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Eye Vegetables and Eye
Candy: How to Visualize Your
Data
Jen Stirrup
Principal, Data Relish Ltd
Level: Intermediate
What is Data Visualisation?
• Data Visualisation tells stories
contained within the data
– focused on analysing large data...
The Business Intelligence
Chocolate Cake problem
• Everybody wants their own chocolate cake…
• They want chocolate cake no...
The Golden Record problem
The Bystander Effect
Why Data Vis
11/20/2015
6
Computers have promised us a fountain of wisdom but
delivered a flood of data (Frawley, 1992)
Challenger
Challenger
Challenger
Why not just tables?
Zimbabwean inflation rates (official) since independence
Date Rate Date Rate Date Rate Date Rate Date...
Thinking with your Eyes
Translated into picture…
Information is Beautiful by David McCandless
13
Context in Charts
14
15
16
Benefits of Data Visualisation
• "A good sketch is better than a long speech..." -- a
quote often attributed to Napoleon B...
Benefits of Data Visualisation
• Visualization as a key enabler of self-service
business intelligence
• Bridging the human...
Anscombe’s Quartet
19
How can we visualize better?
• Flexibility
• Interactivity
• Brushing and linking
20
21
Credit: Visual.ly
22
Advice!
• Never represent something in 3 Dimensions if it
can be represented in two
• NEVER use pie charts, 3-D pie charts...
Advice!
• Remove as much chart junk as possible–
unnecessary gridlines, shading, borders, etc.
• Give your audience a sens...
Guidelines
• Forecasted data - include actuals as well
• Major and minor tick marks
• Standardisation
• Design has 'afford...
Tables
•Tables work best when the data presentation:
• Is used to look up individual values
• Is used to compare individua...
– Sequential Palettes
– Diverging Palettes
– Qualitative Palettes
Guidelines
• white space
• data/ink
• chartjunk
• Context e.g. titles etc
Debates
• Showing axes at zero?
29
The Science!
30
In experiments,
Cleveland and McGill
examined how
accurately our visual
system can process
visual elements...
Credit: Stanford Computer
Graphics Lab
31
Credit: Stanford Computer
Graphics Lab
32
Reference: Russian Blues (Winawer et al, 2007)
33
The Results!
• Judgements about position relative to a baseline
are dramatically more accurate than judgements
about angle...
Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration
Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration
Perceptual Patterns
Attribute Example Assumption
Spatial Position 2D Grouping
2D Position
Sloping to the right = Greater
F...
Perceptual Patterns
Attribute Example Graph Type
Spatial Position 2D Grouping
2D Position
Line Graph
Form Length
Width
Ori...
Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration
• Bullet 1 for the slide
• Sub-bullet
• Sub bullet
• Bullet 2 for the slide
• Just to see how the copy looks if it goes de...
Mobilising Visual Integration
• Affordance
• Highlighting – bright colours
• Increasing Intensity = Increasing Values
• Ey...
Chartjunk
Chartjunk Example
Chartjunk: unintended
Summary
• It’s partly science, partly art
• The principles will help you, regardless of the
technology
• Relish your data!
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Guidelines for data visualisation: eye vegetables and eye candy

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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.

Published in: Technology
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Guidelines for data visualisation: eye vegetables and eye candy

  1. 1. Eye Vegetables and Eye Candy: How to Visualize Your Data Jen Stirrup Principal, Data Relish Ltd Level: Intermediate
  2. 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. 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. 4. The Golden Record problem
  5. 5. The Bystander Effect
  6. 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. 7. Challenger
  8. 8. Challenger
  9. 9. Challenger
  10. 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. 11. Thinking with your Eyes
  12. 12. Translated into picture…
  13. 13. Information is Beautiful by David McCandless 13
  14. 14. Context in Charts 14
  15. 15. 15
  16. 16. 16
  17. 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. 18. Benefits of Data Visualisation • Visualization as a key enabler of self-service business intelligence • Bridging the human – machine learning gap 18
  19. 19. Anscombe’s Quartet 19
  20. 20. How can we visualize better? • Flexibility • Interactivity • Brushing and linking 20
  21. 21. 21
  22. 22. Credit: Visual.ly 22
  23. 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. 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. 25. Guidelines • Forecasted data - include actuals as well • Major and minor tick marks • Standardisation • Design has 'affordances' • Fit for purpose
  26. 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. 27. – Sequential Palettes – Diverging Palettes – Qualitative Palettes
  28. 28. Guidelines • white space • data/ink • chartjunk • Context e.g. titles etc
  29. 29. Debates • Showing axes at zero? 29
  30. 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. 31. Credit: Stanford Computer Graphics Lab 31
  32. 32. Credit: Stanford Computer Graphics Lab 32
  33. 33. Reference: Russian Blues (Winawer et al, 2007) 33
  34. 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. 35. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  36. 36. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  37. 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. 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. 39. Stages of Processing Preattentive Processing Visual Integration Cognitive Integration
  40. 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. 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. 42. Chartjunk
  43. 43. Chartjunk Example
  44. 44. Chartjunk: unintended
  45. 45. Summary • It’s partly science, partly art • The principles will help you, regardless of the technology • Relish your data!

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