The Keys to a Successful Data Design Process

Design is a process, not a product. Yet it is clear that some data design teams produce consistently outstanding visualizations, while others create mostly mediocre ones. So what is the secret? Are some people just “better” designers, or have they established better processes, which tend to result in “successful” designs more often than not?

Transcript

1. The Keys to a Successful
   Data Design Process
   Scott Murray
   @alignedleft
   alignedleft.com
   

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2. Scott Murray
   Assistant Professor
   University of San Francisco
   

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3. 608-283-xxxx
   608-264-xxxx
   202-501-xxxx
   608-652-xxxx
   202-213-xxxx
   347-202-xxxx
   Design is a process,
   not a product.
   

   View Slide

4. 608-283-xxxx
   608-264-xxxx
   202-501-xxxx
   608-652-xxxx
   202-213-xxxx
   347-202-xxxx
   Design(ing) is a process,
   not a product.
   

   View Slide

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   [ [ ] ] ? ? I I } } { { %%| ! ! ! / / + = : : ; , , , = F U U O O 0 E E B B E 0 0 E E B WWWB WB B E E 0 U 4 6 ? . - - , ; ; : = = / / ! | %{ } I I ? ] [ 1 1 * 7 7 L T T J C C 5 5 F F 6 S S S
   [ [ ] ] ? ? I I } } { { %%| | ! ! / %* [ + , ; , , , : 6 U U O 0 E E B B B B 0 0 0 O O E E E E E E O Z Z U Z S J / . . - ' , ; : = = + / ! ! | %{ } I ? ] ] [ 1 * * 7 L L T T J C C 5 F F 6 6 S S S
   [ [ [ ] ] ? ? I I } } { { %%| ! ! * 5 6 6 5 / , , ; , ; T U O 0 0 E B B B B B B E 0 O O E B B WB B E U Z 6 S 6 5 1 . . - ' ' , ; : = = + / ! | %%{ } I ? ] ] [ 1 * * 7 L T T J J C C 5 F F 6 6 S S 4
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   [ [ [ ] ] ] ? ? I I } } { { %{ [ 5 Z Z Z U U Z 4 J [ I I [ C Z 0 E B B B WWWWWE 0 O 0 E E B B 0 O 4 4 S 4 4 6 C T 1 ' . - - ' , ; ; : = + / ! ! | %{ { I I ? ] [ [ * * 7 L L T T J J C 5 5 F F 6 6 S S 4
   1 [ [ [ ] ] ? ? I I } } { { %1 L 4 Z Z Z U U U U Z Z 4 4 4 Z U 0 E B B B WWWWWE 0 O 0 0 0 0 O Z 4 S 6 S 4 U S 5 J * } . . - ' ' , ; : : = + / ! | | %{ } I I ? ] [ 1 * * 7 L L T J J C C 5 5 F F 6 6 S S 4
   1 1 [ [ ] ] ? ? ? I I } } } } F 4 Z Z Z U U O O O O 0 B @WE E E B B WWW@@@WWB E 0 0 O U O O Z 4 S 4 O 0 Z 6 5 L 7 : . . - - ' , , ; : = = / / ! | %%{ } I ? ] ] [ 1 * * 7 L L T J J C C 5 F F 6 6 6 S 4 4
   1 1 1 [ ] ] ] ? ? I I I } } [ 4 Z Z Z U U U O O 0 0 0 0 @WB E B B WWWW@@@@WWB B E 0 0 E E O Z Z Z U 0 Z 6 F L L ? . . - - ' , ; : : = = / / ! | %{ } } I ? ] [ [ 1 * * 7 L T T J J C 5 5 F F 6 6 S S 4 4
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   * 1 1 1 1 1 [ [ ] ] ? ? ] C Z Z Z Z Z Z Z Z U O 0 E B B B B WB B W@@@@& & & & & & & & @@WWB B WWU S F F F F 5 5 5 J T : . - - ' , ; : = = + / / ! | | %{ { } I I ] [ [ 1 * * 7 L L T T J C C C 5 F F 6 6 S S 4 4 4
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   * * * * 1 1 1 [ [ ] ] ? C Z Z Z Z Z Z Z Z Z Z U O 0 B B WWWWWW@@@@& & & & & & @@@@WWB WWB S S S 6 6 6 F F 5 C C T ! - ' ' , : : = = + / ! ! | %{ } } I ? ? [ [ 1 * * * 7 L T T T J C C 5 5 F 6 6 6 S S 4 4 4
   7 * * * * 1 1 1 [ [ ] * 0 B E B E E 0 0 O U U U U O E B B WWW@@@@@@& & & & & & @@@@W@W@0 E 4 4 4 4 S S 6 F 5 5 5 C C | ' , , ; = = + / ! ! | | %%{ } I I ? ] [ [ 1 * * 7 L L T T J J C C 5 F F 6 6 S S S 4 Z Z
   7 * * * * * 1 1 [ [ [ L B @@& @@WB 0 0 O O U O E E B B WW@@@@@@& & & & & & @@@@@@@@E E Z Z 4 4 4 S 6 6 F F F F L } , ; ; : = + + / ! ! | | %{ { } I I ? ] [ 1 1 * * 7 L L T T J J C 5 5 F F 6 6 S S S 4 4 Z
   7 7 7 * * * * * 1 1 1 * W& $ $ # # # $ @@B B 0 0 O 0 E B B W@W@@@& & @& & & @@@@& & & @@E E O U Z Z 4 4 S S S S Z U 4 ? ; ; : = = + / / ! | | %%{ } I I ? ] ] 1 1 * * 7 7 L T T J J C 5 5 F F 6 6 6 S S 4 4 Z Z
   Engineering is a process,
   not a product.
   

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6. Andy Gilmore, Wallpaper*, 2011 Andy Gilmore, 10 28 13
   

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7. Untitled — Paris, Onel, 2013 Untitled — Paris, Navets, 2010
   

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8. 608-283-xxxx
   608-264-xxxx
   202-501-xxxx
   608-652-xxxx
   202-213-xxxx
   347-202-xxxx
   Design is a process,
   not a product,
   so you can't see it
   or point to it.
   

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9. Designer
   person
   Design(ing)
   process
   Designed
   product
   

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10. industrial design
    graphic design
    interior design
    + architecture
    objects
    visuals
    spaces
    

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11. Designer
    person
    Design(ing)
    process
    Designed
    product
    Outcome
    result
    (hopefully the
    intended one!)
    

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12. Data design
    is a specific
    kind of design process.
    

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13. 13
    DATA DESIGN
    Any design process that is heavily informed by data,
    such that the output of the process (designed product)
    changes significantly when the data informing the
    process change.
    

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14. 13
    DATA DESIGN
    Any design process that is heavily informed by data,
    such that the output of the process (designed product)
    changes significantly when the data informing the
    process change.
    DATA VISUALIZATIONS
    Visual solutions to data-driven problems
    

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15. DATA DESIGN FIRMS
    Accurat Halftone Fathom
    

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16. DATA DESIGN FIRMS
    The OCR Periscopic Stamen
    

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17. JOURNALISM
    Bloomberg Visual Data National Geographic The New York Times
    

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18. INDIVIDUALS
    Jan Willem Tulp Jen Lowe Moritz Stefaner
    

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19. INDIVIDUALS
    Nathan Yau Santiago Ortiz Stefanie Posavec
    

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20. COMPANIES + GOVERNMENT
    Google Big Picture Mapbox NASA
    

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21. ACADEMIA
    Selfie City Kindred Britain Poem Viewer
    

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22. Q: How do they do it?
    

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23. Q: What data design
    process results in the
    most successful outcomes?
    

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24. Data Outcome
    

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25. Design process, after Tim Brennan, ~1990, reproduced from How do you design?, Dubberly Design Office, 2004.
    
    

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26. Design process, after Tim Brennan, ~1990, reproduced from How do you design?, Dubberly Design Office, 2004.
    Adapted for local audience in Winchester, England, United Kingdom by Scott Murray, 2014.
    
    £
    

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27. Q: What data design
    process results in the
    most successful outcomes?
    

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28. Q: What data design
    process results in the
    most successful outcomes?
    A:
    

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29. Q: What data design
    process results in the
    most successful outcomes?
    A: It depends.
    

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30. Some factors on which “it depends”:
    team size
    team expertise
    project goals
    data availability
    output medium
    time available
    budget available
    tools available
    

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31. Data Outcome
    Process Maps That Attempt to Clarify
    the Complexity of All This
    

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32. Ben Fry. Visualizing Data. O’Reilly, 2008.
    

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33. Project workflow by Moritz Stefaner, reproduced from The Functional Art by Alberto Cairo. New Riders, 2013.
    

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34. Nathan Yau. Visualize This. Wiley, 2011.
    

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35. Process diagram, Jeff Heer, University of Washington
    

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36. A data journalism workflow, Mark McCormick with Simon Rogers, 2013.
    UNNECESSARY
    COLUMNS OF DATA
    MERGED
    CELLS
    DATA IN WRONG
    FORMAT
    SHARE
    DATA
    BREAKING
    NEWS
    WHAT OTHER DATA
    SETS TO USE WITH IT?
    WHAT TO COMPARE
    OR SHOW CHANGE
    RECURRING
    EVENTS
    SENT
    DATA
    SANITY CHECK
    THE RESULTS
    RECALCULATE
    IF NEEDED
    JUST PUBLISH
    GOOGLE FUSION
    TABLE
    STORY
    OUR GRAPHICS
    TEAM
    SPREAD
    SHEETS
    THEORIES TO
    BE EXPLORED
    PERFORM
    CALCULATIONS
    ON THE DATA
    WHAT DOES THE
    DATA MEAN
    DATA MEASURED IN
    DIFFERENT UNITS
    FREE VIZ
    TOOLS
    OUTPUT
    

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37. UNNECESSARY
    COLUMNS OF DATA
    MERGED
    CELLS
    DATA IN WRONG
    FORMAT
    SHARE
    DATA
    BREAKING
    NEWS
    WHAT OTHER DATA
    SETS TO USE WITH IT?
    WHAT TO COMPARE
    OR SHOW CHANGE
    RECURRING
    EVENTS
    SENT
    DATA
    SPREAD
    SHEETS
    THEORIES TO
    BE EXPLORED
    WHAT DOES THE
    DATA MEAN
    DATA MEASURED IN
    DIFFERENT UNITS
    A data journalism workflow, Mark McCormick with Simon Rogers, 2013.
    UNNECESSARY
    COLUMNS OF DATA
    MERGED
    CELLS
    DATA IN WRONG
    FORMAT
    SANITY CHECK
    THE RESULTS
    RECALCULATE
    IF NEEDED
    JUST PUBLISH
    GOOGLE FUSION
    TABLE
    STORY
    OUR GRAPHICS
    TEAM
    PERFORM
    CALCULATIONS
    ON THE DATA
    DATA MEASURED IN
    DIFFERENT UNITS
    FREE VIZ
    TOOLS
    OUTPUT
    

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38. UNNECESSARY
    COLUMNS OF DATA
    MERGED
    CELLS
    DATA IN WRONG
    FORMAT
    SHARE
    DATA
    BREAKING
    NEWS
    WHAT OTHER DATA
    SETS TO USE WITH IT?
    WHAT TO COMPARE
    OR SHOW CHANGE
    RECURRING
    EVENTS
    SENT
    DATA
    SANITY CHECK
    THE RESULTS
    RECALCULATE
    IF NEEDED
    JUST PUBLISH
    GOOGLE FUSION
    TABLE
    STORY
    OUR GRAPHICS
    TEAM
    SPREAD
    SHEETS
    THEORIES TO
    BE EXPLORED
    PERFORM
    CALCULATIONS
    ON THE DATA
    WHAT DOES THE
    DATA MEAN
    DATA MEASURED IN
    DIFFERENT UNITS
    FREE VIZ
    TOOLS
    OUTPUT
    

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39. Ben Fry. Visualizing Data. O’Reilly, 2008.
    

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40. Decisions,
    decisions,
    decisions...
    

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41. SUCCESS
    FAILURE
    START
    

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42. SUCCESS
    FAILURE
    START
    

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43. SUCCESS
    FAILURE
    START
    

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44. SUCCESS
    FAILURE
    START
    

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45. SUCCESS
    FAILURE
    START
    

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46. SUCCESS
    FAILURE
    START
    

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47. SUCCESS
    FAILURE
    START
    

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48. SUCCESS
    FAILURE
    START
    

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49. SUCCESS
    FAILURE
    START
    

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50. SUCCESS
    FAILURE
    START
    Clarify
    What & Why
    Explore
    & Sketch
    Define
    & Produce
    

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51. SUCCESS
    FAILURE
    START
    Acquire Parse Filter Mine Represent Refine Interact
    

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52. SUCCESS
    FAILURE
    START
    Acquire Parse Filter Mine Represent Refine Interact
    

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53. Expertise is the informed
    capacity to make
    decisions.
    

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54. Q: What expertise,
    exactly, do we need?
    

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55. Q: What expertise,
    exactly, do we need?
    A:
    

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56. Q: What expertise,
    exactly, do we need?
    A: It depends.
    

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57. ESSENTIAL HELPFUL BONUS
    EXPERTISE NEEDED FOR
    DATA DESIGN
    

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58. Cartography
    Mathematics
    Architecture
    Management
    Business
    Art History
    History
    Empathy
    Patience
    Self-
    awareness
    Non-
    defensiveness
    Visual
    Arts
    Illustration
    Graphic
    Design
    Web
    Design
    Design
    Process
    UI/UX
    Psychology
    Journalism
    Computer
    Science
    Cognitive
    Science
    Visual
    Perception
    Scientific
    Method
    Statistics
    Logic
    Writing
    Coding
    Curiosity
    Domain
    Expertise
    Data
    Fluency
    Visual
    Design
    EXPERTISE NEEDED FOR
    DATA DESIGN
    

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59. Cartography
    ecture
    Management
    Business
    Art History
    History
    Empathy
    Patience
    Self-
    awareness
    Non-
    defensiveness
    Visual
    Arts
    Illustration
    Graphic
    Design
    Web
    Design
    Design
    Process
    UI/UX
    Psychology
    Journalism
    Computer
    Science
    Cognitive
    Science
    Visual
    Perception
    Scientific
    Method
    Statistics
    Logic
    Writing
    Coding
    Curiosity
    Domain
    Expertise
    Data
    Fluency
    Visual
    Design
    Cartography
    Mathematics
    Architecture
    Management
    Busi
    History
    Empathy
    Patience
    Self-
    awareness
    Non-
    defensiveness
    Visual
    Arts
    Illustration
    Graphic
    Design
    Web
    Design
    Design
    Process
    UI/UX
    Psychology
    Journalism
    Computer
    Science
    Cognitive
    Science
    Visual
    Perceptio
    Scientifi
    Method
    Statistics
    Logic
    Writing
    Coding
    Curiosity
    Domain
    Expertise
    Data
    Fluency
    Visual
    Design
    

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60. SUCCESS
    FAILURE
    START
    Acquire Parse Filter Mine Represent Refine Interact
    SMALLER LOOPS
    SPEED ITERATION
    

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61. More people with
    more skills
    working more closely together
    making more of the right decisions
    more often.
    

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62. Mathematics
    Empathy
    Patience
    Self-
    awareness
    Non-
    defensiveness
    Illustration
    Psychology
    Journalism
    Co
    S
    Sta
    Logi
    Writing
    Coding
    Curiosity
    Domain
    Expertise
    Data
    Fluency
    Visual
    Design
    PROCESS MAPS FOR BIG-PICTURE GUIDANCE
    MORE EXPERTISE FOR BETTER DECISIONS
    BETTER COMMUNICATION FOR
    FASTER ITERATION
    

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63. What about your process?
    

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64. View Slide

65. What title is on your business card?
    What name do you use for your occupation when talking to people outside your field?
    At a dinner party, what do you say when the other guests ask what you do?
    How many people are in your organization / company / firm?
    How many people are in your immediate team / group / department?
    Please describe your data visualization process.
    What kinds of expertise do you personally contribute during this process?
    What kinds of expertise are contributed by others engaged in the process?
    For whom do you produce work?
    Who is the ultimate audience for your visualizations?
    About how often do you feel your visualizations are successful?
    For you, what constitutes "success"?
    What other questions should I have asked here?
    

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66. For you, what constitutes "success"?
    “Client is happy (firstly); design is clean, code doesn't
    suck from my perspective, vis shows something interesting
    or allows people to find interesting things in it.”
    “Delivery on time. Actual usage by readers of the story.
    [Process produces] reusable code or components.”
    “There was nothing more to improve given the constraints.”
    

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67. First steps in the data visualization process:
    “Collect raw data.”
    “Develop [a] hypothesis about the problem.”
    “Receive the data from client. Sit with client and make sure data
    is fully understood.”
    “I create folders for any idea that pops into my head. When I have
    time I dig into one and see if I can find a data set that fits my
    needs.”
    “Have questions to answer with data. All my projects start by
    having at least one question and some data.”
    “I usually start with a question. What do I want to know? What do
    I want to figure out? It usually starts with some curiosity…”
    “ESTABLISH THE VISUALISATION’S PURPOSE”
    

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68. 1. What problem are we trying to solve?
    2. What question(s) do we have?
    3. What data do we have?
    4. What (additional) data would we need to:
    solve the problem?
    answer the question?
    

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69. Data Vis Process Research
    github.com/alignedleft/data-vis-process
    > take the survey
    > read survey responses
    > related reading list
    > tons of links on process
    

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70. Data Vis Process Research
    github.com/alignedleft/data-vis-process
    

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71. Hugh Dubberly. “What can Steve Jobs and Jonathan Ive teach us about designing?” ACM Interactions, Vol. XIX.3, 2012.
    1. Whole systems thinking
    2. Deep, broad teams
    3. Design conversations
    Hugh Dubberly
    “What can Steve Jobs and Jonathan Ive teach us about designing?”
    

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72. Hugh Dubberly. “What can Steve Jobs and Jonathan Ive teach us about designing?” ACM Interactions, Vol. XIX.3, 2012.
    “But only one form of conversation leads to a
    partnership, to deep trust, and ultimately to
    innovation and a sustained period of good design.
    Such conversations are principally about goals ——
    about beliefs, about values, and about quality…”
    — Hugh Dubberly
    

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73. The Keys to a Successful
    Data Design Process
    Scott Murray
    @alignedleft
    alignedleft.com
    github.com/alignedleft/data-vis-process
    

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