Slides from an interactive workshop focused on exposing M&E practitioners to design thinking approaches to understand the needs and experiences of data users at MERL Tech 2017

1. The Simpsons,
Design, and
Data UseAmanda Makulec | Excella Consulting
Barb Knittel | John Snow Inc.
MERLTech 2017
Featuring work
funded by

4. D E S IGN
THINKING

6. How can we put
ourselves as
designers into the
data user’s shoes?

7. What are our
data user’s wants,
needs, and
interests?

8. What are our
data user’s
challenges and
pain points?

9. EMPATHY

10. STORIES

11. CHAR
ACTERS

12. https://www.youtube.com/watch?v=q1a7tiA1Qzo

13. OUR DE S IGN
JOURNEY

14. Design framework adapted from Stanford d. School
From “Applying User-centered Design to Data Use Challenges:What we Learned”
https://www.measureevaluation.org/resources/publications/tr-17-161

18. 18

20. 20

21. Personas help us move beyond
job titles
stakeholder groups
organization names
and focus on the human side of
your audience.

22. 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.”
“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/2014/08
/a-closer-look-at-personas-part-1/

23. 23

25. • LOVE data
• Hard workers and proactive in work
• Frustrated by environments that
accept
lower performers
• Desire for perfection can push them
to work long hours and burn out
Go Getters

26. • Full of energy
• Seek attention constantly
• Require very hands-on supervision
• Can be creative problem solvers,
but requires focused workplan to
stay on task
Hyperactives

27. • Always “busy” but do not produce
quality outputs
• Always miss deadlines and targets
• Flourish in an environment with no
consequences
• Full of excuses and avoid
responsibilities
Lazy Dodgers

28. design
sprint

29. PE RS ONAS
<CHARACTERS>

30. Identify your
stakeholder group(s).

31. District Health Executive Team
for example

32. Capture data on your
stakeholder group.

33. Analytical ability
Job function
Education
Programmatic knowledge
Access to tools
Motivations to use data
Pain points
Interests
Where they access information
Champion or opposition

34. Talking directly to members of your
audience gives you the greatest insight
into their wants, needs, and pain points.

35. If you’ve worked with this stakeholder
group before, you can also brainstorm
based on experience.

36. Identify 2-3 different
“personas” within each
stakeholder group.

37. District Health Executive Team
The Technocrat The Politician
segmented to

38. Name your personas &
give them stories.

39. District Health Executive Team
Rachel
The Technocrat
Pascal
The Politician
segmented to

41. Credit toThinkPlace Foundation, from the Innovations for Maternal, Newborn & Child Health
Initiative’s Care Community Hub

42. Identify common
characteristics and
differences

43. 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

44. 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
Rachel
the Technocrat
Pascal
the Politician

46. YOUR TURN:
PERSONAS

47. Design framework adapted from Stanford d. School
From “Applying User-centered Design to Data Use Challenges:What we Learned”
https://www.measureevaluation.org/resources/publications/tr-17-161

48. DEFINE

50. JOURNE Y MAPS
<STORIES>

54. Creating a journey map is an excellent way to
systematically think about the steps or milestones
of a process, and can be used in developing
empathy and in communicating about your work.1
You can use the steps on the journey map to
identify pain points for your data user that can be
transformed into opportunities for you in sharing
your data.
1 Definition from https://dschool-old.stanford.edu/groups/k12/wiki/d8073/Journey_Map.html

55. Pick a persona.

56. Map out the various (ideal)
steps that person takes
in making a decision.

57. Pascal
The Politician
Community
scorecard data is
collected in
Pascal’s district
Pascal takes action
with the district
supply chain team
to identify root
causes of the issue
Analysis shows
4 clinics in
Pascal’s district
have stockouts
of condoms
Pascal is
unaware of the
stockouts
NGO meets
with Pascal in
his office to
share the data
Pascal shares the
issue with the
District Health
Executive Team
The DHE checks the
logistics information
system and sees the
stockouts were not
reported

58. I am aware…
I think about my options…
I decide…
I seek…
I receive…
I share…
Use a set of
prompts if you’re
having trouble
mapping out steps

61. Identify the pain points
along that journey.

62. Pascal
The Politician
Community
scorecard data is
collected in
Pascal’s district
Pascal takes action
with the district
supply chain team
to identify root
causes of the issue
Analysis shows
4 clinics in
Pascal’s district
have stockouts
of condoms
Pascal is
unaware of the
stockouts
NGO meets
with Pascal in
his office to
share the data
Pascal shares the
issue with the
District Health
Executive Team
The DHE checks the
logistics information
system and sees the
stockouts were not
reported
Pascal has limited
time and needs
something short he
can easily share
with other DHE
members
The DHE may see
no stockouts in
their system and
doubt the accuracy
of the community
data

63. Identify how you could
address each pain point.

64. Pascal
The Politician
Community
scorecard data is
collected in
Pascal’s district
Pascal takes action
with the district
supply chain team
to identify root
causes of the issue
Analysis shows
4 clinics in
Pascal’s district
have stockouts
of condoms
Pascal is
unaware of the
stockouts
NGO meets
with Pascal in
his office to
share the data
Pascal shares the
issue with the
District Health
Executive Team
The DHE checks the
logistics information
system and sees the
stockouts were not
reported
Pascal has limited
time and needs
something short he
can easily share
with other DHE
members
The DHE may see
no stockouts in
their system and
doubt the accuracy
of the community
data
NGO creates a
one page brief
summarizing the
data and recs
NGO adds a spot
check of stock-
on-hand to the
scorecard
process

65. Credit toThinkPlace Foundation, from the Innovations for Maternal, Newborn & Child Health Initiative’s Care Community Hub

66. YOUR TURN:
JOURNEY MAPS

67. IDE ATE
PROTOTYPE
TE S T

68. Design framework adapted from Stanford d. School
From “Applying User-centered Design to Data Use Challenges:What we Learned”
https://www.measureevaluation.org/resources/publications/tr-17-161

69. IDEATE

70. 70

73. 73

74. 74

75. PROTO
TYPE

78. BONUS
Build aTower, Build aTeam |Tom Wujec
A TED Talk on teams and prototype thinking with the marshmallow challenge
https://www.ted.com/talks/tom_wujec_build_a_tower

79. TEST

80. DO’S O F D E S I G N
T H I N K I N G F O R D A T A U S E
These are reflections from our experience applying design thinking
to data use challenges. Every design process is unique.

81. Use design thinking
as a facilitation tool
(but not a silver bullet)

82. Include data use-
adjacent participants

83. Allow enough time
to see the design
process through

84. Have a follow up
plan (and resources)

86. DON’TS
O F D E S I G N T H I N K I N G
F O R D A T A U S E

87. Rush the process

88. Rely on one
workshop to come
to a ‘perfect’ solution.

89. Design solutions
with a tiny group
more people = more ideas
…to a point

90. Panic if prototypes
are broad when you
finish a workshop.

91. Focus only on the
outcome: there’s
immense value in
the process.

92. “I am exciting about taking [these
activities] forward at a district level to
try and change the way people view
data, and ultimately, the way districts
plan their services within the limited
resources they have.”
- Provincial Deputy Manager for Planning, KZN

93. Favorite Resources for Diving in on Design Thinking
• Stanford d. School Bootcamp Bootleg
dschool.stanford.edu/resources
• IDEO Design Kit
ideo.com/post/design-kit

94. Amanda Makulec
DataVisualization Lead
Excella Consulting
amanda.makulec@excella.com
@abmakulec
Barb Knittel
Senior RME Officer
John Snow Inc.
barbara_knittel@jsi.com
Added acknowledgements to Michelle Li (MEASURE Evaluation) and our colleagues
at Matchboxology (Cal Bruns and Jason Coetzee) for their invaluable contributions to this work.