BEPP-DS

Building Evidence-Based Public Policies with Data Science

Abstract

The exponential growth of data and the advancement of computational tools have made Data Science (DS) an essential discipline for addressing complex societal challenges. In the public sector, Evidence-Based Public Policies (EBPP) leverage data-driven insights to enhance governance transparency, efficiency, and effectiveness. However, the integration of Data Science into policymaking presents challenges, including data quality, interdisciplinary collaboration, and institutional resistance. This paper introduces BEPP-DS, a structured methodology for developing EBPP using DS principles, emphasizing transparency, reproducibility, and scalability. The methodology is informed by real-world applications such as Big Data Social and Big Data Fortaleza, which illustrate how data-driven strategies improve policy design, implementation, and monitoring. BEPP-DS defines a structured framework, from problem identification to policy evaluation, ensuring data-driven decision-making in governance. The methodology provides a replicable model for governments seeking to harness Data Science in policy formulation. Future work includes expanding AI-driven analytics and strengthening citizen engagement in data governance.