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# 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.

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### 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