Integrating Data Governance into Public Sector Decision-Making Processes

Abstract

This research explores the integration of data governance into public sector decision-making processes, emphasising its critical role in enhancing service delivery and addressing societal challenges. Utilizing a systematic literature review of 50 sources, the study identifies three primary themes: policy activities, organisational activities, and informational activities, which collectively facilitate effective data governance. The findings reveal significant barriers to integration, including institutional resistance, resource limitations, and bureaucratic inertia, which hinder the adoption of robust data governance frameworks. The research highlights the necessity for adaptive strategies that promote stakeholder engagement and transparency while ensuring data quality and compliance. Recommendations for practice are structured around five essential elements: people and organisation, processes and methods, technology and data, control and compliance, and culture and attitudes. These elements are crucial for developing effective data governance practices that can support evidence-based policy-making. The study concludes that while integrated data systems can significantly improve decision-making and resource allocation, challenges such as siloed data systems and compliance tensions remain prevalent. Future research directions are suggested, focusing on the implications of emerging technologies like artificial intelligence and big data management for public sector governance. This research contributes valuable insights for policymakers and practitioners aiming to enhance data governance frameworks within public administration.