After the success of the One Chart at a Time video series earlier this year, I decided to start adding more content to my YouTube channel. Over the past few months, I’ve been posting more of my presentations and step-by-step Excel tutorials.
Pondering how I could make YouTube as useful as possible for the data visualization community, I posted this Twitter poll in early May:
What would you like to see/learn more of on my YouTube channel? 📺
— Jon Schwabish (@jschwabish) May 5, 2021
As you can see, nearly half of respondents wanted to see data visualization critiques on the channel. Today, I’m posting the first few of what will hopefully be many data visualization critiques.
Background
Data visualization critiques has been a sensitive topic in the field. Many have expressed outrage that criticism in the data visualization field often comes off as mean and personal, what I call “drive-by critiques”—a quick note or post without considering the creators’ goals, circumstances, and other limitations. It’s partly why I launched HelpMeViz—to give people an opportunity to provide constructive feedback in a longer forum (though, sadly, it doesn’t really do much these days). And it’s why I think community projects like Makeover Monday and Tidy Tuesday are so useful—more in-depth, constructive, community-based feedback can help people grow and create better visualizations.
There has also been a history of how to effectively criticize data visualizations. In 2015, Martin Wattenberg and Fernanda Viegas famously wrote about better ways to criticize people’s work. In the first episode of the Storytelling with Data podcast in 2017, Cole Nussbaumer Knaflic echoed those sentiments and explained how she works with clients and partners to critique their visualizations. In this 2019 blog post, I wrote that critics need to try to take a full consideration of the content creator’s work and try to understand where they are coming from.
But not everyone shares these sentiments. In a blog post that followed his 2018 talk at the Tapestry conference, Elijah Meeks pushed back on being too polite in our critiques: “I have long said that we should be more comfortable with critique in data visualization, but without context the remarks I make might seem arbitrary and mean-spirited.” And in response to the section of my book Better Data Visualizations where I talk about taking this slower criticism approach, a friend wrote, “Naturally, politeness and constructive criticism always beat the opposite, but if it was posted publicly, fair public comment is fair comment. And sometimes it’s a public service to expose error and shoddy work.” Others have sent me similar emails.
The Video Series
As I considered whether to create this series, I weighed how I could simultaneously respect the work of the content creator and provide a resource for people building their own visualizations. After some tests—and fantastic feedback from friends in the field—I think I’ve settled on a strategy that works. I’ll try to be constructive. I’ll try to show alternative approaches. I’ll try to be holistic about the visualization in terms of where it’s positioned on the page and who the target audience might be. And I’ll try to be quick, light-hearted, and fun. In many ways, my model is the baseball breakdown videos from Jomboy Media, which my son and I regularly watch together during breakfast.
But I’m not reaching out to each and every creator. And I’m not remaking each and every visualization. Thinking through it, I felt this would take too long and oftentimes not even be possible. And I considered how movie, film, and food critics conduct their work—they don’t remake the movie or re-cook the meal, they examine the final product and judge it on its merits. I’m pulling visualizations from my existing Data Visualization Catalog, visualizations I see out in the wild (which usually make it to my catalog), and, if you’re interested, things you tell me about (best done on Twitter).
The creator(s) of the visualizations I critique in these videos may have constraints on time or tools or workflow that I am not aware of. They may have made decisions that I critique but actually make complete sense with a different perspective. The purpose of my critiques is to try to offer constructive ideas about the effective presentation of data, but I’m certainly not going to get them all right.
Overall, my critiques are intended to help give you a glimpse of what’s going on inside my head as I view different graphs, charts, and diagrams, not only as a reader but also as someone who specializes in the field. And I hope the videos will help you improve how you visualize your own data. I’m sure some viewers will find what I say mean. And I’m sure many viewers will disagree with aspects of my critique or even my overall approach to this entire project. I hope most will find them valuable in their paths to create better, more effective visualizations, but let me know if I’m straying into the mean or jerk lane.
If you’d like to check out the new Data Viz Critique video series, head over to my YouTube page. I’ll be posting more videos in the next few days as I settle into a pattern.
