II-SDV 2013 Finding Stories and Telling Stories: Two Sides of Data Visualization

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II-SDV 2013 Finding Stories and Telling Stories: Two Sides of Data Visualization

  1. 1. © 2012 Visualising Data Ltd 1 Visualisation’s Duality: Finding Stories and Showing Stories Andy Kirk www.visualisingdata.com
  2. 2. © 2012 Visualising Data Ltd 2 Design architect/consultant Trainer
  3. 3. © 2012 Visualising Data Ltd 3 Author The real craft behind data visualisation design is being able to rationalise choices What to show | How to show it
  4. 4. © 2012 Visualising Data Ltd 4 1. Establish the visualisation’s purpose and identify key factors What is ‘Purpose’? Client project (brief) Internal project (brief) Self-initiated Trigger Its reason for existing How well is it defined? Intent The intended tone and function
  5. 5. © 2012 Visualising Data Ltd 5 How important is accuracy compared to aesthetics? Read data vs Feel data Precision vs Beauty Pragmatism vs Emotion Intent: Tone Who does the work to surface the insights? Find or Show Reader or Designer Explore or Explain Intent: Function
  6. 6. © 2012 Visualising Data Ltd 6 Analytical/Pragmatic Abstract/Emotive Exploratory(FindStories) Explanatory(ShowStories) Analytical | Exploratory
  7. 7. © 2012 Visualising Data Ltd 7 Analytical | Explanatory Emotive | Exploratory
  8. 8. © 2012 Visualising Data Ltd 8 Emotive | Explanatory The brief? Open, strict, helpful, unhelpful, clarity Pressures? Timescales, managerial, financial Format? Static, interactive, video, tools Setting? Issued, presented, instant, prolonged Technical? Software, hardware, infrastructure Audience size? One, group, organisation, outside Audience type? Domain, captive, general Resolution? Headlines, detail Frequency? One-off, regular Rules? Structure, layout, style, colour People? Individual, team, the 8 hats… Potential key factors
  9. 9. © 2012 Visualising Data Ltd 9 2. Acquire and prepare your data Acquisition Examination Transform for quality The hidden burden…
  10. 10. © 2012 Visualising Data Ltd 10 Transform for analysis Consolidation Visual Analysis The hidden cleverness… Using visualisation techniques to familiarise, learn about and discover insights from data Requires curiosity and graphical literacy Visual analysis
  11. 11. © 2012 Visualising Data Ltd 11 Trends and patterns (or lack of) – Up and down vs. flat? – Linear vs. exponential – Steady vs. fluctuating – Seasonal vs. random – Rate of change vs. steepness Graphical literacy 0 10 20 30 40 50 60 70 80 90 Graphical literacy
  12. 12. © 2012 Visualising Data Ltd 12 Relationships – Outliers – Intersections – Correlations – Connections – Clusters – Associations – Gaps Graphical literacy Graphical literacy
  13. 13. © 2012 Visualising Data Ltd 13 3. Establishing editorial focus by finding stories Good content reasoners and presenters are rare, designers are not. Edward Tufte
  14. 14. © 2012 Visualising Data Ltd 14 What questions do you have about this data? What questions do you want readers to be able to answer about this data?
  15. 15. © 2012 Visualising Data Ltd 15 We rejected them because they didn’t do a good job of answering some of the most interesting questions... Different forms do better jobs at answering different questions. Amanda Cox (on NYT Stream Graph)
  16. 16. © 2012 Visualising Data Ltd 16 4. Conceive your visualisation design specification 1. Data representation The 5 layers of a visualisation
  17. 17. © 2012 Visualising Data Ltd 17 What are we trying to say with what we are showing? Which chart? 1. Consistency with purpose 2. Choose the correct visualisation method 3. Effectiveness of visual analysis techniques 4. Consider physical properties of your data 5. Create the appropriate metaphor Data representation ingredients
  18. 18. © 2012 Visualising Data Ltd 18 Comparing categories Assessing hierarchies & part-to-whole relationships
  19. 19. © 2012 Visualising Data Ltd 19 Showing changes over time Charting connections and relationships
  20. 20. © 2012 Visualising Data Ltd 20 Mapping spatial data 2. Colour The 5 layers of a visualisation
  21. 21. © 2012 Visualising Data Ltd 21 Colour used well can enhance and clarify a presentation. Colour used poorly will obscure, muddle and confuse. Maureen Stone Colour (Hue) Represent data values Colour (Saturation)
  22. 22. © 2012 Visualising Data Ltd 22 Distinguish between categorical items Accentuate data
  23. 23. © 2012 Visualising Data Ltd 23 Exploit visual language 3. Interactivity The 5 layers of a visualisation
  24. 24. © 2012 Visualising Data Ltd 24 Immersive interactivity Details on demand
  25. 25. © 2012 Visualising Data Ltd 25 Potential for animation 4. Annotation The 5 layers of a visualisation
  26. 26. © 2012 Visualising Data Ltd 26 The annotation layer is the most important thing we do... otherwise it’s a case of here it is, you go figure it out. Amanda Cox, Graphics Editor, New York Times Layers of user assistance
  27. 27. © 2012 Visualising Data Ltd 27 Layers of user insight 5. Arrangement The 5 layers of a visualisation
  28. 28. © 2012 Visualising Data Ltd 28 Consider the placement of every single visible element in a way that minimises thinking and maximises interpretation Size, sequence, position, grouping, orientation…
  29. 29. © 2012 Visualising Data Ltd 29 5. Construct and launch your data visualisation solution
  30. 30. © 2012 Visualising Data Ltd 30
  31. 31. © 2012 Visualising Data Ltd 31
  32. 32. © 2012 Visualising Data Ltd 32
  33. 33. © 2012 Visualising Data Ltd 33 www.visualisingdata.com andy@visualisingdata.com @visualisingdata

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