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Is big data handicapped by "design"? Seven design principles for communicating data

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Is big data handicapped by "design"? This presentation shares the seven design principles for effective data communication. Good and bad examples for data visualizations highlight the choices designers make in helping non-analytical audiences understand the meaning in data.

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Is big data handicapped by "design"? Seven design principles for communicating data

  1. 1. CEO, Juice Analytics Zach Gemignani Is the Big Data Revolution Handicapped by “Design”? 7 Design Principles for Communicating with Data
  2. 2. Let’s travel back to 1998
  3. 3. What is big data? volume variety velocity Gartner’s 3-Vs 40 definitions from Berkeley School of Information survey of thought leaders
  4. 4. Technologies to capture, gather, store, and manipulate vast quantities and varieties of data Data analysts/scientists who can find meaning in this data Insights Your audience who makes decisions on the data Communication Getting value from data
  5. 5. data junkies information designers Data + Design
  6. 6. via viz.wtf
  7. 7. “…learn how to double your effectiveness as an analyst"
  8. 8. 7 design principles for data communication 1 gradual reveal 2 compactness and modularity 3 guide attention 4 support casual use 5 lead to action 6 relevance explanation before information7
  9. 9. Gradual Reveal Draw the audience in Reveal information as the user expresses interest Build understanding piece by piece
  10. 10. How Many Households http://www.nytimes.com/interactive/2011/06/19/nyregion/how-many-households-are-like-yours.html
  11. 11. How Many Households http://worrydream.com/HowManyHouseholds/
  12. 12. Sprint dashboard
  13. 13. https://www.youtube.com/watch?v=v57sUtvNa5o Phenominal cosmic powers… …itty bitty living space!
  14. 14. Compactness and Modularity “Compact software tools have all the virtues of physical tools that fit well in the hand.They feel pleasant to use, they don’t obtrude themselves between your mind and your work, [and] they make you more productive.” - Eric Steven Raymond,“The Art of Unix Programming.”
  15. 15. Social media viz Google Social Collider, Chrome Experiments
  16. 16. Fitbit
  17. 17. Guide Attention Visual cues and functionality to draw the user to the things that matter most. Color Contrast Positioning Paths Size Animation Photo by David Clapp (http://www.davidclapp.co.uk/)
  18. 18. Shaking Off Oil Addiction http://www.bloomberg.com/graphics/2014-america-shakes-off-oil-addiction/
  19. 19. HR dashboard via Panorama Necto
  20. 20. Support Casual Use Minimize the barrier to entry for new users Avoiding feature overload Providing clear, concise descriptions of what things mean.
  21. 21. AnswerRocket http://vimeo.com/115200957
  22. 22. Lead to Action Empower users to finish their task quickly Direct users to the action that should be taken based on the results
  23. 23. World Chat Clock http://worldchatclock.com/
  24. 24. Buy or Rent? http://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html
  25. 25. “5522 Tweets within the same Second. Sent from all over the world. Preserved and categorized in four Books.” So what? One second http://www.philippadrian.com/project/onesecond/
  26. 26. Relevance Customization to reflect the users’point of view Context to explain how the information relates to the user http://www.businessinsider.com/slow-mo-gopro-vid-of-cliff-jump-gone- wrong-is-nothing-short-of-insane-2013-12
  27. 27. Valuation Analyzer http://labs.juiceanalytics.com/valuation/index.html
  28. 28. Explanation before Information Provide context and explanations to understand new and unfamiliar events "You take the blue pill, the story ends. You wake up in your bed and believe whatever you want to believe. You take the red pill, you stay in wonderland, and I show you how deep the rabbit hole goes."
  29. 29. Gun deaths http://guns.periscopic.com
  30. 30. http://viz.wtf/post/109140768701/common-requests-funky-triangles-in-a-spiral-bound Common Requests?
  31. 31. Technologies to capture, gather, store, and manipulate vast quantities and varieties of data Data analysts/scientists who can find meaning in this data Insights Your audience who makes decisions on the data Communication Getting value from data
  32. 32. Data isn’t like your kids. You don’t have to pretend to love them equally. “ ”» Amanda Cox, New York Times
  33. 33. I don’t like food. I love it. If I don’t love it, I don’t swallow “ ”» Anton Ego, Food Critic, Ratatouille
  34. 34. Our mission is to make communication of data as easy and social as sharing a photo on Instagram. We do this through our technology platform (Fruition), our consulting services, and our thought leadership.
  35. 35. Fruition A solution that abides by these design principles
  36. 36. Data fluency: the ability to use the language of data to fluidly exchange and explore ideas within your organization

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