The use of Data Visualization for user experience research addresses a perceived need of extending the capabilities of the research team or individual; giving us the ability to display large datasets in insightful ways. Attendees to this talk will delve into learning about how to dimensionalize qualitative transcripts against quantitative survey data through a demonstration of a novel, custom visualization platform built from open-source tools. As researchers, we can begin to expand our skillsets and abilities in common tasks such as segmentation, correlation and pattern recognition. Whereas we will continually advocate for the benefits from the abstraction that Data Visualization provides, ethically, these techniques require a narrative that is constantly being validated through human connection. Our goal for the use of Data Visualization is not to become a shortcut that replaces generative ethnography, but instead positioned as a complementary tool for iterative research cycles. By potentially unlocking these patterns of discovery, especially early on in the research process, we can create a more defensible position for our findings overall.
|Data Visualization for UX Research Methods (5.2 MB)||53 Pages||Available after Purchase|
Nick Cawthon helps run Gauge, a small consultancy in Berkeley, California, composed of ethnographers, visualization engineers, interaction and service designers. Past clients have included Adobe, Accenture, Asurion, the San Francisco Giants and other organizations that adopt a user-centric product design and development cycle. Nick has authored “Æ : Aesthetic Effect, Investigating the User Experience of Data Visualization” and teaches Data Visualization in curriculum of the Design Strategy MBA program at his alma mater, CCA in San Francisco.
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