. The conclusions kind of go, can, can be wrong. And if you don't think about like the appropriate inferential framework, you might get to the point where you say, well, we made that decision. We're going to skip the process where we vet it and figure out if the inference was a solid inference. We're just going to go right ahead to automation. We're going to throw this into the machines, we're going to have them automate it to oblivion. Right. And then all of a sudden you get somebody who's got feedback or consideration for what's going on, just implementing whatever random decision you made. Like, to really, like to kingdom come. Right. And I really think there's a risk, especially in this era of automation, that we skip to this human out of the loop stuff way too fast simply because we drew the wrong inference. And part of the book is thinking about how to slow that process down.
- [33:32]
Julie Hoyer
One of the, the parts too. And we have teased it here before and I know we've had a lot of conversations about it and like other sidebars. And so I'm really excited to ask you guys about this is your ladder of evidence, because I know that is not easy to come up with. I have had other conversations with people and it's like you think it's so straightforward and then you get into, oh, but what if you think about it this way or you know, another way? So can we talk about your ladder of evidence that you settled on?
- [33:58]
Dr. Joe Sutherland
We, we can definitely do that, but before we do that, we have to walk through Tim and I's like ideation.
- [34:03]
Julie Hoyer
Process on I need to hear this origin story.
- [34:07]
Tim Wilson
That was intense.
- [34:10]
Dr. Joe Sutherland
It was intense. It was the subject of many like long Zoom FaceTime conversations. And it actually, I think that this section alone single handedly reorganized the book like three times.
- [34:23]
Julie Hoyer
Wow.
- [34:24]
Dr. Joe Sutherland
Is that accurate?
- [34:26]
Tim Wilson
Yeah, very much.
- [34:27]
Dr. Joe Sutherland
I mean, but once we nailed it, I think it actually came home. Like, I really do think it did.
- [34:32]
Julie Hoyer
We need a drum roll.
- [34:33]
Tim Wilson
Yeah, yeah, well. Oh, hold on.
- [34:35]
Julie Hoyer
Come on, Tim.
- [34:38]
Val Croll
The ladders of evidence.
- [34:44]
Dr. Joe Sutherland
Wow.
- [34:45]
Tim Wilson
I mean, so the funny thing is, is that there was a, I think it was like a Shopify blog post buried somewhere that had this idea of a ladder of evidence that I had thought was really useful. And I'd written a little bit on it. So that's kind of where it started. I dug in enough to say, like, oh, wait, this is not like some deeply established way of thinking about things. And where we landed, we're also calling it a ladder of evidence. And it is conceptually consistent, but it gets to that idea of uncertainty, which also gets to this idea of how strong is the evidence I'm using to make a decision. So the latter is very simply, there's anecdotal evidence, which is super, super weak evidence, but it's evidence. And this is in the context of validating hypotheses. If I want to validate a hypothesis, if it's low stakes or if I have no time or any number of factors, all I have a little bit of evidence. You know what, generally speaking is better than no evidence. But we need to recognize that that is anecdotal. There is descriptive evidence, which is, I mean, tons and tons of techniques across lots of different types of data. That's where I think a lot of analytics and a lot of research and insights lives. It is stronger evidence because we're looking with generally have more data. I think actually it's in the book, and this was credit to Joe, that descriptive evidence is when you got a whole bunch of anecdotes kind of gathered together. So it's kind of a continuum. It's stronger evidence. And then the third kind of the strongest evidence is scientific evidence, which is generally speaking, controlled experimentation in one, one form or another. And it's not like these are good versus bad. It is a strength versus weakness of the evidence. It goes to the, you know, criticality of the decision, but it goes to understanding that. You can't just say it goes back to those misconceptions just because I have a billion rows of data and I'm going to run a model on it that is still almost always not going to be as good as running a controlled experiment if I'm trying to actually find evidence for a causal link between two things. So we spend, we spend a whole chapter on descriptive evidence and a whole chapter on scientific. And there are books written on scientific evidence.
- [37:15]
Dr. Joe Sutherland
Is.
- [37:15]
Julie Hoyer
So what. What were some of your earlier, like, words you tried to use? Because I also feel like most of the time when I've seen some version of this, it's using words that you see on like data maturity curves. You know, it's. It's just like it does imply like, descriptive good, better, best, or it.
- [37:34]
Val Croll
Yeah, yeah, predictive.
- [37:35]
Julie Hoyer
It always has to get to predictive or predictive?
- [37:38]
Tim Wilson
Descriptive. Predictive. Prescriptive.
- [37:40]
Julie Hoyer
Yeah, all those, those terms that are much more like talking to the method itself. And I know these kind of are, but the. Your buckets that you ended up on are so much nicer and broad enough that you can't really get down in the dirt on, like, nitty gritty. Like someone can't, I feel like, come in and really be like, oh, my gosh, I completely disagree. So I thought it was very artful how you guys landed there. So what were some of the previous tries?
- [38:07]
Dr. Joe Sutherland
Well, number one, somebody can totally come in and disagree, and I fully expect them to do that. I welcome you to comment on my LinkedIn posts. As much as you care to disagree with us, it'll get the conversation going.
- [38:23]
Tim Wilson
If you're in such disagreement that you want to buy 100 copies of the book and burn them.
- [38:27]
Dr. Joe Sutherland
Yeah, if you wanted to go get 500 copies, you could do that. I mean, the earlier. I think the way we had thought about it before was actually like, in, in terms of, like, analysis rather than like the weight of the evidence. Like, no, the. It was kind of like. And this is why I like where we ended up because it was starting with this, you know, what methodologies can you use to answer your questions? Right. And it was kind of like, well, there's easy methodologies and there's hard ones. Like, that was kind of where we had started with it. But I think as the sort of picture in my head as we were like developing this was actually, I think we ended up with a cartoon in the book about this, with the hand scale. Right. It was kind of the scale which was like, well, actually there's this question that has to be answered and it has to be weighed against some sort of weight of evidence against it. And if it's a really heavy question, you have a lot of heavy evidence to. To come up against it. And that's what I think started to get us towards this idea of like, well, if it's just a light question, you just need a light amount. And what, what are the usual forms of light evidence? Well, it's usually just walking down the hallway and talking to your co workers, see if they're in a good mood or a bad mood. Right. It could be very simple stuff. And that was my memory. Like, the shift was going from the methodological thought process and mental model to. To thinking about it more fundamentally. And that's what I think gave us the elegance of it.
- [39:58]
Tim Wilson
It was kind of your. Yeah, it was like historical data analysis, research Primary and secondary and controlled experimentation. And I mean one of that, as we were going around and around trying to kick the tires on what we had, we had a whole debate around is secondary research. Joe was like, that's anecdotal. And I was like, what do you mean it could be like super robust secondary research. He was like, no, you got that in a study if you got access to the underlying data and, and you knew the research question they were trying to answer and you knew their methodology and that lined up with what you're trying to validate. Sure. But that never happens. Even like a scientific journal, secondary research, it is always one step removed. So I was like, yep, that's touche. You know, totally get that. So, so that's one where like primary research would fall into descriptive evidence. Unless, you know what if you do, if you do a small usability stuff study that's kind of anecdotal. So there's a, there's a little bit of a gray area, but I think that ramp of saying how strong, thinking of it as the strength of evidence, I mean ever since we kind of hit that point, I am using that word, I'm using that phrase a lot.
- [41:12]
Val Croll
Yeah.
- [41:13]
Julie Hoyer
Even after you said it, Joe, like that was a light bulb moment for me when you were like, yeah, it's not so much the methods, it's the, the weight of evidence. It's like, wow.
- [41:21]
Val Croll
It's also just so nicely put for your audience because it's incredibly practical too. Because if you're thinking I'm like a leader inside of an organization, I perhaps have like multiple analytics teams that I work in. Maybe some are embedded, maybe there's a center of excellence, then they're broken out by their specific functions. There's like the digital analytics team which suffer from performance, you know, and so if that's the construct in which you think about all of this, you might not understand how to.
- [41:48]
Dr. Joe Sutherland
Right.
- [41:48]
Val Croll
Size the evidence for the question or problem at hand. And so I think that this is going to be one of those sections that really connects with your audience. I think it was very nicely done. So thank you.
- [41:58]
Dr. Joe Sutherland
I have one more thing which is like, I do think a lot of the books and Tim actually deserves credit for the tone of an approach of the book as more of a fun, entertaining, interactive, very like down to earth tone. Like, you know, I think a lot of the scientific approaches can come across as super heavy handed and super duper. Like this is so full of firepower that you could never deal with it. And, and it's Meant to be very impressive. Right. And it's like in its methodological weight and the way that we've had a lot of fun with this book was thinking about like little simple examples. I mean, this, you know, you might go and be the vice president of analytics at Coca Cola or you might be the CEO of like, you know, Merck Pharmaceuticals or something. You could be any of these people. But on a day to day basis, you're not sitting on the top of a mountain with your hands on your hips being like, ha ha, you know, I mean, what you're doing is like you're going down the hall to talk to Michael and Catherine and they're grumpy because, you know, they discovered the snack room is no longer stocking their, you know, you know, it's, it's a much more down to earth experience. And I know, I'm really thankful that Tim enforced that on the book.
- [43:06]
Val Croll
Well, and on that note, because you did bring up interactive, I did want to bring up the little is it quizzes that you guys are doing at the end of each chapter. Like the performance measurement check in. I think that that's super fun. I think you guys should talk about that.
- [43:21]
Tim Wilson
Sure. This was, yeah, this was my brainchild and Joe's the one who actually built it. But because we, I mean, I think I come from pushing performance measurement and how. And we did an episode of the podcast around goals and KPIs and the two magic questions. And so part of what we're trying to do, it's actually useful was we asked the question, like, how do we measure the performance of, of a book? And you know, the, there's the easy metric, which is, well, how many did you sell? But we're not writing the book because we're trying to drive sales. Like we. So we applied the Chapter 5 is all about performance measurement. But. And you guys are both super familiar with the two magic questions of like, what are we trying to achieve with the book? And we actually said, what is our answer to that question? And we write that in the book and say we want to arm you with a clear and actionable framework and set of techniques for efficiently and effectively effectively getting more value from your data and analytics. And then, okay, well how are we going to decide if we've done that? And we're like, there's lots of ways we could measure that. But we said we should ask people. We want people on a chapter by chapter. And then for the book overall, we're going to ask them. So there is a analytics trw.com, analytics, the right way.com, it's analyticstrw.com but that's where the TRW is, has like an evaluation form. So at the end of every chapter we say, hey, help us measure the performance. We have a target set. We want a certain. And it's published, certain percentage of people to say that they somewhat agreed or strongly agree with. Two questions about the information and ideas presented gave me a new and better way to approach using data. And I expect to apply the information presented to the way I work with data in the next 90 days. So we, we said we will actually measure and when you click submit, you will see how the cumulative respondents to date perform against those targets. Which is kind of terrifying. But it also seems like, well, that's how, you know, if we did do a second edition of the book, we should know which the weakest chapters or which the least impactful chapters were. But so we're doing it kind of there's a meta one to say, yeah, if you think about it, you really do need to think one level beyond what metrics will be available to. What are we really trying to do and how could we best measure that? So yeah, I'm pretty.
- [45:50]
Julie Hoyer
And where on the ladder of evidence will that fall?
- [45:54]
Tim Wilson
Well, that's performance measurement. It's not validating a hypothesis. Right. So that's the. Oh, so that's true. It's just objectively measuring.
- [46:03]
Dr. Joe Sutherland
Yeah, because we're just trying to alert, we need to alert ourselves. The thing that I'm actually worried about.
- [46:11]
Val Croll
Oh, here's a great time to bring it up. I've got concerns.
- [46:19]
Dr. Joe Sutherland
I'm like, you know, and I did do some, you know, I mean, you know, you can go on this website, right. And it's like you could, I'm worried about like a botnet coming and like, you know, over just, you know, giving us a bunch of poor scores, you know. So look, if you come on the website and you see the scores are really low, a botnet got us.
- [46:42]
Tim Wilson
We've had some discussion about correcting for that. But this is, yeah, our assumption is this is not going to. There aren't going to be foreign actors saying, boy, if we can tank the performance measurement of this book, that's going to give us a global leg up. So. But who knows?
- [46:59]
Val Croll
I have a hypothesis about which chapter is going to score the highest on the actionability over the next 90 days. So maybe we should, we used to do our own little back of the napkin target setting, see if we can see how it lines up against real data in the future. Be real meta about it.
- [47:16]
Tim Wilson
You could actually.
- [47:17]
Dr. Joe Sutherland
The other fun thing about the website, if you do go on the website is we have some merch. We're actually not trying to make any money off this merch, but I think it's actually pretty funny. It's funny stuff. So if you're a real fan of the book, you can also get merch online. Printed T shirts, et cetera.
- [47:36]
Julie Hoyer
I was gonna say I'm getting myself a T shirt.
- [47:39]
Tim Wilson
Yeah. Book writing process. Joe makes. Joe makes the crack. Like, oh, go to this URL. It gets in a footnote is like a wisecrack. So then we're going through the process. I'm like, yeah, that's a nice draft of the site, Joe, but you did put store in the footnote. So.
- [47:54]
Dr. Joe Sutherland
And then that actually is what happened. We never intended to do it. And then we realized it'd be kind of. It's actually not a bad idea. So. Yeah.
- [48:04]
Val Croll
I love that. Well, we're going to have to move to rap pretty soon, but I guess. Is there any Last parting thoughts, Dr. Joe or Tim, that you want to share that you're really excited about your future readers being able to take away from analytics the right way?
- [48:17]
Dr. Joe Sutherland
You know, I. Look, I started programming in 1998, okay, in a language called Basic. I don't know how many of the readers will even, or the audience, the listeners will even know what basic is. But, but it was a very EAS to use programming language on Microsoft systems back in the day. And you know, I have seen over the last. How many, you know, 26, seven years, right? Like just things seem to get more and more complicated every, you know, it almost seems like it used to be so much. Maybe I'm just being nostalgic, but you know, everything from the documentation to the methodology, they've gotten more complicated. And I think that that's for no reason in a lot of ways. And I think that that really deprives people like, I think it deprives them of the opportunity to use all these great tools that we have because they, they, you know, not because they don't have access to them. I think that access has improved. But I do think that like the self imposed misunderstanding or feeling like they don't understand the complexity of these things, like almost is like a self deprivation of all the great tools that we have out there. And so my, my hope is just that the book kind of reopens that door, you know, in really simple and direct terms.
- [49:29]
Tim Wilson
And I'm gonna have to go get an advanced degree to self soothe from the fact that I also started programming and on Basic on Apple iic. But I just did the math. It was in, it was in 1980, 1985. So Apple got me there too.
- [49:48]
Dr. Joe Sutherland
Got me there.
- [49:50]
Tim Wilson
Writing with these kids these days, I tell you, was a little rough because he was. Some of the language he was dropping. But I mean, I hope.
- [50:02]
Dr. Joe Sutherland
I would.
- [50:03]
Tim Wilson
Die happy if there were people using some of the language in the book and finding it as a way for them to more like act with more confidence within their organizations. I mean that's fundamentally deeply believe this stuff is not so complicated that needs to be treated as a mystical black box that's so intimidating that I need magical AI to solve it. That there is so much fun and joy and hard creative thinking. And that is like the core of using analytics productively. We're still a few generations away before human creative thought isn't kind of at the core of that. So I'm hoping that there are readers who say, I get it. I now it's not, it's not a hard thing or a scary thing or a frustrating thing to collaborate with my analyst or to poke around in my dashboard because I know what I'm trying to do, why I'm trying to do it, and I have ideas and I can treat those ideas is hypotheses and think about how strong is the evidence I need to validate them. I can feel fine making a decision with very weak evidence because that's okay. You can't like, that's absolutely okay. What's not okay is to not realize that's what you're. You're doing. So, yeah, I guess I'm.
- [51:36]
Val Croll
I like it.
- [51:37]
Tim Wilson
Passionate about it.
- [51:40]
Julie Hoyer
A little.
- [51:42]
Val Croll
All right, so when does the book come out? Where can we find it?
- [51:45]
Dr. Joe Sutherland
It comes out end of January. Was it January 20th?
- [51:49]
Tim Wilson
Tomorrow. If somebody's listening to this pod, January 22nd, if they're listening to the podcast the day that it drops, then you can pre order now and you're effectively ordering it because it is available tomorrow on Amazon, on Walmart, Target, Barnes and Noble, wherever you get your books.
- [52:09]
Val Croll
Oh, you fancy.
- [52:12]
Tim Wilson
You can go to analytics, trw.com and get links to it. You can go to the Wiley, Wiley.com and order it there. It'll be out as an ebook a little bit later and actually it's coming out as an audiobook in about another month.
- [52:30]
Dr. Joe Sutherland
And so if you want to go and listen to the sweet, sweet stories of data and lull yourself to sleep or perhaps keep yourself busy in the car. You can do that.
- [52:41]
Tim Wilson
And Daniel Craig is reading it. No.
- [52:47]
Dr. Joe Sutherland
Actually no. There is a. It's a professionally trained like voice actor. Luckily it was not either of us.
- [52:54]
Tim Wilson
Yeah.
- [52:55]
Dr. Joe Sutherland
Because that would have been difficult.
- [52:57]
Julie Hoyer
I was hoping it would have been.
- [53:00]
Val Croll
I would have listened. Well, this has been such a fun little reunion. Talking about analytics the right way. Long time coming. Very excited for this. So thank you so much for joining us. Dr. Joe, it's been a pleasure.
- [53:12]
Dr. Joe Sutherland
My pleasure. Thank you for having me.
- [53:14]
Val Croll
And Tim, you know, thanks. Thanks for being here. We'll show you. Thank you to you. Even though you're co hosting, somebody had.
- [53:20]
Tim Wilson
Hit the record button. Yeah.
- [53:24]
Val Croll
Well, one of the things that we love to do is just to go around the horn and share a last call. Something that we think our listeners might be interested in. So Dr. Joe, you're our guest. Would you like to share your last call first?
- [53:36]
Dr. Joe Sutherland
So related and also unrelated. You know, I went down to, we have a camp Emory University as a campus in Oxford, Georgia. It's down in Newton County. And I did a quick presentation to their local chamber and I asked how many of you guys feel like you have reasonable facility with artificial intelligence technology such that you could use them in your business today. And it was a big room. Not one hand went up. And you know, I actually realized like, you know, we're all talking about it here and you know, this analytics audience we talk about all the time. Right. But not everybody has access to these tools. And so we went and raised money and started a basically workforce development outreach tour. And if you're interested in learning more about it and how to get involved, we, and you know, we also offer certifications and artificial intelligence, etc. Just go to aiandu georgia.com I know this is a national audience, but AI international.
- [54:34]
Val Croll
Georgia.global. global.
- [54:36]
Dr. Joe Sutherland
Sorry, it's a global audience.
- [54:37]
Tim Wilson
This is Georgia, the U.S. georgia. Not the country, this is the state.
- [54:41]
Val Croll
In the U.S. just clarify. Love it. That's a good one. All right, Julie, how about you? What's your last call?
- [54:49]
Julie Hoyer
My last call is actually a tip I got from actually all of our at least previous or current co worker Ricky Messick. One of our faves, he was telling Walt because I was sharing with him that I struggle to make it all the way through. Like listening to self help books. I was like, sometimes I just want them to get to the freaking point. I'm like, they say it so many ways to fill up pages. We've talked about this before. It's One of my shortcomings, I just cannot finish them. So he told me that he does this thing where he just puts the playback speed like close to 2, like 2x or maybe more. Because he found that when it's going really fast, you actually have to stay more focused on what they're saying. And you will like retain and take in the information instead of letting your mind wander. And he's like, and then you get through the book faster. So I have been trying that slowly. I'm not up to as fast as he listens to it, but I think.
- [55:45]
Tim Wilson
It works ourselves that for all of our listeners, for all of our listeners who listen to us on 1.5 or 2x, we'll tell us because that's. They really want to focus on it. They want to focus on the content of the show. Not. Not because they just want to get, get, get through it. I'm gonna tell myself, okay, but the.
- [56:01]
Julie Hoyer
Fact that they would listen to it all still says something.
- [56:04]
Dr. Joe Sutherland
One of our former co workers actually one time, you know, I missed a meeting and they recorded. It was like a, like a four hour meeting. And, and you know, our co worker goes, no, no, just go back and watch it. I said, oh, should I bill four hours to, you know, to watch the four hour meeting? She goes, no, just watch it at double speed. And so then I think if I watch it at half speed, do I get to Bill 8?
- [56:28]
Val Croll
Nice. That's good, that's good. All right, Tim, you got a last call for us?
- [56:37]
Tim Wilson
I do. It's trivial, but because I'm a sucker for getting a random data set and pursuing it a little too far. This was a while back I got it out of, I think it was out of Philip Bumps how to Read this Chart newsletter. But it's a guy named Colin Morris and he did this kind of deep dive. It's called compound pejoratives on Reddit, from butt face to wank, muffin, wank, puffin. And he basically took hooked, took compound. So to think like dumbass or you know, scumbag, where you have the two words and he went and kind of managed to pull, I don't know, like 20 of the front halves and 20 of the back halves. And then did like, started with just a little heat map of like, what's the like dumbass is the, is the most common occurrence. And you've got ones that you know are like a lib hat, like that's not really used, or a wank sucker. Like there are ones that. So you start to see like Ones that you're like, oh, you could use that. Like, but it's, it almost never shows up. And then you're like, well, that's cool. But then he, he wound up going deeper and deeper as to like, well, which, which like affixes have the most suffixes applied to them. Which suffixes have the most affixes applied to it? So it's quite, quite a bit of a, of a dive and it's, it's really just entertaining. There's nothing you can do with it other than come up with like, you know, oh, you know, you're a, you're a, you're a buttlord. So you wind up, you can't help it, like coming up with pejoratives. You're like, somebody said it. Yeah.
- [58:17]
Val Croll
This is the perfect last call for the explicit rating. Yes, it looks podcast. I love it.
- [58:23]
Tim Wilson
We had to get it there. What about you, Val?
- [58:26]
Dr. Joe Sutherland
Did we just get rated high? Is this like an R rated podcast now?
- [58:30]
Tim Wilson
Oh, it's always, it always was.
- [58:32]
Dr. Joe Sutherland
Yeah.
- [58:35]
Tim Wilson
I steered clear of some of the specifically R plus rated ones, but they're there. What about you, Val? What's your last call?
- [58:45]
Val Croll
Um, so mine, I wanted to keep it in the family. Search discovery, alum, slash, some current family. This is actually a podcast from Experiment Nation when Nick Murphy was a guest on in the summer of 2024 and it was all about building a learning library. And I've actually been sitting on this last call for a long time, so I'm really excited to share this one. If you don't know Nick, he is, he's. He's been a consultant for a couple years at, at Further, but he was an in house practitioner before that and he's incredibly pragmatic with his approach to consulting and helping organizations think about the power of experimentation and such a joy to work with him and his beautiful brain. But in this, he kind of walks through kind of like a base model for how you would think about repository of learnings. Because as we all know, that's the value, that's the reason you experiment, right, is to get smarter and make better decisions as we've touched upon today. So this is a way to make it something that everyone in your organization can access and query and search so it doesn't just live in a PowerPoint presentation on someone's drive. But yeah, he talks about how CROs are often thought of as the numbers go up wizards, which I nearly did a spit take on when he said that. That was so, so funny. But it's good. It's a really great discussion and definitely walked away with some some good tidbits of and some sound bites that I can share with my clients. So definitely recommend that one.
- [60:12]
Tim Wilson
Awesome.
- [60:13]
Dr. Joe Sutherland
Woo.
- [60:13]
Julie Hoyer
Go Nick.
- [60:17]
Val Croll
All right, so this has been an awesome discussion, so I'm so thankful that we were able to dive into analytics the right way with we got both authors on our episode today for the groundbreaking launch of the book, but no show would be complete if we didn't throw a huge shout out to Josh Crowhurst, our producer who does a lot of that work behind the scenes. So thank you Josh. And as always listeners, we would love to hear from you. So you can find us in a couple different places. The Measure Chat, slack group, our LinkedIn page. You can also shoot us an email at contactnalyticshour IO or if you've been listening the past couple episodes, you will know that you can visit us in the comment section of our YouTube channel. So it's another place you can grab and listen to this episode. So feel free to reach out. We'd love to hear from you. So with that, I know I can speak for all of my co hosts, Julie and Tim, when I say no matter what step of the ladder of evidence you are on, keep analyzing.
- [61:18]
Dr. Joe Sutherland
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 Measure Chat Slack Group. Music for the podcast by Josh Crowhurst.
- [61:37]
Tim Wilson
Smart guys wanted to fit in, so.
- [61:39]
Dr. Joe Sutherland
They made up a term called analytics.
- [61:41]
Val Croll
Analytics don't work.
- [61:43]
Dr. Joe Sutherland
Do the analytics say go for it no matter who's going for it.
- [61:46]
Val Croll
So if you and I were on.
- [61:47]
Dr. Joe Sutherland
The field, the analytics say go for it.
- [61:49]
Val Croll
It's the stupidest, laziest, lamest thing I've ever heard for reasoning in competition.
- [61:58]
Dr. Joe Sutherland
So I'm officially. I've been on now three talks. That's. That's a. This is my third talk on the show.
- [62:05]
Tim Wilson
We talked about natural language processing.
- [62:08]
Dr. Joe Sutherland
That's right. Nlp Attribution without cookies. And this is the third one.
- [62:15]
Julie Hoyer
Ding, ding, ding.
- [62:16]
Val Croll
Helm said to Katie Bauer that if you do five, you get the jacket like snl. So.
- [62:22]
Dr. Joe Sutherland
Well, you know what you should tell them? The audience. You should say, joe's in the running for the SNL jacket.
- [62:31]
Julie Hoyer
That'd be fun to design a jacket, though. An aph.
- [62:36]
Dr. Joe Sutherland
I would wear it everywhere to the detriment of my children and wife.
- [62:39]
Tim Wilson
So.
- [62:44]
Dr. Joe Sutherland
I actually was gonna. I was gonna send one to Goose because Goose, like sort of Bright. Me and Tim together, like, in life. And so I just feel like he. I wanted to just send him one. Just be like, you know what? In a way, you. I don't know what is. Yeah. What is the protocol on that? If you sign a book and then gift it to somebody, is it, like, kind of douchey? Like you have to just make sure.
- [63:08]
Val Croll
You do, like, a little red lipstick kiss by it, like, exo. Tim's, like, unamused.
- [63:19]
Tim Wilson
I. Yeah, well, it's more that, like, as we were writing the acknowledgments, Joe's the one who thought to call out Goose. That was, I think, under his. Then there. We then figured out we could have, like a joint acknowledgement section. So that was a good, good, good catch. Good co author stuff. I mean, now he gushes a lot about Sarah in his acknowledgments. Julie gets no mention of my acknowledgments.
- [63:50]
Val Croll
But you dedicated it to her.
- [63:53]
Tim Wilson
Yeah, yeah.
- [63:56]
Dr. Joe Sutherland
It's not a competition too. You don't have to love your wife more than I love mine.
- [64:07]
Val Croll
Julie, I'm kind of disappointed that you don't have a little bit more empathy for me that I have to do this opening. You're like, yeah, I saw. What about it?
- [64:19]
Tim Wilson
Yeah, Joe, this is a first. This is a first for Val.
- [64:23]
Dr. Joe Sutherland
You will suffer. This is like. This is Val's first time doing the intro.
- [64:28]
Val Croll
Yes. And the closing.
- [64:31]
Tim Wilson
Oh, yeah.
- [64:31]
Julie Hoyer
No, it is big. It really is big. I didn't give enough appreciation. Appreciation to that because I would not be mentally prepared for it. So I do greatly feel my first.
- [64:42]
Val Croll
Thought when I wake up. First thought before I go to bed. Still not ready.
- [64:45]
Dr. Joe Sutherland
So.
- [64:47]
Tim Wilson
I'm literally sitting on a. Looking at screens of three people who all rise to the occasion and come across so much more polished and coherent than I do in any situation. I'm feeling. Feeling great about you opening it.
- [65:02]
Val Croll
Okay, well, I'll have to borrow some of your confidence, like I said before, but are we feeling ready to start?
- [65:09]
Tim Wilson
Let's do it.
- [65:13]
Val Croll
Power pose, power pose.
- [65:17]
Julie Hoyer
My favorite is when you said that and you lean away. It was like.
- [65:20]
Dr. Joe Sutherland
Yeah, like the microphone.
- [65:22]
Tim Wilson
Power pose, Power pose, power pose.
- [65:27]
Val Croll
I was definitely. If Tim had The, like the 5, 4, 3, 2, 1, countdown on, I was just gonna start, like, no matter who was talking, I was be like, hey, everyone, and welcome to the Analytics Power hour. My name is Val Crow, and I definitely didn't just take one, but two nervous dumps before I got on this episode tonight.
- [65:47]
Tim Wilson
Josh.
- [65:56]
Dr. Joe Sutherland
It'S. It's out there.
- [66:03]
Val Croll
Okay.
- [66:07]
Julie Hoyer
Rock flag. And you can't eliminate uncertainty.