EPISODE 82
everyday design & data visualization

TRANSCRIPT

Welcome to storytelling with data. The podcast where listeners around the world learn to be better storytellers and presenters with best selling author speaker and workshop guru Cole Nussbaumer Knaflic. We'll cover a wide range of topics that will help you effectively show and tell your data stories. So get ready to separate yourself from the mess of 3-D exploding pie charts and deliver knockout presentations. And with that here's Cole.

Hi, Cole here. Thanks for tuning in! 

It has been a while since I’ve sat on my own to record—much to my husband/producer’s dismay—it’s been primarily due to two reasons. First: I’ve been pretty busy since the publication of Daphne Draws Data in September—visiting schools, doing interviews for other people’s podcasts, and my time on the workfront outside of that has been mainly spent focusing on the next book, which we’ll share more about soon. The other reason that there hasn’t been a new episode lately is because I tend to do them when inspiration strikes. When I have an idea that hits me, and the energy to act on it. 

I’m not sure about you, but when I’m busy and said energy is pretty fully consumed between work and life, it doesn’t leave a ton of room for creative inspiration.

However, there are still moments. One thing that is fascinating about the human brain is our ability to ruminate. I’ve always found it interesting how it seems like the best ideas strike when we aren’t necessarily looking for them. This happens to me—and from the conversations I’ve had and reading I’ve done on the topic, I’m not alone, it’s quite common—that good ideas often come when we’re doing something mundane that is taking some attention, but not super active attention: for example while taking a shower, going for a run, brushing your teeth. Or yesterday, the idea for this podcast came to me while driving in the car.

It didn’t come totally out of nowhere—during our team meeting earlier in the week, we talked about how it’s been a while since we’ve put out an episode. So that was in the back of my mind. Then I had a conversation with one of my colleagues the following day about design and lent her a couple of books to borrow. Somehow, these two conversations connected when I wasn’t actively thinking about either of them—that generated the idea. But an idea on its own isn’t enough, to turn it into something, we have to act upon it. I sent myself an email with the topic so I wouldn’t forget, and then when I got back to my desk, I sketched out some notes for how I’d frame things and structure the episode, and that’s what I’m looking at right now as I transition into what I’d like to talk about today: everyday design and how it can inform how we visually design our graphs and slides.

I share this prelude as a reminder to appreciate these small but powerful moments. I think we sometimes fool ourselves into thinking we need a big block of time or space to generate ideas, but when the conditions are right, some of the space that already exists during our day can be used for this. Provided, that is, that we pay attention to the ideas and do something with them.  

I mentioned the conversation I had with my colleague earlier this week. Kaitlin is our newest team member, she joined in September and is focused on building awareness about Daphne Draws Data, particularly among educators. We’ve had to design a few things as part of this—bookmarks, ads for a couple of magazines. As you might suspect, I have pretty strong feelings when it comes to how things like this look. I expect super high attention to detail, aesthetic layout, and for things to be on brand. We were talking about some edits and Kaitlin said something to me like, I’m working on this, but I’m not a designer, I’m not great at design, I don’t have a good eye for it.

Which I think is nonsense. 

Now, she’s certainly not the first person to have said this, or the only person to feel this way. I call it nonsense because we all know good design when we experience it—and poor design when we’re frustrated by it. It can be useful to take the time to notice when we encounter either lovely or lacking design in the world around us, and think about what we might learn from it to inform how we visually design our graphs and communications.

I’m almost always paying attention to design, noticing the good and the not so good. I’ll share with you some examples that come to mind, and some that I’ve paid special attention to since I started planning to talk about this topic. By calling these out and drawing parallels to the visual design of information, I imagine that you might pay more attention and learn from the design of everyday things, too.

Let’s start with one of my favorite everyday examples: opening a door. I think everyone has likely experienced this at one point or another—where you go to open a door and you try it the wrong way first—you push when you’re meant to pull, or vice versa. Interestingly, people tend to blame themselves for this, “silly me, I can’t even open a door!. When really, this is a failure of design. The designer prioritized something else over affordance. An affordance is something about the design that indicates how the object is meant to be used. In the case of a push/pull door for example, a push plate or bar affords pushing (it’s the visual and physical cue that we’re meant to push on the door) and a handle affords pulling. The scenario in which these are mixed up, or positioned in a way that makes it unclear, leads to our mistake. 

If we translate this scenario to the visual design of graphs: affordances can play an important role here as well. Interestingly, the same phenomenon I described with the door often plays out with poorly designed dataviz, too—when the audience or reader doesn’t immediately understand what they are looking at, they blame themselves. “I’m not a numbers person” “I’m not good with graphs”. To that, I say phooey. It’s not their fault. The blame goes to the designer of the information for not making how to use it clear.

This is a failure of design, where the visual cue—the affordance—wasn’t clear. Just as the door failed to guide you, poorly designed graphs can fail their audiences by not making how to read them obvious.

When it comes to design affordances we can use with our graphs: we can title and label so there’s clarity of information and no one is confused about what they are looking at. We can make it obvious what to see by stating it simply in a takeaway title, and indicate where to look through sparing color or other means of intentional contrast to direct attention.

Another example where design can make things super easy or annoyingly difficult: packaging. 

If you’ve ever had the pleasure of opening an Apple product, you have hopefully taken a moment to appreciate it. The packaging is sleek and modern—I think it’s usually bright white, so it looks really clean, and it’s made of material that feels nice, sort of satiny to the touch. It’s clear in what order to open things, and nothing rips unnecessarily, things seem to slide around seamlessly until your product is revealed—and it’s also beautiful. 

Counter that with my recent experience opening a Barbie doll. Everything was incased in plastic. I had to roughly rip or cut the cardboard backing to get into the package. Once inside, Barbie was secured by what seemed like a totally unnecessarily number of elastic and plastic bands and ties, some nearly impossible to get off without cutting the doll, or myself in the process! On this note, you may have noticed that sometimes Amazon lists an option for a product with “frustration-free” packaging—and I think there’s a even usually price difference: pay more to not be frustrated! 

In data visualization, we also want make the experience seamless. Just like Apple’s packaging invites you in with clean design, clear labeling, and an intuitive structure, our graphs should guide our audience effortlessly to insights.

This reminds me of a question that I’ve been asked a number of times over the years: “Do I need to make it look pretty?”—is there value in making a graph look nice, or a slide aesthetically pleasing? Pretty for the sake of pretty—no. But there is value in aesthetics, in making something look nice. Studies have shown that when someone believes that time and energy was spent on the design of something, they are more likely willing to spend time with it and overlook design flaws. This implicit contract that we can create by being thoughtful about the design of our visual communications is definitely something I’d recommend we use to our advantage. 

If you feel unsure about how to make things look nice, a few things to pay attention to are stylistic choices like color and fonts, and structural components. For the latter, it really starts with attention to detail. Aim for clean vertical and horizontal alignment of elements, separate things with whitespace, allow there to be clean margins free of clutter. For the stylistic choices, if these aren’t set for you through company branding or templates and you don’t trust yourself, look for examples to emulate. When you see a good graph or slide, pause and reflect on what makes it work and how you might make use of similar ideas. Do the same when you encounter a confusing or otherwise not ideal graph or slide—consider what issues exist and how to avoid them in your own work. 

A great place to see a lot of example graphs and slides is in our online SWD community. You can explore users galleries—and, by the way, we’re getting ready to launch the ability to follow other community members, so you can stay up to date when people you follow post new things. I’ll also point your attention to the monthly SWD challenge, it’s a great way to see a lot of example graphs and data viz, I’ll link to it in the show notes.

Let’s take a quick break: when I return, I’ll compare one of my favorite—and a foe—kitchen tool.

[commercial]

Welcome back!

Let’s turn next to product design and discuss another example I’ve touched on before—kitchen devices. In my first book, SWD, I wrote about the OXO brand of kitchen tools. They used to have a slogan or catchphrase that was something like “tools you pick up”—or at least I think that they did, when I searched online I was having trouble finding it, though I did jot down the first sentence on their about page:

At OXO, we look at everyday objects and activities and we see ways to make things simpler, easier, more thoughtfully designed–better.

One of my personal favorites of theirs is the avocado slicer. It’s bright green, which makes it easy to find in the drawer. You can halve, pit, and scoop out and cut the flesh of the avocado all with a single super easy to use tool. It’s intuitive. Even my kids love it. I’ll contrast that with our can opener. My husband bought it recently because he wanted one that doesn’t leave a sharp edge on the lid you cut out, so it’s meant to trim the entire lid away. I like that in theory, however, he’s showed me how to use it multiple times and I can still never get it to work. I hid our old one in the back of the miscellaneous kitchen tool drawer, and it’s the one I dig out when I need to open a can.

In the case of the avocado slicer—the gratifying design experience—it wins on two fronts. Ease of use and, as a result of that, utility. The can opener is awkward and difficult to use, so I just don’t use it. 

When we’re creating graphs to communicate to others, we certainly want the OXO experience: we want the graph to be easy to use and for our audience to actually use it. This is one of the reasons we advocate for simple, common graphs for business communications. There are a trusted few that we find ourselves reaching for again and again and using with our clients: mostly bar charts and line graphs, with the occasional dot plot, slopegraph, or scatterplot, and every once in a while something more specialized.

The goal is never to over simplify, but rather, to not make things more complicated than they need to be. One great resource when it comes to learning more about basic graphs and also some twists and less common but occasionally useful varieties is the SWD chart guide, which I’ll link to in the show notes.

As I record this, we are squarely in the holiday season. Yesterday, I picked up new Christmas wrapping paper. This is another product that has quite a design spectrum, and I’m not even talking about the decorative design, but rather the physical design: look, feel, and performance. Perhaps you’ve cut through one of the great examples before the paper feels almost like satin, but it’s thick, yet scissors slide through cleanly, almost like you’re cutting air. On the other end of the spectrum, the scissors seem to choke on the paper, making a jagged cut or even ripping as the paper catches and snags in their blades.

When it comes to data visualization, the spectrum of design is much like that of wrapping paper. On one end, there are graphs and slides that feel polished and effortless—like the high-quality wrapping paper that glides under scissors. These are the visualizations where every element serves a purpose: the graph type is thoughtfully chosen, clutter is minimized, and the design aligns with the audience’s needs. The result? A seamless experience where the data’s story comes through clearly and effectively, almost as if the graph disappears and the insight speaks for itself.

On the other end of the spectrum are visualizations that feel more like that frustrating, low-quality paper—hard to work with and even harder to interpret. These might include cluttered slides with too many elements competing for attention, confusing graph types that make the audience work to understand the data, or colors and fonts that detract rather than support. Just as the poor design of wrapping paper can lead to a frustrating experience, poor data design leaves the audience struggling to cut through the noise to find the message.

The key lesson here is intentional design. Just as we might choose the right wrapping paper for a special gift, we should design our graphs and slides with care—choosing what fits the data, the message, and the audience. When we focus on clarity, simplicity, and purpose, we create visualizations that feel as satisfying to interpret as cutting through that satin-like wrapping paper.

Speaking of wrapping paper, the SWD holiday shop is open! Delight someone on your list with some of our favorite storytelling with data treasures. From signed books and a charming huggable plush data-drawing dragon to fun and functional office swag, our shop is full of special finds for the special people in your life. Visit storytellingwithdata.com/shop and use the code podcast10 at checkout for 10% off. 

Another parallel I’ve drawn before when it comes to the design of physical objects and the visual design of graphs and slides involves the trusty Swiss Army Knife. Now, don’t get me wrong—Swiss Army Knives are impressive in their versatility. They combine a corkscrew, a screwdriver, scissors, and more into a single compact tool. But here’s the thing: when you need to open a bottle of wine, would you rather use the corkscrew from a Swiss Army Knife or a well-designed, ergonomic corkscrew? When something tries to do many things, it doesn’t do any of them particularly well—or at least not as well as a tool designed specifically for the job.

The same concept applies to data visualization. The Swiss Army Knife of the data viz world is the stacked bar chart. It tries to do it all—showing part-to-whole relationships, comparisons across categories, and sometimes trends over time—all in a single graph. But because it’s doing so much, it often falls short, leaving the audience stuck trying to interpret too much at once. By breaking it into multiple, purpose-driven visuals, we make the data easier to interpret, just like using the right tool for a specific task.

In other words, it’s better to break the stacked bar chart into multiple, simpler graphs, each designed to do one thing well. For example, if you’re showing part-to-whole relationships, a simple bar chart or pie chart might work better. If you’re comparing across categories, grouped bar charts can be clearer. And if you’re looking at trends over time, line graphs usually do the job best. By using the right tool for the job, you create visuals that are easier to interpret and allow your audience to focus on the story the data is telling. Just like with physical tools, the best graph is the one that’s purpose-built for the specific message you want to communicate.

I recently asked people on X for examples of everyday situations where design makes something either disappointing or delightful. One response stood out to me—it was about speed bumps. The person noted that people’s reactions to unexpectedly encountering a speed bump can be quite funny, as long as no damage is done. It’s a perfect example of how design can catch us off guard in unexpected ways.

Speed bumps, when poorly designed or unmarked, can lead to surprise, discomfort, or even damage. But when they’re thoughtfully designed—clearly marked and appropriately placed—they serve their purpose without causing unnecessary distress. 

Similarly, a poorly designed graph can catch your audience off guard—making them slow down or stumble as they try to interpret it. If a graph surprises your audience in a way that’s jarring or confusing—whether through poor labeling, clutter, or unclear purpose—it disrupts the experience. But when the design is intentional and well thought out, the audience feels guided, not frustrated.

I mentioned that I’ve been paying special attention to design since I decided to focus on this topic. From just this morning, there were a few moments that caught my attention:

• My hair conditioner is in a small tub that has a lid that you have to unscrew. The tub is small and wide, which makes the lid too wide for me to get a great grip, particularly since my hands are wet. I find it frustrating every time I try to open it. If we apply the idea to visual communications, this is similar to making text too small. Always make the text on a slide or page bigger than you think it needs to be, assume not everyone’s eyesight is as good as yours. When the lid is too big for my hand, or the text is too small for my eyes, I can force it—stretch my hand in uncomfortable ways, squint so I can make out the letters—but don’t make me do this! Don’t make it feel like work.

• I had another frustrating try-to-get-it-open moment with my vitamins. My vitamin D is in a bag that seals like a ziplock, only with a lot more stick-together force. It’s nearly impossible to open. I need to find a different container for it and I won’t order this brand again. This is another example of why we should avoid frustrating our audiences: when we do, they may look elsewhere for solutions!

• To counter these negative experiences, I also had a nice one. There’s a brand of makeup and skin care products that I like. Last holiday season, they offered limited edition exotic and marine animal designs across some of their products. I realize, hearing myself say this, that it sounds strange, but they are beautifully designed. While picking up a gift for a friend, I decided to treat myself and bought a powder case that is metalic marine blue green with a jellyfish on it, and a brush to go with it that has a gold handle with a crimson snake wrapped around it. They are stunning. And they make me happy every single time I use them.

That’s what I suggest we aim to do through the way that we design: create delight.

Just thinking back over the examples we’ve covered today, a lot of it boils down to the feelings you want your audience to have. When it comes to our graphs and slides, we might set out to evoke feelings of ease and the confidence in our audience that they are looking at things the right way. To achieve that, we should be intentional and thoughtful in our design. We can use the lessons from all sorts of experiences we encounter in the world around us to better understand the ways in which we can do this.

When this happens—when we design our data viz well—people don’t really pay much attention to the graph—instead, they focus on the info—what it means, how to use it, what to do next. The conversations become less about the data, the graph, the slide, and more about the actions to take, the decisions to be made, and what it means in the context of the business.

Like any good design—when it’s done well and if you’re not looking for it, you may not even notice it. 

I suspect, though, as a result of this episode, if you haven’t already, you may start noticing the design of everyday things—which, by the way, is the title of a book on design I highly recommend for those interested in learning more (it was among the titles I mentioned I lent Kaitlin). The design of everyday things is by Don Norman. I’m also a fan of the Universal Principles of Design by Lidwell et al and Susan Weinschenk’s 100 Things Every Designer Needs to Know about People. I’ll link to those in the show notes.

I encourage you to take these lessons from everyday design and bring them into your own work, because thoughtful design isn’t just about aesthetics—it’s about creating clarity and connection.

Before I close, a couple of quick updates:

• Quick reminder that the SWD holiday shop is open! Visit storytellingwithdata.com/shop for unique, personalized gifts for the data enthusiasts on your shopping list. Order today to ensure delivery before the holidays.  Use the code podcast10 at checkout - that’s podcast-1-0 - for 10% off.

• We have just one public virtual workshop left this year and it takes place on December 16th. There are a few spots still available, you can claim yours at storytellingwithdata.com/workshops.

• Speaking of online learning, the next cohort of our highly acclaimed 8-week online course begins in January. Data storytellers Simon and Amy will take you through the world of storytelling with data, curating a multi-media learning journey anchored in core SWD lessons that are expanded upon and explored through weekly lectures, reading, videos, podcasts, exercises, and a course project. Space is limited an filling up quickly. Learn more and register at storytellingwithdata.com/course and use the code PODCAST10 for 10% off the registration fee. 

• For those planning even further ahead, our full 2025 public workshop schedule has been posted, we’re hosting in-person 1-day sessions with the entire SWD team in London and NYC as well as virtual half-day workshops throughout the year. Details and registration at storytellingwithdata.com/workshops.

• If you like to learn via video, check out our growing library on the SWD YouTube channel. You’ll find tips, tool tutorials, data viz makeovers, presentation strategies, and more. Visit storytellingwithdata.com/youtube.

• Finally, if you haven’t already, be sure to join our free online SWD community, where you can practice through our monthly challenge and exercise bank, exchange feedback, and be inspired. We also offer a premium subscription, which includes weekly office hours, live events, early access, special offers, and we have a super exciting new perk coming in the new year—our premium community members will be the first to know. You’ll find all of that at community.storytellingwithdata.com. This and the other links I’ve rattled off here can be found in the show notes.

Thanks for turning in. If you like what you hear, please write a review or share with a friend. 

Happy holidays from me and the entire team at storytelling with data!