Slide deck from our hands-on workshop hosted at the 4th Global Symposium on Health Systems Research, focused on basic design tips, tricks, and best practices to improve your charts and graphs.

1. Designing
Data
Visualizations
to Strengthen
Health
Systems Amanda Makulec Visual Analytics Advisor,JSI
Jeff Knezovich Director,Quaternary Consulting
Health Systems Research Symposium
15 November 2016 |Vancouver,BC,Canada

2. Amanda Makulec, MPH
Visual Analytics Advisor
John Snow Inc.
Jeff Knezovich
Director
Quaternary Consulting

5. conceptual data driven
declarative
exploratory
idea illustration everyday data viz
idea generation visual discovery
Matrix credit: Harvard Business Review’s Good Charts

6. Data Journalism
Learn more on how to craft effective data journalism at
http://datajournalismhandbook.org/

8. Where did viz start?

9. Key terms and
definitions

10. What is data visualisation?
• A way of visually conveying information – often
quantitative in nature – in an accurate, compelling
format.
• Usually makes relationships more apparent (e.g. by
clustering, color coding and by putting items in scale).
• Can be static or interactive.

11. Data visualisation, or
information visualisation?
1001000 1100101
1101100 1101100
1101111

12. Data visualisation, or
information visualisation?
72 101 108
108 111

13. Data visualisation, or
information visualisation?

14. Brief history

15. Are data visualisations new?
• William Playfair
– 1786: line
graph and bar
chart of
economic data
– 1801: pie
chart and
circle graph

16. • Florence
Nightingale
– 1858
polar
area
diagram
Are data visualisations new?

17. Are data visualisations new?
• John Snow
– 1854: Mapping
deaths from a
Cholera
outbreak in
central
London

18. Who’s your audience?

19. Each persona represents a significant
portion of people in the real world
and enables the designer to focus on a
manageable and memorable cast of
characters, instead of focusing on
thousands of individuals.”
persona, n.
“A persona is depicted as a specific
person but is not a real individual;
rather, it is synthesized from observations
of many people.
From:
https://www.smashingmagazine.com/201
4/08/a-closer-look-at-personas-part-1/

20. Interests?

21. Motivations?

22. Action?

23. steps to develop simple
data viz audience
personas

24. 1. Pick your
stakeholder groups

25. 2. Identify key
personas within a
stakeholder group

26. sample template

27. 3. Map personas by
dimension.

28. Factual, analytical Feeling, intuitive Personal gain System gains
External recognition Internal reward Simplicity Complexity
Complacent Driver of change Debilitated by chaos Thrives on chaos
Team oriented Individual/loaner Problem solver Defeatist
Black and white Compromise Receptive Rigid
Team leader Team member Carrot eater Stick driven
Short term focus Long term vision Self accountable Cheater
Internal motive External motive Technical Political
Needs clarification Self-motivated Empowerer Underminer
Works better in group Works better alone Data driven Story motivated
Values independence Values collaboration Head Heart
Personas on Continuums
Much like the Myers-Briggs personality scale, personas can be ranked along continuums of
characteristics that may impact their use of data for decisionmaking.Some examples of different
“poles” identified in workshops are below.

29. Ministry of Health
Rachel
The Technocrat
Pascal
The Politician
for example
segmented to

30. Wants
stories
Wants
numbers
Motivated
by passion
Motivated
by money
Act based
on feelings
Act based
on data
Resilient
problem solver
Frustrated
bureaucrat
Champion for
your issue
Oppose your
issue
mapping by
dimension

31. Wants
stories
Wants
numbers
Motivated
by passion
Motivated
by money
Act based
on feelings
Act based
on data
Resilient
problem solver
Frustrated
bureaucrat
Champion for
your issue
No knowledge
of your issue
mapping by
dimension
Rachel
the Technocrat
Pascal
the Politician

32. find your persona

33. Not just
job titles
stakeholder groups
organization names
Focus on the human side of
your data viz audience.

34. Let’s look at some
visualizations.

35. WTF is wrong with
the following
visualisations?

36. WTF is wrong with:
Image from Patients Association: http://www.patients-association.org.uk/reports/waiting-times-report-feeling-wait/

37. WTF is wrong with:
Image from WTFviz: http://visual.ly/buzzing-trends-real-estate-market-bangalore

38. WTF is wrong with:
Image from WTFviz: http://visual.ly/beyond-facebook-marketing-new-generation

39. WTF is wrong with:
Image from WTFviz: http://viz.wtf/image/110276700184

40. WTF is wrong with:
Image from WTFviz: https://twitter.com/BofA_News/status/558242696415166464

41. On Think Tanks Data
Visualisation Competition
Objectives:
Inspire
Strengthen
capacity
Encourage

42. Designing your
visualizations

43. “The two optic nerves [in the
eyes] are sending what we
now know are 20 megabits a
second of information back to
the brain.”
- Edward Tufte

44. System 1 vs System 2 thinking
17 x 24 = ?
Example from Graham Odds

45. Conscious v sub-conscious bandwidth
0 2 4 6 8 10
Taste
Auditory
Olfactory
Tactile
Visual
Sub-conscious (millions of bits
per second)
0 10 20 30 40
Conscious
(bits per second)
Adapted from: Tor Norretranders' The User Illusion

46. make data sticky

47. Gestalt design: taking
advantage of sub-concsious
processing

48. Gestalt design principles:
Implications for data visualisation
Adapted from Alberto Cairo

49. Gestalt design principles:
Semiotics and iconography
☺

50. Who makes a good
data visualisation?

51. Who makes a good
visualisation?
Communication
Research
Design
Technology
From: https://onthinktanks.org/art
icles/visualising-data-both-a-
science-and-an-art/

52. • Data literacy – merging and tidying datasets
• Statistical competencies – mean v median,
ordinal versus scalar
• Research methods - sampling
• Research context
Research

53. • For data collection – e.g. web scrapers
• For data storage – e.g. database and SQL
• For data manipulation – e.g. SPSS, R
• For data visualisation – e.g. coding, like jQuery,
HTML5
Technology

54. • Appropriate visualisation types
• Chart fundamentals
• Colour and form
Design

55. • Understanding of users’ needs and
perceptions
• Finding and refining messages
• Telling stories
Communication

56. What makes a good
data visualisation?

57. What makes a good visualisation?
Interesting
Function
Form
Integrity
(McCandless, 2012)

58. Function
Source: Gregor Aisch - http://slidesha.re/1jTg5Eq

59. Source: Adapted from from The Wall Street Journal guide to information graphics
Form

60. Integrity

61. From: XKCD
CONSUMERS OF
BOILED EGGS
Interestingness

62. What do you think are key
elements of effective
data visualisation?

63. preattentive attributes
strategic chart selection

64. “ t h i n k i n g w i t h
y o u r l i z a r d
b r a i n ”
preattentive attributes

65. Paired ColumnColumn
Bar Paired Bar Stacked Bar
Stacked Column
Slope
compare categories

66. Histogram Box and Whiskers Confidence Interval
distribution

67. Scatterplot Bubble
relationship

68. Line
Stacked Area SparkLines
Dot Plot
time series
0 20 40 60 80

69. why dots?
0
20
40
60
80
Facility
1
Facility
2
Facility
3
Facility
4
Facility
5
Facility
6
Facility
7
Facility
8
Year 1
Year 5
0 20 40 60 80
Facility 1
Facility 2
Facility 3
Facility 4
Facility 5
Facility 6
Facility 7
Facility 8

70. Pie Donut
part-to-whole

71. a cautionary tale

72. Image credit from:https://www.biostat.wisc.edu/~kbroman/topten_worstgraphs/bell_fig3.jpg

73. Image Source:
http://www.nytimes.com/imagepages/2008/
03/16/magazine/16wwln-
lede.graphic.ready.html

74. Tree Map
0% 20% 40% 60% 80% 100%
Icon Matrix
part-to-whole
better
839
294
145
50
Small Multiples Chart
Horizontal Stacked Bar

76. icon matrix

77. pick your icon

79. for social share &
infographics

80. View & order a copy at http://policyviz.com/graphic-continuum/
chart type quick reference

81. Common Pitfalls

82. Things to avoid when
designing your
visualizations.
Many courtesy of Gregor Aisch
(http://slidesha.re/1jTg5Eq)

83. Using 3d
25%
50%
25%
33%
33%
33%
vs

84. Going colour crazy
0
1
2
3
4
5
6
7
8
9
10
Category 1 Category 2 Category 3 Category 4
0
1
2
3
4
5
6
7
8
9
10
Category 1 Category 2 Category 3 Category 4
vs

85. Quick colour aside
Analogous (similar
colours)
monochromatic
complementary
From: Data visualisation: a practical guide to producingeffective visualisations for research communication
http://resyst.lshtm.ac.uk/resources/data-visualisation-practical-guide-producing-effective-visualisations-research

86. Not remembering the objective
Team 1
Team 2
Person A
Person B
Person C
Person D
Person E
Person F
Person
G
Person
H
vs

87. Not thinking about order
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
A B C D
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
D A C B
vs

88. Not scaling to zero
11.5
12
12.5
13
13.5
14
14.5
15
Category
1
Category
2
Category
3
Category
4
0
2
4
6
8
10
12
14
16
Category
1
Category
2
Category
3
Category
4
vs

89. Not scaling to zero (exceptions)
vs

90. Not labelling directly
Series 1
Series 2
Series 3
0
1
2
3
4
5
6
1990 1995 2000 2005
0
1
2
3
4
5
6
1990 1995 2000 2005
Series 1 Series 2 Series 3
vs

91. Non-descriptive titles

92. Non-descriptive titles
Youth Unemployment Rates in Europe

93. Non-descriptive titles
Youth Unemployment on Historical High

94. Non-descriptive titles
Youth Unemployment Divides Europe

95. Non-descriptive titles
Youth Unemployment Divides Europe
Seasonally adjusted unemployment rates of under 25s

96. Seven deadly sins
of
data visualisation
https://onthinktanks.org/articles/on-datavis-judging-jeff-knezovichs-advice/
From: https://onthinktanks.org/articles/on-
datavis-judging-jeff-knezovichs-advice/

97. Not telling
a story

98. Misrespresentation

99. Shifting
scales
(without mentioning it)

100. Over-
reliance on
text

101. Ambiguity

102. Over-
complication

103. Forgetting
the point

104. Break time!
Download me:
tinyurl.com/DataVizatHSR

105. Designing in
Excel

106. Think like a
designer

107. Simple
LESS IS MORE
white space is nice

108. three things
to improve every chart

109. 1: declutter

110. “Erase non-data-ink,
within reason.”

111. 8
14.9
11.7
38.9
19.2
16.6
20.4
26.2
0
5
10
15
20
25
30
35
40
45
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Year 1
Year 1

112. 0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8

115. 2: color sparingly

116. 0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8

117. 0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8

118. 0
1
2
3
4
5
6
Category 1 Category 2 Category 3 Category 4
Series 1 Series 2 Series 3
Super special brand-compliant graph!
Inspired by Cole Naussbaumer at Stanford’s Data on Purpose, February 2016

119. 4.3
2.5
3.5
4.5
Category 1 Category 2 Category 3 Category 4
Series 1 Series 2 Series 3
Easier to see a key data series graph!
Inspired by Cole Naussbaumer at Stanford’s Data on Purpose, February 2016

120. be mindful

121. custom styles

122. 3: purposeful title

123. 0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Quality of Care Score by Facility
Ghana, 2015

124. 0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Facility 4 showed the highest quality of care.
Despite scoring highest, its overall score was below 50%, indicating there is
work to be done to improve quality of care.

125. 38.9
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Facility 4 showed the highest quality of care.
Despite scoring highest, its overall score was below 50%, indicating there is
work to be done to improve quality of care.

126. chart templates

127. favorite things
Graphic Continuum
Dot plots
Icon matrix
Canva
Piktochart
Noun Project
Visage DataViz 101
Pexels
Slidedocs
Google Sheets
High Charts

128. Inspiration
Skill building
data viz recs

129. duarte.com // storycorps.org // skillshare.com
storytelling recs

130. Amanda Makulec
amanda_makulec@jsi.com
@abmakulec
Jeff Knezovich
jeff@quarternary.co
@knezovjb