We respond well when people remember things about us—specific things, like our names or our birthdays or our favorite foods or our unusually fuzzy pet llama named Roberto. It makes us feel important. This same concept applies to data visualization. However, it is often disregarded. Thus, you end up making bad data visualization.

Bad Data Visualization Example

Your audience will care about your data visualization if you show you care about your audience’s needs. You can show you care (and in turn persuade your audience to care about the viz) by working to understand specifics about your audience.

Google Maps is really good at knowing specific, meaningful things about you when you use the tool. It knows because it asks.

Behold, one of the simplest and most powerful questionnaires on the Internet:

Where to Find Good Data Visualization Examples

I type in “ice cream,” and Google now knows something specific and important about me. It knows I want ice cream. It provides a data visual to help me fill my specific, named, identified need:

Good Data Visualization Example

This is why people love Google Maps. It cares about their needs.

If Google Maps didn’t care, it might give me something like this:

Bad Data Visualization Example

There’s nothing specific to me in this bad data visualization. I can’t see what I want to see—a way to get my triple chocolate, caramel chunk two-scoop into my mouth as fast as possible. I’m lost in a sea of irrelevant locations.

An Effective Way to Avoid Making Bad Data Visualization

Often times the bad data visualizations we make are more like the latter map than the former. We give people too much information.

This ultimately waters down our visuals and weakens their impacts. To make a bad data visualization meaningful to someone, you absolutely must filter it according to their specific needs. Remove anything that is not important to your audiences’ needs, and you are left with something that can make a difference.

Identifying specific needs and writing them down is often overlooked. Its power is excruciatingly underestimated. In truth, it’s the core of everything we do as data scientists. It is the foundation.

In every data project you ever touch, you should dedicate enough time to be able to write down—in specific terms—what your audiences’ needs are.

Steps in Identifying Needs to Avoid Bad Data Visualization

There is a lot we can discuss on this topic, but here is a simple procedure you can follow today, right now, to have a measurable impact on the effectiveness of your data visuals. Here is the process:

  • Write down in specific terms three to five needs you think your audience has
  • Share these items with your audience and get feedback
  • Revise your list according to the feedback
  • Repeat as needed

There it is. Write, Talk, Revise, Repeat. And the key to it all—be specific.

Check out our Tableau Dashboard best practices to learn more about data visualization or enroll now and learn from our Tableau courses! Avoid creating bad data visualizations today!