Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science

I provide a philosophical perspective on the characteristics of data-centric research and the conceptualization of data that underpins it. The transformative features of contemporary data science derive not only from the availability of Big Data and powerful computing, but also from a fundamental shift in the conceptualization of data as research materials and sources of evidence. A relational view of data is proposed, within which the meaning assigned to data depends on the motivations and instruments used to analyze them and to defend specific interpretations. The presentation of data, the way they are identified, selected and included (or excluded) in databases and the information provided to users to re-contextualize them are fundamental to producing knowledge - and significantly influence its content. Concerns around interpreting data and assessing their quality can be tackled by cultivating governance strategies around how data are collected, managed and processed.Keywordsdata philosophy; data history; data-centric research; inference; data management; data curation; modelling.