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The Zen of Visual Analysis

The Zen of Visual Analysis. Chris Stolte Vice President, Engineering & co-founder Jock Mackinlay Director, Visual Analysis. How do people reason about data?. The Cycle of Visual Analysis. “Wildlife Strike Database” http://wildlife-mitigation.tc.faa.gov/public_html/index.html.

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The Zen of Visual Analysis

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  1. The Zen of Visual Analysis Chris StolteVice President, Engineering & co-founder Jock Mackinlay Director, Visual Analysis

  2. How do people reason about data?

  3. The Cycle of Visual Analysis

  4. “Wildlife Strike Database”http://wildlife-mitigation.tc.faa.gov/public_html/index.html

  5. Not just“Aha!”

  6. advice:Iterate, explore, andexperiment…regardless of task

  7. How do we empowerindividuals and groups toeffectively analyze and share data?

  8. Support the“Cycle of Visual Analysis”

  9. Incremental Expressive Unified Direct

  10. Supporting the “Cycle of Visual Analysis”: Incremental Allow people to easily and incrementally change the data and how they are looking at it click click

  11. advice:Startsimple…

  12. Supporting the “Cycle of Visual Analysis”:Expressive There is no single viewfor all tasks and all data

  13. Supporting the “Cycle of Visual Analysis”:Expressive

  14. Supporting the “Cycle of Visual Analysis”:Expressive

  15. advice:Reverse your thinking –startwiththe data

  16. Supporting the “Cycle of Visual Analysis”:Unification with the database Traditional Reporting Tools versus Tableau Traditional Visualization Tools

  17. Supporting the “Cycle of Visual Analysis”:Unification with the database Leverage the revolutionarychangesindatabase technology

  18. Supporting the “Cycle of Visual Analysis”:Direct Interaction Make the tool disappear. Allow the user to directly interact with the data.

  19. How is this possible? VizQL

  20. Generate Effective Views of Data

  21. People shouldnothave to be graphic designers orpsychologists.

  22. Generating Effective Views of Data An effective presentation of data: • Communicates all of the data • Communicates only the data • Leverages the human perceptual system • Is understandable • Is interpretable

  23. Generating Effective Views of Data Communicatesall of relevant data Bad Bad Good

  24. Generating Effective Views of Data Communicatesonly the data Bad Bad

  25. Generating Effective Views of Data Leveragesthehuman perceptual system Bad Good

  26. Generating Effective Views of Data Isunderstandable

  27. advice:Keepyour visualssimple

  28. Generating Effective Views of Data Isinterpretable

  29. advice:Annotate but remainfocused

  30. How does Tableau support generating effective views of data? Best practices are built into the product Great defaults Show Me and Show Me Alternatives Small multiples Limiting the visual properties to a proven set Titling, captioning, and annotation Generating Effective Views of Data

  31. Generate Beautiful Results

  32. Generate Beautiful Results

  33. Generate Beautiful Results

  34. Share Interactive Views

  35. Share Interactive Views • Communicate all the data • Demonstrate yourconfidence • Allow people to test your conclusions • Let your audience engage directly with the data

  36. advice:Empoweryouraudience – • share interactive views

  37. Share Interactive Views Underlying Aggregated

  38. advice:Datais a dish • best served raw

  39. Share Interactive Views • Ad hoc analysis often reveals effective patterns of analysis.

  40. advice:Leave a trail – model effective analysis with actions.

  41. Share Interactive Views How do I design for interaction?

  42. Share Interactive Views Ben Shneiderman’s mantra: • “Overview first, • zoom&filter, • then details-on-demand.” Quick filters Filtering actions View underlying data Tooltips Master-detail

  43. Share Interactive Views Jacques Bertin’s permutation matrices: Programmatic sort 1-click sort

  44. advice:knowyour audience • and designfor • their questions

  45. Share Interactive Views • Task: Finding an object and viewing it “in context” Wildcard filter Text lists Highlighting

  46. Share Interactive Views • Task: Finding and understanding relationships Dashboards with multiple perspectives Highlighting

  47. Share Interactive Views • Interaction “resiliency”:Think about how your views will change when people interact

  48. “Wildlife Strike Database”http://wildlife-mitigation.tc.faa.gov/public_html/index.html

  49. Who can Visual Analysis help? Anybodywith data and questions

  50. advice:Do not limityourself to • large data or • special projects

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