Best practices of data visualization to to communicate information clearly and efficiently

1. Data Visualization
Christian Stade-Schuldt
Project-A Ventures
BI Team Knowledge Transfer

2. Outline
Motivation
Principles of Data Visualization
Types of Visualization
Summary
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3. Why visualizing data?
Visualization lets you see things that would rather go unnoticed
Visualization gives answers faster
Color pictures are pretty and fun to look at
Simple example: Anscombe’s Quartet
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4. Anscombe’s Quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.10 4.74 5.0 5.73 8.0 6.89
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5. Anscombe’s Quartet II
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6. Edward Tufte
Professor emeritus at Yale
University
Pioneer in the field of data
visualization
Notable works: The Visual
Display of Quantitative
Information
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7. Principles of graphical excellence and
integrity
1. Serve a purpose
2. Make large data sets coherent
3. Present many numbers in a small space
4. Don’t lie
5. Use clear labels to defeat ambuigity and graphical distortion
6. Show entire scales
7. Show in context
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8. Small multiples
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9. The Lie factor
Lie factor =
Size of effect in graphic
Size of effect in data
(1)
=
5.3 − 0.6
0.6
/
27.5 − 18
18
= 14.8 (2)
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10. Scale contorsions
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11. Scale contorsions
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12. Scale contorsions and context
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13. Principle of data graphics
1. Above all else show the data
2. Maximize the data-ink ratio
3. Erase non-data-ink
4. Erase redundant data-ink
5. Revise and edit
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14. Data-Ink
Data-ink the non-erasable ink used for the presentation of data
If removed the graphic would lose the content
Non-Data-Ink is accordingly the ink that does not transport the
information
Data-ink ratio = (data ink)/(total ink used to print the graphic)
Chartjunk: unecessary to comprehend the information represented or
distractive
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15. Data-Ink-Ratio Example
Low Data-Ink-Ratio High Data-Ink-Ratio
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16. Chart types
The good
Bar charts
Line charts
Scatter plots
Boxplots
The bad
Pie charts
Area charts
The ugly
All 3d charts
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17. Bar charts
Go-to graph for comparing accross categories, discrete data or
continous data
Proximity: Set white space width separating contiguous bars equal to
50%-150% width of bars
Fills: Avoid pattern lines, use soft but distinct colors
Borders: Avoid
Tick marks: Do not overdo
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18. Bar charts
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19. Line chart
Used for continous data
Intervals should be equal in size
Values should only direct connect values in adjacent intervals
Indicate missing data
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20. Line chart
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21. Scatter plots
Great for correlation between two quantitave dimensions
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22. Pie charts
Pie charts are the Aquaman of data visualization
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23. Pie charts
The same data represented as a column chart
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24. 3d Pie charts
What is worse than a pie chart? Meet the 3d pie chart
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25. Maps
provide specific
information
about particular
locations
provide general
information
about spatial
patterns
can be used to
compare
patterns on two
or more maps
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26. Maps
Relevant xkcd
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27. Remember Color Blindness
Approximately 10% of males and 1% of females suffer color vision
deficiency
Original colors Perceived colors
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28. Summary
Visualize your data
Choose the right type for your visualization
Aim for a high Data-Ink-Ratio
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29. For Further Reading I
Tufte, Edward R
The Visual Display of Quantitative Information.
Graphics Press, 2001.
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