#307: AI Does (and Does Not) Work for Data Storytelling

00:00:00.00 | Announcer: Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language.

00:00:15.90 | Tim Wilson: Hi, everyone. Welcome to the Analytics Power Hour. This is episode number 307. I’m Tim Wilson, and I’m joined today by my co-hosts Moe Kiss and Julie Hoyer. Are you two ready to dive into a discussion about how and where AI can be effectively employed when doing data visualization and data storytelling? Oh, absolutely. Yeah. And maybe where it is best left out of the process. All right. Well, for this discussion, I am super excited that we have brought on one of my absolute professional heroes. Cole Nussbaumer Knaflic is the founder and CEO of Storytelling with Data and the author of five bestselling books. Including? Storytelling with Data, a data visualization guide for business professionals, which I have personally recommended like in over 20 different trainings and presentations. She’s also written a children’s book, Daphne Draws Data, which I think has gotten Val’s four-year-old drawing all sorts of charts. Cole has spent the last 15 years teaching people around the world how to turn messy graphs into clear, compelling stories. And today she is our guest. So welcome to the show, Cole.

00:01:22.15 | Cole Nussbaumer Knaflic: Hi, Tim. It’s so great to be here.

00:01:24.50 | Tim Wilson: Awesome. So I think maybe a good place to start might be to just have you kind of reflect or look back on your own transition from kind of the pre-AI, not the semantically 1960s, but like, you know, when most of us kind of, it came on the scene from pre-AI to kind of where you are today. Like, were you skeptical or dismissive or did you like immediately see how you’d be able to incorporate it? Like, what was your experience in that transition?

00:01:53.65 | Cole Nussbaumer Knaflic: I was absolutely not. I was not an early adopter. Skeptical is probably a good word for it. I joked more than once that I planned to retire before I had to figure out how AI was going to impact data storytelling, because it clearly it’s going to change how we work. But it took me some time to get on board. And actually, it wasn’t really even a conscious decision, I guess. I had been approached by Canva to speak at their national or their… annual client conference and didn’t realize until after I’d already said yes, that the topic was data storytelling in the age of AI, which meant and that was April of 2026. So that meant I needed to spend some time in AI and start using it for data storytelling and understanding where it makes sense and where it maybe doesn’t so that I can start speaking about these things. And I kind of kicked myself a little bit. At first for saying yes to the conference. But then once I started using AI, I started kicking myself for waiting so long because it definitely is one of those things that the more you can play with it in a thoughtful way, the more you can see where all of the opportunities are, also where the dangers are. So it continues to be this simultaneously fascinating and terrifying tool from my perspective. But we’re very much trying to… to teach people how to use the fascinating parts of it and really finding that there are ways it can accelerate and 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.

00:03:43.80 | 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. As you’re working through a problem with AI. And I remember looking at one example and it took one of your colleagues like a few iterations with AI to get it right. 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. And it took like maybe four or five prompts to get it right. So I’m really curious, like as you started exploring, like what got you over that hump? Because I feel like that hump is the like hardest bit.

00:04:22.45 | Cole Nussbaumer Knaflic: I think that that is… The way that different people at different skill levels use AI should look different. 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. So for the person who isn’t proficient making graphs, hasn’t spent a ton of time. And actually, I encountered this recently. We’re in the middle of a five post series on using… 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. But the recent one I did was on choosing an effective graph. And I struggled with this one so much because I thought I was going to do it originally with Copilot. 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. But instead, I started testing across some different… tools and also across paid versus unpaid levels. 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. 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. If it’s what your company is using, that probably makes sense. Maybe with the… exception of Copilot, but we can talk more about that. 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. 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. And they said, well, yeah, but that’s because it’s you. 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. And so for me, it was trying to take a step back that felt really gross. 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. But I think still the prompt shouldn’t be give me the thing I want. The prompt should be help me look at some options and assess which are going to work in my scenario. Help me understand what the trade offs of each one are. Yeah. Got it. Great. Bye. Bye. Bye. Bye. of those are. Let me see them, mock them up so that I can see if I understand them or if I would feel comfortable explaining them or recreating them in my tool. So I think part of it’s a different way to think about prompting and not be prompting for the final output immediately, because you’re going to be disappointed when you do that. But really thinking of the way you use AI and the data storytelling workflow as a partner, as a sounding board, as somebody you can brainstorm with or get feedback on to check your grammar, to double check your math, to do all of these things that would take a person a lot of time and the results might be inconsistent. You can get beautifully efficient and consistent feedback in those areas from AI. I think that’s where we can think about using it smartly. And I think of it when it comes to any sort of analysis, you want to think of it almost like your junior analyst who knows enough to be dangerous to be able to do the right thing. And I think of it when it comes to any sort of but not so much that they’re going to be making the right decision all the time. And so you want to bring that lens in to the things that it gives you and be skeptical in order to have the right sort of back and forth to come up with something good. And I think that when you take the time to do that, I’ve seen the instances when I’ve worked with it myself, that I can come up with something partnering with AI in a smart and thoughtful way that is certainly better than what AI would have done on its own, but also better than what I would have done on my own, because it’s caused me to think in ways that I didn’t on my own. And I think that’s where the power is.

00:08:34.70 | Tim Wilson: I like the prompting part. I just, I get nervous. And this is kind of, I think where Moe was as well, that it’s kind of tough to take your expertise out of it. That if you, if you give it a prompt and it comes back and you’re like, that’s not good where I, I get concerned is pre AI. I would watch analysts and business users. They’re so caught up in just getting the data displayed accurately that as soon as they get to some first chart, they’re like, this is good. And then they, over time have to develop that. Well, yeah, but that’s not effective. Like it’s, it’s accurate. And I worry that without having built up some of that experience, like doing the critiquing that you’re like, it’s so quick to say, and they’ll get caught up in saying, is it accurately representing it? Cause I think even Simon on the, uh, the YouTube, um, video called that out too, that it’s like, oh, you get caught

00:09:35.02 | Cole Nussbaumer Knaflic: up in, is it accurate and not, and not stepping back and saying, is it good? Is it effective? And Tim, I definitely don’t disagree with the idea that the human should learn the foundation. I don’t, the part I get very afraid for is, you know, you take somebody who has experience, then they can partner. With AI in ways that make everything stronger. You take somebody without experience though. And I think anybody who’s worked with AI has gone through where, you know, the first thing it gives back or the thing it gets back, it looks shiny. And if you look at it quickly, you’re like, oh, this is great. This takes me so much time, but then you start looking more closely and like, oh, but no, I would have done this differently. And you know, oh, this is inconsistent. And oh, that data is outright wrong or whatever the case is. And so the human having the foundation, is essential to be able to direct AI, whether it’s doing things for you or acting more like a thought partner or brainstorming partner to be able to do those things more effectively. Jumping straight to AI without the foundation is a truly terrifying thing.

00:10:43.02 | Michael Helbling: Tim, we need everyone to mark their calendars for November 10th for a very special free conference from Stape. You have my attention. Stape is hosting Stape Reads. They’re a free virtual event for technical marketers, analysts, and server-side tracking practitioners. And this isn’t six hours of product demos. Nope. There are 18 practical sessions with speakers, including Simo Ahava, Julius Fedorovicius from Analytics Mania, Juliana Jackson, and more.

00:11:11.74 | Tim Wilson: Oh, that’s a pretty serious lineup. I see many previous guests of the Analytics Power Hour on

00:11:17.52 | Michael Helbling: the speakers list. Yeah, absolutely. There’s two different tracks. You can move between product focus sessions and the analytics power hour. And you can also do a lot of different things.

00:11:23.32 | Tim Wilson: And broader strategy. Plus live Q&A, some swag, and even a certificate of attendance if LinkedIn

00:11:28.72 | Michael Helbling: credentials are your thing. Oh, yeah. And did we mention it’s free? We did, but it’s worth mentioning again. Click the link in the show description and register for Stape Relay, November 10th. You don’t want to miss it. Michael, what’s the most annoying thing about working with AI? Besides it confidently explaining my own business back to me incorrectly?

00:11:49.64 | Tim Wilson: Well, that was fair, but I was thinking memory.

00:11:53.32 | Michael Helbling: Oh, yeah. Every new chat is like meeting a very smart co-worker who got hit on the head

00:11:57.92 | Tim Wilson: over the weekend. That’s what Prism from Ask-Y is trying to fix. Its memory portal lets you actually manage what your agents know about your data and your business. Manage it how? You can search it. You can tune it. You can endorse what’s right, reject what’s wrong, even adjust it in plain English. So if the AI thinks customer means

00:12:16.34 | Michael Helbling: anyone with an email address, I can say absolutely not, Kevin. Or George or Fred. Exactly. You control the knowledge layer and you can manage it. And that’s what we’re doing.

00:12:23.32 | Tim Wilson: And your users benefit from that context every time they ask a question.

00:12:27.30 | Michael Helbling: Oh, which means less re-explaining what revenue means, which table is correct, and why Q2 has that weird little situation. Your team gets the benefit of curated context without becoming professional AI babysitters. Finally, institutional memory without having to ask, who remembers why we did this?

00:12:47.04 | Tim Wilson: Want to try it? Click the link in the show description and sign up for the Ask-Y waitlist. Use code APH and the link will take you to the link. And they’ll move you to the top of the list.

00:12:55.20 | Michael Helbling: Give your AI some memory, preferably better than ours.

00:12:59.08 | Moe Kiss: So I’m going to be full disclosure. Often when I’m doing a data viz using AI, I will literally be like, you’re Cole Knaflic. You’re a data visualization expert. That is like

00:13:07.92 | 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.

00:13:13.44 | Tim Wilson: Okay. But then I want to give the ability to tee off because I said, I was like, oh, Cole said she did that. So I’m going to see if I can make a skill in Claude. And it did. But that’s different. And I haven’t really tried it. And now I’m ready for Cole to just take a big old swing like, no!

00:13:28.12 | Cole Nussbaumer Knaflic: I was trying to understand why that made me so uncomfortable because I like the idea of it. My first thing was like, oh, that’s really cool, right? You take storytelling with data and you just kind of give it to AI and then what you get should be great, right? I don’t know. Something was working the wrong way with that.

00:13:43.42 | Moe Kiss: It feels icky also though because you’re not getting credit for your work. That to me feels icky. Your work is being used. You mean my work?

00:13:51.40 | Cole Nussbaumer Knaflic: Yes. But that’s a whole separate conversation.

00:13:52.88 | Moe Kiss: That part.

00:13:53.32 | Cole Nussbaumer Knaflic: Yeah. That part I actually am. I mean, I probably should care about that, but I’m less concerned by. My view is the more that it’s out there and that means people are doing better things. Like I’m good with that outcome. I think what gave me pause was the skill thing for Claude is much more, here are rules. Claude, go apply these rules. Whereas I don’t think that’s the goal. And that runs the risk of really taking it. Yeah. Yeah. That’s what I think is really important. Because if you don’t apply those rules, then you’re just taking the thinking out of it. And even the responsibility out of it. Like, oh, I gave Claude the thing and Claude did this. So Claude’s wrong, not me. Like, oh, it’s not going to work that way. But I think more than that, it’s the human who needs the foundation because the goal isn’t to give rules. The goal is that the human understands things, understands the principles, and then can understand when and how to flex those. than with AI. It’s not AI go do the thing. That might be fine for routine tasks or things that can be very prescriptive. But I think the magic when a data visualization is striking and effective or when a data story is, you know, when there’s that magic, that power is because a person has looked at that and they’ve decided, when do we do things in the way that, you know, that we should? When do we follow the principles and when do we not? And why does that make sense in this case? And how do we make those things balance in a way that’s going to make the outcome that we want more likely than it otherwise would have been? And when people come to workshops or when people are early learning these skills, they often are asking for rules. They’re asking for checklists, which I understand, but it’s not the right spirit of things. Or at least you’ve got to learn maybe the rules and the checklist, but then you’ve got to move beyond that. Because it’s the knowing the ground rules and then knowing when to flex that I think is when the good stuff happens. And if we go straight to a tool and we say, here are the rules, all of that runs the risk of getting mixed, missed.

00:16:04.89 | Tim Wilson: So I’m, I feel like I want to get stronger on that because I, that feels right. I will just to the skills credit, it did step number one was understand the context. And before designing anything explicitly ask, who’s the audience? What do they need? How will it be consumed? So it was like the skill wasn’t saying take it and just run with it. It was going to kind of push back. So that feels like there’s a little bit of that. The skill is saying you can’t just apply rules. You got to get more. I still am not feeling great about it for everything you just

00:16:37.60 | Moe Kiss: said, but. But so I guess the nut, oh, I don’t know the crux of the problem though, that I’m trying to understand previously we would have a slide and I have this a lot where it has a very strong sense of where we want to go and where we want to go and where we want to go and very shitty, shitty, shitty data visualization on it. And I feel sick to my stomach. And still, despite managing a big team, I end up fixing people’s slides. Or I mean, I do go back to them and be like, you need to fix your slide and here’s why. I feel like these are the same people that are going to be writing prompts like this or skills like this or doing the going back and forth with AI, right? Like you still need to care and understand data visualization to your point, Cole. And I suppose the thing I’m thinking about is, and I’ll come full circle on this at the end of the episode when I get to my last call, but it’s like people are using AI more for data. But then it’s like, how do we still get them to care about investing that time in the visualization and the data story that has to accompany it versus the shitty first draft you get back? And it’s like, from an organizational perspective, how do you still encourage that and build that into the AI workflow?

00:17:39.27 | Cole Nussbaumer Knaflic: In my experience, the way that that works well is you get some part of the organization or you get some individuals who do care and who are spending the time and you get them the resources that they need to continue to spend time and get better doing those things so that over time you can see the efficacy of when graphs are designed thoughtfully and when PowerPoints aren’t just thrown together. There’s thought in the experience or the journey you take your audience on. And when you can see the reaction and not only reaction, but the change, or the decision that those things drive, that then that can start to amplify in a really nice way. Because now it’s not somebody top down saying, team, you need to learn these skills. It’s check out how this is effective. And by the way, this is a skill we can all develop. We can all get better at this. And here’s what that looks like. And by the way, 90% of it is out of AI. Oftentimes the thing that is most helpful in those instances, and I think it’s, is things that people are the least likely to do because they don’t necessarily feel productive because they involve archaic tools like pencils and paper, but really stepping away from, Moe in your example, step away from the data, right? You’ve done the data. Yeah, Tim’s holding up a page of sketches. I love it, but get out of the data and think about your audience and what you now know and how you can turn that into something that’s going to land with them and resonate with them. And what other information needs to be brought in so that they’ll be on board? How do you take them through that to, in a way that’s going to line you up for success? And when you do those things in an analog way, there is friction and it can feel bad. It slows you down, but that slowness is what makes it effective because then once you’ve got a plan in place, whether you spent time getting really succinct on your message, or you might’ve spent time storyboarding, looking at your email, it looked like a storyboard maybe that Tim showed, and you’re vetting what potential content could look like and the flow, you’re maybe sketching what graphs could look like. Though I do think that’s an area that AI can help expedite for us. But when you spend that time thinking upfront and you introduce friction on purpose and with purpose, that actually makes the whole rest of the process much more efficient. And if you invest the time doing that upfront, these like human things, then you’re going to be able to do a lot more. You’re in a position where you could probably pull AI in and use it in ways that’ll help make the other parts of the process more efficient. You don’t have to. And as we’ve talked about somebody who’s good at this stuff, they’re going to be faster doing it on their own anyway. Or they can look for points where there’s friction of the not great type, right? Friction like, you have to manually enter all of this data into from one tool to the other, or recreate the same graph 20 times. Like those sorts of things AI can do really well. Careful, still check your data. But there was an article that my colleague Alex posted on the blog recently. And actually, this was one of the early uses of AI for making slides that I saw where my mind was kind of blown and I was thinking, wow, this could change the way we work immediately and forever if it’s really this good. Which was, she had an example. It was a client makeover that we were doing. It was a client that was doing a workshop at the time. And it was this, it was like a bar, two series bar chart where one of the bars was stacked and it had a lot of different categories. It was revenue that was broken down over time and then also across regions. And it just, it was almost impossible to see anything in the bar chart. So she had an idea of what she wanted to do, which was small multiples. She wanted to change everything into line graphs. And so she had, she sketched one large version, that was for everything. And then each of the small multiples that was for the individual regions. And just sketched it without even data, right? Here’s just the layout and what I want it to look like. And she then turned to Claude in PowerPoint. There’s an add-in, ChatGPT has a similar thing. I would hope Copilot would have similar functionality, but I’m not sure that it does. In any case, so she gave her sketch and the original graph to Claude in PowerPoint. And said, can you remake this? And it did. And the first version it does isn’t bad. It’s exactly what she drew out. It’s all the right data. And part of her point, because she goes through these iterations in the blog that she later wrote, or she anonymized the data, but kept it true to the path. And she said, many people would have stopped here. And that actually probably would have been okay. But because I love the design piece, I’m going to take things a little further. And so she spends a little bit more time, more time now, time that she has free. Because she didn’t just have to go put all the data in and make those iterations of the different graphs. And yet her final version looks 20x what Claude came up with because she’s got that skill set. And I think that’s a really interesting use of… Making use of AI where you can gain efficiency on the things that were gonna happen behind the scenes. Nobody was gonna appreciate really the time spent there anyway. And it would have been grunt work. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. Or teamwork. tedious work, I should say for her. And she’s able to then instead spend that time or more time on the design, the part that she actually enjoys doing and is really good at and get this superior result as a result. And she would have gotten there on her own anyway, it just would have taken longer. And so the way that she’s able to now divide her time differently because of the

00:23:38.72 | Julie Hoyer: efficiency gains you get, I thought that was a neat example. You’ve had some great points throughout the conversation so far where I’ve been jotting down different effectiveness of AI or the efficiency gains. You’ve listed a couple of different ones. And I’ve been wanting to ask you, because we’re saying that the human in the beginning is still so important and the person using AI to help them with their visualizations and their storytelling, they need to have those foundations. Where then does AI… Give a benefit. Is it solely in the… Does it grant greater access to someone to the data to be able to do this and spin something up? Is it solely speed? Is it the repeatability piece? Is it maybe a mix of a lot of things? But I’m curious to hear specifically, how would you summarize that?

00:24:38.41 | Cole Nussbaumer Knaflic: Yeah, I think it can be pieces of each of those things. And I think where people stand to get the most benefit, we touched on this a little bit earlier, but might come back to where their skill level is and what kind of work they enjoy doing. Because to some extent, we can think about the pieces we enjoy less, some of that, those might be opportunities to bring in AI. They won’t always, right? Because I also mentioned people don’t want to stop and build a storyboard or spend time physically writing out their message and then proofreading and adjusting and making it better. But I do think where we find… We find things where like, eh, this feels gross. I don’t want to do it, but I know I should. We can still bring in AI in ways that it almost makes it more fun. Fun’s maybe the wrong word, but can help us be more robust. I think, and that was missing maybe from your list, because we haven’t talked about this yet. Because I do think that AI to help poke holes in our thinking or our logic, or to help us even like wordsmith or come up with other ideas of what our audience might care about. And I think that’s a really good point. And I think that’s a really good point. And I think that’s a really good point. And I think that’s a really good point. That we actually haven’t considered that it can be helpful for putting pressure on our initial ideas in ways that can be useful and just help us be more critical in how we’re thinking about things versus less. And I actually love that because I think a lot of the headlines, we see, oh, there’s a study that AI makes people think less. And it’s like, well, yeah, it easily could do, but it doesn’t have to. That all comes down to how you’re using it, what you’re using it for. And so I think if our goal is not, let’s show where AI can fail, right? We have a good sense of where it can fail. We can give it prompts that it will do that immediately. I did a video like that a few months ago now, but it was intentionally wrong, if you will, of like, I’m just going to go to all these different AIs and ask it to make a graph and ask it to tell me a data story. It’s not going to do that well. We knew it wasn’t going to do that well. So then we can kind of laugh at aha and feel sorry. And then we can kind of laugh at aha and feel sorry. Oh, humans are still useful. It didn’t do that well. But that’s not how we want to be thinking about using it. And it’s not a reason to discount it. Rather, I think if we are looking at our own workflow and saying, where could I bring AI in, right? If my workflow, once I’m communicating data looks like, you know, I want to start by considering the audience and my message and plan what my story is going to look like, then I want to really kind of make sure that that’s a coherent story. And then I want to kind of make sure that that’s a coherent story. And that one point leads logically to the next. Then I want to think about where does data come in? What data do I have? What could it look like? What would be compelling for my audience? Now that I’m working with the data, I’m making graphs, what graph is going to work for what I want to show or what I want my audience to see? How do I get rid of clutter that doesn’t belong, drive attention to where I want it and really build an experience to walk my audience through that’s going to communicate the thing that I want to do? I want to communicate and hopefully drive them to act in the way I want them to. And we can think about, okay, what has that looked like for me historically as the human? And now where can I bring AI in, in pieces of that to help make my work and my thought process stronger? And if we do that and we’re thoughtful around all the pieces that go around it, then we can have really smart conversations with AI. And it’s not always going to be right, right? It doesn’t, it never gives like the answer. So I think really, really important to have that conversation with AI. And I think that’s one of the things that we need to be thinking about. And I think that’s one of the skeptical through that. But we can be thoughtful about when it might make sense to do that, to get output that is different than we would have come up with on our own.

00:28:26.63 | Julie Hoyer: Back to the benefit thing. I think it’s interesting, though, a lot of people, as we’ve talked about on some previous episodes, default to the idea that it’s going to make you faster. And I think especially when you’re talking about data storytelling, like that is definitely not the goal of using AI. A lot of times, I was just talking to a colleague and he came up with a great, like, I guess you would say, more than a hundred percent, but he came up with a great, like, I guess you would say more like infographic. It was like a client value loop that he had been working on building in it. And it looks amazing. And I asked him, I said, I know you had to have used AI to help because I don’t know what tool you would have gone and made that yourself. It looks great. And I asked him how long it had taken him. And he said, oh, it took me at least a week of going back and forth. And so I was really curious his process. And he said that he had to be super thoughtful before going to AI and go back and forth with it and give it a pre-drawing like you were just saying in your example. And so I think it’s really important to have that for people to understand. Like, I don’t think any of us have an example where you’re able to give a kind of like a hand wavy gray story and have AI refine it. Like I’ve noticed you can give AI something succinct and it tends to make it longer and stress maybe not the exact point. So you have to, like you’re saying, you just, you have to go in, I think, with a lot of conviction of those main pieces you need out of it. And with that said, I am curious if you have any guidance on prompting it or what you do. Bring specifically to using AI. I know you said there are no rules, like perfect rules, but.

00:29:48.93 | Cole Nussbaumer Knaflic: Well, I mean, there are ways to do things better and less well, right? Just on that note, though, I think one thing for people to be aware of is just how AI works and some of these tendencies it has because of what it was built to do and how it’s been programmed. So AI will typically default to breadth, right? And bringing in everything versus concision, which means if you want it to act against that natural tendency, and natural feels like the wrong word there, but against that tendency, then you need to instruct it to do so, right? Don’t add more information or suggest more things to bring in. I’m working on making this concise or I have this limitation. AI also, I think everybody’s aware of this, but it, it gauges its own success on whether you, the user are satisfied. So it is more likely to tell you, you are right. And anything else. And so guard against that. You can tell it, you know, you don’t need to be nice to me, be direct. I want you to challenge my thinking. And this is one of the reasons that I, you know, personally, I jump around across different AIs. I have certain ones that I like for certain things, but I also, I’ll pit them against each other. I was like, Hey, I, I, a friend, a friend told me this, I disagree. What do you think where you can kind of do that? You can do that actually for a double checking data integrity, as well, have them double check each other. But so some of this is understanding how AI is working so that when you need something that is different from that, you are aware and looking out and directing it. Otherwise, I think when it comes to useful prompt one, I mean, I do tell it who I am. So it knows and let it know that I want you to follow the principles outlined. You never forget who I am. Well, I think it was the, I think it was the YouTube live event that we, we’ve talked about, but where I was going back and forth, I was working with a lot of different ones, but Gemini at one point gave me a really good one. And then the next iteration gave me not a great one. And, or no, I remember what it did. It followed like this awful purple template that I was pointing it to, but in a way where like the whole background was purple and the whole graph was yellow. And I was like, okay, you like, you really followed that to the T rather than the spirit of what I was going for. And I thought to myself, if you only knew who I was, then I was like, wait, I didn’t tell it. And as soon as I said who I was, you know, then you get like the color palette and everything. That works out. Right. But I think for prompts, another one that I find useful is I end almost any prompt with something along the lines of, if there are questions I can answer for you, that would help you give me better input. Start by asking those. Cause if you don’t actually ask for that, it’ll jump straight to the output. It’ll make assumptions along the way. And that’s where I the expectation of what you’re going to get and the mismatch of what comes up becomes wider. Whereas if you, recognize that you’re thinking a lot of things in your head that you haven’t taken the time to type, and it’s not necessarily clear which of those is going to be really important and which isn’t then having it ask you. So it’s not making those assumptions. And so that it can be, being more robust in, in what it comes back with. I find very useful. I will say that different AIs come back with different amounts of questions. Some are overwhelming. Some are easier to answer. Some are more difficult to answer. Some are more difficult to answer. Some are easier to answer. Some are more difficult to answer. Some are more difficult to answer. Some are more easier to work with when it comes to that. And so some of it’s finding a fit that way, I think as well. I don’t know. It’s I’m curious to see what’s going to happen in the tool landscape over time. And so I am. I think careful at this point, not to go, not to anchor myself too much to any single tool, knowing that that landscape is going to change. And so for the things that we teach at storytelling

00:33:34.32 | Tim Wilson: with data. Groks charting is amazing. I’m not going anywhere outside of Grok. I’m kidding. I’m not. Oh yeah. I was thinking through. I’m not even calling it an actual tool.

00:33:41.92 | Cole Nussbaumer Knaflic: I was thinking through how to respond to that.

00:33:46.00 | 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? 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? Or do you like it when I hear you talk about it? And for those listening, like when Cole talks about data viz, like her whole face lights up. Like you can tell she loves this topic and she’s so passionate about it. And I’m like, when we look at the future, do you imagine this being a prompt thing? Or is the editability still really important to you?

00:34:20.21 | Cole Nussbaumer Knaflic: I don’t have the answer here. I suspect that will come down to how people like to work. I can imagine some people getting there through the prompting entirely. It’s like, do you go through the drop down menu where you’re actually writing code? Different people will do that differently, sometimes because of skill set, sometimes because it’s what they were taught. Sometimes it’s because for the thing that they’re doing in the moment. That makes sense. 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. 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. I would like to go in and move the title. I would like to go in and move the title. I would like to go in and move the title. I would like to go in and move the title. I would like to go in and move the title. But sometimes people don’t see that. It drives me nuts. I’m like, why don’t you see that needs to move a little bit? That’s the kind of the counter to that, right? It depends. 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. So it’s like everything. It’s when do you flex it? Like when? Does it need to be perfect? When do you when is good enough? Okay. And I definitely used to be of the mindset that like good enough is never okay. It should always be perfect. And like somebody can easily drive themselves crazy with that approach.

00:35:54.98 | Julie Hoyer: I just want all the fonts to match when I get sent to that.

00:35:57.79 | Cole Nussbaumer Knaflic: You know, I feel like that’s fair, but sometimes that’s also and that’s actually Julie. 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.

00:36:09.29 | Julie Hoyer: Oh, I’ve never thought. To use it in my feedback. That’s I’d be like just baseline. Hey, go through this. And these are my nitpicky things like flag them for me.

00:36:20.78 | Tim Wilson: You’ve said it a couple of times that thinking of AI is the junior analyst. And I think I’m trying to bring a few different themes together. 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. If there’s someone who’s got the foundation. And they’re using a junior analyst that can work. 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? Like that’s one way for a junior human analyst to learn, which also seems like a junior. So it’s kind of in that. Yes. Check for the font consistency. But it could also be. Who do you think? What’s the what’s the message that you’re most getting out of this deck or what? You know, what is really clear about this? Because. Junior analysts, when forced to kind of assess a visualization or a slide deck, can actually learn as they go and give good feedback.

00:37:19.08 | Cole Nussbaumer Knaflic: And the same with your AI, right? 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. And I think that is one of the great benefits we can get from AI is just it’s another perspective. It’s somebody. It’s like another lens on. Your work. 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. 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. And that’s where I think the consistency of a machine versus a human can be really beneficial. It’s just have it check for errors. And so. You’re not spending your time maybe as much there. And then you’re spending your time on the critical thinking of like, you know, what should the content be? How do I get from one thing to the next? How do I make this work for my audience and the setting and all of the other things?

00:38:24.09 | Julie Hoyer: It’s funny. That feels so obvious now that we’ve said it. 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. So that’s amazing.

00:38:32.13 | 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. I will. 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. 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. But there are a lot of really, really useful videos. 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. But. So this has been a great discussion. I wish we could talk for another hour and a half, but we can’t. 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. And Cole, you’re our guest. Do you have a last call?

00:39:33.53 | Cole Nussbaumer Knaflic: I do. I took this in maybe a strange direction because it has nothing to do with what we’ve just been talking about. Or it’s maybe the antithesis. This is what we’re just talking about. I’m not sure. Although it is an app. So maybe not entirely. But it is the Merlin bird ID. And this this isn’t new, but I’ve recently. So it’s migratory starting to be migratory season where I live. And so lots of birds. And so it’s it’s come up more for us lately. But it’s put out by the Cornell Lab of Ornithology. 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. Versus less. And one of my sons in particular is he’s like our outdoor kid. And we just got done with summer here. 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. And he came back. He calls it bird fishing. He’s like, I got 87 birds. It tells them what they all are and will sing their song. I love that.

00:40:33.87 | 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. And my my octogenarian parents will do that as well. And I’m like, I know exactly what you’re doing. Like you’re you’re looking up in the trying to figure out where is that? That’s right. Yeah. Catching bird fishing. Yeah. That’s awesome. Mo, what’s your last call?

00:40:57.69 | Moe Kiss: I’m the total opposite direction. I’m a recent episode of Choiceology. Katie Milkman has had me deep thinking. So the episode was on algorithm appreciation. And it’s new research from Jennifer Logg that shows that people trust and act on data from LLMs. Well, actually, technically, it’s from the research specified algorithms more than when it came from a person. And they looked at it like in particular research for like song rankings, business forecasts, political calls, stuff like that. 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. And. Stakeholders use LLMs and data outputs. So anyway, I thought I’d share that and I’ll hand over to Julie.

00:42:16.91 | Tim Wilson: Always. Always a good Katie Milkman. Yeah. Julie, what’s your best call?

00:42:22.37 | Julie Hoyer: Mine is just a fun little quiz that I took. I love reading, but this quiz was what type of reader are you? And I was like, huh, I don’t know if I could honestly answer that. I could tell you generically, like, I don’t ever really finish a self-help book, you know, things like that. Um, so what really captures me? So I was like, I want to know. So you take the quiz. It’s maybe 20 questions total. Um, it’s like, does this catch your eye? Would you read this book? And a couple like, um, scale prompts of like, would you agree or disagree with these statements? Um, so it does not take long. And then they have a really cool visualization at the end. It’s like a quadrant format. And then they have these colors to talk about the different categories of readers. I think there’s like nine, eight or nine. Anyways, I’m a yellow reader. If anyone wants to take it and see if you’re a yellow, I’m like immersive yellow. And then it gives you a nice summary. And if you want, you could sign up and get your type of reading books sent to you every month.

00:43:18.55 | Tim Wilson: I thought I was going to say you were an academic paper formula reader or something.

00:43:22.25 | Julie Hoyer: No, no, I care about the people in the relationships or something like that. But it’s true. I was like, wow, I know so much more about myself now. What about you, Tim?

00:43:33.11 | Tim Wilson: So mine is a little silly as well. I was in Nashville a few weeks ago for work. And, uh, then wound up because I was there and things worked out. I wound up spending a couple of nights with a guy named Matt Cohen, who anybody who’s ever heard me talk about the two magic questions of performance measurement. They’re a hundred percent. I got them from Matt when we worked together years ago. He’s now at Adobe and integrated services. So he’s analytics tech by day, but he’s really a musician. He’s got his master’s in music production from Berkeley. He has a whole studio behind his log cabin in Nashville. But all of that, just to say, I got to wander around this amazing studio. Um, and he played a song that he actually made a video with kind of silly AI animations in it. And it’s just a delightful song called funky Saris. Like it’s a dinosaur that is like funky, but he’s in Nashville and he’s like good friends with like Victor Wooten and like these big like name people. And so he had like legit like session, sax player, percussionist playing. It was like 48 tracks by the time. He was done. And it is a hilarious video. I’ve watched the song three or four times. Um, and that just makes me, it makes me smile every time. Cause I met him through analytics and there are a lot of musician types in our industry. Um, but he’s one of them and it’s, it’s a delightful little video to watch and laugh at. So that, uh, Cole, thank you again for coming on. Um, we could so much. I, I’m going to keep checking back. And as you guys continue to figure out more and more stuff, these little nuggets on how to use AI effectively, I feel like you guys have really are nailing that. So for you, our listeners, um, if you are up for leaving us a review or a rating on whatever platform you’re listening on, supposedly that will help us out. Now we love to do it. We do like to read the reviews when we get them. Um, if you’d like a sticker, that’s also, that’s a fun, our fulfillment warehouse, uh, has some extra supply. Um, and I mean the stack of little stickers sitting on my desk. So if you go to analyticshour.io, you can, we’ll send you a sticker, reach out to us on LinkedIn, on the measure slack. 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. Um, we keep trying to create, and we hope you keep trying to create what we most care about is that you keep analyzing.

00:46:17.00 | Announcer: Thanks for listening. Let’s keep the conversation going with your comments, suggestions, and questions on Twitter at analytics hour on the web at analytics hour.io, our LinkedIn group and the measured chat slack group music for the podcast by Josh Crowhurst.

00:46:35.52 | Charles Barkley: Oh, smart guys wanted to fit in. So they made it. Made up a term called analytics. Analytics don’t work. Do the analytics say go for it no matter who’s going for it. So if you and I were on the field, the analytics say go for it. It’s the stupidest, laziest, lamest thing I’ve ever heard for reasoning in competition.

00:47:08.59 | Tim Wilson: No one ever listen to me. No one ever heard me say go for it. No one ever listen to me.