Enhancing (Publications on) Data Quality: Deeper Data Minding and Fuller Data Confession

Abstract

Abstract Statistics typically treats data as inputs for analysis, whereas the broader data science enterprise deals with the entire data life cycle, including the phases that output data. This commentary argues that it would benefit statistics and (data) science if we statisticians were also to treat data as products in and of themselves, and accordingly subject them to data minding, a stringent quality inspection process that scrutinizes data conceptualization, data pre-processing, data curation and data provenance, in addition to data collection, the traditional objective of our emphasis before data analysis. A concrete step in promoting deeper data minding is to encourage fuller data confession in (statistical) publications, that is, to entice—or at least not to disincentivize—the authors into providing more details on the genealogy of a given body of data, including an account of its deliberations, especially with respect to sources of adverse influence on data quality. The collection of articles in this special issue (on data science for societies) provides both the inspiration and aspiration for deeper data minding and fuller data confession.