Data-Driven Policy Making in Digital Governments
Keywords:
Digital government, data-driven policy making, data governance, evidence-based decision-making, thematic analysisAbstract
Objective: This study aimed to identify and explain the dimensions, requirements, and challenges of data-driven policy making in digital governments through a systematic conceptual review.
Methods and Materials: This qualitative systematic review explored conceptual frameworks of data-driven policy making. The research sample included scholarly articles on data governance, digital government, and evidence-based decision-making. After a systematic search in Scopus, Web of Science, and Google Scholar, twelve relevant and high-quality papers were selected. Data were analyzed thematically using Nvivo 14 software, following open, axial, and selective coding until theoretical saturation was reached.
Findings: Thematic analysis revealed three main themes: “Data and Technological Infrastructure,” “Institutional Capacity and Data Governance,” and “Data-Driven Policy Process.” Each theme included multiple subthemes such as data standardization, data security, leadership and literacy, transparency, predictive analytics, and organizational learning. The results indicated that data-driven policy making requires not only technological readiness but also institutional maturity, ethical frameworks, and cultural adaptation within public organizations.
Conclusion: The study concluded that data-driven policy making, as a core component of digital governance, depends on integrating data technologies with institutional and cultural mechanisms for evidence-based decision-making. Effective implementation requires balancing transparency, privacy, and innovation. These findings offer valuable insights for policymakers seeking to design data governance frameworks, improve public decision-making, and enhance public trust.
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