BROAD DATA: Ethnographic methods in the age of interdisciplinary collaboration

Abstract

Under the aegis of what we term “broad data ethnography”, we outline four principles for the standardized collection, processing, and analysis of ethnographic data, particularly fieldnotes: compilability, compatibility, counting, and computability. As we demonstrate via examples from an interdisciplinary study at a Danish politics festival: compilability denotes the need for a shared data structure; compatibility refers to the demand for high similarity in data content; counting captures how standardizing both form and content emables systematic use of quantitative measures in the field, which in turn, and finally, allows for subsequent computational analysis. Finally, we address the potentials and pitfalls of adhering to these four principles, suggesting that in an age of increasing interdisciplinary collaboration and ubiquitous digital platforms and AI tools, “broad data ethnography” offers an attractive alternative to conventional “thick data” by allowing for short-term, computationally-augmented, team-based ethnography in situations where more individual and idiosyncratic approaches are not feasible.