DATA-DRIVEN GOVERNANCE AND INSTITUTIONAL PERFORMANCE: A REVIEW OF FRAMEWORKS AND APPLICATIONS
TL;DR
Findings indicate that data-driven governance can enhance decision quality, operational efficiency, and institutional accountability, but significant challenges remain including data quality issues, privacy concerns, digital divides, and organizational resistance.
Abstract
Data-driven governance has emerged as a critical paradigm for enhancing institutional performance, transparency, and accountability in both public and private sectors. This review synthesizes the existing literature on data-driven governance, examining conceptual frameworks, key principles, applications, and the relationship between data-driven approaches and institutional performance. The review identifies core components of data-driven governance including data infrastructure, analytics capabilities, governance structures, and organizational culture. Applications span diverse domains including policy formulation, service delivery, performance management, and stakeholder engagement. Findings indicate that data-driven governance can enhance decision quality, operational efficiency, and institutional accountability, but significant challenges remain including data quality issues, privacy concerns, digital divides, and organizational resistance. Success factors include strong leadership, investment in data infrastructure and capabilities, robust governance frameworks, and a culture of data-informed decision-making. This paper contributes to the literature by providing a comprehensive overview of data-driven governance frameworks and applications, and identifying critical success factors and future research directions.future research directions.