Talk by Andy Kirk, delivered during a Sage webinar on Friday 1st July.

1. DATA VISUALISATION
A GAME OF DECISIONS
Andy Kirk
andy@visualisingdata.com
www.visualisingdata.com
@visualisingdata

2. IMPERFECTIONS
COMPLEXITIES

3. The visual representation and presentation
of data to facilitate understanding
A game of decisions: There’s no such thing as perfect

4. A game of decisions: There’s no such thing as perfect
Perceiving Interpreting Comprehending
What does it mean?
Is it good or bad?
Meaningful or insignificant?
Unusual or expected?
What does it show?
What’s plotted?
How do things compare?
What relationships exist?
What does it mean to me?
What are the main messages?
What have I learnt?
Any actions to take?
CREATOR’S RESPONSIBILITY CONSUMER’S RESPONSIBILITY

5. What colour shall we make the axis lines?
How thick should the lines be?
How long should the lines be?
How will we label them?
Should we label them?
Do we want tick marks as well?
Do we even need the lines?
It depends.
A game of decisions: Complex more than complicated

6. To make the best decisions you need to be familiar with all your
options and aware of the things that will influence your choices.
A game of decisions: Complex more than complicated
THINGS YOU
COULD DO
THINGS YOU
WILL DO
“IT DEPENDS”

7. Workflow
A framework for optimising your
critical thinking

8. Effective
visualisation is
TRUSTWORTHY
Effective
visualisation is
ACCESSIBLE
Effective
visualisation is
ELEGANT
Design workflow: Effective decisions, efficiently made
Do I have believe that
what I see is faithful to
the data and the
subject?
Am I able to
understand this work
with a proportionate
amount of effort?
Does the way this work
is presented inspire me
to engage with it?

9. Design workflow: Effective decisions, efficiently made
Stage 1
Formulating
your brief
Stage 2
Working
with data
Stage 3
Establishing
your editorial
thinking
Stage 4
Developing your
design solution

10. Design workflow: Effective decisions, efficiently made
Stage 1
Formulating
your brief
Stage 2
Working
with data
Stage 3
Establishing
your editorial
thinking
Stage 4
Developing your
design solution
What’s the curiosity? What are the project conditions? What’s the purpose?

11. http://filmographics.visualisingdata.com/
“What is the pattern of success or failure in the
movie careers of a range of notable actors/directors?”
What’s the curiosity? “An eagerness to understand something”

12. What are the conditions? The factors and requirements
https://github.com/propublica/weepeople

13. What are the conditions? The factors and requirements
http://chartmaker.visualisingdata.com/

14. What’s the purpose? How will understanding be facilitated?
https://www.bbc.co.uk/weather
Explanatory Exploratory
Exhibitory

15. Design workflow: Effective decisions, efficiently made
Stage 1
Formulating
your brief
Stage 3
Establishing
your editorial
thinking
Stage 4
Developing your
design solution
Stage 2
Working
with data
Data acquisition, examination, transformation, and exploration

16. Working with data: Understanding its properties and qualities
Qualitative (Textual)
Bolt quote: “It wasn't perfect today, but I got it done
and I’m pretty proud of what I've achieved.
Nobody else has done it or even attempted it”
Categorical (Nominal) The athletics event: Men's 100m
Categorical (Ordinal) The medal category: Gold
Quantitative (Interval)
The estimated temperature at track level
during the Men's 100m: 28℃
Quantitative (Ratio) Usain Bolt’s winning time: 9.81 seconds

17. HEADING SUMMARY STATS
CREDITS
LOGO
63 matches =
8 x 8 grid
Working with data: Understanding its properties and qualities
http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/

18. Working with data: Understanding its properties and qualities
http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/
X-axis = 0 to 120 minutes
Y-axis = -3 to +6 goal difference

19. Working with data: Understanding its properties and qualities

20. Working with data: Understanding its properties and qualities

21. Working with data: Understanding its properties and qualities
WHO?
WHAT?
HOW
MUCH?

22. Working with data: Understanding its properties and qualities

23. Design workflow: Effective decisions, efficiently made
Stage 1
Formulating
your brief
Stage 4
Developing your
design solution
Stage 2
Working
with data
Stage 3
Establishing
your editorial
thinking
What questions are you trying to answer in support of the overriding curiosity?

24. Editorial: Which angle(s) of analysis are relevant/interesting?
How good was my run?
What distance did I run?
What time/pace did I run it in?
What were my main achievements?
What was the route elevation?
What were my 1km splits?

25. Editorial: Which angle(s) of analysis are relevant/interesting?
How good was my run?

26. Editorial: How will you frame your data (include vs. exclude)?

27. Design workflow: Effective decisions, efficiently made
Stage 1
Formulating
your brief
Stage 2
Working
with data
Stage 3
Establishing
your editorial
thinking
Stage 4
Developing your
design solution
Making data representation, interactivity, annotation, colour, and composition choices

28. Data representation: A recipe of marks and attributes
Shape
Line
Form
Point
Size
Position
Angle
Pattern
Quantity Containment
Connection
Symbol
Colour
Visual placeholders to
represent data items
Visual properties to
represent data values
Direction

29. Data representation: A recipe of marks and attributes
Size
Colour
Line

30. Data representation: A recipe of marks and attributes
Shape
Colour
Size

31. Data representation: How to show what you want to say?
CATEGORICAL
Comparing categories and
distributions of quantitative values
TEMPORAL
Showing trends and activities
over time
HIERARCHICAL
Charting part-to-whole relationships
and hierarchies
SPATIAL
Mapping spatial patterns through
overlays and distortions
RELATIONAL
Graphing relationships to explore
correlations and connections

32. Data representation: How to show what you want to say?

33. Interactivity: Controlling what and how your data is presented
http://www.visualisingdata.com/olympics2016/

34. Annotation: Judging the right level of assistance
http://www.visualisingdata.com/2016/05/boom-bust-shape-roller-coaster-season/

35. Annotation: Judging the right level of assistance

36. Colour: Colouring all your chart and project contents
http://filmographics.visualisingdata.com/

37. Colour: Colouring all your chart and project contents
Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1

38. Colour: Colouring all your chart and project contents
Visualisation by FinViz https://finviz.com/map.ashx?t=sec&st=w1
Colour blindness
simulator
colororacle.org

39. Colour: Colouring all your chart and project contents

40. BAR CHART UNIVARIATE BUBBLE PLOT
BUBBLE PLOT
SLOPE GRAPH
MATRIX CHART
Composition: Making layout, sizing and positioning decisions
TITLE
ABOUT THE DATA
HEADLINES
ABOUT THE SUBJECT
SECTIONS & COMMENTARY

41. Composition: Making layout, sizing and positioning decisions
Visualisation by Andy Kirk http://www.visualisingdata.com/olympics2016/

42. Composition: Making layout, sizing and positioning decisions
WHO?
WHAT?
HOW
MUCH?

43. Composition: Making layout, sizing and positioning decisions

44. Composition: Making layout, sizing and positioning decisions

45. Demonstration
A framework for optimising your
critical thinking

46. The importance of critical thinking to improve visual sophistication

47. The importance of critical thinking to improve visual sophistication

48. The importance of critical thinking to improve visual sophistication

49. Design workflow: Effective decisions, efficiently made
Stage 1
Formulating
your brief
Stage 2
Working
with data
Stage 3
Establishing
your editorial
thinking
Stage 4
Developing your
design solution

50. Single slide overview to present analysis that shows
“how staff feel about working here” to key stakeholders
Formulating the brief: Requirements

51. Formulating the brief: Tool constraints

52. Working with data: Understanding its properties and qualities
SURVEY RESPONSES
8 x question categories about work issues
5 x response categories for scale of feelings
40 x question-response quantities (%, 100% total per question)
RESPONDENT DEMOGRAPHICS
4 x gender categories, 4 x quantities (% and abs. numbers)
3 x employment categories, 3 x quantities (% and abs. numbers)
6 x service length categories, 6 x quantities (% and abs. numbers)

53. 1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?
Editorial thinking: What questions are you trying to answer?

54. Data representation: How to show what you want to say?
CATEGORICAL
Comparing categories and
distributions of quantitative values
TEMPORAL
Showing trends and activities
over time
HIERARCHICAL
Charting part-to-whole relationships
and hierarchies
SPATIAL
Mapping spatial patterns through
overlays and distortions
RELATIONAL
Graphing relationships to explore
correlations and connections
1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?

55. Chart types: How to show what you want to say?
Q1. Do you feel appreciated
in your role?
Q2. Are you sa sfied with
your personal performance?
Q3. Are you sa sfied with
your team's performance?
Q4. Is poor-performance
effec vely managed by your
manager?
Q5. Does the organisa on
allow you to raise issues of
unfairness?
Q6. Do you think the
organisa on gets the most
out of its talented employee..
Q7. Do you consider this to be
a learning organisa on?
Q8. Does this organisa on's
leader mo vate and inspire
you?
Category
Strongly Agree
Agree
Disagree
Strongly Disagree
No opinion
1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?

56. Chart types: How to show what you want to say?
0 10 20 30 40 50 60 70 80 90 100
Q1. Do you feel appreciated in your
role?
Q2. Are you sa sfied with your
personal performance?
Q3. Are you sa sfied with your
team's performance?
Q4. Is poor-performance effec vely
managed by your manager?
Q5. Does the organisa on allow you
to raise issues of unfairness?
Q6. Do you think the organisa on
gets the most out of its talented
employees?
Q7. Do you consider this to be a
learning organisa on?
Q8. Does this organisa on's leader
mo vate and inspire you?
Category
No opinion
Strongly Disagree
Disagree
Agree
Strongly Agree
Strongly Agree Agree Disagree Strongly Disagree No opinion
0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60
Q1. Do you feel appreciated in your
role?
Q2. Are you sa sfied with your personal
performance?
Q3. Are you sa sfied with your team's
performance?
Q4. Is poor-performance effec vely
managed by your manager?
Q5. Does the organisa on allow you to
raise issues of unfairness?
Q6. Do you think the organisa on gets
the most out of its talented employees?
Q7. Do you consider this to be a learning
organisa on?
Q8. Does this organisa on's leader
mo vate and inspire you?
Category
Strongly Agree
Agree
Disagree
Strongly Disagree
No opinion
1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?

57. Chart types: How to show what you want to say?
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70
Q1. Do you feel appreciated in
your role?
Q2. Are you sa sfied with your
personal performance?
Q3. Are you sa sfied with your
team's performance?
Q4. Is poor-performance
effec vely managed by your
manager?
Q5. Does the organisa on allow
you to raise issues of unfairness?
Q6. Do you think the
organisa on gets the most out
of its talented employees?
Q7. Do you consider this to be a
learning organisa on?
Q8. Does this organisa on's
leader mo vate and inspire you?
0 10
Agreement Disagreement No-opinion
1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?

58. Chart types: How to show what you want to say?
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70
Q1. Do you feel appreciated in
your role?
Q2. Are you sa sfied with your
personal performance?
Q3. Are you sa sfied with your
team's performance?
Q4. Is poor-performance
effec vely managed by your
manager?
Q5. Does the organisa on allow
you to raise issues of unfairness?
Q6. Do you think the
organisa on gets the most out
of its talented employees?
Q7. Do you consider this to be a
learning organisa on?
Q8. Does this organisa on's
leader mo vate and inspire you?
Agreement Disagreement No-opinion
1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?

59. Chart types: How to show what you want to say?
Gender
Female
Male
Other
No response
Employment Status
Full-Time
Part-Time
No response
Length of Service
Less than 1 year
Between 1 and 3 years
Between 3 and 5 years
Between 5 and 10 years
Over 10 years
No response
Female
Male
Other
No response
0 20 40 60 80 100 120 140
Gender
Full-Time
Part-Time
No response
0 20 40 60 80 100 120 140 160
Employment Status
Less than 1 year
Between 1 and 3 years
Between 3 and 5 years
Between 5 and 10 years
Over 10 years
No response
0 10 20 30 40 50 60 70 80 90
Length of Service
1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?

60. Chart types: How to show what you want to say?
Back-to-back bar chart Bar chartBubble chart
1. For each statement, what is the proportion of response sentiment?
2. For each respondent demographic group, what is the breakdown?

61. Interactivity: Controlling what and how your data is presented
Q3. Strongly Agree = 45%
More info | Download data | Contact
Results filtered
for female
respondents

62. Annotation: Judging the right level of assistance
Main
observations
verbalised

63. Colour: Colouring all your chart and project contents

64. Colour: Colouring all your chart and project contents

65. Colour: Colouring all your chart and project contents
Response categories
Demographic bars
Background shading
Title text
Section title text
Chart axis and value labels

66. Colour: Colouring all your chart and project contents

67. Composition: Defining all size and position decisions
Survey results
breakdown
Demographic
breakdown
Title

68. Composition: Defining all size and position decisions

69. Developing your design solution

70. Developing your design solution

71. Developing your design solution
Demonstrate the value
of your work, its
accuracy, and be
transparent
Show the most relevant
things in the most
appropriate form that
minimises obstructions
Optimise the aesthetic
presentation to seduce
and sustain an
audience’s attention
Effective
visualisation is
TRUSTWORTHY
Effective
visualisation is
ACCESSIBLE
Effective
visualisation is
ELEGANT

72. Learn more! ‘Introduction to Data Visualisation’ online course
https://campus.sagepub.com/introduction-to-data-visualisation

73. DATA VISUALISATION
A GAME OF DECISIONS
Andy Kirk
andy@visualisingdata.com
www.visualisingdata.com
@visualisingdata