Andy Kirk's Facebook Talk


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  • With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • So we can classify the purposes but there is another dimension, that of ‘tone’.We mentioned earlier the battle between art and science…
  • On one hand the ‘scientists’ believe…
  • On the other hand, more creative and abstract practitioners are creating works that blow away the conventional bar charts…
  • With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • Use scatter plots to undertake visual analysis and immerse yourself in your raw material
  • With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • Your role is almost that of the movie director, managing all the different streams of tasks and thoughts in order to bring together a cohesive final work
  • Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • Whilst serendipitous discovery should be encouraged and accommodated, the parameters surrounding a project mean that tactics, efficiency and focus are paramount Often linked to brief: Are you commissioned to create a specific design to tell a specific story or rather encouraged to find your own important story to tell?
  • Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • Whereas here the efforts to gather insights are so great that we get very little reward
  • Never use colour/Hue to portray quantitative values
  • Every visual property should serve a purpose
  • Importance of visual annotation to highlight a key insight – as seen by these trend line and reference markers
  • Discussion about tools, when to finish and how to evaluate
  • Books
  • With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • 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’.
  • Andy Kirk's Facebook Talk

    1. 1. Visualisation Workflow: Finding Stories and Telling Stories Andy Kirk
    2. 2. Hebden Bridge
    3. 3. Data Visualisation Blogger
    4. 4. Architect | Consultant
    5. 5. Trainer tion duct ion to DataVisualisa Training Courses Intro le Current Public Schedu Visualisation The Growth of Data le of 2012: e through to the midd publi c traini ng cours mean s for These are the scheduled ded us with ubiquitous Arts, Copenhagen | £250 COP2 in technology have provi e once data h Academy of Fine Exponential advances amounts of data. Wher Thu 8 Mar | Royal Danis Copenhagen | £250 COP1 mobi lising incredible mers have h Academy of Fine Arts, creati ng, recording and Our attitudes as consu Fri 9 Mar | Royal Danis London | £235 LON3 captured in abundance. for visual insight House, University of was scarce, now it is openn ess and yearn Thu 26 Apr | Senat e York City | £250 NY C1 nd transparency and ournalism, CUNY, New also evolved: we dema Fri 1 May | Grad Schoo 1 l of J n DC | £250 WDC1 to aid our understand ing. ation Center, Washingto £250 BAL1 for the Mon 1 May | Found 4 widespread capabilities Wed 1 May go | £250 CHI1 s to fantastic tools and iques requi red 6 Center Conference, Chica Yet, whilst we have acces knowledge and techn Fri 1 Jun | University 5 analysis of data, the Toron to | £250 TOR1 storage, handl ing and Mon 1 J | Venue TBC, 8 un £235 BRS 1 instin ct ach based on intuit ion, Fri 29 J un Edinburgh | £235 EDI1 e world, a design appro Hotel , University of a cluttered, competitiv Fri 6 J | Salisury Green ul 1 Amst erdam | £250 AMS data visualisation comes in. Fri 1 Jul | Venue TBC, 3 overload. This is where l A 1 discount 0% comm unications that appea Train ing page on and innovation, designing unleashing creati vity regist er to atten d an event . way our eyes and brain s process om where you can also exploiting the www.visualisingdata.c aimed at under standing and recen t times h in popularity over lisation and its growt sizes and The interest in data visua isations of all shapes, story. As a result , organ ster now to reserve a Places are limited so regi has been a remar kable value. ation of its poten tial waking up to the realis domain are now training workshop. place on your preferred tent Training Course Con Visit the www.visualising, select the with a comprehensive, d location. The objective of the traini ng is to provi de delegates Training page and click on your preferre excitement event s buzzi ng with tion. You will leave the have acqui red, impact and ampli fy cogni ical capabilities you knowledge and pract rtunit ies about the foundation n challenges and oppo on future data visualisatio inspir ing you to take Further Information in the courses will include: environment main topics cover ed Class size a supportive learn ing The size is 20 to facilit ate of data visualisation The maxim um class and modern context Historical background n visual system en all attendees. of design and the huma group discussion betwe Foundation principles and select ion The essen tials of chart design and resources Refreshments tial visualisation tools centr al locati ons. Exploration of the essen process ed. All event s will be held in city n methodology and lunch will not be includ The visualisation desig n ing to visualisation desig Applying critical think itioners ice exam ples and pract Laptops Showcase of best pract s Visualisation project case studie lisation challenges g the day’s activi ties. re your own data visua across the group durin Opportunit ies to explo have a some devices Times ? end of the Who Should Attend time allocated at the g from 9:00 and extra regist ration comm encin r discussion s. nsibil ity for, or is intere sted in quest ions or hold furthe for anyone who has respo comm unicating data. session to pick up any The courses are suited and for visually exploring best pract ice approaches . Visualising Data Ltd body who lex datasets, or some st with large and comp t be an You might be an analy gement repor t. You migh the occasional mana visualisation just wants to enhance Ltd, a UK based data er of Visualising Data ber of this crowd. You might be a Andy Kirk is the found has been an active mem g to stand out from the traini ng service. He to adver tising and are lookin ner witho ut progr amm ing skills. design consultancy and design traini ng or a desig sector. progr amm er with no eering or the publi c cine, the media, engin popular blog www.visua You might be in medi we’ve all Data is everywhere and is no typical delegate. is most The point is that there Anyone and everyone with it, so let’s do it right. got to do something to atten d! welcome and encouraged
    6. 6. Trainer
    7. 7. Speaker
    8. 8. Author
    9. 9. CareerLancaster University | 1995 to 1999Degree in Operational Research + Year in IndustryCo-operative Insurance Society (CIS) | 1999 to 2001Business AnalystWest Yorkshire Police | 2001 to 2007Performance Analyst > Information ManagerUniversity of Leeds | 2007 to 2012Information ManagerUniversity of Leeds | 2007 to 2009Masters Degree (Research) in Data VisualisationVisualising Data Ltd. | 2010 to dateFreelance Jack of All Trades / Visualisation Mercenary
    10. 10. Visualisation Workflow: Finding Stories and Telling Stories
    11. 11. “The aggregation of marginal gains” Dave Brailsford
    12. 12. 1. Establish the visualisation‟s purpose and identify key factors2. Acquire, prepare and 3. Establish editorial focus explore your data with your subject matter 4. Conceive your visualisation design 5. Construct your data visualisation solution
    13. 13. 1. Establish the visualisation‟spurpose and identify key factors
    14. 14. What is „Purpose‟? Trigger Intent Its reason for existing The intended function and tone How well is it defined?Client project (brief)Internal project (brief) Self-initiated
    15. 15. Intent: Function Who does the work?Designer driven or Reader driven
    16. 16. Intent: Tone SciencArt e
    17. 17. Intent: ToneGetting [visualisation] right ismuch more a science than an art,which we can only achieve by studying human perception. Stephen Few
    18. 18. Intent: ToneI have this fear that we aren’t feeling enough. Chris Jordan, TED Talk
    19. 19. Intent: Function + ToneExploratory (Find Stories) Analytical/Pragmatic Explanatory (Tell Stories) Abstract/Emotive
    20. 20. Analytical +Exploratory
    21. 21. 512 Paths to the White House | New York Times
    22. 22. Analytical +Explanatory
    23. 23. Why Is Her Paycheck Smaller? | New York Times
    24. 24. Emotive/Abstract + Exploratory
    25. 25. OECD Better Life Index | Moritz Stefaner
    26. 26. Emotive/Abstract + Explanatory
    27. 27. What A Hundred Million Calls To 311 Reveal About New York | Pitch Interactive
    28. 28. Potential Key FactorsThe aim? Open, strict, helpful, unhelpful, clarityPressures? Timescales, managerial, financialFormat? Static, interactive, video, toolsSetting? Issued report, presentedTechnical? Software, hardware, infrastructureAudience size?One, group, organisation, outsideAudience type? Domain, captive, generalResolution? Headlines, detailFrequency? One-off, regularRules? Structure, layout, style, colourPeople? Individual, team, the 8 hats…
    29. 29. 2. Acquire andprepare your data
    30. 30. The Hidden BurdenThe Hidden Cleverness
    31. 31. 80% perspiration,10% great idea, 10% output Simon Rogers The Guardian, „Facts Are Sacred: The Power of Data‟
    32. 32. 3. Establishingeditorial focus by finding stories
    33. 33. Good content reasonersand presenters are rare, designers are not. Edward Tufte
    34. 34. Finding Stories
    35. 35. Finding Stories is…Using visualisation techniques to familiarise, learn about and discover insights from data
    36. 36. Graphical Literacy
    37. 37. Visual Analysis to Find StoriesComparisons – Categorical comparison and proportions – Ranking: big, small, medium – Measurements/values: absolutes – Range and distribution – Context: Targets, forecasts, averages – Hierarchical relationships
    38. 38. Visual Analysis to Find Stories: Comparisons
    39. 39. Visual Analysis to Find StoriesTrends and patterns (or lack of) – Up and down vs. flat? – Linear vs. exponential – Steady vs. fluctuating – Seasonal vs. random – Rate of change vs. steepness
    40. 40. Visual Analysis to Find Stories: Trends
    41. 41. Visual Analysis to Find StoriesRelationships – Outliers – Intersections – Correlations – Connections – Clusters – Associations – Gaps
    42. 42. Visual Analysis to Find Stories: Relationships
    43. 43. 4. Conceive yourvisualisation design
    44. 44. Telling [or Framing] Stories
    45. 45. Telling Stories is… Identifying and caring for thereader – taking responsibility tomaximise their potential insight
    46. 46.
    47. 47.
    48. 48.
    49. 49. The Visualisation Anatomy
    50. 50. Data representation
    51. 51. Showing what we are trying to say
    52. 52. The Ebb and Flow of Movie Box Office Takings | New York Times
    53. 53. We rejected them because they didn’t do a good job of answering some of the mostinteresting questions... Different forms do better jobs at answering different questions. Amanda Cox (on NYT Stream Graph)
    54. 54. Comparing categories
    55. 55. Assessing hierarchies & part-to-whole relationships
    56. 56. Showing changes over time
    57. 57. Charting connections and relationships
    58. 58. Mapping geo-spatial data
    59. 59. Colour and background
    60. 60. Colour used well can enhance and clarify a presentation. Colour used poorly willobscure, muddle and confuse. Maureen Stone
    61. 61. Confusion…
    62. 62. OMG
    63. 63. To represent data values Colour (Hue)Colour (Saturation)
    64. 64. To distinguish between categorical items
    65. 65. To help distinguish foreground and background
    66. 66. To create signals/accentsFrom “Information Dashboard Design” and l
    67. 67. Annotation
    68. 68. The annotation layer is themost important thing we do... otherwise it’s a case ofhere it is, you go figure it out. Amanda Cox, Graphics Editor, New York Times
    69. 69. TEDTalks “Myths about the developing world“ (2006) | Hans Rosling
    70. 70. The Growth of Newspapers Across the US | Stanford
    71. 71. The Growth of Newspapers Across the US | Stanford
    72. 72. Arrangement
    73. 73. Consider the placement of every single visible element in a way that minimises thinking and maximises interpretation
    74. 74. Deliberate design
    75. 75. „Narrative Visualization: Telling Stories with Data‟, Edward Segel and Jeff Heer
    76. 76. 1. Magazine StyleDot point map of cholera deaths | Jon Snow
    77. 77. 2. Annotated ChartWhy Is Her Paycheck Smaller? | New York Times
    78. 78. 3. Partitioned PosterSteroids or not, the pursuit is on | New York Times
    79. 79. 4. Flow ChartGraphic of Napoleons March (1869) | Charles Joseph Minard
    80. 80. 5. Comic Strip Drought‟s footprint | New York Times
    81. 81. 6. Slide Show Rise of the Megacities | The Guardian
    82. 82. 7. Video/AnimationVisualizing how a population grows to 7 billion | NPR
    83. 83. Interactivity
    84. 84. Interactive Features and FunctionsVariable adjustment – selectionhighlighting/brushing, filtering, excluding, sortingView adjustment – pan, zoom, scale, rotate,transpose, arrange, tabsAnnotation – hovering/annotate, drop linesAnimation – play, pause, reset, chapternavigation, grab the slider, show newdata/changed data
    85. 85. Wind map | Fernanda Viegas and Martin Wattenberg
    86. 86. cubase-nuendo/
    87. 87. 5. Construct and evaluate your datavisualisation solution
    88. 88.
    89. 89. Sample Project
    90. 90. Visualizing the London 2012 OlympicGames we will see the best of the best compete for pride,This summer,glory, and, of course, medals at the Olympics. From kilogramslifted in weightlifting to the number of individual countriescompeting to the number of medals won by competing nations - the Olympicsprovides a barrage of numbers that are ripe for designers to analyze andvisualize.We challenge you to use data and design to visualize the Olympics, helping usunderstand and enjoy as we watch. For instance, you could create a piece thatcontextualizes each country‟s medal count with information about theirpopulation, GDP, and athletic training resources. Or you could illuminate theresults of a particular event or the impact of hosting the London 2012 OlympicsGames on the UKs economy. “We‟re looking for any data-driven project that brings new insight, context, or comparison to our
    91. 91. Find stories…
    92. 92. Find stories…
    93. 93. Establish Narrative/Data QuestionsRepeat for all relevant sports and events:• Comparison between patterns for different medals?• What % improvement in time has there been?• Which events have improved the most and the least?• Comparison between progress of men and women? - Is one gender improving more than the other? - Any evidence of women getting closer to men?
    94. 94. Tell (or frame) stories…
    95. 95. Tell (or frame) stories… Data representation - line chart, dot plots, small multiples, tables Colour and background - gold, silver, bronze, blue, orange Animation and interaction - Data/view manipulation Arrangement - intro, selections, chart, filters, extra stats The annotation layer - context, annotated detail, stats
    96. 96. Construction
    97. 97. EvaluationUnderstanding (10 Points):How effectively does the visualization communicate?How well does it help you make sense of this issue?Originality (5 Points):Are the approach and design innovative?Style (5 Points):Is the visualization aesthetically compelling?
    98. 98. EvaluationAndy Kirks “The Pursuit of Faster” also earned an honorable mention for its level of data analysis that was unmatched by most, if not all, of the challenge entries. Its focused andthorough narrative further distinguished the project from the other interactive entries.
    99. 99. Learning the Craft
    100. 100. |
    101. 101.
    102. 102. Multi-disciplinary: Art & Science
    103. 103. The 8 Hats of Data Visualisation ProjectInitiator Journalist Communicator ManagerCognitive Design Computer Data Science Science Science
    104. 104. CraftPractice, practice, practice – experience is the keySeek potential projects – paid, curiosity, contestsLearn about yourself – take notes, self critiqueTechnical skills – push yourself out of comfort zoneEvaluate others – silently or provide reviewsPublish yourself – encourage and digest peercritique
    105. 105. TheoryOnline content – immerse yourself in the communityBooks – so many invaluable references and inspirationsAcademia – papers, journalsConferences – within the field and around itTraining/education – look for good training provider…
    106. 106. @visualisingdata