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Is Your User Hunting or Gathering Insights? Identifying Insight Drivers Across Domains.
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Is Your User Hunting or Gathering Insights? Identifying Insight Drivers Across Domains.

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Presenter: Michael Smuc, Eva Mayr, Hanna Risku …

Presenter: Michael Smuc, Eva Mayr, Hanna Risku
BELIV 2010 Workshop
http://www.beliv.org/beliv2010/

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  • 1. Is Your User Hunting or Gathering Insights? Identifying Insight Drivers Across Domains Michael Smuc, Eva Mayr, Hanna Risku
  • 2. Motivation
    • Goal: Evaluate a visualization tool for temporal pattern analysis
    • Expert-User Dilemma
      • Real domain experts are rare , hard to find (and to motivate)
      • Sometimes they don‘t even exist
    • Across-domain development
    • What if you wanted to develop a tool suitable for different domains?
  • 3. Solutions
    • Use heuristics instead of insight analysis
      • In case you find suitable ones for your tool, you can‘t keep in touch with the users
    • Educate non-experts
      • time consuming, „they will tell you what you taught them to tell”
      • even more time-consuming for multiple domains
    • => we would like to propose another solution
  • 4. Our approach
    • Across-domain testing: Experts have to solve tasks in data visualizations of their own domain and from other domains
    • Additional aid story about the data
    • Research questions
      • How do experts differ in different domains? More insights?
      • Results only useful when experts work in own domain?
      • … and what makes an domain expert?
  • 5. Specific knowledge of domain experts
  • 6. Common ground of domain experts in our case „temporal data explorers“
  • 7.  
  • 8. Insight Study
    • 9 experts in temporal data analysis from 4 different domains
    • think-aloud
    • Insights: quantitative & qualitative analysis
    • Q: Effect of domain expertise?
  • 9. => Across domain testing works! Effect of domain expertise on insights t = -0.29, df = 24, p > .05
  • 10. Different kinds of insights in a catering business dataset
    • Type 1: "At noon there is a red belt."
    • Type 2: "There is quite some breakfast business."
  • 11. Type 1 Type 2
  • 12. Typology
    • Type 1: insight-gatherers: simple description
    • Type 2: insight-hunters: active search for insights, driven by prior knowledge
      • “… . even the smallest domain information is used to create novel interpretations and make as much sense of the data as possible ”
    • no sign. differences for the number of insights
    • but significant differences for use of prior knowledge & hypotheses
  • 13.
    • Is insight hunting driven by domain-expertise?
    • NO , 50% of the domain experts, but 25% of the non-domain experts showed this behavior
    • (no sign., but small dataset)
    • only every second domain expert hunted for insights
  • 14. Discussion
    • Across-domain-testing works, but domain expertise needs a differentiated approach? (redefinition?)
    • experts common ground & the story are insight-drivers
    • Even shallow insights are useful, insights interrelate
    • Typology iHunters | iGatherers
      • allows selective sampling
      • “ hunters’ insights are the best argument to sell“
      • sampling is easier
      • “ sometimes it is sufficient to gather the second choice experts ”
  • 15. Questions ? what is expertise? across domain testing shallow insights Relational Insight Organizer RIO what is a domain? insight hunting insight gathering experimental setting story about the data sampling what makes an expert? prior knowledge compensation by experts common ground applicability future research generalizability
  • 16. additional
  • 17. t = 0.16, df = 14, p > .05 t = -3.80, df = 7, p < .01 t = -4.87, df = 14, p < .001
  • 18. Definition of insights “… the understanding gained by an individual using a visualization tool (or parts thereof) for the purpose of data analysis, which is a gradual process toward s discovering new knowledge “