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[Announcer]: Welcome to the Analytics Power Hour.

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[Announcer]: Analytics topics covered conversationally and sometimes with explicit language.

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[Tim Wilson]: Hi, everyone.

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[Tim Wilson]: Welcome to the Analytics Power Hour.

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[Tim Wilson]: This is episode number 307.

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[Tim Wilson]: I'm Tim Wilson, and I'm joined today by my co-hosts Moe Kiss and Julie Hoyer.

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[Tim Wilson]: Are you two ready to dive into a discussion about how and where AI can be effectively

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[Tim Wilson]: employed when doing data visualization and data storytelling?

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[Tim Wilson]: Oh, absolutely.

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[Tim Wilson]: Yeah.

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[Tim Wilson]: And maybe where it is best left out of the process.

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[Tim Wilson]: All right.

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[Tim Wilson]: Well, for this discussion, I am super excited that we have brought on one of my absolute

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[Tim Wilson]: professional heroes.

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[Tim Wilson]: Cole Nussbaumer Knaflic is the founder and CEO of Storytelling with Data and the author

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[Tim Wilson]: of five bestselling books.

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[Tim Wilson]: Including?

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[Tim Wilson]: Storytelling with Data, a data visualization guide for business professionals, which I

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[Tim Wilson]: have personally recommended like in over 20 different trainings and presentations.

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[Tim Wilson]: She's also written a children's book, Daphne Draws Data, which I think has gotten Val's

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[Tim Wilson]: four-year-old drawing all sorts of charts.

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[Tim Wilson]: Cole has spent the last 15 years teaching people around the world how to turn messy

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[Tim Wilson]: graphs into clear, compelling stories.

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[Tim Wilson]: And today she is our guest.

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[Tim Wilson]: So welcome to the show, Cole.

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[Cole Nussbaumer Knaflic]: Hi, Tim.

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[Cole Nussbaumer Knaflic]: It's so great to be here.

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[Tim Wilson]: Awesome.

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[Tim Wilson]: So I think maybe a good place to start might be to just have you kind of reflect or look

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[Tim Wilson]: back on your own transition from kind of the pre-AI, not the semantically 1960s, but like,

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[Tim Wilson]: you know, when most of us kind of, it came on the scene from pre-AI to kind of where

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[Tim Wilson]: you are today.

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[Tim Wilson]: Like, were you skeptical or dismissive or did you like immediately see how you'd be

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[Tim Wilson]: able to incorporate it?

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[Tim Wilson]: Like, what was your experience in that transition?

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[Cole Nussbaumer Knaflic]: I was absolutely not.

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[Cole Nussbaumer Knaflic]: I was not an early adopter.

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[Cole Nussbaumer Knaflic]: Skeptical is probably a good word for it.

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[Cole Nussbaumer Knaflic]: I joked more than once that I planned to retire before I had to figure out how AI was going

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[Cole Nussbaumer Knaflic]: to impact data storytelling, because it clearly it's going to change how we work.

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[Cole Nussbaumer Knaflic]: But it took me some time to get on board.

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[Cole Nussbaumer Knaflic]: And actually, it wasn't really even a conscious decision, I guess.

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[Cole Nussbaumer Knaflic]: I had been approached by Canva to speak at their national or their...

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[Cole Nussbaumer Knaflic]: annual client conference and didn't realize until after I'd already said yes, that the

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[Cole Nussbaumer Knaflic]: topic was data storytelling in the age of AI, which meant and that was April of 2026.

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[Cole Nussbaumer Knaflic]: So that meant I needed to spend some time in AI and start using it for data storytelling

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[Cole Nussbaumer Knaflic]: and understanding where it makes sense and where it maybe doesn't so that I can start

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[Cole Nussbaumer Knaflic]: speaking about these things.

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[Cole Nussbaumer Knaflic]: And I kind of kicked myself a little bit.

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[Cole Nussbaumer Knaflic]: At first for saying yes to the conference.

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[Cole Nussbaumer Knaflic]: But then once I started using AI, I started kicking myself for waiting so long because it

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[Cole Nussbaumer Knaflic]: definitely is one of those things that the more you can play with it in a thoughtful way, the more

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[Cole Nussbaumer Knaflic]: you can see where all of the opportunities are, also where the dangers are.

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[Cole Nussbaumer Knaflic]: So it continues to be this simultaneously fascinating and terrifying tool from my perspective.

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[Cole Nussbaumer Knaflic]: But we're very much trying to...

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[Cole Nussbaumer Knaflic]: to teach people how to use the fascinating parts of it and really finding that there are ways it can accelerate and

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[Cole Nussbaumer Knaflic]: strengthen, make more robust the way that we communicate with data in a lot of different settings, which I think we'll get a lot more into today.

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[Moe Kiss]: Can I just ask, watching some of the training that you've done, which is such an incredible resource and I highly recommend it to folks, one of the things that I observed, you know, there were some improvements to graphs and things like that.

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[Moe Kiss]: As you're working through a problem with AI.

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[Moe Kiss]: And I remember looking at one example and it took one of your colleagues like a few iterations with AI to get it right.

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[Moe Kiss]: And I remember looking at that chart and being like Cole or also my sister or Tim or like any of the people that I know who are really good at database, like that would have been their first draft.

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[Moe Kiss]: And it took like maybe four or five prompts to get it right.

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[Moe Kiss]: So I'm really curious, like as you started exploring, like what got you over that hump?

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[Moe Kiss]: Because I feel like that hump is the like hardest bit.

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[Cole Nussbaumer Knaflic]: I think that that is...

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[Cole Nussbaumer Knaflic]: The way that different people at different skill levels use AI should look different.

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[Cole Nussbaumer Knaflic]: And I think one of the things that AI can do, it was a colleague of mine, Alex, who pointed this out, that it basically can raise the floor on those that are not skilled, let's say, in a certain area.

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[Cole Nussbaumer Knaflic]: So for the person who isn't proficient making graphs, hasn't spent a ton of time.

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[Cole Nussbaumer Knaflic]: And actually, I encountered this recently.

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[Cole Nussbaumer Knaflic]: We're in the middle of a five post series on using...

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[Cole Nussbaumer Knaflic]: AI in the context of data storytelling with really the idea being that you want it to be a partner and use it where it's going to help and not hinder.

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[Cole Nussbaumer Knaflic]: But the recent one I did was on choosing an effective graph.

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[Cole Nussbaumer Knaflic]: And I struggled with this one so much because I thought I was going to do it originally with Copilot.

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[Cole Nussbaumer Knaflic]: And the output I got, even after a lot of time and back and forth, was so awful that I almost scrapped the whole thing.

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[Cole Nussbaumer Knaflic]: But instead, I started testing across some different...

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[Cole Nussbaumer Knaflic]: tools and also across paid versus unpaid levels.

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[Cole Nussbaumer Knaflic]: And so one thing I didn't realize is just how different the results you get from a paid subscription are versus the off the shelf sort of thing.

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[Cole Nussbaumer Knaflic]: So I will say anyone who is using AI for work or when it is anything of importance, make sure you've got a subscription, a paid version.

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[Cole Nussbaumer Knaflic]: If it's what your company is using, that probably makes sense.

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[Cole Nussbaumer Knaflic]: Maybe with the...

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[Cole Nussbaumer Knaflic]: exception of Copilot, but we can talk more about that.

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[Cole Nussbaumer Knaflic]: But I went back and forth and I was eventually able to get some help with the post from AI in ways that were useful to be able to use it as a teaching construct.

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[Cole Nussbaumer Knaflic]: But I then I shared it with some of my team for feedback before finalizing and I shared the struggle that I had as well.

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[Cole Nussbaumer Knaflic]: And they said, well, yeah, but that's because it's you.

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[Cole Nussbaumer Knaflic]: Like you look at a data set and you've done this so many times, you already know what graphical form might work or where to start.

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[Cole Nussbaumer Knaflic]: And so for me, it was trying to take a step back that felt really gross.

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[Cole Nussbaumer Knaflic]: But for the person who doesn't have those years and those reps of experience choosing graphs for a given message or a given data set or a given audience, that's where things get interesting.

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[Cole Nussbaumer Knaflic]: But I think still the prompt shouldn't be give me the thing I want.

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[Cole Nussbaumer Knaflic]: The prompt should be help me look at some options and assess which are going to work in my scenario.

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[Cole Nussbaumer Knaflic]: Help me understand what the trade offs of each one are.

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[Cole Nussbaumer Knaflic]: Yeah.

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[Cole Nussbaumer Knaflic]: Got it.

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[Cole Nussbaumer Knaflic]: Great.

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[Cole Nussbaumer Knaflic]: Bye.

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[Cole Nussbaumer Knaflic]: Bye.

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[Cole Nussbaumer Knaflic]: Bye.

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[Cole Nussbaumer Knaflic]: Bye.

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[Cole Nussbaumer Knaflic]: of those are. Let me see them, mock them up so that I can see if I understand them or if I would

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[Cole Nussbaumer Knaflic]: feel comfortable explaining them or recreating them in my tool. So I think part of it's a

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[Cole Nussbaumer Knaflic]: different way to think about prompting and not be prompting for the final output immediately,

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[Cole Nussbaumer Knaflic]: because you're going to be disappointed when you do that. But really thinking of the way you use

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[Cole Nussbaumer Knaflic]: AI and the data storytelling workflow as a partner, as a sounding board, as somebody you

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[Cole Nussbaumer Knaflic]: can brainstorm with or get feedback on to check your grammar, to double check your math, to do all

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[Cole Nussbaumer Knaflic]: of these things that would take a person a lot of time and the results might be inconsistent.

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[Cole Nussbaumer Knaflic]: You can get beautifully efficient and consistent feedback in those areas from AI. I think that's

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[Cole Nussbaumer Knaflic]: where we can think about using it smartly. And I think of it when it comes to any sort of analysis,

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[Cole Nussbaumer Knaflic]: you want to think of it almost like your junior analyst who knows enough to be

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[Cole Nussbaumer Knaflic]: dangerous to be able to do the right thing. And I think of it when it comes to any sort of

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[Cole Nussbaumer Knaflic]: but not so much that they're going to be making the right decision all the time. And so you want

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[Cole Nussbaumer Knaflic]: to bring that lens in to the things that it gives you and be skeptical in order to have the right

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[Cole Nussbaumer Knaflic]: sort of back and forth to come up with something good. And I think that when you take the time to

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[Cole Nussbaumer Knaflic]: do that, I've seen the instances when I've worked with it myself, that I can come up with something

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[Cole Nussbaumer Knaflic]: partnering with AI in a smart and thoughtful way that is certainly better than what AI

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[Cole Nussbaumer Knaflic]: would have done on its own, but also better than what I would have done on my own, because it's

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[Cole Nussbaumer Knaflic]: caused me to think in ways that I didn't on my own. And I think that's where the power is.

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[Tim Wilson]: I like the prompting part. I just, I get nervous. And this is kind of, I think where Moe was as well,

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[Tim Wilson]: that it's kind of tough to take your expertise out of it. That if you,

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[Tim Wilson]: if you give it a prompt and it comes back and you're like, that's not good where I,

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[Tim Wilson]: I get concerned is pre AI. I would watch analysts and business users. They're so

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[Tim Wilson]: caught up in just getting the data displayed accurately that as soon as they get to some

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[Tim Wilson]: first chart, they're like, this is good. And then they, over time have to develop that. Well,

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[Tim Wilson]: yeah, but that's not effective. Like it's, it's accurate. And I worry that without having built

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[Tim Wilson]: up some of that experience, like doing the critiquing that you're like, it's so quick to say,

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[Tim Wilson]: and they'll get caught up in saying, is it accurately representing it? Cause I think even

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[Tim Wilson]: Simon on the, uh, the YouTube, um, video called that out too, that it's like, oh, you get caught

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[Cole Nussbaumer Knaflic]: up in, is it accurate and not, and not stepping back and saying, is it good? Is it effective?

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[Cole Nussbaumer Knaflic]: And Tim, I definitely don't disagree with the idea that the human should learn the foundation.

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[Cole Nussbaumer Knaflic]: I don't, the part I get very afraid for is, you know, you take somebody who has experience,

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[Cole Nussbaumer Knaflic]: then they can partner.

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[Cole Nussbaumer Knaflic]: With AI in ways that make everything stronger. You take somebody without experience though. And

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[Cole Nussbaumer Knaflic]: I think anybody who's worked with AI has gone through where, you know, the first thing it gives

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[Cole Nussbaumer Knaflic]: back or the thing it gets back, it looks shiny. And if you look at it quickly, you're like, oh,

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[Cole Nussbaumer Knaflic]: this is great. This takes me so much time, but then you start looking more closely and like,

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[Cole Nussbaumer Knaflic]: oh, but no, I would have done this differently. And you know, oh, this is inconsistent. And oh,

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[Cole Nussbaumer Knaflic]: that data is outright wrong or whatever the case is. And so the human having the foundation,

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[Cole Nussbaumer Knaflic]: is essential to be able to direct AI, whether it's doing things for you or acting more like a

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[Cole Nussbaumer Knaflic]: thought partner or brainstorming partner to be able to do those things more effectively.

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[Cole Nussbaumer Knaflic]: Jumping straight to AI without the foundation is a truly terrifying thing.

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[Michael Helbling]: Tim, we need everyone to mark their calendars for November 10th for a very special free

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[Michael Helbling]: conference from Stape. You have my attention.

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[Michael Helbling]: Stape is hosting Stape Reads.

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[Michael Helbling]: They're a free virtual event for technical marketers, analysts,

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[Michael Helbling]: and server-side tracking practitioners. And this isn't six hours of product demos.

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[Michael Helbling]: Nope. There are 18 practical sessions with speakers, including Simo Ahava,

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[Michael Helbling]: Julius Fedorovicius from Analytics Mania, Juliana Jackson, and more.

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[Tim Wilson]: Oh, that's a pretty serious lineup. I see many previous guests of the Analytics Power Hour on

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[Michael Helbling]: the speakers list. Yeah, absolutely. There's two different tracks. You can move between product

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[Michael Helbling]: focus sessions and the analytics power hour. And you can also do a lot of different things.

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[Tim Wilson]: And broader strategy. Plus live Q&A, some swag, and even a certificate of attendance if LinkedIn

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[Michael Helbling]: credentials are your thing. Oh, yeah. And did we mention it's free?

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[Michael Helbling]: We did, but it's worth mentioning again. Click the link in the show description and

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[Michael Helbling]: register for Stape Relay, November 10th. You don't want to miss it.

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[Michael Helbling]: Michael, what's the most annoying thing about working with AI?

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[Michael Helbling]: Besides it confidently explaining my own business back to me incorrectly?

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[Tim Wilson]: Well, that was fair, but I was thinking memory.

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[Michael Helbling]: Oh, yeah. Every new chat is like meeting a very smart co-worker who got hit on the head

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[Tim Wilson]: over the weekend. That's what Prism from Ask-Y is trying to fix. Its memory portal lets you

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[Tim Wilson]: actually manage what your agents know about your data and your business.

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[Tim Wilson]: Manage it how? You can search it. You can tune it. You can endorse what's right,

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[Tim Wilson]: reject what's wrong, even adjust it in plain English. So if the AI thinks customer means

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[Michael Helbling]: anyone with an email address, I can say absolutely not, Kevin. Or George or Fred. Exactly. You control

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[Michael Helbling]: the knowledge layer and you can manage it. And that's what we're doing.

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[Tim Wilson]: And your users benefit from that context every time they ask a question.

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[Michael Helbling]: Oh, which means less re-explaining what revenue means, which table is correct,

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[Michael Helbling]: and why Q2 has that weird little situation.

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[Michael Helbling]: Your team gets the benefit of curated context without becoming professional AI babysitters.

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[Michael Helbling]: Finally, institutional memory without having to ask, who remembers why we did this?

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[Tim Wilson]: Want to try it? Click the link in the show description and sign up for the Ask-Y

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[Tim Wilson]: waitlist. Use code APH and the link will take you to the link.

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[Tim Wilson]: And they'll move you to the top of the list.

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[Michael Helbling]: Give your AI some memory, preferably better than ours.

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[Moe Kiss]: So I'm going to be full disclosure. Often when I'm doing a data viz using AI,

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[Moe Kiss]: I will literally be like, you're Cole Knaflic. You're a data visualization expert. That is like

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[Cole Nussbaumer Knaflic]: part of my prompt. I tell it that that's who I am and it gives me better results when I do that.

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[Tim Wilson]: Okay. But then I want to give the ability to tee off because I said, I was like, oh,

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[Tim Wilson]: Cole said she did that. So I'm going to see if I can make a skill in Claude. And it did.

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[Tim Wilson]: But that's different.

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[Tim Wilson]: And I haven't really tried it.

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[Tim Wilson]: And now I'm ready for Cole to just take a big old swing like, no!

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[Cole Nussbaumer Knaflic]: I was trying to understand why that made me so uncomfortable because I like the idea of it. My

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[Cole Nussbaumer Knaflic]: first thing was like, oh, that's really cool, right? You take storytelling with data and you

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[Cole Nussbaumer Knaflic]: just kind of give it to AI and then what you get should be great, right? I don't know. Something

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[Cole Nussbaumer Knaflic]: was working the wrong way with that.

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[Moe Kiss]: It feels icky also though because you're not getting credit for your work. That to me feels

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[Moe Kiss]: icky. Your work is being used.

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[Moe Kiss]: You mean my work?

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[Cole Nussbaumer Knaflic]: Yes. But that's a whole separate conversation.

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[Moe Kiss]: That part.

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[Cole Nussbaumer Knaflic]: Yeah. That part I actually am. I mean, I probably should care about that, but I'm less concerned

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[Cole Nussbaumer Knaflic]: by. My view is the more that it's out there and that means people are doing better things. Like

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[Cole Nussbaumer Knaflic]: I'm good with that outcome. I think what gave me pause was the skill thing for Claude is much more,

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[Cole Nussbaumer Knaflic]: here are rules. Claude, go apply these rules. Whereas I don't think that's the goal. And that

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[Cole Nussbaumer Knaflic]: runs the risk of really taking it.

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[Cole Nussbaumer Knaflic]: Yeah.

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[Cole Nussbaumer Knaflic]: Yeah. That's what I think is really important. Because if you don't apply those rules, then

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[Cole Nussbaumer Knaflic]: you're just taking the thinking out of it. And even the responsibility out of it. Like, oh, I gave Claude

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[Cole Nussbaumer Knaflic]: the thing and Claude did this. So Claude's wrong, not me. Like, oh, it's not going to work that way. But I

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[Cole Nussbaumer Knaflic]: think more than that, it's the human who needs the foundation because the goal isn't to give rules.

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[Cole Nussbaumer Knaflic]: The goal is that the human understands things, understands the principles, and then can understand

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[Cole Nussbaumer Knaflic]: when and how to flex those.

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[Cole Nussbaumer Knaflic]: than with AI. It's not AI go do the thing. That might be fine for routine tasks or things that

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[Cole Nussbaumer Knaflic]: can be very prescriptive. But I think the magic when a data visualization is striking and effective

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[Cole Nussbaumer Knaflic]: or when a data story is, you know, when there's that magic, that power is because a person has

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[Cole Nussbaumer Knaflic]: looked at that and they've decided, when do we do things in the way that, you know, that we

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[Cole Nussbaumer Knaflic]: should? When do we follow the principles and when do we not? And why does that make sense in this

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[Cole Nussbaumer Knaflic]: case? And how do we make those things balance in a way that's going to make the outcome that we want

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[Cole Nussbaumer Knaflic]: more likely than it otherwise would have been? And when people come to workshops or when people

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[Cole Nussbaumer Knaflic]: are early learning these skills, they often are asking for rules. They're asking for checklists,

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[Cole Nussbaumer Knaflic]: which I understand, but it's not the right spirit of things. Or at least you've got to learn maybe

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[Cole Nussbaumer Knaflic]: the rules and the checklist, but then you've got to move beyond that.

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[Cole Nussbaumer Knaflic]: Because it's the knowing the ground rules and then knowing when to flex that I think is when

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[Cole Nussbaumer Knaflic]: the good stuff happens. And if we go straight to a tool and we say, here are the rules,

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[Cole Nussbaumer Knaflic]: all of that runs the risk of getting mixed, missed.

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[Tim Wilson]: So I'm, I feel like I want to get stronger on that because I, that feels right. I will just

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[Tim Wilson]: to the skills credit, it did step number one was understand the context. And before designing

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[Tim Wilson]: anything explicitly ask, who's the audience? What do they need? How will it be consumed?

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[Tim Wilson]: So it was like the skill wasn't saying take it and just run with it. It was going to kind of

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[Tim Wilson]: push back. So that feels like there's a little bit of that. The skill is saying you can't just

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[Tim Wilson]: apply rules. You got to get more. I still am not feeling great about it for everything you just

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[Moe Kiss]: said, but. But so I guess the nut, oh, I don't know the crux of the problem though,

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[Moe Kiss]: that I'm trying to understand previously we would have a slide and I have this a lot where it has

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[Moe Kiss]: a very strong sense of where we want to go and where we want to go and where we want to go and

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[Moe Kiss]: very shitty, shitty, shitty data visualization on it.

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[Moe Kiss]: And I feel sick to my stomach.

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[Moe Kiss]: And still, despite managing a big team,

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[Moe Kiss]: I end up fixing people's slides.

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[Moe Kiss]: Or I mean, I do go back to them and be like,

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[Moe Kiss]: you need to fix your slide and here's why.

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[Moe Kiss]: I feel like these are the same people

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[Moe Kiss]: that are going to be writing prompts like this

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[Moe Kiss]: or skills like this

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[Moe Kiss]: or doing the going back and forth with AI, right?

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[Moe Kiss]: Like you still need to care

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[Moe Kiss]: and understand data visualization to your point, Cole.

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[Moe Kiss]: And I suppose the thing I'm thinking about is,

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[Moe Kiss]: and I'll come full circle on this

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[Moe Kiss]: at the end of the episode

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[Moe Kiss]: when I get to my last call,

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[Moe Kiss]: but it's like people are using AI more for data.

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[Moe Kiss]: But then it's like,

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[Moe Kiss]: how do we still get them to care

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[Moe Kiss]: about investing that time

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[Moe Kiss]: in the visualization and the data story

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[Moe Kiss]: that has to accompany it

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[Moe Kiss]: versus the shitty first draft you get back?

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[Moe Kiss]: And it's like, from an organizational perspective,

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[Moe Kiss]: how do you still encourage that

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[Moe Kiss]: and build that into the AI workflow?

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[Cole Nussbaumer Knaflic]: In my experience, the way that that works well

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[Cole Nussbaumer Knaflic]: is you get some part of the organization

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[Cole Nussbaumer Knaflic]: or you get some individuals who do care

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[Cole Nussbaumer Knaflic]: and who are spending the time

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[Cole Nussbaumer Knaflic]: and you get them the resources that they need

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[Cole Nussbaumer Knaflic]: to continue to spend time

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[Cole Nussbaumer Knaflic]: and get better doing those things

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[Cole Nussbaumer Knaflic]: so that over time you can see the efficacy

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[Cole Nussbaumer Knaflic]: of when graphs are designed thoughtfully

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[Cole Nussbaumer Knaflic]: and when PowerPoints aren't just thrown together.

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[Cole Nussbaumer Knaflic]: There's thought in the experience

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[Cole Nussbaumer Knaflic]: or the journey you take your audience on.

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[Cole Nussbaumer Knaflic]: And when you can see the reaction

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[Cole Nussbaumer Knaflic]: and not only reaction,

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[Cole Nussbaumer Knaflic]: but the change,

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[Cole Nussbaumer Knaflic]: or the decision that those things drive,

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[Cole Nussbaumer Knaflic]: that then that can start to amplify

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[Cole Nussbaumer Knaflic]: in a really nice way.

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[Cole Nussbaumer Knaflic]: Because now it's not somebody top down saying,

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[Cole Nussbaumer Knaflic]: team, you need to learn these skills.

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[Cole Nussbaumer Knaflic]: It's check out how this is effective.

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[Cole Nussbaumer Knaflic]: And by the way, this is a skill we can all develop.

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[Cole Nussbaumer Knaflic]: We can all get better at this.

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[Cole Nussbaumer Knaflic]: And here's what that looks like.

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[Cole Nussbaumer Knaflic]: And by the way, 90% of it is out of AI.

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[Cole Nussbaumer Knaflic]: Oftentimes the thing that is most helpful

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[Cole Nussbaumer Knaflic]: in those instances,

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[Cole Nussbaumer Knaflic]: and I think it's,

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[Cole Nussbaumer Knaflic]: is things that people are the least likely to do

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[Cole Nussbaumer Knaflic]: because they don't necessarily feel productive

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[Cole Nussbaumer Knaflic]: because they involve archaic tools like pencils and paper,

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[Cole Nussbaumer Knaflic]: but really stepping away from,

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[Cole Nussbaumer Knaflic]: Moe in your example, step away from the data, right?

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[Cole Nussbaumer Knaflic]: You've done the data.

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[Cole Nussbaumer Knaflic]: Yeah, Tim's holding up a page of sketches.

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[Cole Nussbaumer Knaflic]: I love it, but get out of the data

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[Cole Nussbaumer Knaflic]: and think about your audience and what you now know

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[Cole Nussbaumer Knaflic]: and how you can turn that into something

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[Cole Nussbaumer Knaflic]: that's going to land with them and resonate with them.

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[Cole Nussbaumer Knaflic]: And what other information needs to be brought in

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[Cole Nussbaumer Knaflic]: so that they'll be on board?

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[Cole Nussbaumer Knaflic]: How do you take them through that to,

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[Cole Nussbaumer Knaflic]: in a way that's going to line you up for success?

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[Cole Nussbaumer Knaflic]: And when you do those things in an analog way,

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[Cole Nussbaumer Knaflic]: there is friction and it can feel bad.

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[Cole Nussbaumer Knaflic]: It slows you down,

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[Cole Nussbaumer Knaflic]: but that slowness is what makes it effective

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[Cole Nussbaumer Knaflic]: because then once you've got a plan in place,

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[Cole Nussbaumer Knaflic]: whether you spent time getting really succinct

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[Cole Nussbaumer Knaflic]: on your message,

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[Cole Nussbaumer Knaflic]: or you might've spent time storyboarding,

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[Cole Nussbaumer Knaflic]: looking at your email,

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[Cole Nussbaumer Knaflic]: it looked like a storyboard maybe that Tim showed,

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[Cole Nussbaumer Knaflic]: and you're vetting what potential content could look like

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[Cole Nussbaumer Knaflic]: and the flow,

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[Cole Nussbaumer Knaflic]: you're maybe sketching what graphs could look like.

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[Cole Nussbaumer Knaflic]: Though I do think that's an area

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[Cole Nussbaumer Knaflic]: that AI can help expedite for us.

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[Cole Nussbaumer Knaflic]: But when you spend that time thinking upfront

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[Cole Nussbaumer Knaflic]: and you introduce friction on purpose and with purpose,

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[Cole Nussbaumer Knaflic]: that actually makes the whole rest of the process

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[Cole Nussbaumer Knaflic]: much more efficient.

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[Cole Nussbaumer Knaflic]: And if you invest the time doing that upfront,

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[Cole Nussbaumer Knaflic]: these like human things,

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[Cole Nussbaumer Knaflic]: then you're going to be able to do a lot more.

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[Cole Nussbaumer Knaflic]: You're in a position where you could probably pull AI in

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[Cole Nussbaumer Knaflic]: and use it in ways that'll help make the other parts

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[Cole Nussbaumer Knaflic]: of the process more efficient.

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[Cole Nussbaumer Knaflic]: You don't have to.

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[Cole Nussbaumer Knaflic]: And as we've talked about somebody who's good at this stuff,

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[Cole Nussbaumer Knaflic]: they're going to be faster doing it on their own anyway.

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[Cole Nussbaumer Knaflic]: Or they can look for points where there's friction

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[Cole Nussbaumer Knaflic]: of the not great type, right?

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[Cole Nussbaumer Knaflic]: Friction like, you have to manually enter all of this data

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[Cole Nussbaumer Knaflic]: into from one tool to the other,

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[Cole Nussbaumer Knaflic]: or recreate the same graph 20 times.

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[Cole Nussbaumer Knaflic]: Like those sorts of things AI can do really well.

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[Cole Nussbaumer Knaflic]: Careful, still check your data.

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[Cole Nussbaumer Knaflic]: But there was an article that my colleague Alex

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[Cole Nussbaumer Knaflic]: posted on the blog recently.

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[Cole Nussbaumer Knaflic]: And actually, this was one of the early uses of AI

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[Cole Nussbaumer Knaflic]: for making slides that I saw where my mind was kind of blown

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[Cole Nussbaumer Knaflic]: and I was thinking, wow, this could change the way we work

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[Cole Nussbaumer Knaflic]: immediately and forever if it's really this good.

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[Cole Nussbaumer Knaflic]: Which was, she had an example.

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[Cole Nussbaumer Knaflic]: It was a client makeover that we were doing.

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[Cole Nussbaumer Knaflic]: It was a client that was doing a workshop at the time.

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[Cole Nussbaumer Knaflic]: And it was this, it was like a bar, two series bar chart

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[Cole Nussbaumer Knaflic]: where one of the bars was stacked and it had

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[Cole Nussbaumer Knaflic]: a lot of different categories.

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[Cole Nussbaumer Knaflic]: It was revenue that was broken down over time

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[Cole Nussbaumer Knaflic]: and then also across regions.

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[Cole Nussbaumer Knaflic]: And it just, it was almost impossible to see

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[Cole Nussbaumer Knaflic]: anything in the bar chart.

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[Cole Nussbaumer Knaflic]: So she had an idea of what she wanted

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[Cole Nussbaumer Knaflic]: to do, which was small multiples.

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[Cole Nussbaumer Knaflic]: She wanted to change everything into line graphs.

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[Cole Nussbaumer Knaflic]: And so she had, she sketched one large version,

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[Cole Nussbaumer Knaflic]: that was for everything.

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[Cole Nussbaumer Knaflic]: And then each of the small multiples

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[Cole Nussbaumer Knaflic]: that was for the individual regions.

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[Cole Nussbaumer Knaflic]: And just sketched it without even data, right?

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[Cole Nussbaumer Knaflic]: Here's just the layout and what I want it to look like.

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[Cole Nussbaumer Knaflic]: And she then turned to Claude in PowerPoint.

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[Cole Nussbaumer Knaflic]: There's an add-in, ChatGPT has a similar thing.

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[Cole Nussbaumer Knaflic]: I would hope Copilot would have similar functionality,

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[Cole Nussbaumer Knaflic]: but I'm not sure that it does.

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[Cole Nussbaumer Knaflic]: In any case, so she gave her sketch

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[Cole Nussbaumer Knaflic]: and the original graph to Claude in PowerPoint.

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[Cole Nussbaumer Knaflic]: And said, can you remake this?

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[Cole Nussbaumer Knaflic]: And it did.

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[Cole Nussbaumer Knaflic]: And the first version it does isn't bad.

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[Cole Nussbaumer Knaflic]: It's exactly what she drew out.

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[Cole Nussbaumer Knaflic]: It's all the right data.

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[Cole Nussbaumer Knaflic]: And part of her point,

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[Cole Nussbaumer Knaflic]: because she goes through these iterations in the blog

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[Cole Nussbaumer Knaflic]: that she later wrote, or she anonymized the data,

366
00:22:35,299 --> 00:22:38,479
[Cole Nussbaumer Knaflic]: but kept it true to the path.

367
00:22:38,479 --> 00:22:40,899
[Cole Nussbaumer Knaflic]: And she said, many people would have stopped here.

368
00:22:40,899 --> 00:22:42,619
[Cole Nussbaumer Knaflic]: And that actually probably would have been okay.

369
00:22:42,619 --> 00:22:44,459
[Cole Nussbaumer Knaflic]: But because I love the design piece,

370
00:22:44,459 --> 00:22:45,919
[Cole Nussbaumer Knaflic]: I'm going to take things a little further.

371
00:22:45,919 --> 00:22:47,679
[Cole Nussbaumer Knaflic]: And so she spends a little bit more time,

372
00:22:47,680 --> 00:22:49,740
[Cole Nussbaumer Knaflic]: more time now, time that she has free.

373
00:22:49,740 --> 00:22:52,640
[Cole Nussbaumer Knaflic]: Because she didn't just have to go put all the data in

374
00:22:52,640 --> 00:22:54,960
[Cole Nussbaumer Knaflic]: and make those iterations of the different graphs.

375
00:22:54,960 --> 00:22:58,940
[Cole Nussbaumer Knaflic]: And yet her final version looks 20x what Claude came up with

376
00:22:58,940 --> 00:23:01,380
[Cole Nussbaumer Knaflic]: because she's got that skill set.

377
00:23:01,380 --> 00:23:04,759
[Cole Nussbaumer Knaflic]: And I think that's a really interesting use of...

378
00:23:06,160 --> 00:23:09,220
[Cole Nussbaumer Knaflic]: Making use of AI where you can gain efficiency

379
00:23:09,220 --> 00:23:11,380
[Cole Nussbaumer Knaflic]: on the things that were gonna happen behind the scenes.

380
00:23:11,380 --> 00:23:12,680
[Cole Nussbaumer Knaflic]: Nobody was gonna appreciate really

381
00:23:12,680 --> 00:23:14,220
[Cole Nussbaumer Knaflic]: the time spent there anyway.

382
00:23:14,220 --> 00:23:16,980
[Cole Nussbaumer Knaflic]: And it would have been grunt work.

383
00:23:16,980 --> 00:23:17,100
[Cole Nussbaumer Knaflic]: Or teamwork. Or teamwork. Or teamwork.

384
00:23:17,100 --> 00:23:17,500
[Cole Nussbaumer Knaflic]: Or teamwork. Or teamwork.

385
00:23:17,500 --> 00:23:17,600
[Cole Nussbaumer Knaflic]: Or teamwork. Or teamwork.

386
00:23:17,600 --> 00:23:17,640
[Cole Nussbaumer Knaflic]: Or teamwork. Or teamwork.

387
00:23:17,640 --> 00:23:17,680
[Cole Nussbaumer Knaflic]: Or teamwork. Or teamwork. Or teamwork.

388
00:23:17,680 --> 00:23:22,600
[Cole Nussbaumer Knaflic]: tedious work, I should say for her. And she's able to then instead spend that time or more time

389
00:23:22,600 --> 00:23:27,820
[Cole Nussbaumer Knaflic]: on the design, the part that she actually enjoys doing and is really good at and get this superior

390
00:23:27,820 --> 00:23:33,060
[Cole Nussbaumer Knaflic]: result as a result. And she would have gotten there on her own anyway, it just would have taken

391
00:23:33,060 --> 00:23:38,720
[Cole Nussbaumer Knaflic]: longer. And so the way that she's able to now divide her time differently because of the

392
00:23:38,720 --> 00:23:45,620
[Julie Hoyer]: efficiency gains you get, I thought that was a neat example. You've had some great points

393
00:23:45,619 --> 00:23:55,419
[Julie Hoyer]: throughout the conversation so far where I've been jotting down different effectiveness of AI

394
00:23:55,419 --> 00:24:00,399
[Julie Hoyer]: or the efficiency gains. You've listed a couple of different ones. And I've been wanting to ask you,

395
00:24:00,399 --> 00:24:06,159
[Julie Hoyer]: because we're saying that the human in the beginning is still so important and the person

396
00:24:06,159 --> 00:24:11,519
[Julie Hoyer]: using AI to help them with their visualizations and their storytelling, they need to have those

397
00:24:11,519 --> 00:24:15,399
[Julie Hoyer]: foundations. Where then does AI...

398
00:24:15,619 --> 00:24:23,439
[Julie Hoyer]: Give a benefit. Is it solely in the... Does it grant greater access to someone

399
00:24:23,439 --> 00:24:29,519
[Julie Hoyer]: to the data to be able to do this and spin something up? Is it solely speed? Is it the

400
00:24:29,519 --> 00:24:35,519
[Julie Hoyer]: repeatability piece? Is it maybe a mix of a lot of things? But I'm curious to hear specifically,

401
00:24:35,899 --> 00:24:38,119
[Julie Hoyer]: how would you summarize that?

402
00:24:38,419 --> 00:24:41,500
[Cole Nussbaumer Knaflic]: Yeah, I think it can be pieces of each of those things. And I think

403
00:24:41,500 --> 00:24:45,139
[Cole Nussbaumer Knaflic]: where people stand to get the most benefit,

404
00:24:45,619 --> 00:24:49,299
[Cole Nussbaumer Knaflic]: we touched on this a little bit earlier, but might come back to where their skill level is

405
00:24:49,299 --> 00:24:55,099
[Cole Nussbaumer Knaflic]: and what kind of work they enjoy doing. Because to some extent, we can think about

406
00:24:55,099 --> 00:25:01,339
[Cole Nussbaumer Knaflic]: the pieces we enjoy less, some of that, those might be opportunities to bring in AI. They won't

407
00:25:01,339 --> 00:25:05,819
[Cole Nussbaumer Knaflic]: always, right? Because I also mentioned people don't want to stop and build a storyboard or

408
00:25:05,819 --> 00:25:11,839
[Cole Nussbaumer Knaflic]: spend time physically writing out their message and then proofreading and adjusting and making it

409
00:25:11,839 --> 00:25:15,579
[Cole Nussbaumer Knaflic]: better. But I do think where we find...

410
00:25:15,619 --> 00:25:18,979
[Cole Nussbaumer Knaflic]: We find things where like, eh, this feels gross. I don't want to do it, but I know I should.

411
00:25:19,319 --> 00:25:26,459
[Cole Nussbaumer Knaflic]: We can still bring in AI in ways that it almost makes it more fun. Fun's maybe the wrong word,

412
00:25:26,459 --> 00:25:31,199
[Cole Nussbaumer Knaflic]: but can help us be more robust. I think, and that was missing maybe from your list,

413
00:25:31,259 --> 00:25:38,019
[Cole Nussbaumer Knaflic]: because we haven't talked about this yet. Because I do think that AI to help poke holes in our

414
00:25:38,019 --> 00:25:44,779
[Cole Nussbaumer Knaflic]: thinking or our logic, or to help us even like wordsmith or come up with other ideas of what

415
00:25:44,779 --> 00:25:45,599
[Cole Nussbaumer Knaflic]: our audience might care about. And I think that's a really good point. And I think that's a really

416
00:25:45,599 --> 00:25:45,659
[Cole Nussbaumer Knaflic]: good point. And I think that's a really good point. And I think that's a really good point.

417
00:25:45,659 --> 00:25:53,259
[Cole Nussbaumer Knaflic]: That we actually haven't considered that it can be helpful for putting pressure on our initial ideas

418
00:25:53,259 --> 00:25:59,959
[Cole Nussbaumer Knaflic]: in ways that can be useful and just help us be more critical in how we're thinking about things

419
00:25:59,959 --> 00:26:05,500
[Cole Nussbaumer Knaflic]: versus less. And I actually love that because I think a lot of the headlines, we see,

420
00:26:05,759 --> 00:26:10,240
[Cole Nussbaumer Knaflic]: oh, there's a study that AI makes people think less. And it's like, well, yeah, it easily could

421
00:26:10,240 --> 00:26:14,740
[Cole Nussbaumer Knaflic]: do, but it doesn't have to. That all comes down to how you're using it, what you're using it for.

422
00:26:14,740 --> 00:26:23,720
[Cole Nussbaumer Knaflic]: And so I think if our goal is not, let's show where AI can fail, right? We have a good sense

423
00:26:23,720 --> 00:26:29,259
[Cole Nussbaumer Knaflic]: of where it can fail. We can give it prompts that it will do that immediately. I did a video

424
00:26:29,259 --> 00:26:35,819
[Cole Nussbaumer Knaflic]: like that a few months ago now, but it was intentionally wrong, if you will, of like,

425
00:26:35,880 --> 00:26:39,359
[Cole Nussbaumer Knaflic]: I'm just going to go to all these different AIs and ask it to make a graph and ask it to tell me

426
00:26:39,359 --> 00:26:43,440
[Cole Nussbaumer Knaflic]: a data story. It's not going to do that well. We knew it wasn't going to do that well. So then we

427
00:26:43,440 --> 00:26:44,720
[Cole Nussbaumer Knaflic]: can kind of laugh at aha and feel sorry. And then we can kind of laugh at aha and feel sorry.

428
00:26:44,740 --> 00:26:50,140
[Cole Nussbaumer Knaflic]: Oh, humans are still useful. It didn't do that well. But that's not how we want to be thinking

429
00:26:50,140 --> 00:26:59,339
[Cole Nussbaumer Knaflic]: about using it. And it's not a reason to discount it. Rather, I think if we are looking at our own

430
00:26:59,339 --> 00:27:05,120
[Cole Nussbaumer Knaflic]: workflow and saying, where could I bring AI in, right? If my workflow, once I'm communicating

431
00:27:05,120 --> 00:27:10,660
[Cole Nussbaumer Knaflic]: data looks like, you know, I want to start by considering the audience and my message and plan

432
00:27:10,660 --> 00:27:14,519
[Cole Nussbaumer Knaflic]: what my story is going to look like, then I want to really kind of make sure that that's a

433
00:27:14,519 --> 00:27:14,720
[Cole Nussbaumer Knaflic]: coherent story. And then I want to kind of make sure that that's a coherent story.

434
00:27:14,740 --> 00:27:21,460
[Cole Nussbaumer Knaflic]: And that one point leads logically to the next. Then I want to think about where does data come in?

435
00:27:21,859 --> 00:27:26,000
[Cole Nussbaumer Knaflic]: What data do I have? What could it look like? What would be compelling for my audience?

436
00:27:26,180 --> 00:27:30,859
[Cole Nussbaumer Knaflic]: Now that I'm working with the data, I'm making graphs, what graph is going to work for what I

437
00:27:30,859 --> 00:27:36,120
[Cole Nussbaumer Knaflic]: want to show or what I want my audience to see? How do I get rid of clutter that doesn't belong,

438
00:27:36,599 --> 00:27:43,440
[Cole Nussbaumer Knaflic]: drive attention to where I want it and really build an experience to walk my audience through

439
00:27:43,440 --> 00:27:44,519
[Cole Nussbaumer Knaflic]: that's going to communicate the thing that I want to do?

440
00:27:44,519 --> 00:27:49,559
[Cole Nussbaumer Knaflic]: I want to communicate and hopefully drive them to act in the way I want them to. And we can think

441
00:27:49,559 --> 00:27:54,119
[Cole Nussbaumer Knaflic]: about, okay, what has that looked like for me historically as the human? And now where can I

442
00:27:54,119 --> 00:28:04,680
[Cole Nussbaumer Knaflic]: bring AI in, in pieces of that to help make my work and my thought process stronger? And if we do that

443
00:28:04,680 --> 00:28:08,619
[Cole Nussbaumer Knaflic]: and we're thoughtful around all the pieces that go around it, then we can have really smart

444
00:28:08,619 --> 00:28:12,859
[Cole Nussbaumer Knaflic]: conversations with AI. And it's not always going to be right, right? It doesn't, it never gives like

445
00:28:12,859 --> 00:28:14,500
[Cole Nussbaumer Knaflic]: the answer. So I think really, really important to have that conversation with AI. And I think

446
00:28:14,519 --> 00:28:15,019
[Cole Nussbaumer Knaflic]: that's one of the things that we need to be thinking about. And I think that's one of the

447
00:28:15,019 --> 00:28:21,000
[Cole Nussbaumer Knaflic]: skeptical through that. But we can be thoughtful about when it might make sense to do that,

448
00:28:21,299 --> 00:28:26,420
[Cole Nussbaumer Knaflic]: to get output that is different than we would have come up with on our own.

449
00:28:26,639 --> 00:28:29,279
[Julie Hoyer]: Back to the benefit thing. I think it's interesting, though, a lot of people,

450
00:28:29,339 --> 00:28:33,059
[Julie Hoyer]: as we've talked about on some previous episodes, default to the idea that it's going to make you

451
00:28:33,059 --> 00:28:37,619
[Julie Hoyer]: faster. And I think especially when you're talking about data storytelling, like that is definitely

452
00:28:37,619 --> 00:28:43,220
[Julie Hoyer]: not the goal of using AI. A lot of times, I was just talking to a colleague and he came up with a

453
00:28:43,220 --> 00:28:43,359
[Julie Hoyer]: great, like, I guess you would say, more than a hundred percent, but he came up with a great, like,

454
00:28:43,359 --> 00:28:48,339
[Julie Hoyer]: I guess you would say more like infographic. It was like a client value loop that he had been

455
00:28:48,339 --> 00:28:52,439
[Julie Hoyer]: working on building in it. And it looks amazing. And I asked him, I said, I know you had to have

456
00:28:52,439 --> 00:28:55,939
[Julie Hoyer]: used AI to help because I don't know what tool you would have gone and made that yourself. It

457
00:28:55,939 --> 00:29:01,119
[Julie Hoyer]: looks great. And I asked him how long it had taken him. And he said, oh, it took me at least a week

458
00:29:01,119 --> 00:29:06,479
[Julie Hoyer]: of going back and forth. And so I was really curious his process. And he said that he had

459
00:29:06,479 --> 00:29:10,839
[Julie Hoyer]: to be super thoughtful before going to AI and go back and forth with it and give it a pre-drawing

460
00:29:10,839 --> 00:29:13,339
[Julie Hoyer]: like you were just saying in your example. And so I think it's really important to have that

461
00:29:13,359 --> 00:29:18,299
[Julie Hoyer]: for people to understand. Like, I don't think any of us have an example where you're able to give a

462
00:29:18,299 --> 00:29:24,019
[Julie Hoyer]: kind of like a hand wavy gray story and have AI refine it. Like I've noticed you can give AI

463
00:29:24,019 --> 00:29:29,319
[Julie Hoyer]: something succinct and it tends to make it longer and stress maybe not the exact point. So you have

464
00:29:29,319 --> 00:29:34,919
[Julie Hoyer]: to, like you're saying, you just, you have to go in, I think, with a lot of conviction of those

465
00:29:34,919 --> 00:29:41,219
[Julie Hoyer]: main pieces you need out of it. And with that said, I am curious if you have any guidance on

466
00:29:41,959 --> 00:29:43,339
[Julie Hoyer]: prompting it or what you do.

467
00:29:43,359 --> 00:29:48,559
[Julie Hoyer]: Bring specifically to using AI. I know you said there are no rules, like perfect rules, but.

468
00:29:48,939 --> 00:29:54,139
[Cole Nussbaumer Knaflic]: Well, I mean, there are ways to do things better and less well, right? Just on that note, though,

469
00:29:54,179 --> 00:29:59,899
[Cole Nussbaumer Knaflic]: I think one thing for people to be aware of is just how AI works and some of these

470
00:29:59,899 --> 00:30:07,379
[Cole Nussbaumer Knaflic]: tendencies it has because of what it was built to do and how it's been programmed. So AI will

471
00:30:07,379 --> 00:30:12,699
[Cole Nussbaumer Knaflic]: typically default to breadth, right? And bringing in everything versus concision,

472
00:30:12,700 --> 00:30:18,759
[Cole Nussbaumer Knaflic]: which means if you want it to act against that natural tendency, and natural feels like the

473
00:30:18,759 --> 00:30:22,799
[Cole Nussbaumer Knaflic]: wrong word there, but against that tendency, then you need to instruct it to do so, right?

474
00:30:22,900 --> 00:30:28,440
[Cole Nussbaumer Knaflic]: Don't add more information or suggest more things to bring in. I'm working on making this concise

475
00:30:28,440 --> 00:30:36,160
[Cole Nussbaumer Knaflic]: or I have this limitation. AI also, I think everybody's aware of this, but it, it gauges

476
00:30:36,160 --> 00:30:41,460
[Cole Nussbaumer Knaflic]: its own success on whether you, the user are satisfied. So it is more likely to tell you,

477
00:30:41,460 --> 00:30:42,340
[Cole Nussbaumer Knaflic]: you are right.

478
00:30:42,700 --> 00:30:45,680
[Cole Nussbaumer Knaflic]: And anything else. And so guard against that. You can tell it, you know,

479
00:30:45,759 --> 00:30:49,920
[Cole Nussbaumer Knaflic]: you don't need to be nice to me, be direct. I want you to challenge my thinking.

480
00:30:50,640 --> 00:30:56,460
[Cole Nussbaumer Knaflic]: And this is one of the reasons that I, you know, personally, I jump around across different AIs.

481
00:30:56,580 --> 00:31:01,240
[Cole Nussbaumer Knaflic]: I have certain ones that I like for certain things, but I also, I'll pit them against each

482
00:31:01,240 --> 00:31:06,819
[Cole Nussbaumer Knaflic]: other. I was like, Hey, I, I, a friend, a friend told me this, I disagree. What do you think where

483
00:31:06,819 --> 00:31:12,480
[Cole Nussbaumer Knaflic]: you can kind of do that? You can do that actually for a double checking data integrity,

484
00:31:12,700 --> 00:31:18,039
[Cole Nussbaumer Knaflic]: as well, have them double check each other. But so some of this is understanding how AI is working

485
00:31:18,039 --> 00:31:23,980
[Cole Nussbaumer Knaflic]: so that when you need something that is different from that, you are aware and looking out and

486
00:31:23,980 --> 00:31:32,360
[Cole Nussbaumer Knaflic]: directing it. Otherwise, I think when it comes to useful prompt one, I mean, I do tell it who I am.

487
00:31:32,500 --> 00:31:37,340
[Cole Nussbaumer Knaflic]: So it knows and let it know that I want you to follow the principles outlined.

488
00:31:37,340 --> 00:31:39,340
[Cole Nussbaumer Knaflic]: You never forget who I am.

489
00:31:39,500 --> 00:31:42,640
[Cole Nussbaumer Knaflic]: Well, I think it was the, I think it was the YouTube live event that we,

490
00:31:42,640 --> 00:31:45,720
[Cole Nussbaumer Knaflic]: we've talked about, but where I was going back and forth, I was working with a lot of different

491
00:31:45,720 --> 00:31:50,660
[Cole Nussbaumer Knaflic]: ones, but Gemini at one point gave me a really good one. And then the next iteration gave me

492
00:31:50,660 --> 00:31:56,120
[Cole Nussbaumer Knaflic]: not a great one. And, or no, I remember what it did. It followed like this awful purple template

493
00:31:56,120 --> 00:31:59,540
[Cole Nussbaumer Knaflic]: that I was pointing it to, but in a way where like the whole background was purple and the

494
00:31:59,540 --> 00:32:03,700
[Cole Nussbaumer Knaflic]: whole graph was yellow. And I was like, okay, you like, you really followed that to the T rather

495
00:32:03,700 --> 00:32:07,660
[Cole Nussbaumer Knaflic]: than the spirit of what I was going for. And I thought to myself, if you only knew who I was,

496
00:32:07,660 --> 00:32:11,020
[Cole Nussbaumer Knaflic]: then I was like, wait, I didn't tell it. And as soon as I said who I was, you know,

497
00:32:11,020 --> 00:32:12,520
[Cole Nussbaumer Knaflic]: then you get like the color palette and everything.

498
00:32:12,640 --> 00:32:19,240
[Cole Nussbaumer Knaflic]: That works out. Right. But I think for prompts, another one that I find useful is I end almost

499
00:32:19,240 --> 00:32:25,860
[Cole Nussbaumer Knaflic]: any prompt with something along the lines of, if there are questions I can answer for you,

500
00:32:25,860 --> 00:32:30,780
[Cole Nussbaumer Knaflic]: that would help you give me better input. Start by asking those. Cause if you don't actually ask

501
00:32:30,780 --> 00:32:34,960
[Cole Nussbaumer Knaflic]: for that, it'll jump straight to the output. It'll make assumptions along the way. And that's where I

502
00:32:34,960 --> 00:32:40,620
[Cole Nussbaumer Knaflic]: the expectation of what you're going to get and the mismatch of what comes up becomes wider.

503
00:32:40,960 --> 00:32:42,520
[Cole Nussbaumer Knaflic]: Whereas if you,

504
00:32:42,640 --> 00:32:47,120
[Cole Nussbaumer Knaflic]: recognize that you're thinking a lot of things in your head that you haven't taken the time to type,

505
00:32:47,120 --> 00:32:52,180
[Cole Nussbaumer Knaflic]: and it's not necessarily clear which of those is going to be really important and which isn't

506
00:32:52,180 --> 00:32:57,300
[Cole Nussbaumer Knaflic]: then having it ask you. So it's not making those assumptions. And so that it can be,

507
00:32:57,360 --> 00:33:03,980
[Cole Nussbaumer Knaflic]: being more robust in, in what it comes back with. I find very useful. I will say that different AIs

508
00:33:03,980 --> 00:33:10,840
[Cole Nussbaumer Knaflic]: come back with different amounts of questions. Some are overwhelming. Some are

509
00:33:10,840 --> 00:33:10,960
[Cole Nussbaumer Knaflic]: easier to answer. Some are more difficult to answer. Some are more difficult to answer. Some are

510
00:33:10,960 --> 00:33:12,620
[Cole Nussbaumer Knaflic]: easier to answer. Some are more difficult to answer. Some are more difficult to answer. Some are more

511
00:33:12,640 --> 00:33:17,200
[Cole Nussbaumer Knaflic]: easier to work with when it comes to that. And so some of it's finding a fit that way, I think as well.

512
00:33:17,200 --> 00:33:22,800
[Cole Nussbaumer Knaflic]: I don't know. It's I'm curious to see what's going to happen in the tool landscape over time. And so

513
00:33:22,800 --> 00:33:30,640
[Cole Nussbaumer Knaflic]: I am. I think careful at this point, not to go, not to anchor myself too much to any single tool,

514
00:33:30,640 --> 00:33:34,320
[Cole Nussbaumer Knaflic]: knowing that that landscape is going to change. And so for the things that we teach at storytelling

515
00:33:34,320 --> 00:33:34,920
[Tim Wilson]: with data.

516
00:33:34,920 --> 00:33:41,280
[Tim Wilson]: Groks charting is amazing. I'm not going anywhere outside of Grok. I'm kidding. I'm not.

517
00:33:41,280 --> 00:33:41,920
[Tim Wilson]: Oh yeah. I was thinking through. I'm not even calling it an actual tool.

518
00:33:41,920 --> 00:33:43,500
[Cole Nussbaumer Knaflic]: I was thinking through how to respond to that.

519
00:33:46,000 --> 00:33:53,380
[Moe Kiss]: One thing that's been on my mind a lot is you talk a lot about like still having the need to edit at the last step, right?

520
00:33:53,800 --> 00:34:00,600
[Moe Kiss]: Like, do you think there is a world in data viz where we go back and forth and we get to the finished product with prompting?

521
00:34:00,779 --> 00:34:04,360
[Moe Kiss]: Or do you like it when I hear you talk about it?

522
00:34:04,420 --> 00:34:08,860
[Moe Kiss]: And for those listening, like when Cole talks about data viz, like her whole face lights up.

523
00:34:08,940 --> 00:34:11,599
[Moe Kiss]: Like you can tell she loves this topic and she's so passionate about it.

524
00:34:11,599 --> 00:34:16,079
[Moe Kiss]: And I'm like, when we look at the future, do you imagine this being a prompt thing?

525
00:34:16,299 --> 00:34:19,259
[Moe Kiss]: Or is the editability still really important to you?

526
00:34:20,219 --> 00:34:21,940
[Cole Nussbaumer Knaflic]: I don't have the answer here.

527
00:34:22,099 --> 00:34:26,219
[Cole Nussbaumer Knaflic]: I suspect that will come down to how people like to work.

528
00:34:26,639 --> 00:34:29,579
[Cole Nussbaumer Knaflic]: I can imagine some people getting there through the prompting entirely.

529
00:34:30,099 --> 00:34:32,860
[Cole Nussbaumer Knaflic]: It's like, do you go through the drop down menu where you're actually writing code?

530
00:34:33,179 --> 00:34:38,420
[Cole Nussbaumer Knaflic]: Different people will do that differently, sometimes because of skill set, sometimes because it's what they were taught.

531
00:34:38,420 --> 00:34:41,579
[Cole Nussbaumer Knaflic]: Sometimes it's because for the thing that they're doing in the moment.

532
00:34:41,599 --> 00:34:43,099
[Cole Nussbaumer Knaflic]: That makes sense.

533
00:34:44,099 --> 00:35:00,719
[Cole Nussbaumer Knaflic]: I can imagine scenarios where even someone like me who's spent a lot of time on this stuff where I might be where I could get to a point that's good enough for something without having that final my hands in it doing things.

534
00:35:01,079 --> 00:35:08,259
[Cole Nussbaumer Knaflic]: I mean, I say that I could imagine that, but it does kind of make my skin crawl a little bit because I'm a control freak.

535
00:35:08,259 --> 00:35:11,239
[Cole Nussbaumer Knaflic]: I would like to go in and move the title.

536
00:35:11,599 --> 00:35:13,480
[Cole Nussbaumer Knaflic]: I would like to go in and move the title.

537
00:35:13,480 --> 00:35:14,559
[Cole Nussbaumer Knaflic]: I would like to go in and move the title.

538
00:35:14,559 --> 00:35:15,659
[Cole Nussbaumer Knaflic]: I would like to go in and move the title.

539
00:35:15,659 --> 00:35:16,400
[Cole Nussbaumer Knaflic]: I would like to go in and move the title.

540
00:35:16,400 --> 00:35:17,980
[Cole Nussbaumer Knaflic]: But sometimes people don't see that.

541
00:35:18,059 --> 00:35:18,960
[Cole Nussbaumer Knaflic]: It drives me nuts.

542
00:35:19,139 --> 00:35:23,839
[Cole Nussbaumer Knaflic]: I'm like, why don't you see that needs to move a little bit?

543
00:35:23,839 --> 00:35:26,880
[Cole Nussbaumer Knaflic]: That's the kind of the counter to that, right?

544
00:35:27,000 --> 00:35:28,139
[Cole Nussbaumer Knaflic]: It depends.

545
00:35:28,279 --> 00:35:36,599
[Cole Nussbaumer Knaflic]: If this is like your team update and it's your colleagues and you really should just be done with it, not spending the time designing to the nth degree.

546
00:35:38,259 --> 00:35:39,779
[Cole Nussbaumer Knaflic]: So it's like everything.

547
00:35:39,900 --> 00:35:41,000
[Cole Nussbaumer Knaflic]: It's when do you flex it?

548
00:35:41,099 --> 00:35:41,480
[Cole Nussbaumer Knaflic]: Like when?

549
00:35:41,599 --> 00:35:42,719
[Cole Nussbaumer Knaflic]: Does it need to be perfect?

550
00:35:42,719 --> 00:35:45,960
[Cole Nussbaumer Knaflic]: When do you when is good enough?

551
00:35:45,960 --> 00:35:46,659
[Cole Nussbaumer Knaflic]: Okay.

552
00:35:46,659 --> 00:35:49,480
[Cole Nussbaumer Knaflic]: And I definitely used to be of the mindset that like good enough is never okay.

553
00:35:49,480 --> 00:35:50,839
[Cole Nussbaumer Knaflic]: It should always be perfect.

554
00:35:51,559 --> 00:35:54,920
[Cole Nussbaumer Knaflic]: And like somebody can easily drive themselves crazy with that approach.

555
00:35:54,980 --> 00:35:57,799
[Julie Hoyer]: I just want all the fonts to match when I get sent to that.

556
00:35:57,799 --> 00:36:02,420
[Cole Nussbaumer Knaflic]: You know, I feel like that's fair, but sometimes that's also and that's actually Julie.

557
00:36:02,420 --> 00:36:09,259
[Cole Nussbaumer Knaflic]: That's a beautiful example where you could say to AI point out every time in this deck that it's not like we're font is inconsistent.

558
00:36:09,299 --> 00:36:11,299
[Julie Hoyer]: Oh, I've never thought.

559
00:36:11,300 --> 00:36:13,600
[Julie Hoyer]: To use it in my feedback.

560
00:36:14,440 --> 00:36:16,360
[Julie Hoyer]: That's I'd be like just baseline.

561
00:36:16,480 --> 00:36:17,240
[Julie Hoyer]: Hey, go through this.

562
00:36:17,300 --> 00:36:20,780
[Julie Hoyer]: And these are my nitpicky things like flag them for me.

563
00:36:20,780 --> 00:36:25,200
[Tim Wilson]: You've said it a couple of times that thinking of AI is the junior analyst.

564
00:36:25,560 --> 00:36:28,800
[Tim Wilson]: And I think I'm trying to bring a few different themes together.

565
00:36:29,180 --> 00:36:38,120
[Tim Wilson]: If a junior analyst who doesn't have the fundamentals is using AI, which is also a junior analyst, that is kind of a recipe for you.

566
00:36:38,340 --> 00:36:41,260
[Tim Wilson]: If there's someone who's got the foundation.

567
00:36:41,360 --> 00:36:43,980
[Tim Wilson]: And they're using a junior analyst that can work.

568
00:36:43,980 --> 00:36:52,760
[Tim Wilson]: But it does also seem like a junior analyst can be used as a hey, you review this thing that I produced and tell me, is it clear to you?

569
00:36:52,840 --> 00:36:57,840
[Tim Wilson]: Like that's one way for a junior human analyst to learn, which also seems like a junior.

570
00:36:58,200 --> 00:36:59,560
[Tim Wilson]: So it's kind of in that.

571
00:36:59,820 --> 00:37:00,260
[Tim Wilson]: Yes.

572
00:37:00,340 --> 00:37:01,560
[Tim Wilson]: Check for the font consistency.

573
00:37:02,040 --> 00:37:03,640
[Tim Wilson]: But it could also be.

574
00:37:04,040 --> 00:37:04,580
[Tim Wilson]: Who do you think?

575
00:37:04,660 --> 00:37:07,880
[Tim Wilson]: What's the what's the message that you're most getting out of this deck or what?

576
00:37:08,400 --> 00:37:10,680
[Tim Wilson]: You know, what is really clear about this?

577
00:37:10,680 --> 00:37:10,840
[Tim Wilson]: Because.

578
00:37:11,260 --> 00:37:19,080
[Tim Wilson]: Junior analysts, when forced to kind of assess a visualization or a slide deck, can actually learn as they go and give good feedback.

579
00:37:19,080 --> 00:37:21,540
[Cole Nussbaumer Knaflic]: And the same with your AI, right?

580
00:37:21,580 --> 00:37:32,760
[Cole Nussbaumer Knaflic]: Because hopefully, as we've talked about, you're using a subscription, like you're going back to the same conversation so that so that this history can play forward so that it's not only helping you in the given project, but can help more broadly than that.

581
00:37:33,180 --> 00:37:38,760
[Cole Nussbaumer Knaflic]: And I think that is one of the great benefits we can get from AI is just it's another perspective.

582
00:37:39,060 --> 00:37:39,760
[Cole Nussbaumer Knaflic]: It's somebody.

583
00:37:39,900 --> 00:37:41,240
[Cole Nussbaumer Knaflic]: It's like another lens on.

584
00:37:41,239 --> 00:37:41,899
[Cole Nussbaumer Knaflic]: Your work.

585
00:37:41,899 --> 00:37:48,539
[Cole Nussbaumer Knaflic]: And my view is there should never have been a spelling mistake or grammar error or math that didn't add up in the first place.

586
00:37:48,599 --> 00:37:58,539
[Cole Nussbaumer Knaflic]: But now that there's AI, there certainly should never be these things because there is no reason not to take your important thing and have AI check for issues when it comes to that.

587
00:37:58,879 --> 00:38:07,079
[Cole Nussbaumer Knaflic]: And that's where I think the consistency of a machine versus a human can be really beneficial.

588
00:38:07,399 --> 00:38:08,719
[Cole Nussbaumer Knaflic]: It's just have it check for errors.

589
00:38:10,399 --> 00:38:11,159
[Cole Nussbaumer Knaflic]: And so.

590
00:38:11,239 --> 00:38:14,179
[Cole Nussbaumer Knaflic]: You're not spending your time maybe as much there.

591
00:38:14,179 --> 00:38:18,139
[Cole Nussbaumer Knaflic]: And then you're spending your time on the critical thinking of like, you know, what should the content be?

592
00:38:18,239 --> 00:38:19,659
[Cole Nussbaumer Knaflic]: How do I get from one thing to the next?

593
00:38:19,679 --> 00:38:23,699
[Cole Nussbaumer Knaflic]: How do I make this work for my audience and the setting and all of the other things?

594
00:38:24,099 --> 00:38:24,319
[Julie Hoyer]: It's funny.

595
00:38:24,459 --> 00:38:26,379
[Julie Hoyer]: That feels so obvious now that we've said it.

596
00:38:26,459 --> 00:38:31,439
[Julie Hoyer]: But I'm so glad we said it because I'm like, oh, I hadn't thought of it exactly like a twist that way.

597
00:38:31,539 --> 00:38:32,139
[Julie Hoyer]: So that's amazing.

598
00:38:32,139 --> 00:38:40,199
[Tim Wilson]: And it's good that we got it right in under the wire of when we need to move to wrap, which clearly.

599
00:38:40,199 --> 00:38:41,159
[Tim Wilson]: I will.

600
00:38:41,319 --> 00:38:48,399
[Tim Wilson]: I will throw out like part of what led to this through back weird ways was the the we've mentioned the YouTube, the video.

601
00:38:48,519 --> 00:38:58,059
[Tim Wilson]: But there is storytelling with data dot com slash AI has a whole bunch of resources, including if you want Cole's team to do a workshop with you.

602
00:38:58,099 --> 00:39:00,199
[Tim Wilson]: But there are a lot of really, really useful videos.

603
00:39:01,379 --> 00:39:09,599
[Tim Wilson]: So I will just kind of put a plug for that out there because clearly there's so much to think about and learn here.

604
00:39:09,599 --> 00:39:10,159
[Tim Wilson]: But.

605
00:39:10,199 --> 00:39:12,899
[Tim Wilson]: So this has been a great discussion.

606
00:39:13,199 --> 00:39:17,059
[Tim Wilson]: I wish we could talk for another hour and a half, but we can't.

607
00:39:17,719 --> 00:39:30,299
[Tim Wilson]: So before we wrap up, though, we like to go around and have everyone share a last call, something that's interesting, funny, worth sharing that people may get a kick out of.

608
00:39:30,419 --> 00:39:32,079
[Tim Wilson]: And Cole, you're our guest.

609
00:39:32,159 --> 00:39:33,000
[Tim Wilson]: Do you have a last call?

610
00:39:33,539 --> 00:39:33,799
[Cole Nussbaumer Knaflic]: I do.

611
00:39:33,799 --> 00:39:38,419
[Cole Nussbaumer Knaflic]: I took this in maybe a strange direction because it has nothing to do with what we've just been talking about.

612
00:39:38,779 --> 00:39:39,799
[Cole Nussbaumer Knaflic]: Or it's maybe the antithesis.

613
00:39:40,199 --> 00:39:41,199
[Cole Nussbaumer Knaflic]: This is what we're just talking about.

614
00:39:41,239 --> 00:39:41,599
[Cole Nussbaumer Knaflic]: I'm not sure.

615
00:39:41,960 --> 00:39:42,819
[Cole Nussbaumer Knaflic]: Although it is an app.

616
00:39:42,919 --> 00:39:43,879
[Cole Nussbaumer Knaflic]: So maybe not entirely.

617
00:39:44,079 --> 00:39:47,019
[Cole Nussbaumer Knaflic]: But it is the Merlin bird ID.

618
00:39:47,279 --> 00:39:50,139
[Cole Nussbaumer Knaflic]: And this this isn't new, but I've recently.

619
00:39:50,419 --> 00:39:53,119
[Cole Nussbaumer Knaflic]: So it's migratory starting to be migratory season where I live.

620
00:39:53,179 --> 00:39:54,279
[Cole Nussbaumer Knaflic]: And so lots of birds.

621
00:39:54,519 --> 00:39:57,460
[Cole Nussbaumer Knaflic]: And so it's it's come up more for us lately.

622
00:39:57,460 --> 00:40:00,879
[Cole Nussbaumer Knaflic]: But it's put out by the Cornell Lab of Ornithology.

623
00:40:00,879 --> 00:40:09,879
[Cole Nussbaumer Knaflic]: And one of the things I love about it is it's a rare example of technology that actually makes you more observant of the world around you.

624
00:40:10,199 --> 00:40:11,960
[Cole Nussbaumer Knaflic]: Versus less.

625
00:40:11,960 --> 00:40:16,579
[Cole Nussbaumer Knaflic]: And one of my sons in particular is he's like our outdoor kid.

626
00:40:16,579 --> 00:40:19,079
[Cole Nussbaumer Knaflic]: And we just got done with summer here.

627
00:40:19,079 --> 00:40:26,039
[Cole Nussbaumer Knaflic]: But this last week of summer, he was out with his iPad and he had the app up and it will listen for all the different birds.

628
00:40:26,039 --> 00:40:26,879
[Cole Nussbaumer Knaflic]: And he came back.

629
00:40:26,879 --> 00:40:27,899
[Cole Nussbaumer Knaflic]: He calls it bird fishing.

630
00:40:27,899 --> 00:40:30,119
[Cole Nussbaumer Knaflic]: He's like, I got 87 birds.

631
00:40:30,119 --> 00:40:32,879
[Cole Nussbaumer Knaflic]: It tells them what they all are and will sing their song.

632
00:40:32,879 --> 00:40:33,480
[Cole Nussbaumer Knaflic]: I love that.

633
00:40:33,879 --> 00:40:39,879
[Tim Wilson]: So if it's funny when I'm in like nature, I may be out with a camera and you're watching people walk around with their phones.

634
00:40:40,199 --> 00:40:43,859
[Tim Wilson]: And my my octogenarian parents will do that as well.

635
00:40:43,859 --> 00:40:46,079
[Tim Wilson]: And I'm like, I know exactly what you're doing.

636
00:40:46,079 --> 00:40:49,859
[Tim Wilson]: Like you're you're looking up in the trying to figure out where is that?

637
00:40:49,859 --> 00:40:51,039
[Tim Wilson]: That's right.

638
00:40:51,039 --> 00:40:52,699
[Tim Wilson]: Yeah. Catching bird fishing.

639
00:40:52,699 --> 00:40:53,699
[Tim Wilson]: Yeah.

640
00:40:53,699 --> 00:40:54,699
[Tim Wilson]: That's awesome.

641
00:40:55,439 --> 00:40:56,779
[Tim Wilson]: Mo, what's your last call?

642
00:40:57,699 --> 00:40:59,319
[Moe Kiss]: I'm the total opposite direction.

643
00:40:59,439 --> 00:41:03,159
[Moe Kiss]: I'm a recent episode of Choiceology.

644
00:41:03,159 --> 00:41:05,960
[Moe Kiss]: Katie Milkman has had me deep thinking.

645
00:41:06,739 --> 00:41:09,439
[Moe Kiss]: So the episode was on algorithm appreciation.

646
00:41:09,440 --> 00:41:16,440
[Moe Kiss]: And it's new research from Jennifer Logg that shows that people trust and act on data from LLMs.

647
00:41:16,440 --> 00:41:23,440
[Moe Kiss]: Well, actually, technically, it's from the research specified algorithms more than when it came from a person.

648
00:41:23,440 --> 00:41:30,440
[Moe Kiss]: And they looked at it like in particular research for like song rankings, business forecasts, political calls, stuff like that.

649
00:41:30,440 --> 00:41:38,440
[Moe Kiss]: And yeah, like you think about it, like even in our day to day, like you're going to text your friend for a recipe or you can ask an LLM.

650
00:41:38,440 --> 00:41:38,679
[Moe Kiss]: And.

651
00:42:09,199 --> 00:42:12,639
[Moe Kiss]: Stakeholders use LLMs and data outputs.

652
00:42:12,899 --> 00:42:16,719
[Moe Kiss]: So anyway, I thought I'd share that and I'll hand over to Julie.

653
00:42:16,919 --> 00:42:17,259
[Tim Wilson]: Always.

654
00:42:17,539 --> 00:42:18,759
[Tim Wilson]: Always a good Katie Milkman.

655
00:42:19,319 --> 00:42:19,599
[Tim Wilson]: Yeah.

656
00:42:19,759 --> 00:42:20,939
[Tim Wilson]: Julie, what's your best call?

657
00:42:22,379 --> 00:42:26,579
[Julie Hoyer]: Mine is just a fun little quiz that I took.

658
00:42:26,579 --> 00:42:31,019
[Julie Hoyer]: I love reading, but this quiz was what type of reader are you?

659
00:42:31,119 --> 00:42:33,879
[Julie Hoyer]: And I was like, huh, I don't know if I could honestly answer that.

660
00:42:33,960 --> 00:42:38,559
[Julie Hoyer]: I could tell you generically, like, I don't ever really finish a self-help book, you know,

661
00:42:38,579 --> 00:42:39,179
[Julie Hoyer]: things like that.

662
00:42:39,199 --> 00:42:41,000
[Julie Hoyer]: Um, so what really captures me?

663
00:42:41,039 --> 00:42:41,839
[Julie Hoyer]: So I was like, I want to know.

664
00:42:41,919 --> 00:42:43,219
[Julie Hoyer]: So you take the quiz.

665
00:42:43,319 --> 00:42:45,579
[Julie Hoyer]: It's maybe 20 questions total.

666
00:42:45,980 --> 00:42:47,519
[Julie Hoyer]: Um, it's like, does this catch your eye?

667
00:42:47,559 --> 00:42:48,379
[Julie Hoyer]: Would you read this book?

668
00:42:48,419 --> 00:42:53,899
[Julie Hoyer]: And a couple like, um, scale prompts of like, would you agree or disagree with these statements?

669
00:42:54,319 --> 00:42:55,879
[Julie Hoyer]: Um, so it does not take long.

670
00:42:55,879 --> 00:42:58,399
[Julie Hoyer]: And then they have a really cool visualization at the end.

671
00:42:58,519 --> 00:42:59,619
[Julie Hoyer]: It's like a quadrant format.

672
00:42:59,759 --> 00:43:02,899
[Julie Hoyer]: And then they have these colors to talk about the different categories of readers.

673
00:43:03,000 --> 00:43:05,219
[Julie Hoyer]: I think there's like nine, eight or nine.

674
00:43:05,960 --> 00:43:07,460
[Julie Hoyer]: Anyways, I'm a yellow reader.

675
00:43:07,659 --> 00:43:09,139
[Julie Hoyer]: If anyone wants to take it and see if you're a yellow,

676
00:43:09,199 --> 00:43:11,139
[Julie Hoyer]: I'm like immersive yellow.

677
00:43:11,559 --> 00:43:12,859
[Julie Hoyer]: And then it gives you a nice summary.

678
00:43:13,159 --> 00:43:18,399
[Julie Hoyer]: And if you want, you could sign up and get your type of reading books sent to you every month.

679
00:43:18,559 --> 00:43:21,799
[Tim Wilson]: I thought I was going to say you were an academic paper formula reader or something.

680
00:43:22,259 --> 00:43:27,239
[Julie Hoyer]: No, no, I care about the people in the relationships or something like that.

681
00:43:27,279 --> 00:43:27,759
[Julie Hoyer]: But it's true.

682
00:43:27,859 --> 00:43:30,239
[Julie Hoyer]: I was like, wow, I know so much more about myself now.

683
00:43:31,480 --> 00:43:32,539
[Julie Hoyer]: What about you, Tim?

684
00:43:33,119 --> 00:43:35,039
[Tim Wilson]: So mine is a little silly as well.

685
00:43:35,039 --> 00:43:37,980
[Tim Wilson]: I was in Nashville a few weeks ago for work.

686
00:43:38,359 --> 00:43:39,119
[Tim Wilson]: And, uh,

687
00:43:39,199 --> 00:43:42,619
[Tim Wilson]: then wound up because I was there and things worked out.

688
00:43:42,679 --> 00:43:44,939
[Tim Wilson]: I wound up spending a couple of nights with a guy named Matt Cohen,

689
00:43:44,939 --> 00:43:49,119
[Tim Wilson]: who anybody who's ever heard me talk about the two magic questions of performance measurement.

690
00:43:49,119 --> 00:43:50,019
[Tim Wilson]: They're a hundred percent.

691
00:43:50,099 --> 00:43:52,739
[Tim Wilson]: I got them from Matt when we worked together years ago.

692
00:43:52,739 --> 00:43:55,039
[Tim Wilson]: He's now at Adobe and integrated services.

693
00:43:55,739 --> 00:43:59,579
[Tim Wilson]: So he's analytics tech by day, but he's really a musician.

694
00:43:59,679 --> 00:44:02,419
[Tim Wilson]: He's got his master's in music production from Berkeley.

695
00:44:02,419 --> 00:44:07,659
[Tim Wilson]: He has a whole studio behind his log cabin in Nashville.

696
00:44:08,239 --> 00:44:09,059
[Tim Wilson]: But all of that,

697
00:44:09,059 --> 00:44:09,420
[Tim Wilson]: just to say,

698
00:44:09,420 --> 00:44:12,719
[Tim Wilson]: I got to wander around this amazing studio.

699
00:44:12,779 --> 00:44:13,259
[Tim Wilson]: Um,

700
00:44:13,820 --> 00:44:18,059
[Tim Wilson]: and he played a song that he actually made a video with kind of silly AI

701
00:44:18,059 --> 00:44:19,059
[Tim Wilson]: animations in it.

702
00:44:19,079 --> 00:44:21,820
[Tim Wilson]: And it's just a delightful song called funky Saris.

703
00:44:22,259 --> 00:44:25,199
[Tim Wilson]: Like it's a dinosaur that is like funky,

704
00:44:25,219 --> 00:44:29,559
[Tim Wilson]: but he's in Nashville and he's like good friends with like Victor Wooten and

705
00:44:29,559 --> 00:44:31,639
[Tim Wilson]: like these big like name people.

706
00:44:31,639 --> 00:44:33,500
[Tim Wilson]: And so he had like legit like session,

707
00:44:33,500 --> 00:44:34,559
[Tim Wilson]: sax player,

708
00:44:35,219 --> 00:44:36,860
[Tim Wilson]: percussionist playing.

709
00:44:37,239 --> 00:44:38,880
[Tim Wilson]: It was like 48 tracks by the time.

710
00:44:38,880 --> 00:44:39,519
[Tim Wilson]: He was done.

711
00:44:39,519 --> 00:44:41,000
[Tim Wilson]: And it is a hilarious video.

712
00:44:41,000 --> 00:44:43,539
[Tim Wilson]: I've watched the song three or four times.

713
00:44:43,619 --> 00:44:44,099
[Tim Wilson]: Um,

714
00:44:44,340 --> 00:44:45,840
[Tim Wilson]: and that just makes me,

715
00:44:45,960 --> 00:44:47,119
[Tim Wilson]: it makes me smile every time.

716
00:44:47,119 --> 00:44:51,599
[Tim Wilson]: Cause I met him through analytics and there are a lot of musician types in

717
00:44:51,720 --> 00:44:52,500
[Tim Wilson]: our industry.

718
00:44:53,000 --> 00:44:53,500
[Tim Wilson]: Um,

719
00:44:53,960 --> 00:44:55,700
[Tim Wilson]: but he's one of them and it's,

720
00:44:55,700 --> 00:44:58,440
[Tim Wilson]: it's a delightful little video to watch and laugh at.

721
00:44:59,720 --> 00:45:01,500
[Tim Wilson]: So that,

722
00:45:01,680 --> 00:45:02,180
[Tim Wilson]: uh,

723
00:45:02,420 --> 00:45:02,780
[Tim Wilson]: Cole,

724
00:45:02,780 --> 00:45:05,579
[Tim Wilson]: thank you again for coming on.

725
00:45:05,640 --> 00:45:06,180
[Tim Wilson]: Um,

726
00:45:06,539 --> 00:45:08,880
[Tim Wilson]: we could so much.

727
00:45:08,960 --> 00:45:09,240
[Tim Wilson]: I,

728
00:45:10,119 --> 00:45:11,380
[Tim Wilson]: I'm going to keep checking back.

729
00:45:11,380 --> 00:45:14,519
[Tim Wilson]: And as you guys continue to figure out more and more stuff,

730
00:45:14,519 --> 00:45:17,400
[Tim Wilson]: these little nuggets on how to use AI effectively,

731
00:45:17,400 --> 00:45:20,220
[Tim Wilson]: I feel like you guys have really are nailing that.

732
00:45:21,480 --> 00:45:22,800
[Tim Wilson]: So for you,

733
00:45:22,800 --> 00:45:23,880
[Tim Wilson]: our listeners,

734
00:45:23,940 --> 00:45:24,440
[Tim Wilson]: um,

735
00:45:24,900 --> 00:45:30,780
[Tim Wilson]: if you are up for leaving us a review or a rating on whatever platform you're listening on,

736
00:45:31,059 --> 00:45:32,760
[Tim Wilson]: supposedly that will help us out.

737
00:45:32,940 --> 00:45:34,019
[Tim Wilson]: Now we love to do it.

738
00:45:34,019 --> 00:45:35,880
[Tim Wilson]: We do like to read the reviews when we get them.

739
00:45:36,599 --> 00:45:37,019
[Tim Wilson]: Um,

740
00:45:37,019 --> 00:45:38,079
[Tim Wilson]: if you'd like a sticker,

741
00:45:38,079 --> 00:45:38,760
[Tim Wilson]: that's also,

742
00:45:38,760 --> 00:45:39,600
[Tim Wilson]: that's a fun,

743
00:45:39,600 --> 00:45:41,160
[Tim Wilson]: our fulfillment warehouse,

744
00:45:41,160 --> 00:45:41,460
[Tim Wilson]: uh,

745
00:45:41,460 --> 00:45:43,500
[Tim Wilson]: has some extra supply.

746
00:45:43,500 --> 00:45:44,000
[Tim Wilson]: Um,

747
00:45:44,000 --> 00:45:47,520
[Tim Wilson]: and I mean the stack of little stickers sitting on my desk.

748
00:45:47,520 --> 00:45:49,800
[Tim Wilson]: So if you go to analyticshour.io,

749
00:45:49,800 --> 00:45:50,400
[Tim Wilson]: you can,

750
00:45:50,400 --> 00:45:51,360
[Tim Wilson]: we'll send you a sticker,

751
00:45:51,880 --> 00:45:54,060
[Tim Wilson]: reach out to us on LinkedIn,

752
00:45:54,060 --> 00:45:55,020
[Tim Wilson]: on the measure slack.

753
00:45:55,020 --> 00:46:08,200
[Tim Wilson]: You could just email us at contact at analyticshour.io with that for Julie and for Moe and for all the wonderful visualizations and data stories.

754
00:46:08,200 --> 00:46:08,700
[Tim Wilson]: Um,

755
00:46:08,760 --> 00:46:10,800
[Tim Wilson]: we keep trying to create,

756
00:46:10,800 --> 00:46:17,000
[Tim Wilson]: and we hope you keep trying to create what we most care about is that you keep analyzing.

757
00:46:17,000 --> 00:46:19,440
[Announcer]: Thanks for listening.

758
00:46:19,440 --> 00:46:22,260
[Announcer]: Let's keep the conversation going with your comments,

759
00:46:22,260 --> 00:46:22,980
[Announcer]: suggestions,

760
00:46:22,980 --> 00:46:28,800
[Announcer]: and questions on Twitter at analytics hour on the web at analytics hour.io,

761
00:46:28,800 --> 00:46:35,520
[Announcer]: our LinkedIn group and the measured chat slack group music for the podcast by Josh Crowhurst.

762
00:46:35,520 --> 00:46:36,420
[Charles Barkley]: Oh,

763
00:46:36,420 --> 00:46:38,340
[Charles Barkley]: smart guys wanted to fit in.

764
00:46:38,340 --> 00:46:38,600
[Charles Barkley]: So they made it.

765
00:46:38,599 --> 00:46:40,460
[Charles Barkley]: Made up a term called analytics.

766
00:46:40,460 --> 00:46:42,920
[Charles Barkley]: Analytics don't work.

767
00:46:42,920 --> 00:46:45,980
[Charles Barkley]: Do the analytics say go for it no matter who's going for it.

768
00:46:45,980 --> 00:46:47,380
[Charles Barkley]: So if you and I were on the field,

769
00:46:47,380 --> 00:46:49,099
[Charles Barkley]: the analytics say go for it.

770
00:46:49,099 --> 00:46:50,659
[Charles Barkley]: It's the stupidest,

771
00:46:50,659 --> 00:46:51,719
[Charles Barkley]: laziest,

772
00:46:51,719 --> 00:46:55,219
[Charles Barkley]: lamest thing I've ever heard for reasoning in competition.

773
00:47:08,599 --> 00:47:11,819
[Tim Wilson]: No one ever listen to me.

774
00:47:11,819 --> 00:47:15,000
[Tim Wilson]: No one ever heard me say go for it.

775
00:47:15,000 --> 00:47:16,880
[Tim Wilson]: No one ever listen to me.