Storytelling with Data - See | Show | Tell | Engage

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Stories have been recognized for their power of communication & persuasion for centuries and we need to operate at that intersection of data, visual and stories to fully harness the power of data.

I take your through a short tour of the science and the art of visualization and storytelling. Then give you an introduction through examples and exemplar on the four different layers in a data-story: See - Show - Tell - Engage.

Used in the session on Business Analytics and Intelligence at IIM Bangalore in July 2014.

Storytelling with Data - See | Show | Tell | Engage

  1. 1. Amit Kapoor narrativeVIZ Storytelling with Data Data Visual Story *
  2. 2. Approach Fundamentals Learn from first principles Know the science Understand the art Experiential I hear and I forget I see and I remember I do and I understand I experience and I learn (for life)
  3. 3. Learning the Djembe Source: The Visitor - Learning the Djembe
  4. 4. da - da - da - da tak - tak - tak 1 - 2 - 3 - 4 1 - 2 - 3
  5. 5. Linguistic (Verbal) Symbolic (Math-Logic) Interactive (Kinesthetic) Geometric (Visual-Spatial)
  6. 6. Linguistic (Verbal) The Pythagoras' theorem is a relation in Euclidean geometry among the three sides of a right triangle. It states: “The square of the hypotenuse (the side opposite the right angle) is equal to the sum of the squares of the other two sides.”
  7. 7. Symbolic (Math-Logic)
  8. 8. Geometric (Visual) Book: The First Six Books of The Element of Euclid by Oliver Byrne
  9. 9. Interactive (Kinesthetic) Source: Setosa.io - Pythagorean
  10. 10. Source: Explorable Explanation - Bret Victor
  11. 11. “ To develop a complete mind, study the science of art, the art of science. Learn how to see. Realize that everything connects to everything else. “ - Leonardo da Vinci
  12. 12. Visual Thinking Spectrum Hand me the Pen I can draw but... I’m not visual
  13. 13. Pointing, Waving, Grabbing, Holding, Reaching out, Dancing Smiling, Frowning, Disinterest, Concern, Full Attention, Surprise “Put this there” Gesture with Pen
  14. 14. Visual Wired Brain 70% of the sensory receptors are in the eyes 50% of the brain used for visual processing 100ms to get a sense of the visual scene
  15. 15. Visual Language While you are travelling down this road there is a chance that one or more rocks of varying size may fall from the slopes. You should be aware of this before you travel this way so that you are cautious of this particular type of hazard.
  16. 16. ˌvɪʒʊəlaɪˈzeɪʃən (noun) Derived from the Latin verb videre, "to look, to see" The act or instance to form a mental image or picture (without an object) Visualization The act or instance to make visible or visual (with an object)
  17. 17. “Why should we be interested in visualization? Because the human visual system is a pattern seeker of enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers. At higher levels of processing, perception and cognition are closely interrelated, which is the reason why the words ’understanding’ and ‘seeing’ are synonymous. ” —  Colin Ware Pattern Seekers
  18. 18. Pattern Recognition Driving a Car
  19. 19. Pattern Recognition Facial & Emotion Recognition
  20. 20. Pattern Recognition CAPTCHA Completely Automated Public Turing test to tell Computers and Humans Apart
  21. 21. Pattern Recognition Chess Go
  22. 22. Pattern Recognition Weather Forecasts
  23. 23. Patterns in Random Noise Choropleth maps of cancer deaths in Texas, where darker colors = more deaths. Can you spot which of the nine plots is made from a real dataset and not from under the null hypothesis of spatial independence? Source: Graphical Inference for Infovis
  24. 24. “Transformation of the symbolic into the geometric” - McCormick et al. 1987 “The use of computer-generated, interactive, visual representations of abstract data to amplify cognition.” - Card, Mackinlay, & Shneiderman 1999 Visualization
  25. 25. Value of Visualization Expand memory Answer questions Find patterns See data in context Make decisions Persuade | Tell a story Share | Collaborate Inspire
  26. 26. Exploration Explanation Expression Value of Visualization
  27. 27. Data Tool for engagement, exploration and discovery Exploration | Interactive
  28. 28. Source: Gramener Cricket Stats
  29. 29. Source: Strategy& Working Capital Profiler
  30. 30. Source: Pindecode Pincode decoder
  31. 31. Data Stories for telling a specific and (linear) visual narrative Explanatory | Narrative
  32. 32. Source: Hans Rosling The Joy of Stats
  33. 33. Source: Politizane Wealth Inequality
  34. 34. Source: Pitch Interactive Drone Attacks
  35. 35. Data Art for visual expression, delight (and impact, insight) Exhibition | Expression
  36. 36. Source: hint.fm/wind Wind Map
  37. 37. Source: Aaron Koblin Flight Patterns
  38. 38. Source: Internet Census Internet Census
  39. 39. “The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it— that’s going to be a hugely important skill in the next decades, ... because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.” —  Hal Varian, Google’s Chief Economist Making Sense of Data
  40. 40. Approach for Creating Data-Visual- Stories Design Framework
  41. 41. Word Writer Note Frame Musician Film Maker | | | Data Artist|Datum
  42. 42. ??? ??? ??? ??? Datum Data-Visual-Story
  43. 43. See the Data Show the Visual Tell the Story Engage the Audience Datum Data-Stories
  44. 44. See the Data Pattern Deviation Outlier Trend Data Abstraction
  45. 45. Anscombe’s Quartet x1 y1 x2 y2 x3 y3 x4 y4 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
  46. 46. Anscombe’s Quartet x(mean) = 9 y(mean) = 7.5 x(var) = 11 y(var) = 4.12 y = 3.00 + 0.500 x
  47. 47. Anscombe’s Quartet
  48. 48. This is hard work "80% perspiration, 10% great idea, 10% output." - Simon Rogers
  49. 49. See the Data Acquire Prepare Refine Explore 1 3 2 4
  50. 50. See the Data Acquire Prepare Refine Explore Data Wrangling Exploratory Data Analysis 1 3 2 4
  51. 51. Explore "Visualization gives you answers to questions you didn't know you had." - Ben Schneiderman
  52. 52. Directed Approach ? ExploreQuestion Insight
  53. 53. Exploratory Approach ? ExploreQuestion InsightExplore
  54. 54. Visually Exploring Active Seeing Skill Building over Time
  55. 55. Comparison, Deviations ● Range, Distribution: high, low, shape ● Ranking: big, medium, small ● Categorical Comparison: proportion ● Measurement: absolutes ● Context: target, average, forecast ● Hierarchical: category, subcategories
  56. 56. Trends ● Direction: up, down or flat ● Optima: highs. lows ● Rate of Change: linear, exponential ● Fluctuation: seasonal, rhythm ● Significance: signal vs. noise ● Intersection: overlap, crossover
  57. 57. Patterns, Relationships ● Exceptions: outliers ● Boundaries: highs. lows ● Correlation: weak, strong ● Association: variables, values ● Clusters: bunching, gaps ● Intersection: overlap, crossover
  58. 58. Show the Visual Framing Transition Visual Representation
  59. 59. How Many? When? Why? Who & What? Where? How?
  60. 60. Portrait Distribution Representation How Many? When? Why? Who & What? Where? How?
  61. 61. Strip Plot Frequency Polygon Histogram Dot Plot
  62. 62. Column (Bar) Chart Pie Chart CoxComb Wind Rose
  63. 63. Comparison Comparative Representation Portrait Distribution Representation How Many? When? Why? Who & What? Where? How?
  64. 64. Small Multiple Frequency Polygon Histogram Box Plot
  65. 65. Column (Bar) Chart Stacked Chart CoxComb MariMekko / Mosaic
  66. 66. Comparison Comparative Representation Portrait Distribution Representation Map Position in Space ? How Many? When? Why? Who & What? Where? How?
  67. 67. Chloropleth Cartogram Dorling Cartogram Graduated Symbol
  68. 68. Map Connection Flow Map
  69. 69. Comparison Comparative Representation Timeline Position in Time Portrait Distribution Representation Map Position in Space ? How Many? When? Why? Who & What? Where? How?
  70. 70. Strip Plot Line Chart Bar Chart Area Chart Index Chart
  71. 71. Stacked Column Stacked Area % Stacked Column % Stacked Area
  72. 72. Horizon Chart Stream Graph Sparklines Slopegraph
  73. 73. Comparison Comparative Representation Timeline Position in Time Portrait Distribution Representation Map Position in Space ? How Many? When? Why? Who & What? Where? How? Flowchart Relationship, Hierarchy
  74. 74. Tree - Node Linkage Tree Radial Force Directed Matrix View
  75. 75. Enclosure Radial Enclosure Arc Diagram Sankey Diagram
  76. 76. Comparison Comparative Representation Timeline Position in Time Multi-Variable Plot Deduction & Prediction y x Portrait Distribution Representation Map Position in Space ? How Many? When? Why? Who & What? Where? How? Flowchart Relationship, Hierarchy
  77. 77. Scatter Plot Scatter Plot - Color Scatter Plot - Multiple Scatter Plot Matrix
  78. 78. Parallel Coordinates Data : n x quantitative, n x categorical Encoding : position, connection, color
  79. 79. Bubble Chart Data : 4 x quantitative, 1 x categorical Encoding : position, size, color, motion
  80. 80. Comparison Comparative Representation Timeline Position in Time Multi-Variable Plot Deduction & Prediction y x Portrait Distribution Representation Map Position in Space ? How Many? When? Why? Who & What? Where? How? Flowchart Relationship, Hierarchy
  81. 81. Tell the Story Ordering & Structure Messaging (Verbal & Text) Point of View Relatability TRF JQL VWX DFR RGT DEF ZEF LXR
  82. 82. Tone of Visualization Analytical & Pragmatic Emotive & Abstract
  83. 83. I think people have begun to forget how powerful human stories are, exchanging their sense of empathy for a fetishistic fascination with data, networks, patterns, and total information... Really, the data is just part of the story. The human stuff is the main stuff, and the data should enrich it. - Jonathan Harris
  84. 84. People tell stories Words Pictures tell stories tell stories | | | Comics tell stories| Movies tell stories|
  85. 85. Rhetoric
  86. 86. logos reason ethos pathos credible emotional | | | Persuasion
  87. 87. Body Mass Index (BMI) BMI = mass (kg) [ height (m) ]2h m
  88. 88. Living on the edge 5’ 1 6’ 5 5’ 7 5’ 5 5’ 3 6’ 3 6’ 1 5’ 11 5’ 9 6’ 7 Obese Over Normal Under 18 25 30 mass (in kg) height (in ft)
  89. 89. Data Story Visual Graph Art Tale *
  90. 90. analysis SYNTHESIS numbers argument VISUALISE STORY | | |
  91. 91. logic | EMPATHY
  92. 92. Data & Stories The focus of stories is on individual people rather than averages, on motives rather than movements, on point of view rather than the view from nowhere, context rather than raw data. Moreover, stories are open-ended and metaphorical rather than determinate and literal.
  93. 93. The Story Mindset In listening to stories we tend to suspend disbelief in order to be entertained, whereas in evaluating statistics we generally have an opposite inclination to suspend belief in order not to be beguiled. - John Allen Paulos
  94. 94. Stories are emotional Stories are Stories are memorable impactful | | | Why Stories?
  95. 95. Dual Coding Aural Visual
  96. 96. /ˈnærətiv / (noun) A narrative (or story) is any account of connected events, presented to a reader or listener in a sequence of written or spoken words, or in a sequence of (moving) pictures. Derived from the Latin verb narrare, "to tell" Narrative
  97. 97. Narrative Structure
  98. 98. Cognitive Flow Start Frame it as a Journey Finish
  99. 99. Making Comics
  100. 100. Don’t just add a chart... Source: Economist
  101. 101. ...or complex visualization Source: Joshua Gallagher
  102. 102. Think Stories, not Charts
  103. 103. Telling Compelling Stories Source: Gapminder
  104. 104. Strong Order Heavy Messaging Limited Interactivity Author Driven Explanatory (Narrative) Exploration (Interactive) Weak Order Light Messaging Free Interactivity Reader Driven
  105. 105. Genres of Story Source: Narrative Visualization
  106. 106. Think about the structure Source: Narrative Visualization Explanatory (Narrative) Exploration (Interactive)
  107. 107. Choose the Visualization Source: Bloomberg
  108. 108. Make it Simple Source: Capabilities Premium
  109. 109. Representation Matters Source: South China Post
  110. 110. More Linear, More Story Like Source: Inconvenient Truth
  111. 111. Linear Narrative Source: Pitch Interactive
  112. 112. Story Structure
  113. 113. Story Structure
  114. 114. Focus Attention Choose a Canvas Put Visuals Focus Attention --------------------- --------------------- --------------------- Rocks Hieroglyph Carving (Hand) Pointing Paper Pen Drawing Pen Movement Transparency Marker Pens Stick Whiteboard Marker Drawings Pen Movement Presentation Slides Next Slide Please??
  115. 115. Focus Attention
  116. 116. Focus Attention Choose a Canvas Put Visuals Focus Attention --------------------- --------------------- --------------------- Rocks Hieroglyph Carving (Hand) Pointing Paper Pen Drawing Pen Movement Transparency Marker Pens Stick Whiteboard Marker Drawings Pen Movement Presentation Slides Next Slide Please?? Genre Data Viz Highlight, CloseUp, Zoom, Framing Feature Distinct Motion, Audio
  117. 117. Explain and Guide Reader Source: Guns - Periscopic
  118. 118. Single Frame Dominates Source: Walmart & Target Store Expansion
  119. 119. Establish & Focus Source: OECD Better Life Consistent Visual Framework
  120. 120. Establish & Focus Source: OECD Better Life
  121. 121. Use Staging & Animation Source: Gapminder
  122. 122. Say it with Text
  123. 123. Weave Text into Graphics Source: Napolean’s Campaign
  124. 124. Provide Meaningful Annotation Source: New York Times
  125. 125. Source: Hans Rosling | Joy of Stats Power of Verbal Messaging
  126. 126. Answer the why? We are good at who, what, where, when. Not why?
  127. 127. Provide Relatability
  128. 128. Engage the Audience TRF JQL VWX DFR RGT DEF ZEF LXR Emotion Takeaway Interactivity
  129. 129. Attention & Engagment Source: Cost of Sick
  130. 130. Animation Helps Source: Multibar Transition
  131. 131. Be Explicit about Actions Source: Gapminder
  132. 132. Restrict Interactivity Source: One Report, Many Perspective
  133. 133. Make it look live Source: 512 Paths to White House
  134. 134. Make it look live Source: Obama's Path
  135. 135. Make Interaction Easy Source: Health and Wealth of Nation
  136. 136. Linear Navigation: Story Like Source: NY Times
  137. 137. Science or Art? Science Perceptual Psychology Cognitive Science Graphic Design Data Analysis Art Emotional Aesthetic sense Craft and Skill Creativity
  138. 138. Six Thinking Hats Facts Creativity Feelings Benefits Caution Process
  139. 139. Visualization Skill Hats Data Scientist Visual Designer Storyteller Explorer Programmer Manager
  140. 140. Visualization Tools
  141. 141. Tools Landscape Abstract Flexible Difficult Slow Code Expressive Blackbox Limited Simple Quick GUI Efficient
  142. 142. Tools Landscape Abstract, Flexible, Difficult Slow, Code, Expressive Blackbox, Limited, Simple Quick, GUI, Efficient
  143. 143. Tools Landscape Abstract, Flexible, Difficult Slow, Code, Expressive Blackbox, Limited, Simple Quick, GUI, Efficient Canvas Grammar Charting Collection of fixed charts that require data to be shaped in a particular way Excel Mondrian Many Eyes Google Charts HighCharts Fusion Charts Collection of graphical primitives for creating data driven graphics R-ggplot2 SPSS raw d3.js Vega Bokeh Paint directly on a pixel grid. Design & manage every element of chart Processing Nodebox sketchpad Raphael.js Paper.js Processing.js Visual Visual analysis languages allowing flexibility to design many variants Tableau Gephi plot.ly ...
  144. 144. Amit Kapoor @amitkaps Partner, narrativeVIZ Consulting amit@narrativeviz.com Find this presentation and more at http://narrativeviz.com/playbook

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