Data Visualization dataviz superpower! Guidelines on using best practice data visualization principles for Power BI, Excel, SSRS, Tableau and other great tools!

1. Data Visualization Super Power
Jen Stirrup
Data Whisperer
Data Relish Ltd
Level: Novice

2. JenStirrup
• Boutique
Consultancy
Owner of Data
Relish
• Postgraduate
degrees in
Artificial
Intelligence and
Cognitive Science
• Twenty year
career in industry
• Author
JenStirrup.com
DataRelish.co
m
http://bit.ly/JenStirrupRD
http://bit.ly/JenStirrupLinkedI
n
http://bit.ly/JenStirrupMVP
http://bit.ly/JenStirrupTwitter

3. Jen Stirrup
• Boutique Consultancy
Owner of Data Relish
• Postgraduate degrees
in Artificial Intelligence
and Cognitive Science
• Twenty year career in
industry
• Author
• http://bit.ly/JenStirrupRD
• http://bit.ly/JenStirrupLinked
In
• http://bit.ly/JenStirrupMVP
• http://bit.ly/JenStirrupTwitter

4. • As a general rule, the most
successful man in life is the
man who has the best
information. (Disraeli, 19th
Century)

5. Parkinson’s Law
• ‘Whatever
information
capacity you give
to humans, they
will use up’
• Structured data
grows by about
30% each year

6. Data Proliferation

7. Solutions

8. • The endless cycle of idea and
action,
Endless invention, endless
experiment,
Brings knowledge of motion, but
not of stillness;
Knowledge of speech, but not of
silence;
..
Where is the wisdom we have lost
in knowledge?
Where is the knowledge we have
lost in information?
Excerpt from The Rock by TS Eliot (1934)

9. Secret
Sauce?

11. Genius depends
upon the data
within its reach.
Ernest Dimnet

12. You have to start with the truth. The
truth is the only way that we can get
anywhere. Because any decision-
making that is based upon lies or
ignorance can't lead to a good
conclusion.
Julian Assange, Wikileaks

13. You have to start with the truth. The
truth is the only way that we can get
anywhere. Because any decision-
making that is based upon lies or
ignorance can't lead to a good
conclusion.
Julian Assange, Wikileaks

16. Objectives

18. Objectives

19. What?

20. Objectives

21. Use of colour:
dark colours
are considered
to have higher
values

22. What?

24. Objectives

25. Inaccurate

30. 3
0

36. Bad Dataviz Examples

37. Bad Dataviz Examples

38. Bad Dataviz Examples

39. Lost Finale: Mins Breakdown
Filler
Adverts
Questions
Answered

40. Chartjunk Example

41. Linear vs Quadratic

42. 12/5/2018
42

43. Chartjunk: unintended

44. Designing visualizations
that communicate clearly
doesn’t have to be
complicated.

45. Keep it simple.

46. 46
Data where you want it
46

47. Data Visualisation Background
47
We have the tools. All
we’ve got to
do is imagine what
could be.
We can reinvent the
present;
we can transform the
world around us.

48. 48
Almost 50% of your
brain is dedicated
to visual
processing.
David van Essen
About 70% of your
sensory receptors are in your
eyes.
Researchers found that colour
visuals increase the willingness to
read by 80%

49. Why is Data Visualisation
Important?• It’s clearly a
budget. It has a
lot of numbers in
it. George W
Bush The different branches
of Arithmetic -
Ambition, Distraction,
Uglification, and
Derision. (Lewis
Carroll)

50. • The use of computer-
supported, interactive,
visual representations of
data to amplify cognition.
(Stu Card, Jock Mackinlay
& Ben Shneiderman)

51. • Computer-based visualization
systems provide visual
representations of datasets
intended to help people carry
out some task more
effectively. (Tamara Munzner)

53. Challenger

54. Challenger

55. Challenger

56. Why are we doing this?

57. Anscombe’s Quartet
mean(X) = 9, var(X) = 11, mean(Y) = 7.5, var(Y) = 4.12,
cor(X,Y) = 0.816, linear regression line Y = 3 + 0.5*X

58. Data Visualisation

59. BusinessFocus
business intelligence to win the race
businessFocusednobusinessFocused
strategictactical
Innovating Despite
Business
•Cool gadgets
•Buzz Word BI
•Not Actionable
Winning the Race
•Differentiation
•Listening to Customers
•Data Aware
•Actionable Knowledge
“Ticking along”
•Minimum Maintenance
•No New BI Functionality
•Low Adoption
Running on the Spot
•Regurgitation of the
same
•Focus on only known
metrics
•Standing Still

60. 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
1,281.1
1%
2007
66,212.
3%
2008
231,15
0,888.8
7%
(July)

61. Thinking with your Eyes

62. Translated into picture…

65. Why Data Vis
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Computers have promised us a fountain of wisdom
but delivered a flood of data (Frawley, 1992)

66. Why is Data Visualisation
Important?
• Computers have promised us a
fountain of wisdom but delivered a
flood of data (Frawley, 1992)
• Challenging to understand data on
its own
• Computers as anti-Faraday
machines

67. Why is Data Visualisation
Important?
• Networks allow us unprecedented
access to data
• Creative Thinking about data
• See relationships better
• Visual literacy

70. Data First
Data

71. Data First
Data
Tabular Spatial

72. Data First
Data
Tabular
Categorical Ordered
Spatial

73. Abstract
Data First
Data
Tabular
Categorical
Ordered
Quantitative Ordinal Relational
Spatial

74. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

75. Visual Building Blocks
Points Lines Shapes

76. 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

77. 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

78. Visual Building Blocks

79. Quantitative Ordinal Nominal
Position Position Position
Length Gray gradient Shape
Angle Colour gradient Colour hue
Area Colour hue Gray gradient
Gray gradient Length Colour gradient
Colour gradient Angle Length
Colour hue Area Angle
Shape Shape Area

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

82. 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)

83. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

84. Information Seeking Mantra
• Ben Schneiderman
Summary
Zoom and
Filter
Details on
Demand

85. Information Seeking Mantra
• Ben Schneiderman
Summary
Zoom and
Filter
Details on
Demand

86. Information Seeking Mantra
• Ben Schneiderman
Summary
Zoom and
Filter
Details on
Demand

87. Visualising Big Data
87
Self-Service
Insights
Actions

88. 88
Different Tools for Different Jobs
88
• Power View • Power Map
▪ Highly Visual Design Experience
▪ Power View is an interactive, ad hoc, query and
visualization experience.
▪ It is for business question ‘mystery’ solving
▪ Power Map is a new 3D visualization add-in for
Excel helping you to analyse geographical and
temporal data
– Mapping
– Exploring
– Interacting

89. 12/5/2018
Copper Blue Business Intelligence
Ltd
89

90. Sploms
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90

91. Back to the Royal Road
• Questions?
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91

92. Revealing Patterns
• Patternicity
– Finding meaningful patterns in
noise
– This can be seen as an error
in cognition
– Brain as belief systems

93. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

94. Pre-attentive Attributes
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

95. Pre-attentive Attributes
Attribute Example Graph Type
Spatial
Position
2D Grouping
2D Position
Line Graph
Form Length
Width
Orientation
Size
Bar Chart
Colour Hue
Intensity
Scatter Chart

96. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

97. Visual Integration
• Chartjunk
• Data/Ink
Ratio

98. 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)

99. Mobilising Visual Integration
– Sequential Palettes
– Diverging Palettes
– Qualitative Palettes

100. Visual Integration

101. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

102. Cognitive Integration
• Building an understanding of the graph
• Eye Path going from cluster to cluster, rather than cluster to legend
(Ratwani, 2008)

103. Cognitive Integration
• Summary first
• Zoom and filter
• Then details ‘on-demand’
» (Schneiderman, 1999)

104. Cognitive Integration
• Comparison
• Sorting
• Bookmarks – analytical view of browsing

105. Mobilising Cognitive
Integration
• Humans are not good at judging:
– 2D Area
– Angles
– 3D pie chart

106. Mobilising Cognitive
Integration
• Humans are not good at judging:
– 2D Area
– Angles
• Pie Charts and Gauges rely on these characteristics…

107. Find Patterns in your data
• Demo – Sparklines
• What did we learn?
• Making patterns in small spaces
Session Code | Session Title
107

108. 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.

109. – Sequential Palettes
– Diverging Palettes
– Qualitative Palettes

110. Moire Illusion

111. Mobilising Cognitive
Integration• Humans are not good at judging:
– 2D Area
– Angles

112. Mobilising Cognitive
Integration• Humans are not good at judging:
– 2D Area
– Angles
• Pie Charts and Gauges rely on these
characteristics…

113. Summary
• SSRS can help businesses to implement business
performance management
– Based on sound Business Intelligence principles
– SSRS provides data visualisation components that are
consistent with best practice
– However, some components are not
• There are different types of Dashboards, to cover
different purposes

114. Reporting Services

115. IT Oriented
Structured Reporting
Business Oriented
Click as you Think AnalysisGuided Analysis
Reporting Services
PerformancePoint Services Report Builder
Power View
Excel
PowerPivot

116. Colour
• 2D representation is better (Few, 2009)
• brighter and darker colours = higher values
Colour usage:
• to highlight
• to encode quantity
• grouping items as well

117. Stages of Processing
Preattentive
Processing
Visual
Integration
Cognitive
Integration

118. Cognitive Integration
• Building an understanding of the graph
– Eye Tracking Studies
• Eye Path going from cluster to
cluster, rather than cluster to legend
(Ratwani, 2008)

120. 70% 30%

121. DATA RELATIONSHIPS
NOMINAL COMPARISON DEVIATION
.
TIME-SERIES DISTRIBUTION
CORRELATION PART-TO-WHOLE
RELATIONSHIPS
RANKING
4
RELATIONSHIPS

124. We’re not the only ones who
are overwhelmed
Everyone is trying to make sense of the data deluge (“big data”)

125. Choose your metrics wisely.

126. Make your Big Data Sing

127. Demo: Hortonworks Sandbox,Tableau, PowerBI

128. Balance depth with big picture

129. Balance depth with big picture

130. Balance depth with big picture

131. Balance depth with big picture

132. Q & A

133. Back to the Royal Road
• Questions?
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133