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Myths About Designing with Data

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There is a lot of buzz about data-driven design, but very little agreement about what that really means. Even deciding how to define data is difficult for teams with spotty access to data within their organizations, uneven understanding, and little shared language. For any site or app, it's standard practice to have analytics, A/B tests, surveys, intercepts, benchmarks, scores of usability tests, ethnographic studies, and interviews. So what counts as data? And more importantly, what will inform design in a meaningful way? This deck explores 6 myths about data and design.

Published in: Design, Data & Analytics
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Myths About Designing with Data

  1. 6 Myths About DATA  &     DESIGN   @changesciences changesciences.com
  2. 1  DATA  =  NUMBERS   myth
  3. Think of it like   ARCHEOLOGY  
  4. It’s incomplete TRACES  
  5. …about PEOPLE  
  6. 2  DATA  =  OBJECTIVE  TRUTH   myth
  7. Big  data  can  have  SIGNAL  BIAS  
  8. Big  data  can  have  ALGORITHMIC  BIAS  
  9. Be  a  bit  Bayesian   –  Updates beliefs –  Iterative –  Incorporates multiple sources –  Yes, and... –  Never 100%
  10.   DATA  WITH  A  SOUL   Don’t collect it without purpose Use only what is needed Use data with empathy  
  11. 3  BIGGER  =  BETTER   myth
  12. BIG  DATA   THICK  DATA  
  13. Big  Data   •  What, where, when, how •  Multi-structured •  Collected by machines •  Broad •  Behaviors & actions of many people •  Collected as people do what they do •  People are not highly aware of data being collected •  Analysis uses statistical methods          
  14. ü  Transactional data ü  Customer service logs ü  Analytics ü  A/B tests ü  Social media posts ü  Sensor data
  15. Li:le  Data   •  It’s big data for one •  Focused on personal goals •  Individuals grant access to it, rather than companies  
  16. Thick  Data   •  How and why •  Description •  Collected by people •  In-depth •  Behaviors, actions, emotions, intentions, motivations of a few •  Collected as part of a study •  People are highly aware of data being collected •  Analysis includes developing codes, summaries, and themes                
  17. ü  Usability tests ü  Contextual research ü  Interviews ü  Diaries ü  Any study
  18. Strive for BALANCE  
  19. 4  DATA  IS  FOR  MANAGERS   myth
  20. poverty rainfall global happiness copyright value culture fit attitudes employee performance DESIGN automobile safety emotion economic growth engagement size of an atom corruption healthcare outcomes learning intelligence emotional intelligence potential output social media ROI risk dolphin population love innovation national security reputation public influence customer satisfaction team productivity violence in households cooperation air pollution asset value of advertising trust pH length of Saturn’s day online readership stress level accountability
  21.   MEASURE  WITH  MEANING   Personal well-being Collective well-being Markets and money  
  22. 5  DATA  KILLS  INNOVATION   myth
  23. The key is   PAIRING  
  24. Data & DISCOVERY  
  25. Public datasets Interviews Behavioral analytics Observations Social media data Lean research Competitive data Ideation DISCOVERY  PAIRINGS   Emotional analytics Diaries
  26. Data & IMPROVING  
  27. A/B tests Usability tests Surveys Intercept interviews Customer service data Interviews Analytics Usability tests IMPROVEMENT  PAIRINGS  
  28. 6  THERE  IS  ONE  RIGHT  WAY   myth
  29. THERE  IS  NO   PERFECT  METHOD    
  30. Decide on MEANINGFUL   MEASURES  
  31. Choose the right SIGNALS  
  32. Be sensitive to COMPLEXITY  
  33. changesciences.com   @changesciences      

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