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Strata preview 2014: Design thinking for dummies (data scientists)

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Data scientists often face ambiguous challenges and, as a group, should use and make use of the design process to address these challenges. These slides briefly make the case for using the design process. Interested in more, reach out!

Publicado en: Diseño, Tecnología, Meditación
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Strata preview 2014: Design thinking for dummies (data scientists)

  1. 1. design thinking for dummies (data scientists) tuesday, february 11, 9:00 a.m. @deanmalmgren @mstringer @laurieskelly 2014 february strata preview
  2. 2. data scientists thrive with ambiguity solve for x project evolution x=5+2 @deanmalmgren | bit.ly/design-data
  3. 3. data scientists thrive with ambiguity solve for x Ax=b project evolution x=5+2 @deanmalmgren | bit.ly/design-data
  4. 4. data scientists thrive with ambiguity solve for x Ax=b project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
  5. 5. data scientists thrive with ambiguity solve for x Ax=b optimize f(x) project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
  6. 6. data scientists thrive with ambiguity solve for x Ax=b optimize f(x) optimize “our profitability” project evolution x=5+2 optimize Ax=b subject to f(x) > 0 @deanmalmgren | bit.ly/design-data
  7. 7. origins of ambiguity many feasible approaches @deanmalmgren | bit.ly/design-data
  8. 8. origins of ambiguity unclear problems identify the best locations to plant new trees @deanmalmgren | bit.ly/design-data
  9. 9. origins of ambiguity unclear problems identify the best locations to plant new trees how many? what kinds of trees? move old trees? replace old trees? @deanmalmgren | bit.ly/design-data
  10. 10. origins of ambiguity unclear problems identify the best locations to plant new trees aesthetically pleasing? maximize growth? increase folliage? offset CO2 emissions? how many? what kinds of trees? move old trees? replace old trees? @deanmalmgren | bit.ly/design-data
  11. 11. “design process” is used everywhere anticipate failure generate hypotheses evaluate feedback 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
  12. 12. “design process” is used everywhere anticipate failure human-centered design lean startup agile programming evaluate feedback generate hypotheses personas, scenarios, use cases business/product requirements story/user cards 1-4 week iterations build prototype surveys, interviews, focus groups split testing, A/B testing QA; requirements churn build device prototypes minimum viable product write code @deanmalmgren | bit.ly/design-data
  13. 13. design and data science challenges in practice problem lost in translation evaluate feedback generate hypotheses 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
  14. 14. design and data science challenges in practice problem lost in translation generate hypotheses takes a long time to collect data, analyze, and build visualization evaluate feedback 1-4 week iterations build prototype @deanmalmgren | bit.ly/design-data
  15. 15. design and data science challenges in practice problem lost in translation generate hypotheses takes a long time to collect data, analyze, and build visualization evaluate feedback 1-4 week iterations build prototype proof is in the pudding @deanmalmgren | bit.ly/design-data
  16. 16. solve ambiguous problems with an iterative approach http://bit.ly/design-data ! @deanmalmgren dean.malmgren@datascopeanalytics.com

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