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The 8 Hats of Data Visualisation
The document discusses the principles and roles involved in effective data visualization design, referencing Edward de Bono's '6 Thinking Hats' to introduce eight roles, including data scientist, journalist, computer scientist, and designer. It emphasizes the importance of human skills in visual communication, highlighting the need for collaboration among various experts to successfully convey data-driven narratives. The content is structured around the design process, focusing on cognitive understanding and the diverse capabilities needed to create compelling visual representations.
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The 8 Hats of Data Visualisation
- 1. The 8 Hatsof Data Visualisation Design Andy Kirk
- 3. The popular emergenceof data visualisation
- 4. What is datavisualisation? The representation and presentation of data that exploits our visual perception abilities in order to amplify cognition
- 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‟
- 11.
- 12. What’s Missing? Doing datavisualisation well is less a technology problem, more a people problem. Paraphrasing Aron Pilhofer, New York Times
- 13.
- 14. So, why the‘8 hats of data visualisation design’?
- 15. Edward de Bono‟s6 Thinking Hats http://www.debonogroup.com/six_thinking_hats.php
- 16. Mr Benn, aman 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
- 17. Initiator Data Scientist Journalist Computer Scientist Designer CognitiveScientist Communicator Project Manager
- 18.
- 19.
- 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
- 23.
- 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
- 29.
- 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
- 31.
- 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/
- 35.
- 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/
- 39.
- 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 datavisualisation 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
- 44.
- 45.
- 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 Visiblepixels 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/
- 50.
- 51. Communicator The „negotiator‟– needs a hard hat Acts at the client-designer gateway Manage expectations Present possibilities Launch and publicise
- 52.
- 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
- 63. Initiator Data Scientist Journalist Computer Scientist Designer CognitiveScientist Communicator Project Manager
- 64.
- 65. The 8 Hatsof Data Visualisation Design Andy Kirk
- 66.
Editor's Notes
- #6 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.
- #7 If we take another look at Google Insights, this time for the term Infographic, we see a similar trend of interest.
- #8 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
- #9 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
- #14 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’.
- #16 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.
- #17 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’.
- #65 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’.