You might have landed here because you thought this was going to be a post full of snarky commentary on mediocre graphs. It isn’t. In fact, I’m not going to include a single data viz.

Because what makes a data viz suck or sing isn’t just about its outward appearance. That’s only going to get you so far and it isn’t even the most important part.

The most important part of data visualization is what happens to people after they’ve seen it.

I’ve got a story for you about a surefire sign that my dataviz sucked.

I was giving one of my first presentations ever as a bright-eyed grad student. The conference was in Oslo, at that, so you know I was blissed out and a little smug about being on international travel.

My talk was on child labor, to a room of about 100 people, seating auditorium-style. I was armed with data points on transparency film (do NOT ask me what transparency film is, you are younger than me and know how to use the internet).

When I was done and opened the floor to questions, I was met with total silence.

My English-speaking host thought perhaps something was lost in translation so prompted the audience herself to pitch me a question. I eagerly searched the audience for signs of engagement but it was a sea of this:

Britney Spears glances up with a hint of boredom and even lack of care.

Silence is a sign that your dataviz sucks.

When I start a dataviz training project with a new client, we talk about what’s going on that has put them in a position to need my help. Lemme tell ya, their answers are full of more signs to monitor.

Your audience asks about things you already addressed.

Nothing like preparing a well-detailed talk only to have to repeat yourself, right?

Kelly Kapoor says “Yeah, I have a lot of questions.”

Questions about topics you think you clearly covered could be coming at you because your first attempt was unclear. Sometimes this is due to the outward appearance of your visual. It’s also often due to the language you used when discussing your data.

It’s really easy to get too jargon-istic, especially when you know a lot about your topic.

“Current resource levels forecast a downwind supply shortage by the end of the fiscal year” is going to bring up more questions than “We need to hire more people before June.”

You might also be getting redundant questions if your audience had already zoned out the first time around and now they need to quickly get the gist so they don’t seem unaware. Maybe you can keep people engaged with some fun.

You aren’t getting repeat customers.

You make advocates and evangelizers when your data viz is good. People enthusiastically gush about you to their friends and colleagues. When you inspire them to rethink, make decisions, and take action, they remember you and want more of it.

You get asked back.

But when you fail to spark the fire, you and your work become forgettable.

Mariah Carey shakes her head and says “I don’t know her.”

When your data viz is getting off-key reactions or no reaction at all, take it as a sign that your data viz sucks and you need to rework your approach.

If this post sounds a little too familiar, you can take some next steps right now.

Double check that your graph’s outward appearance isn’t the issue. Test your work against my Data Visualization Checklist.

Learn how to pair your message with your data in my Online Courses.

I’ll be back soon with Signs Your Data Viz Sings.