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.

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
8. But what about predicting Cesarean sections? Image credit: Flickr user Tord Sollie, Creative Commons
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
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
24. Image credit: Flickr user Simon Hammond, Creative Common
25. rachel@akashiclabs.com @rachelshadoan www.akashiclabs.com Thank you!