Quantitative Information Architecture

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Quantitative methods are applicable for IA thinking be it for hypothesis generation, instrumentation, data collection and analysis of information at scales never before possible with insights that are comparable over time, generalizable and extensible.

Quantitative skills can allow IAs to interpret and analyze others’ designs and research more readily, as well as combine methods and models for meta-analysis to help IAs move from description to prediction in designing and developing future interfaces and architectures.

This presentation will review why you should use quantitative methods and discuss both foundational and emerging ideas that are applicable for content analysis, behavioral modeling, social media usage, informetrics and other IA-related issues.

http://donturn.com or http://twitter.com/donturn

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Quantitative Information Architecture

  1. 1. Quantitative Information Architecture Don Turnbull, Ph.D. twitter.com/donturn #quantia donturn@gmail.com http://donturn.com
  2. 2. whois? •  Software Developer •  M.S. @ Georgia Tech •  Software Design & Management •  Ph.D. @ U Toronto •  Principal @ Startup (Google) •  Faculty @ U Texas (Austin) •  Consultant & Entrepreneur
  3. 3. Ways of Knowing & Doing The fox knows many things, but the hedgehog knows one big thing. Archilochus •  Quantitative IA is one big thing: a specialty, a mindset •  Designing appropriate experiments •  Leveraging existing quantitative data •  Conducting rigorous analysis
  4. 4. What does the world look like to a Quantitative Researcher?
  5. 5. Quantitative Methods •  What’s Quantitative good for? •  Understanding what users actually do instead of what they said they do. •  Making comparisons over time •  Generalizable and extensible •  Useful for interpreting and analyzing others results
  6. 6. Why you should go Quant •  It is a discipline •  Hypothesis-based •  More applicable to (peer) review •  It requires a set of skills that have a (much) higher market value •  It examines characteristics that are constants •  Behavior •  Physical traits & abilities
  7. 7. The Power of Quant •  Fight fire with Fire •  Numbers speak the language of business & technology (C-level execs) •  (Almost) infallible results •  Qualitative decisions for Quantitative measurement
  8. 8. Predict everything?
  9. 9. What about Qualitative? •  Anyone can do qualitative research… •  …and anyone does •  Hard to replicate, hard to validate, easier to do •  Domain of study (who) is main focus •  Variability is often wide •  Technique is critical
  10. 10. Quantitative & Qualitative should complement each other
  11. 11. Because if they don’t
  12. 12. Things go wrong - fast
  13. 13. Why Quant, Why Now? •  Computational power & networked systems •  We need new modeling techniques, even new metaphors to examine the complex systems we interact with •  Finance, Psychology, Physics & Computer Science •  Verifiable or provable by means of observation or experiment: empirical laws.
  14. 14. The Network Effect(s) •  Understanding how people interact •  Metcalfe •  Milgram •  Wellman, Watts & others
  15. 15. A (new) Era of Instrumentation •  We are undoubtedly in a new era of reasoning •  Scientific Engineering enabled the original Age of Reason •  Now to understand intent & interactions
  16. 16. Statistics
  17. 17. Statistics & every day life •  Weather •  Stock Market •  Whole channels on TV devoted to both •  Sports…. All the time…. Everywhere •  Your Net Worth, IQ, Postal Code, SAT score, GPA...
  18. 18. Data Science
  19. 19. A New Kind of (Empirical) Science
  20. 20. What’s next ?
  21. 21. Analytics
  22. 22. HCIR (IRUX)
  23. 23. Atomic Information Architecture (a marketplace)
  24. 24. Pervasive, Emotive & Suggestive
  25. 25. Summary New Age of (Empirical) Reason Quant at scale = new Qual insights Data Science (& its affect on IA/UX) What’s next isn’t that far ahead…
  26. 26. Thanks donturn@gmail.com http://donturn.com twitter.com/donturn

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