The Keys to a Successful Data Design Process

A464352136da1292242d810c001ed275?s=47 Scott Murray
August 28, 2014

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?

A464352136da1292242d810c001ed275?s=128

Scott Murray

August 28, 2014
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I I I } } { { %| | | ! / / + = : : : ; , : J Z Z O 0 0 U U O 0 0 E E E E E 0 Z 4 Z 0 E B B B E 0 U 5 6 S F C | . . - , , ; : = + / ! ! | %} } I ? ] ] 1 1 * * 7 L T T J J C 5 5 F 6 6 6 S S ] ] ] ? ? ? } } } { %%| | ! ! / / + + = : : ; , , | F U O 0 0 U U O 0 0 E E E E 0 O O O O 0 0 E E B E 0 U 6 F S F 5 ; . - - ' , ; : = + / ! ! %%} } I ? ] [ 1 1 * * 7 L T T J J C 5 5 F 6 6 6 S S [ ] ] ] ? ? I I } { { %| | ! ! ! / + + = = : ; ; , ; %4 O O O U O O 0 E E B B E E 0 E E E E E E E E 0 U Z S 6 S 6 C - . - ' ' , ; : = + / ! | %%} } I ? ] [ 1 1 * 7 7 L T T J J C 5 5 F F 6 6 S S [ [ ] ] ? ? I I } { { { %| | ! ! / / + = = : ; ; , , ' = 5 Z Z O O 0 E E E B E E E E B WWWWWWB E E O U Z 4 6 * . - - ' , ; : = = + / ! | %{ } } I ? ] [ 1 1 * 7 7 L T T J J C 5 5 F 6 6 6 S S [ [ ] ] ? ? 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 [ [ ] ] ] ? ? I I } } { { %%| | %6 Z Z Z 4 F 1 , ' , , } F U 0 0 E B B B WWB B E 0 0 E E B WWWB O Z 6 S S 5 T * - . . - ' ' , ; : = + + ! ! | %{ { } I ? ] ] [ 1 * 7 7 L T T J J C 5 5 F F 6 6 S S S [ [ [ ] ] ] ? ? 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 1 1 1 [ [ [ ] ] ? ? I I } } 5 U Z U O O O O O 0 0 0 0 0 E B B E B WWW@@@@@@@@@@@@WWWB E O 5 C C C J T T L 7 - . - ' ' , ; ; : = + / / / | | %{ } } ? ? ] [ 1 1 * 7 L L T T J C C 5 5 F F 6 6 S S 4 4 * 1 1 [ [ [ ] ] ? ? I I } } C U Z U O O O 0 0 0 0 0 0 0 E E B E WW@@@@@@@@& & & & & @@@WWE 0 5 C J J J T T T L ! . . - ' ' , ; ; = = + / ! ! | %%{ } I ? ? [ [ 1 * * 7 L L T T J C C 5 5 F F 6 6 S S 4 4 * * 1 1 [ [ ] ] ? ? ? I I I S U Z U U U O O 0 0 0 0 E E E E E 0 WW@@@@& & & & & & & & & & @@WB E 0 6 F C C J J J J T * = . - ' ' , ; ; : = = + + ! ! | %{ } } I ? ] [ 1 1 * 7 7 L T T J J C C 5 F F 6 6 S S S 4 4 * * 1 1 [ [ [ [ ] ? ? ? I * Z U Z U U U U O 0 0 E E E E B B E E WW@@@@& & & & & & & & & & @WB E B E 4 6 5 5 C C C C J T 1 . - - ' , , ; : = = + / ! ! | %%{ } I I ] ] [ 1 * * 7 7 L T T J J C C 5 F F 6 6 S S S 4 4 * 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 * * * * 1 1 [ [ [ ] ? ? T S 4 Z Z Z Z Z Z Z Z U 0 0 B B WWWWWW@@@@& & & & & & @@@@WWB B WB 4 S 6 6 6 F F 5 5 C C * . - - , , ; : = = + / ! ! | %%{ } I I ? ] [ 1 1 * * 7 L L T T J C C 5 5 F 6 6 6 S S 4 4 4 * * * * 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.
  2. 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.
  3. 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
  4. 25.

    Design process, after Tim Brennan, ~1990, reproduced from How do

    you design?, Dubberly Design Office, 2004.  
  5. 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.   £
  6. 30.

    Some factors on which “it depends”: team size team expertise

    project goals data availability output medium time available budget available tools available
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 61.
  13. 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
  14. 64.
  15. 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?
  16. 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.”
  17. 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”
  18. 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?
  19. 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
  20. 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?”
  21. 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
  22. 73.

    The Keys to a Successful Data Design Process Scott Murray

    @alignedleft alignedleft.com github.com/alignedleft/data-vis-process