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The 8 Hats of
Data Visualisation Design



       Andy Kirk
The popular emergence of
    data visualisation
What is data visualisation?

     The representation and
presentation of data that exploits
 our visual perception abilities in
    order to amplify cognition
Popularity
          Google Insights: Keyword Infographic




http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
#1: Data
Periscopic: Yahoo! C.O.R.E Data Visualization (2012)




           http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/
#2: Technology
The „eyeo‟ Festival (2011-2012)




          http://eyeofestival.com/
#3: Exposure
Hans Rosling: TEDTalks “Myths about the developing world“ (2006)




               http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html
What’s Missing?

    The skills required for most
 effectively displaying information
are not intuitive and rely largely on
 principles that must be learned.

       Stephen Few, „Show Me the Numbers‟
Art & Science
What’s Missing?


Doing data visualisation well is
 less a technology problem,
   more a people problem.
   Paraphrasing Aron Pilhofer, New York Times
http://images.wikia.com/marvel_dc/images/9/93/Adventures_of_Superman_424.jpg | http://www.adobenido.com/blog/wp-content/uploads/2012/01/wonder_woman.jpg
So, why the ‘8 hats of data
  visualisation design’?
Edward de Bono‟s 6 Thinking Hats




          http://www.debonogroup.com/six_thinking_hats.php
Mr Benn, a man wearing a black suit and bowler hat, leaves his house at 52
Festive Road and visits a fancy-dress costume shop where he is invited by the
moustachioed, fez-wearing shopkeeper to try on a particular outfit. He leaves
the shop through a magic door at the back of the changing room and enters a
world appropriate to his costume, where he has an adventure (which usually
contains a moral) before the shopkeeper reappears to lead him back to the
changing room, and the story comes to an end. Mr Benn returns to his normal
life, but is left with a small souvenir of his magical adventure.

            http://realtimeshortstories.files.wordpress.com/2011/10/mr_benn.jpg | http://www.youtube.com/watch?v=FMSJNrzQ3PM
Initiator
Data Scientist
Journalist
Computer Scientist
Designer
Cognitive Scientist
Communicator
Project Manager
Design Process




Mindsets
 /Roles
Initiator
Initiator




            http://www.ratestogo.com/blog/wp-content/uploads/2009/01/thinker.jpg
Initiator
 The „leader‟ – seeks a solution
 Person with problem/curiosity/ opportunity
 Appetite to explore, find answers
 Researcher mindset, seek evidence
 Creates the analytical direction
 Sets the tone of the project
 Identifies and sets parameters
Initiator
 Brief: Open, strict, helpful, unhelpful
 Format: Static, interactive, video
 Audience size: One, group, www
 Audience type: Domain experts, general
 Resolution: High level, detail, exploratory
Initiator
Initiator




     From “Information Dashboard Design” and http://centerview.corda.com/corda/dashboards/examples/sales/main.dashxm l
Initiator




            http://www.npr.org/2011/10/31/141816460/visualizing-how-a-population-grows-to-7-billion
Initiator




            http://oecdbetterlifeindex.org/countries/united-kingdom/
Initiator




            http://hci.stanford.edu/jheer/files/zoo/
Initiator




            http://www.chrisjordan.com/gallery/rtn2/#gyre2
Data Scientist
Data Scientist
 The „data miner‟ – acquires the data
 Addresses the data for quality
 Prepares the data for its purpose
 Enhances and consolidate the data
 Strong statistical knowledge
 Undertakes initial descriptive analysis
 Undertakes exploratory visual analysis
Journalist
Journalist
 The „storyteller‟ – establishes narrative
 Formulates the questions
 Finds the stories/key angles
 Deeper researcher mindset
 Validates the analytical enquiry
 Gets answers
Journalist

     What questions or curiosities
      are you hoping to answer
      through this visualisation?

 What stories should users/readers be
 able to derive from this visualisation?
Journalist


    Good content reasoners
    and presenters are rare,
      designers are not.
                          Edward Tufte



         http://adage.com/article/adagestat/edward-tufte-adagestat-q-a/230884/
Computer Scientist
Computer Scientist




      http://collider.com/wp-content/uploads/WarGames-Sheedy-and-Broderick-on-computer.jpg
Computer Scientist
 The „executor‟ – brings the project alive
 Has the critical technical capability
 Acquires, handles and analyses data
 Technical illustration skills
 Technical programming skills
Computer Scientist




         http://www.visualisingdata.com/index.php/resources/
Designer
Designer




           http://degaryan.blogspot.com/2011/03/introduction.html
Designer
 The „creative‟ – conceives the solution
 Understands the message
 Understands the possibilities
 Explores and pursues different options
 Rationalises and reasons design options
 Balances form and function
Designer
 The data visualisation anatomy…
 Data representation layer
 Colour and background layer
 Animation and interaction layer
 Layout, placement and apparatus layer
 The annotation layer
Designer
      Length                                        Volume
                                 Size
    Area          Texture                 Colour            Label
                              Direction
      Saturation                                        Position
                              Slope
 Height        Angle                      Radius/Diameter
                               Speed
           Curvature/Arc                            Shape
                                      Orientation
     Transparency
                            Luminance                   Glyph
           Flow                                Motion
                       Blur/Focus
Designer
Cognitive Scientist
Cognitive Scientist
 The „thinker‟ – visual perception
 Knows how the eye and brain work
 Understands principles like „Gestalt Laws‟
 Colour theories, HCI
 Memory, attention, decision making
Cognitive Scientist




       Images from http://psychology.about.com/od/sensationandperception/ss/gestaltlaws.htm
Cognitive Scientist




 Visible pixels on left graph: blue = 82% pink =18%

 Visible pixels on right graph: blue = 91% pink = 9%

       Office for National Statistics: Presentation by Alan Smith, “The Curious Incident of Kevins in Zurich…and other stories”
Cognitive Scientist




             http://colorbrewer2.org/
Communicator
Communicator
 The „negotiator‟ – needs a hard hat
 Acts at the client-designer gateway
 Manage expectations
 Present possibilities
 Launch and publicise
Project Manager
Project Manager




        http://www.bat-mania.co.uk/main/heroes/images/alfred_batphone.JPG
Project Manager
 The „manager‟ – looks after the project
 Manages the progress, cohesively
 Understands brief
 Understands capabilities
 Finishes, checks, attention to detail
 Concerned with visualisation/stats ethics
 Identifies and sets parameters
Project Manager
 Pressures: Timescales, editorial
 Rules: Structure, layout, style, colour
 Capability: Design, technical, technology
 People: Individual, team, collaboration
Project Manager




          http://v2.centralstory.com/about/squiggle/
Purpose &     Prepare &     Formulate     Design     Construct &
               parameters   explore data   questions   concepting     launch

   Initiator

   Data
  Scientist

  Journalist

 Computer
 Scientist

  Designer

  Cognitive
  Scientist

Communicator

  Project
  Manager
Purpose &     Prepare &     Formulate     Design     Construct &
               parameters   explore data   questions   concepting     launch

  Initiator




Communicator

  Project
  Manager
Purpose &     Prepare &     Formulate     Design     Construct &
             parameters   explore data   questions   concepting     launch

 Initiator

 Data
Scientist




Computer
Scientist




Project
Manager
Purpose &     Prepare &     Formulate     Design     Construct &
               parameters   explore data   questions   concepting     launch

   Initiator

   Data
  Scientist

  Journalist




Communicator

  Project
  Manager
Purpose &     Prepare &     Formulate     Design     Construct &
            parameters   explore data   questions   concepting     launch




Computer
Scientist

Designer

Cognitive
Scientist




Project
Manager
Purpose &     Prepare &     Formulate     Design     Construct &
               parameters   explore data   questions   concepting     launch




 Computer
 Scientist

  Designer

  Cognitive
  Scientist

Communicator

  Project
  Manager
Initiator
Data Scientist
Journalist
Computer Scientist
Designer
Cognitive Scientist
Communicator
Project Manager
http://images.wikia.com/marvel_dc/images/9/93/Adventures_of_Superman_424.jpg | http://www.adobenido.com/blog/wp-content/uploads/2012/01/wonder_woman.jpg
The 8 Hats of
Data Visualisation Design



       Andy Kirk
Thank you!

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The 8 Hats of Data Visualisation

  • 1. The 8 Hats of Data Visualisation Design Andy Kirk
  • 2.
  • 3. The popular emergence of data visualisation
  • 4. What is data visualisation? The representation and presentation of data that exploits our visual perception abilities in order to amplify cognition
  • 5.
  • 6. Popularity Google Insights: Keyword Infographic http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q
  • 7. #1: Data Periscopic: Yahoo! C.O.R.E Data Visualization (2012) http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/
  • 8. #2: Technology The „eyeo‟ Festival (2011-2012) http://eyeofestival.com/
  • 9. #3: Exposure Hans Rosling: TEDTalks “Myths about the developing world“ (2006) http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html
  • 10. What’s Missing? The skills required for most effectively displaying information are not intuitive and rely largely on principles that must be learned. Stephen Few, „Show Me the Numbers‟
  • 12. What’s Missing? Doing data visualisation well is less a technology problem, more a people problem. Paraphrasing Aron Pilhofer, New York Times
  • 14. So, why the ‘8 hats of data visualisation design’?
  • 15. Edward de Bono‟s 6 Thinking Hats http://www.debonogroup.com/six_thinking_hats.php
  • 16. Mr Benn, a man wearing a black suit and bowler hat, leaves his house at 52 Festive Road and visits a fancy-dress costume shop where he is invited by the moustachioed, fez-wearing shopkeeper to try on a particular outfit. He leaves the shop through a magic door at the back of the changing room and enters a world appropriate to his costume, where he has an adventure (which usually contains a moral) before the shopkeeper reappears to lead him back to the changing room, and the story comes to an end. Mr Benn returns to his normal life, but is left with a small souvenir of his magical adventure. http://realtimeshortstories.files.wordpress.com/2011/10/mr_benn.jpg | http://www.youtube.com/watch?v=FMSJNrzQ3PM
  • 20. Initiator http://www.ratestogo.com/blog/wp-content/uploads/2009/01/thinker.jpg
  • 21. Initiator The „leader‟ – seeks a solution Person with problem/curiosity/ opportunity Appetite to explore, find answers Researcher mindset, seek evidence Creates the analytical direction Sets the tone of the project Identifies and sets parameters
  • 22. Initiator Brief: Open, strict, helpful, unhelpful Format: Static, interactive, video Audience size: One, group, www Audience type: Domain experts, general Resolution: High level, detail, exploratory
  • 24. Initiator From “Information Dashboard Design” and http://centerview.corda.com/corda/dashboards/examples/sales/main.dashxm l
  • 25. Initiator http://www.npr.org/2011/10/31/141816460/visualizing-how-a-population-grows-to-7-billion
  • 26. Initiator http://oecdbetterlifeindex.org/countries/united-kingdom/
  • 27. Initiator http://hci.stanford.edu/jheer/files/zoo/
  • 28. Initiator http://www.chrisjordan.com/gallery/rtn2/#gyre2
  • 30. Data Scientist The „data miner‟ – acquires the data Addresses the data for quality Prepares the data for its purpose Enhances and consolidate the data Strong statistical knowledge Undertakes initial descriptive analysis Undertakes exploratory visual analysis
  • 32. Journalist The „storyteller‟ – establishes narrative Formulates the questions Finds the stories/key angles Deeper researcher mindset Validates the analytical enquiry Gets answers
  • 33. Journalist What questions or curiosities are you hoping to answer through this visualisation? What stories should users/readers be able to derive from this visualisation?
  • 34. Journalist Good content reasoners and presenters are rare, designers are not. Edward Tufte http://adage.com/article/adagestat/edward-tufte-adagestat-q-a/230884/
  • 36. Computer Scientist http://collider.com/wp-content/uploads/WarGames-Sheedy-and-Broderick-on-computer.jpg
  • 37. Computer Scientist The „executor‟ – brings the project alive Has the critical technical capability Acquires, handles and analyses data Technical illustration skills Technical programming skills
  • 38. Computer Scientist http://www.visualisingdata.com/index.php/resources/
  • 40. Designer http://degaryan.blogspot.com/2011/03/introduction.html
  • 41. Designer The „creative‟ – conceives the solution Understands the message Understands the possibilities Explores and pursues different options Rationalises and reasons design options Balances form and function
  • 42. Designer The data visualisation anatomy… Data representation layer Colour and background layer Animation and interaction layer Layout, placement and apparatus layer The annotation layer
  • 43. Designer Length Volume Size Area Texture Colour Label Direction Saturation Position Slope Height Angle Radius/Diameter Speed Curvature/Arc Shape Orientation Transparency Luminance Glyph Flow Motion Blur/Focus
  • 46. Cognitive Scientist The „thinker‟ – visual perception Knows how the eye and brain work Understands principles like „Gestalt Laws‟ Colour theories, HCI Memory, attention, decision making
  • 47. Cognitive Scientist Images from http://psychology.about.com/od/sensationandperception/ss/gestaltlaws.htm
  • 48. Cognitive Scientist Visible pixels on left graph: blue = 82% pink =18% Visible pixels on right graph: blue = 91% pink = 9% Office for National Statistics: Presentation by Alan Smith, “The Curious Incident of Kevins in Zurich…and other stories”
  • 49. Cognitive Scientist http://colorbrewer2.org/
  • 51. Communicator The „negotiator‟ – needs a hard hat Acts at the client-designer gateway Manage expectations Present possibilities Launch and publicise
  • 53. Project Manager http://www.bat-mania.co.uk/main/heroes/images/alfred_batphone.JPG
  • 54. Project Manager The „manager‟ – looks after the project Manages the progress, cohesively Understands brief Understands capabilities Finishes, checks, attention to detail Concerned with visualisation/stats ethics Identifies and sets parameters
  • 55. Project Manager Pressures: Timescales, editorial Rules: Structure, layout, style, colour Capability: Design, technical, technology People: Individual, team, collaboration
  • 56. Project Manager http://v2.centralstory.com/about/squiggle/
  • 57. Purpose & Prepare & Formulate Design Construct & parameters explore data questions concepting launch Initiator Data Scientist Journalist Computer Scientist Designer Cognitive Scientist Communicator Project Manager
  • 58. Purpose & Prepare & Formulate Design Construct & parameters explore data questions concepting launch Initiator Communicator Project Manager
  • 59. Purpose & Prepare & Formulate Design Construct & parameters explore data questions concepting launch Initiator Data Scientist Computer Scientist Project Manager
  • 60. Purpose & Prepare & Formulate Design Construct & parameters explore data questions concepting launch Initiator Data Scientist Journalist Communicator Project Manager
  • 61. Purpose & Prepare & Formulate Design Construct & parameters explore data questions concepting launch Computer Scientist Designer Cognitive Scientist Project Manager
  • 62. Purpose & Prepare & Formulate Design Construct & parameters explore data questions concepting launch Computer Scientist Designer Cognitive Scientist Communicator Project Manager
  • 65. The 8 Hats of Data Visualisation Design Andy Kirk

Hinweis der Redaktion

  1. I’m going to briefly present some contemporary visualisation projects from prominent designers across the globe and pick out some key tips learned from each to help achieve effective and efficient visualisation results.
  2. If we take another look at Google Insights, this time for the term Infographic, we see a similar trend of interest.
  3. Volume: 95 million front page viewsVariety: Just imagine the range of data captured about every visitor and user of a yahoo searchVelocity: This was based on a fairly unpredictable near-real-time feed from Yahoo’s backend engine
  4. Volume: 95 million front page viewsVariety: Just imagine the range of data captured about every visitor and user of a yahoo searchVelocity: This was based on a fairly unpredictable near-real-time feed from Yahoo’s backend engine
  5. You’re also expected to be a super hero with all the abilities perfectly aligned, balanced and deployable at a moment’s notice.  It’s just not that likely, so you will usually rely on a team.  The advice I received from most people was ‘stay close, connected and together as a team’.
  6. Six Thinking Hats® is a simple, effective parallel thinking process that helps people be more productive, focused, and mindfully involved. And once learned, the tools can be applied immediately!You and your team members can learn how to separate thinking into six clear functions and roles. Each thinking role is identified with a colored symbolic "thinking hat." By mentally wearing and switching "hats," you can easily focus or redirect thoughts, the conversation, or the meeting.
  7. You’re also expected to be a super hero with all the abilities perfectly aligned, balanced and deployable at a moment’s notice.  It’s just not that likely, so you will usually rely on a team.  The advice I received from most people was ‘stay close, connected and together as a team’.
  8. You’re also expected to be a super hero with all the abilities perfectly aligned, balanced and deployable at a moment’s notice.  It’s just not that likely, so you will usually rely on a team.  The advice I received from most people was ‘stay close, connected and together as a team’.