Welcome to another infrequent edition of Will's Newsletter, and happy first day of May! (can it please stop being cold now, ty)
If Elon Musk really does buy Twitter, this newsletter might become my only way to launch my thoughts into the void—then you'll get more than one of these emails every couple months, for better or worse. For now, my feelings on the whole Twitter thing can be summed up by this faux Onion-esque headline I came across, "Heartbreaking: The Worst Person You Know Bought the Website You Hate but Spend All of Your Time On"
From June 14–18 I'll be at EYEO Festival in Minneapolis, if you'll be there hit me up and we'll hang out!
Lately, I've been doing a lot of teaching. I gave several talks on data journalism, I've been writing and teaching for the Elevate membership, and approaching the finish line on my dataviz design course.
All of this has reminded me that the best way to learn is by teaching. My dataviz design course involved me writing down just about everything I know about dataviz, which made me realize how little I actually know about certain things. Sometimes it's patching holes in your knowledge, but more often it's codifying things that you do instinctually. Why do certain colors work well together? Why do you choose certain fonts? A lot of times as designers we simply learn by doing or by imitation, but teaching requires you to go deeper.
Same goes for the work I'm doing with Elevate. Recently, I ran a challenge that walked our members through the process of developing a data story from ideation to research to outlining. Developing this mini-course was an interesting challenge for me, because storytelling is something that I do instinctually. I've studied and practiced storytelling in one form or another for about 10 years, and a lot of what I know I learned by practicing. So having to explain the process to people of all experience levels forced me to synthesize my collected knowledge, and in the process develop new ideas and connections. One of those new connections was a framework I developed around the "genres of data stories", and we've decided to make this post free to the public, enjoy!
The inspiration for my genres of data stories idea came from a podcast I listen to called Writing Excuses. It's a show hosted by 4 speculative fiction (scifi/fantasy) authors on the business and craft of writing. I don't plan to write a fantasy novel any time soon, but I love the podcast because I learn more about how some of my favorite novels came to be, and pick up tons of useful writing and storytelling tips that I've applied to my own work.
For their eleventh season, they focused the entire year on a concept called "elemental genre". These aren't genres like Romance or SciFi, but rather the core archetypes that make up all stories: mystery, thriller, relationship, ensemble, humor, etc. For example, we all think of The Lord of the Rings as a fantasy story, but its elemental genre is adventure. And the contemporary Disney classic Encanto combines two elemental genres—mystery (what is wrong with the magic?) and relationship (intergenerational trauma). The elemental genres are important to understand because they each come with some pre-set touchstones and structures that make them successful. For example, if your elemental genre is thriller, you know you will need fast pacing, high tension that never relents, and great cliffhangers.
I started brainstorming what the elemental genres of data stories would be. I came up with 4 main genres, and a handful of subgenres. The classifications fit well with how I think about data stories, but I'm sure there's other genres you could include, or different ways to think about it, so I'd love to hear what you think! You can hit reply to this email and tell me your thoughts.
I could also delve much deeper into each one, going into specifics of how to tell a great Question story, or How it Happened story. I touched a bit on that in the post, but I'll save the deep dives for another time 😉
If that whole discussion of genres was a bit too nebulous for you, I'll leave you with some practical advice. Recently my work for Elevate and my dataviz course intersected on the topic of where to find good affordable fonts.
I talk a lot about typography, but I realize that for most people who aren't graphic designers, big professional font families are just not in the budget. But that doesn't mean you have to settle for bad fonts! So I wrote down a ton of advice on what makes a good font, where to find them, and several strategies for saving cents and pinching pennies without sacrificing good typography.
That's all for now folks! My May and June are packed full, and I'm looking forward to a nice vacation after EYEO. So look for the next edition some time this summer 🏝️