Bridging Data Quality and Business Value Through Flexible, Results-Driven Data Governance Frameworks

Abstract

In an era of data-driven decision-making, organizations increasingly struggle to convert high-volume data into meaningful business value due to persistent challenges in data quality and rigid governance structures. This study examines how flexible, results-driven data governance frameworks can effectively bridge the gap between data quality and organizational performance. Using a mixed-methods research design, data were collected from 240 professionals across multiple data-intensive industries, integrating governance practices, data quality dimensions, analytics capability, and business value indicators. The analysis employed descriptive statistics, correlation analysis, multiple regression, and structural equation modeling to test the proposed relationships. The results reveal that governance flexibility significantly improves data quality and analytics capability, which in turn strongly influence decision-making effectiveness and operational efficiency. Cluster analysis further demonstrated distinct organizational performance groups based on governance maturity and data quality strength. The findings provide empirical evidence that outcome-oriented, adaptive governance models are more effective than traditional control-driven approaches in generating sustainable business value. This study contributes to the existing literature by offering an integrated framework that links governance, data quality, and analytics to measurable business outcomes, and provides practical insights for organizations seeking to enhance their data-driven strategies.