Data-Driven Governance: Conceptualizing Evidence-Based Decision Making in the Public Sector
Keywords:
Data-driven governance, evidence-based decision making, public sector, digital government, public governanceAbstract
The increasing complexity of public governance requires governments to adopt decision-making approaches that are adaptive, transparent, and evidence-based. The rapid development of big data, artificial intelligence, and digital government has accelerated the emergence of data-driven governance, where data become a strategic asset for improving policy quality and public service performance. This study aims to conceptualize data-driven governance as a comprehensive framework for evidence-based decision-making in the public sector. Using a conceptual review approach, the study synthesizes contemporary literature on digital government, evidence-based policymaking, artificial intelligence, data governance, and public administration to identify the key dimensions that shape effective governance. The analysis reveals that successful data-driven governance depends on the interaction of five strategic dimensions: data quality, analytical capability, organizational readiness, ethical and accountable governance, and collaborative governance. These dimensions collectively support governments in transforming data into actionable evidence for more responsive, transparent, and accountable public policies. The study proposes an integrated conceptual framework that connects these dimensions within a unified governance model, providing a broader perspective than previous studies that generally examined them separately. The proposed framework contributes theoretically by enriching the literature on public governance and digital transformation while offering practical guidance for policymakers in strengthening evidence-based governance initiatives. Future studies are encouraged to validate the framework empirically across different governmental contexts to enhance its applicability and explanatory power.
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