Virtual Reality Will Be A Game Changer For Data Visualization

7 most common data visualization mistakes

7 most common data visualization mistakes

Stacked

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.

Read Also:
Data quality: Whose job is it? -

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
Read Also:
How to use data to create strong visuals

Resources

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


Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
How To Hire Analytical Workers: 10 Traits To Seek

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
How To Hire Analytical Workers: 10 Traits To Seek

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
How to Use Data Visualizations to Win Over Your Audience

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
A History of DataViz

HR & Workforce Analytics Innovation Summit 2017 London

12
Jun
2017
HR & Workforce Analytics Innovation Summit 2017 London

$200 off with code DATA200

Read Also:
Exploring High-End Visualization for Research and Education

Leave a Reply

Your email address will not be published. Required fields are marked *