If you have one underlying philosophy that guides your graph-making, let it be this: People are meaning makers.

It’s what we do.

People are so good at making meaning that we’ll find meaning in places it doesn’t exist. That’s how good we are.

Don’t believe me? Try this.

What do you see when you look at this Rorschach ink blot?

rorschach ink blot

An insect? A fox hide? A manta ray?

Babe, you made meaning.

Wait, are you one of those people who says “I see an inkblot, Stephanie. I’m not some amateur. I’m pragmatic and smart.”

You still made meaning. In fact, you made this exercise even more meaningful, by making it a reflection of your character, than the person who said this is a space craft out of Star Trek.

Don’t sweat it. You can’t help but make meaning.

It’s what humans do.

Let’s accept that fact and then move on to thinking about how it applies to data visualization.

The implication for us, the data designers, is that every. single. part. of a visual will be interpreted and assigned some meaning. Whether you like it or not. Which means we’d better start getting real thoughtful about our designs.

Found this chart on Tableau’s website (not the public space where data designers post their work, from Tableau itself):

You might go into your chart making just blindly trusting that the software has your best storytelling interests at heart. So you quick pop out this column chart and move on with your day.

But your audience starts thinking:

Why are these columns green? Does that mean they’re good?

Why is Age at the top?

What do these numbers mean at the bottom? Are they ages? If so, why are there only 6?

By that time, it’s gotten weird, and they quit engaging.

Or, take this one:

What’s going on in your mind when you look at this? What are you wondering? Where are you assigning meaning?

You’ve probably noticed that the chart has three colors. And you wonder what they mean, don’t you?

People are meaning makers.

What does “Year of Date” mean? I know what a year is and I know what a date is but “Year of Date”? IDK.

When the answer is “that’s just Tableau’s way of talking about this data and I didn’t think to change it.”

And the danger there is that we’ve confused people and wasted the little bit of time and attention they’re willing to extend to us, all because we weren’t being hyperaware of how people are meaning makers.

And there’s another danger that we’ve unintentionally created some meaning that would be incorrect – and people run with it.

Listen, you can’t dictate people’s thoughts. But you can control your controllables.

Your controllables are the design choices in your chart.

Your colors. Your annotations. Your legend. Your axes titles. Your chart choice.

That’s your sphere on influence.

But here’s the trick: It isn’t about what you think about your colors, your axes, your titles. It’s about what your audience will think.

Go in with eyes wide open to the fact that people are meaning makers. Go in asking yourself “what could someone read into this?” Go in, putting your audience first.

Learn something new?

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