The Socio-Organizational Dimension of Data Quality: People, Processes, and Culture

Abstract

Data quality is commonly understood as a technical challenge requiring measurement, validation, and correction of data errors. However, this perspective overlooks the fundamental socio-organizational dimensions that shape data quality outcomes. This review examines the role of people, processes, and organizational culture in determining data quality, drawing on literature from information systems, public administration, and organizational studies. We analyze how human factors—including training, skills, motivation, and behavior—influence data quality at the point of collection and throughout the data lifecycle. We examine the importance of process design, with particular attention to how data quality requirements can be embedded into workflows rather than addressed through post-hoc correction. We analyze the role of organizational culture in shaping attitudes toward data quality and supporting or undermining governance efforts. The review synthesizes evidence from diverse sectors, including healthcare, public administration, and small and medium enterprises, identifying common patterns and sector-specific challenges. We propose a socio-organizational framework for data quality management that complements technical approaches by addressing the human and organizational factors that ultimately determine data quality outcomes.