Scale, Nuance, and New Expectations in Ethnographic Observation and Sensemaking

Abstract

We consider new expectations for ethnographic observation and sensemaking in the next 20–25 years, as technology industry ethnographers’ work unfolds in the increasing presence of the type of analytical capabilities specially trained (and self‐training) machines can do ‘better’ and ‘cheaper’ than humans as they can take in, analyze and model digital data at much higher volumes and with an attention to nuance not achievable through human cognition alone. We do so by re‐imagining three of our existing ethnographic research projects with the addition of very specific applications of machine learning, computer vision, and Internet of Things sensing and connectivity technologies. We draw speculative conclusions about: (1) how data in‐and‐of‐the world that drives tech innovation will be collected and analyzed, (2) how ethnographers will approach analysis and findings, and (3) how the evidence produced by ethnographers will be evaluated and validated. We argue that these technology capabilities do offer compelling new ways to model and understand the contexts in which ethnographic encounters take place. Yet because ethnography has never been solely about describing behavior, or about testing hypotheses to ultimately generate laws, these new tools will never get us on their own to the type of truths the ethnographer values above all else: the meanings given to experiences by humans.