Edge Case: Adventures in Hybrid User Research

27047f6f7d4b93e4180ff0611b99d304?s=47 Akashic Labs
January 28, 2015

Edge Case: Adventures in Hybrid User Research

Big Data is certainly having a moment. Proponents promise that data will serve up answers to all of our most pressing questions. But what happens when all that data leads to a dead end? Rachel Shadoan, CEO and research scientist at Akashic Labs, tells the story of the data dead end that put her on the path from data science to ethnography--and where that path has led since. Through a variety of case studies featuring the blending of qualitative and quantitative research methodologies, Shadoan shows where data shines, where qualitative research techniques are necessary, and how visualization can bridge the gap between the two.

27047f6f7d4b93e4180ff0611b99d304?s=128

Akashic Labs

January 28, 2015
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Transcript

  1. Edge Case Adventures in Hybrid User Research

  2. Rachel Shadoan @rachelshadoan @akashiclabs

  3. Machine Learning Research: Neural Nets and CAPTCHAs and Robots, oh

    my!
  4. What happens when data dead ends?

  5. Using Oklahoma Mesonet data…

  6. …to predict the birth of babies Image credit: Flickr user

    a4gpa, Creative Commons
  7. None
  8. But what about predicting Cesarean sections? Image credit: Flickr user

    Tord Sollie, Creative Commons
  9. None
  10. Design Ethnography

  11. Research methods are like colored light: the “color” of a

    method impacts what we can see with it. Combining methods allows us to see more clearly.
  12. Data Mining • Predicting future behavior • Answering “what” and

    “how” questions with great specificity • Finding certain kinds of patterns • Identifying users who exhibit specific use behaviors Great For: • Answering “why” questions • Identifying new design opportunities Not Great For:
  13. Data Visualization • Leveraging human pattern-matching ability • Exploring data

    • Providing participants with specific context • Communicating patterns to stakeholders Great For: • Statistically robust analysis • Very large data sets Not Great For:
  14. Design Ethnography • Answering “why” questions • Finding out things

    you didn’t know you needed to know • Generating new opportunities Great For: • Nailing down specifics about behavior • Predicting future behavior Not Great For:
  15. PATTERNS OF PLAY Data Visualization as Scaffolding for Ethnographic Inquiry

  16. Patterns of Play

  17. None
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  19. LOCAL EXPERIENCES OF AUTOMOBILITY Data Analysis to Support Ethnographic Findings

    and Parameterize Design
  20. Routines in Space

  21. Routines in Time

  22. CULTURAL DATA PROJECT’S DATA PROFILE REVISION Ethnographic Inquiry to Guide

    Data Collection for Future Analysis and Visualization
  23. None
  24. Image credit: Flickr user Simon Hammond, Creative Common

  25. rachel@akashiclabs.com @rachelshadoan www.akashiclabs.com Thank you!