"Good enough" charts for work

I often think about what constitutes "good enough" work. It's a vague concept in work settings because the standards of what is considered good enough for a situation vary between the creator as well as the receiving stakeholder. Each and every one of us has built up an internal model of the world that leads us to determine whether our work is good enough for whatever it is we're doing.

That internal model of "good enough-ness" is, as far as I can tell, built up over time based on our past experiences. For data practitioners, we primarily build up our models based on getting feedback from our stakeholders. Every one of us has at least one story where we put a ton of work and extra hours into making a really fancy analysis or chart – only to have everyone pay practically no attention to it in favor of something much simpler. Hit that a few times and you start learning that certain kinds of extra effort aren't worth the trouble.

Feedback of course doesn't only push us to cut down our efforts. They can easily push us to try harder. This can come in both positive and negative feedback. All the times that people suggested that you could improve on something with some changes. All the times that jerk Reviewer #2 gave a frivolous bit of feedback. All the times a client kicked something back because they weren't satisfied with it. All of those can be motivation to do better.

I occasionally think about the craftspeople who now currently do great work because the people they trained under refused to let them get away with sloppy work. I also think about how I spent much of my career working in data teams of size one. While I had a couple of good mentors and managers over the years, I sometimes wonder if there's any part where my work level just isn't as good as it could be for lack of external motivation to make it better.

Nowhere is this more apparent in my skill set than the data visualizations I make on a regular basis. As a student and early in my career, I paid attention to all the data vis wisdom available in the late 2000s. I have a couple of the Tufte books somewhere (they make decent monitor stands). I read blog posts about cool visualizations. I still respect, to this day, the dataviz work that comes out of various news rooms like the NY Times or Washington Post. I'd like to believe that I have a working knowledge of what makes a good visualization.

But guess what my go-to, "advanced" data visualization is? It's a line chart, often generated in Excel or Google Sheets, with some simple text boxes and lines marking significant points. I have a handful of other simple visualizations I reach for as needed, but in terms of "amount of story told for effort put in" this is my primary tool.

All my other data presentations are even simpler... line charts, the occasional bar or histogram. Sometimes it's just a TABLE. On top of that, since I have the design skills of a squirrel, I even do the rather ugly thing of using the default color palettes. Changing the color of something usually signals that I'm being Extra Fancy.

Meanwhile, I look at some of my UX teammates and they're actual designers, full degrees, design school, the works. Their stuff is night and day different because their internal quality bar for what looks good enough is a couple of dimensions higher than mine. Just like I mentally cannot report on a survey result without having the sample size information visible, they'd never let my "black text on white background" slides fly in their own presentations. If they let an incomprehensible slide go through, it'd reflect on them as an actual designer. Just like I'd damage my reputation for working with data if I tried to sneak in survey results when n=5.

I highlight this difference not because I want to become like my designer friends. It's because despite the huge, extremely noticeable differences in presentational styles to the exact same group of stakeholders... both work. No one particularly gets upset that my stuff looks very simple. The people I present findings to get the story I want to tell and make better decisions as a result. At most, we joke about how my stuff is almost 99% pure content and 1% presentation compared to how almost everyone else (even other researchers) have a better balance. For my little professional universe, that is "good enough" because I don't get any pressure to make serious attempts at improvement.

Earlier in my career, I sorta wondered if my lack of pretty viz skills would be a problem in the wider data world and I'm happy to report that the answer of my lived experience is – nope. Unless I plan on moving to a role that specifically requires me to do much more refined visualization work, like say publishing for a visualization oriented blog, I don't see a pressing need to, My way of doing things pretty much has people expecting little in the way of eye-pleasing design, so they expect to just take the content and insights.

For this one narrow topic, this is where I've found my "good enough" line. I compensate for my utter lack of color sense and aesthetics with utility. I put extra time and effort into making sure that people looking at my graphs get the useful information they need quickly, and with as little ambiguity as I can manage.

So hey, wherever you personally stand on the spectrum of chart fanciness, if it works for you and you're satisfied with where you are, you're probably good.

Standing offer: If you created something and would like me to review or share it w/ the data community — just email me by replying to the newsletter emails.

Guest posts: If you’re interested in writing something a data-related post to either show off work, share an experience, or need help coming up with a topic, please contact me. You don’t need any special credentials or credibility to do so.

About this newsletter

I’m Randy Au, Quantitative UX researcher, former data analyst, and general-purpose data and tech nerd. Counting Stuff is a weekly newsletter about the less-than-sexy aspects of data science, UX research and tech. With some excursions into other fun topics.

All photos/drawings used are taken/created by Randy unless otherwise credited.

• randyau.com — Curated archive of evergreen posts. Under re-construction thanks to *waves at everything

Supporting the newsletter

All Tuesday posts to Counting Stuff are always free. The newsletter is self hosted, so support from subscribers is what makes everything possible. If you love the content, consider doing any of the following ways to support the newsletter:

• Consider a paid subscription – the self-hosted server/email infra is 100% funded via subscriptions
• Share posts you like with other people!
• Join the Approaching Significance Discord — where data folk hang out and can talk a bit about data, and a bit about everything else. Randy moderates the discord. We keep a chill vibe.
• Get merch! If shirts and stickers are more your style — There’s a survivorship bias shirt!