What's your data visualization vegetables? What's your candy? This session will look at data visualization theory and practice of hot data visualization topics such as: how can you choose which chart to choose and when? How can you best structure your dashboard? What about pie charts? What is the fuss about, and when are they best used? Color blindness - how can you cater for the 1 out of 12 color blind males (and not forgetting the 1 out of 100 color blind females?) To 3D or not to 3D? Why is it missing in Power View? And any other data visualization topics you care to mention! Come along for dataviz fun, and to learn the "why" along with practical advice.

1. Eye Vegetables and Eye
Candy: How to Visualize Your
Data
Jen Stirrup
Principal, Data Relish Ltd
Level: Intermediate

2. What is Data Visualisation?
• Data Visualisation tells stories
contained within the data
– focused on analysing large datasets
– which allows the data consumer to draw their own
conclusions.
• Very often, the data is not static in
nature, but fluid and dynamic.

3. The Business Intelligence
Chocolate Cake problem
• Everybody wants their own chocolate cake…
• They want chocolate cake now….
• Their way….
• But who cleans up the mess?
• Who is going to pay for it?
• How can we stop the cakes from mixing?

4. The Golden Record problem

5. The Bystander Effect

6. Why Data Vis
11/20/2015
6
Computers have promised us a fountain of wisdom but
delivered a flood of data (Frawley, 1992)

7. Challenger

8. Challenger

9. Challenger

10. Why not just tables?
Zimbabwean inflation rates (official) since independence
Date Rate Date Rate Date Rate Date Rate Date Rate Date Rate
1980 7% 1981 14% 1982 15% 1983 19% 1984 10% 1985 10%
1986 15% 1987 10% 1988 8% 1989 14% 1990 17% 1991 48%
1992 40% 1993 20% 1994 25% 1995 28% 1996 16% 1997 20%
1998 48% 1999 56.9% 2000 55.22% 2001 112.1% 2002 198.93% 2003 598.75%
2004 132.75% 2005 585.84% 2006 1,281.11% 2007 66,212.3% 2008
231,150,88
8.87%
(July)

11. Thinking with your Eyes

12. Translated into picture…

13. Information is Beautiful by David McCandless
13

14. Context in Charts
14

15. 15

16. 16

17. Benefits of Data Visualisation
• "A good sketch is better than a long speech..." -- a
quote often attributed to Napoleon Bonaparte
• Data has increased in quality, timeliness,
granularity, and volume
17

18. Benefits of Data Visualisation
• Visualization as a key enabler of self-service
business intelligence
• Bridging the human – machine learning gap
18

19. Anscombe’s Quartet
19

20. How can we visualize better?
• Flexibility
• Interactivity
• Brushing and linking
20

21. 21

22. Credit: Visual.ly
22

23. Advice!
• Never represent something in 3 Dimensions if it
can be represented in two
• NEVER use pie charts, 3-D pie charts, stacked
bar charts, or 3-D bar charts.
23

24. Advice!
• Remove as much chart junk as possible–
unnecessary gridlines, shading, borders, etc.
• Give your audience a sense of the noise present
in your data–draw error bars or confidence bands
if you are plotting estimates.
24

25. Guidelines
• Forecasted data - include actuals as well
• Major and minor tick marks
• Standardisation
• Design has 'affordances'
• Fit for purpose

26. Tables
•Tables work best when the data presentation:
• Is used to look up individual values
• Is used to compare individual values
• Requires precise values
• Values involve multiple units of measure.

27. – Sequential Palettes
– Diverging Palettes
– Qualitative Palettes

28. Guidelines
• white space
• data/ink
• chartjunk
• Context e.g. titles etc

29. Debates
• Showing axes at zero?
29

30. The Science!
30
In experiments,
Cleveland and McGill
examined how
accurately our visual
system can process
visual elements or
“perceptual units”
representing
underlying data

31. Credit: Stanford Computer
Graphics Lab
31

32. Credit: Stanford Computer
Graphics Lab
32

33. Reference: Russian Blues (Winawer et al, 2007)
33

34. The Results!
• Judgements about position relative to a baseline
are dramatically more accurate than judgements
about angles, area, or length (with no baseline).
• Cleveland and McGill suggests that we replace
pie charts with bar charts or dot plots and that
we substitute stacked bar charts for grouped bar
charts.
34

35. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

36. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

37. Perceptual Patterns
Attribute Example Assumption
Spatial Position 2D Grouping
2D Position
Sloping to the right = Greater
Form Length
Width
Orientation
Size
Longer = Greater
Higher = Greater
Colour Hue
Intensity
Brighter = Greater
Darker = Greater

38. Perceptual Patterns
Attribute Example Graph Type
Spatial Position 2D Grouping
2D Position
Line Graph
Form Length
Width
Orientation
Size
Bar Chart
Colour Hue
Intensity
Scatter Chart

39. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

40. • Bullet 1 for the slide
• Sub-bullet
• Sub bullet
• Bullet 2 for the slide
• Just to see how the copy looks if it goes deep enough
to reach the bottom.
You never know how much copy will be on a slide
• Bullet 3 for the slides
• This is a critical point that needs to be communicated

41. Mobilising Visual Integration
• Affordance
• Highlighting – bright colours
• Increasing Intensity = Increasing Values
• Eye Tracking Studies
• Eye Path going from cluster to legend, and back
again (Ratwani, 2008)

42. Chartjunk

43. Chartjunk Example

44. Chartjunk: unintended

45. Summary
• It’s partly science, partly art
• The principles will help you, regardless of the
technology
• Relish your data!