Data Quality with Information Governance Practices and Knowledge Transfer across Infrastructure Delivery Programs

Abstract

Modern infrastructure delivery programs are increasingly characterized by high levels of digital complexity, generating massive volumes of data across extended lifecycles. However, asset owners and delivery consortia frequently struggle with poor data quality, which directly contributes to cost overruns, schedule delays, and compromised asset operational efficiency. While technological solutions are commonly proposed to address these challenges, this study argues that data quality is fundamentally a socio-technical phenomenon. We investigate how formal information governance practices and structured knowledge transfer mechanisms explain variation in data quality within large-scale infrastructure programs. Using structural equation modeling applied to empirical data collected from project delivery professionals across major transport, energy, and utility infrastructure programs, we analyze the direct and indirect pathways connecting governance to data quality. The results demonstrate that while information governance practices establish the necessary rules and accountability frameworks, their direct impact on data quality is significantly amplified when mediated by robust knowledge transfer processes. This research provides a novel theoretical synthesis of information governance and knowledge management in project-based environments, offering actionable insights for program directors seeking to leverage data as a strategic asset.