3 Common Data Visualization Mistakes to Avoid

4 minutes

Data visualization can be a powerful tool for uncovering and spotlighting the patterns hidden in a dataset — it’s as much an artform as it is a science.

“The right visualization can basically make the relationships in the data just appear on the page, which is pretty cool,” says Eva Sibinga, Codecademy Senior Curriculum Developer. Just think how easy it is to glance at charts on your weather or finance apps and quickly get the gist of the temperature or stock prices.

But that’s only when done right. An unpolished visualization can be confusing or hard to follow, and even average visualizations can lack that extra oomph that really drives home the points they’re trying to make. Luckily, there are a few tips and best practices that can take your visualizations from good to great, Eva says. We explore them all in greater detail in our free course Learn Data Visualizations with Python, but here’s an overview.

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1. Everything, everywhere, all at once

After spending hours, or even days, crafting your visualization, you’re going to be pretty familiar with your data; and you’ll be able to follow your charts pretty intuitively. But it’s important to remember to put yourself in your audience’s shoes. Would someone who’s seeing this for the first time understand the chart or graph?

Take a step away from your visualizations and return to them with fresh eyes. In fact, Eva recommends using an old painter’s trick: “My favorite trick for recognizing if the information on a page is well balanced is to just unfocus my eyes,” she says. “The details kind of fade away and you immediately see what the main emphasis is. If it’s not clear, that might mean you have too much stuff on the page squished together and your eye doesn’t know where to go first or what to prioritize.”

If your charts are looking a little cramped, consider breaking them down; splitting information into more digestible chunks can help clarify your message and make it easier for your audience to follow along.

And be mindful of cognitive load, a term used to describe how well we can take in new info (basically, it’s like RAM for your brain). We can only absorb so much at once, especially when presented with new information. Every audience will have their own cognitive load, but Eva explains that you can gauge how complex your data visualization is by:

  • Evaluating how well your charts fit your data
  • Distinguishing between big-picture and detailed takeaways
  • Considering how your audience will encounter your visualization

2. Unnecessarily superfluous, convoluted headings and annotations

The questions and topics you’ll delve into as you create your visualizations can get pretty highbrow, and it can be tempting to show off our big brains with smart-sounding words and isms. But that’s a great way to lose an audience (and it’s a snoozefest — who wants to sit through a lecture?).

“If we’re asking them to learn something new from the visualization, we won’t help them out by filling the title with unfamiliar words as well,” Eva says.

Is your title easy to read? Is the accompanying text easy to understand and interpret? “It’s not that you need to bring every dataset down to an ‘explain it like I’m five’ level, but try to avoid double negatives or language that makes things needlessly confusing,” Eva explains.

3. Clashing color schemes

If you’ve never taken color theory, here’s a crash course: Color matters.

You don’t have to be an artist to understand the logic of color schemes. Following common patterns, like gradients and sequential color palettes, makes it easier for your audience to follow along. “If you have sequential data that goes from zero to 100, it makes sense to visualize that with a sequential color palette — like from light green to dark green — than a color palette that doesn’t have an inherent order that human eyes can understand,” Eva says.

And remember to be accessible for your whole audience. Some degree of color blindness is more common than we usually assume, and using distinct hues and adjusting brightness and saturation can help make your visualizations more accessible for people with visual impairments and easier to read for everyone.

If you want to learn more about how to choose the right colors in your visualizations, Eva suggests taking our free course Principles of Data Literacy.

Build your data visualization skills

The tips above can be helpful guidelines when you’re creating your next data visualization project, but if you want to learn more about creating effective (and attractive) visualizations, check out our free course Learn Data Visualizations with Python. We’ll show you how to build your own charts with Python, Seaborn, and Matplotlib.

Data visualization is a great way to take your Python skills to the next level. If you’ve never used Python for data visualization, don’t worry — this is a beginner-friendly course, and we’ll teach you everything you need to know.

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