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7 most common data visualization mistakes

7 most common data visualization mistakes

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In dataviz, as in any other field, there are rules, best practices, guidelines and then there is common sense. And contrary to what we might believe, common sense gets ignored quite often, as we’ll see in the examples below.

Let’s get started with a few most common mistakes!

When you draw a pie, stacked-bar or stacked-area chart, the numbers should add up to 100. This might sound too silly a mistake to point out here, but you will be surprised to see how many times people make it.

You don’t need anything special to avoid this mistake. We all have seen enough charts to have a general idea of what is normal. Just do that and you’ll be fine. There may be cases where you will need to go against the convention. But that will be an exception rather than the rule.

Axes values provide context to charts. You mess with axis and you have a visualization that will paint a completely wrong picture.

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Bubble charts are very useful for displaying three dimensional data in two dimensions. Not only you have x- and y-axes, but you can depict a third quantity by varying the size of the bubble.

By far the most common mistake I’ve seen people making in bubble charts is that they vary ‘radius’ of bubble instead of varying ‘area’ to display different values.

Dataviz is supposed to make the task of interpreting data easier and not harder.

You can avoid this or other similar mistakes if you try to think from a readers perspective. Or try to get feedback from your colleague or friend before publishing it. Ask them how easy it is to interpret the charts? Are they able to easily compare the two charts? Also use ‘labels’ wherever necessary.

Data visualization is a vast field and if you are reading this post it means you are interested in getting better. So now that you know common mistakes, here are some resources that will help you improve:

  • Numbers Dont Add Up
  • Not Following Conventions
  • Cropped Axes
  • Not Using Annotations
  • Improper Bubble Sizes
  • Incomplete Data
  • Hard to Compare
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Resources

  • Get your basics right
  • Use the right tools
  • Avoid mistakes


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