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Enhancing Report Clarity: Fixing Bad Reports in Data Communication

This session focuses on improving the clarity and effectiveness of reports, particularly in the context of data presentation within police reports. Participants will learn to identify common pitfalls, such as chart junk and unnecessary complexity, and will be introduced to strategies for enhancing data density and readability. We will explore various types of charts and tables, emphasizing the importance of reducing non-data pixels, optimizing whitespace, and using color judiciously. The session aims to foster a culture of continual improvement in data reporting.

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Enhancing Report Clarity: Fixing Bad Reports in Data Communication

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Presentation Transcript

  1. Bad Reports: Fixing Their Mistakes Roger Noble Consultant LobsterPot Solutions

  2. My bad report NSW Police report Just an example, not really from the NSW Police Dept. (source data.gov.au) Report is used to: • Track incident response times • Incident rates across divisions • Incident rates by offence BID-210

  3. Bad Report Demo

  4. First Some Theory What are we trying to communicate? • Remove non-data pixels and chartjunk • Increase data density • Use colour sparingly BID-210

  5. Data ink and non-data ink BID-210

  6. Chartjunk = 38.7% BID-210

  7. Colour BID-210

  8. Data-Ink Ratio • Ink used to present the data • Total ink used BID-210

  9. Process for continual improvement • Identify non-data pixels • Can it be removed? • Can it be deemphasised? • Identify data pixels • Is it meaningful? • Can it be emphasised? • Repeat BID-210

  10. Let’s fix it! Demo

  11. Charts BID-210

  12. Charts BID-210

  13. Charts BID-210

  14. Data types Dimensions • Nominal Location • Ordinal Days of week • Interval Time Measures • Additive Sales amount • Non-additive Temperature BID-210

  15. Data types - Charting Dimensions Measures Nominal Ordinal Interval Additive Non-additive Measures Additive Non-additive BID-210

  16. Charting – Line vs Bar Nominal BID-210

  17. Charting – Line vs Bar Nominal BID-210

  18. Charting – Line vs Bar Interval BID-210

  19. Charting – Line vs Bar Interval BID-210

  20. Let’s fix it! Demo

  21. Tables BID-210

  22. Tables Remove unnecessary colour and gridlines BID-210

  23. Tables Remove gridlines and unnecessary colour BID-210

  24. Tables Add lines and emphasis where necessary BID-210

  25. Tables Add white space to aid with reading BID-210

  26. Tables Add white space to aid with readingWhite space allows the eye to easily scan down columns and across rows BID-210

  27. Indicators Be optimistic add indications only for exceptions BID-210

  28. Indicators Be optimistic add indications only for exceptions BID-210

  29. Let’s fix it! Demo

  30. Problems with Pie charts Requires mental conversion from size/angle to percentage. Works well when values are close to 25% and 50% (90o and 180o) BID-210

  31. Problems with Pie charts Distortion from perspective Slices that are closer appear larger than they are BID-210

  32. Problems with Pie charts 32% (40) 24% (30) 20% (25) 24% (30) = Actual values can be listed in a smaller space (higher data-ink ratio) BID-210

  33. Problems with Pie charts VS Patterns are harder to perceive BID-210

  34. Area based charts • IncorrectSized by diameter • CorrectSized by area N 2N Area is difficult to quantify Bubbles must be sized by area not diameter (or radius or circumference) BID-210

  35. What else is bad? Size can be perceived differently based on surroundings BID-210

  36. Let’s fix it! Demo

  37. Summary Identify key information Improve the data-ink ratio (remove chartjunk) Use whitespace to aid in scanning rows and columns Be optimistic with indicators Only use area based charts when accuracy isn’t important BID-210

  38. Recommended Reading Edward Tufte Steven Few Also: William S. Cleveland, Colin Ware, Nathan Yau and Benjamin Willers BID-210

  39. Thank you for attending this session and the 2012 PASS Summit in Seattle

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