What to Do About Data Distance? Responsible Alternatives to Data Sharing · Issue 7.2, Spring 2025

"In this commentary, I take issue with one assumption that underlies...the idea that data reuse requires the sharing of data, and that transparency is therefore a key principle guiding data work, including the practices of data formatting, cleaning, filtering, modeling, curation, and visualization carried out by knowledge intermediaries. By contrast, I argue that data distance is sometimes so large and fraught with challenges, that a better way to facilitate data reuse is to employ intelligent methods of data governance and interpretation that do not involve the sharing of data. I focus on two such methods in this commentary: mining algorithms facilitating data analysis (sometimes also called ‘data visiting’ methods) and narratives (‘data stories’) forged to contextualize and interpret data in specific ways. These methods have a key characteristic in common: they require the explicit articulation of specific visions for prospective data use, thereby moving away from the quest to open data to any possible usage, and rather placing emphasis on the need to account for how choices are made when circulating data end up affecting—and, indeed, constraining—the interpretation of such data in new contexts...."