Data visualization is a useful tool to leverage when using data to communicate information. Done correctly, dataviz can substantially boost your audience's engagement and understanding. However, poorly-visualized data can override your message. This presentation provides an overview of common dataviz mistakes and how to avoid them.

1. 5 Common Data
Visualization Mistakes
(and how to avoid making them)

2. Wrong Chart Type
Dataviz Mistake #1

3. This chart
falsely implies
that 1/3 of all
preterm births
are to Non-
Hispanic Black
mothers.

4. A bar chart
more
accurately
portrays the
relationship of
preterm births
to race.

5. Too Complicated
Dataviz Mistake #2

6. This chart
displays
information
culled from a
141 page report.
Its complexity
hampers
communication.

7. The point of the
infographic is that
BrandZ can provide
insight on the world
marketplace. This
small portion of the
visualization
accomplishes the
same goal.

8. Skewed Correlation
Dataviz Mistake #3

9. The circles’ sizes
exaggerate the
number of deaths
per disease in
relation to
money raised.

10. While not as
dramatic, this
visualization
correctly
correlates money
donated with
deaths per
disease.

11. Omitting Key Information
Dataviz Mistake #4

12. Without
context it’s
impossible to
determine the
significance of
the U.S.’s
increase in oil
production.
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13. Providing data
enables viewers
to see the extent
of the U.S.’s
increase in oil
production
compared to
other countries.

14. Bucking Conventions
Dataviz Mistake #5

15. The inversion
of this chart’s
y axis makes
it seem that
gun deaths
decreased
after 2005.

16. Flipping the
chart makes it
much more
clear that gun
deaths actually
increased after
2005.

17. Five dataviz mistakes to avoid:
Wrong Chart
Type
Too
Complicated
Skewed
Correlation
Omitting Key
Information
Bucking
Conventions
Summary

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