Mediating data quality and data governance in a data-intensive world

Abstract

Data is a critical resource in healthcare research, yet ensuring high data quality remains a persistent challenge. Errors introduced at the point of data entry can propagate through the data lifecycle, affecting usability, integrity, and research outcomes. In response to this challenge, we designed and developed a digital infrastructure over the course of four years capable of accommodating diverse structured health data while enforcing predefined data standards through a validation system. We use that digital infrastructure to examine the root causes of data quality issues at the input stage by analysing the type of data work needed for meaningful data curation. Through a combination of feasibility evaluation, surveys, and semi-structured interviews, we identified recurring mistakes, user behaviour patterns, and underlying reasons for poor data quality. We contribute six key elements of data work necessary for meaningful decision-making and research purposes, grouped under three categories: (i) the data work needed for reaching intrinsic data quality in data governance, (ii) the data work needed for reaching contextual data quality in data governance, and (iii) the data work needed for reaching representational and accessibility data quality in data governance.