Flink Labs Data Visualisation

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Flink Labs' Data Visualisation presentation to the Sydney Data Miners group.

Content from the Flink Labs Masterclass Data Visualisation workshop.

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Flink Labs Data Visualisation

  1. 1. Data Visualisation Ben Hosken ben@flinklabs.com @flinklabs
  2. 2. Melbourne trains
  3. 3. Mobile phone locates
  4. 4. Live Where?
  5. 5. Interactive visual representationof data to improve cognition
  6. 6. Data telling stories
  7. 7. Exploration and Engagement
  8. 8. Good and Bad
  9. 9. Visual Cortex FTW
  10. 10. Accelerating data
  11. 11. 3 Vs of Big Data Velocity Volume Variety
  12. 12. Accelerating power
  13. 13. Changing expectations
  14. 14. Consumer devices leading the way
  15. 15. Tells a story you can interact with
  16. 16. Data Visualisation Process Define Data Design Develop DeliverDefine the problem and Gather the Data Brainstorm ideas Develop "architecture Deliver final producthigh level goals Load and explore data Sketch rough designs tracer bullet" Install softwareAgree on scope and Produce data patterns Create concepts Code iterations of product Handover to clienttimings Identify potential narrative Develop interactive Test and resolve issues Prepare support channelsUnderstand the target "hot spots" prototypes Conduct final QA Launch visualisationaudience and platforms Discuss with target Present alternatives Conduct post mortemKick off project audience
  17. 17. Audience ?
  18. 18. What is the action outcome!
  19. 19. Storytelling
  20. 20. Data’s Journey
  21. 21. Tell the story don’t just show the data
  22. 22. Draw the viewer into the data
  23. 23. Toiling in the Data Mines
  24. 24. Not just the averages
  25. 25. Get the basics right
  26. 26. Count the number of 5s 7 6 2 7 8 7 3 2 3 6 7 7 5 8 2 3 7 1 5 6 8 6 0 0 9 7 4 8 5 8 5 4 1 4 3 5 7 7 0 3 3 4 0 2 3 6 8 1 7 3 8 3 1 8 2 3 7 9 8 1 0 1 4 9 9 3 0 7 8 6 6 8 6 8 6 3 8 3 5 0 7 7 6 2 8 7 1 4 6 1 9 7 7 6 3 5 0 8 0 5 8 1 2 1 5 1 8 0 9 3
  27. 27. Count the number of 5s 7 6 2 7 8 7 3 2 3 6 7 7 5 8 2 3 7 1 5 6 8 6 0 0 9 7 4 8 5 8 5 4 1 4 3 5 7 7 0 3 3 4 0 2 3 6 8 1 7 3 8 3 1 8 2 3 7 9 8 1 0 1 4 9 9 3 0 7 8 6 6 8 6 8 6 3 8 3 5 0 7 7 6 2 8 7 1 4 6 1 9 7 7 6 3 5 0 8 0 5 8 1 2 1 5 1 8 0 9 3
  28. 28. Visual EncodingForm Orientation Line Length Line Width Size Shape Curvature Enclosure MotionColour Intensity Hue Position 2-D Grouping
  29. 29. Common Pitfalls
  30. 30. Tools
  31. 31. Hans Rosling Video

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