BEPP-DS

Building Evidence-Based Public Policies with Data Science

Authors

  • José Antonio F. Macedo Insight Lab, Federal University of Ceará, Brazil https://orcid.org/0000-0002-0661-2978
  • Rossana Maria de Castro Andrade Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, Brazil https://orcid.org/0000-0002-0186-2994
  • Regis P. Magalhães Insight Lab, Federal University of Ceará, Brazil
  • Livia A. Cruz Insight Lab, Federal University of Ceará, Brazil
  • José Florêncio Q. Neto Insight Lab, Federal University of Ceará, Brazil
  • Samir B. Chavez Insight Lab, Federal University of Ceará, Brazil
  • Mauricio Feijo B. M. Filho Federal University of Ceará, Brazil https://orcid.org/0000-0002-8652-4328
  • Joaquim José Escola Trás-os-Montes and Alto Douro University, Portugal https://orcid.org/0000-0002-6676-6928
  • Amanda Sousa Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, Brazil
  • Pedro Almir M. Oliveira Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará | e Laboratory of Innovation and Scientific Computing (LICC), Federal Institute of Maranhão, Brazil https://orcid.org/0000-0002-3067-3076
  • Davyson S. Ribeiro Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, Brazil
  • Paulo V. A. Fabrício Group of Computer Networks, Software Engineering and Systems (GREat), Federal University of Ceará, Brazil

DOI:

https://doi.org/10.59490/dgo.2025.1045

Keywords:

Data Science, Evidence-Based Public Policies, E-Government, Public Administration, Big Data, Policy Analytics, Digital Governance, Decision Support Systems

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.

Downloads

Download data is not yet available.

References

Anderson, L. M., Brownson, R. C., Fullilove, M. T., Teutsch, S. M., Novick, L. F., Fielding, J., & Land, G. H. (2005). Evidence-based public health policy and practice: Promises and limits. American journal of preventive medicine, 28(5), 226–230.

Batista, É., Andrade, R. M., Santos, I. S., Nogueira, T. P., Oliveira, P. A., Lelli, V., & Oliveira, V. T. (2024). Fortaleza city hall strategic planning based on data analysis and forecasting. Congresso Ibero-Americano em Engenharia de Software (CIbSE), 433–436.

Furtado, L. S., Moura, G., Vasconcelos, D. J., Fernandes, G. S., Cruz, L. A., Magalhães, R. P., & Coelho da Silva, T. L. (2023). An analytical citizen relation management system (czrm) for social vulnerability mapping and policy recommendation in brazil. Decision Support Systems, 172, 113995. DOI: https://doi.org/10.1016/j.dss.2023.113995.

MacArthur, B. D., Dorobantu, C. L., & Margetts, H. Z. (2022). Resilient government requires data science reform. Nature Human Behaviour, 6(8), 1035–1037.

Musafir, V. E. N. (2018). Brazilian e-government policy and implementation. International E-Government Development: Policy, Implementation and Best Practice, 155–186.

Pereira, G., Monteiro, I., Vasconcelos, D., Braz, L., & Silva, C. (2021). Classificação taxonômica de categorias de serviços públicos para aplicações digitais. Anais do IX Workshop de Computação Aplicada em Governo Eletrônico, 119–130. DOI: https://doi.org/10.5753/wcge.2021.15982.

Rahul, K., & Banyal, R. K. (2020). Data life cycle management in big data analytics. Procedia Computer Science, 173, 364–371.

Saltelli, A., Bammer, G., Bruno, I., Charters, E., Di Fiore, M., Didier, E., Nelson Espeland, W., Kay, J., Lo Piano, S., Mayo, D., Pielke Jr, R., Portaluri, T., Porter, T. M., Puy, A., Rafols, I., Ravetz, J. R., Reinert, E., Sarewitz, D., Stark, P. B., … Vineis, P. (2020). Five ways to ensure that models serve society: A manifesto. Nature, 582(7813), 482–484. DOI: https://doi.org/10.1038/d41586-020-01812-9.

Santos, I. S., Oliveira, P. A. M., Oliveira, V. T., Nogueira, T. P., Dantas, A. B. O., Menescal, L. M., Batista, É., & Andrade, R. M. C. (2023). Big Data Fortaleza: Plataforma Inteligente para Políticas Públicas Baseadas em Evidências. Workshop de Computação Aplicada em Governo Eletrônico (WCGE), 200–211. DOI: https://doi.org/10.5753/wcge.2023.230796.

Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model [CENTERIS 2020 - International Conference on ENTERprise Information Systems / ProjMAN 2020 - International Conference on Project MANagement / HCist 2020 - International Conference on Health and Social Care Information Systems and Technologies 2020, CENTERIS/ProjMAN/HCist 2020]. Procedia Computer Science, 181, 526–534. DOI: https://doi.org/10.1016/j.procs.2021.01.199.

Silcock, R. (2001). What is e-government. Parliamentary affairs, 54(1), 88–101.

Silva, W. C. P., Macedo, J. A. F. D., & De Queiroz Neto, J. F. (2022). Usando um modelo de classificação para a adequada implantação do patrulhamento policial para o enfrentamento à assaltos a bancos no nordeste do Brasil. Revista Brasileira de Ciências Policiais, 13(9), 185–205. DOI: https://doi.org/10.31412/rbcp.v13i9.845.

Sucupira Furtado, L., Da Silva, T. L. C., Ferreira, M. G. F., De Macedo, J. A. F., & De Melo Lima Cavalcanti Moreira, J. K. (2023a). A framework for Digital Transformation towards Smart Governance: Using big data tools to target SDGs in Ceará, Brazil. Journal of Urban Management, 12(1), 74–87. DOI: https://doi.org/10.1016/j.jum.2023.01.003.

Sucupira Furtado, L., da Silva, T. L. C., Ferreira, M. G. F., de Macedo, J. A. F., & de Melo Lima Cavalcanti Moreira, J. K. (2023b). A framework for digital transformation towards smart governance: Using big data tools to target sdgs in ceará, brazil [Digital Technologies in Urban Planning and Urban Management]. Journal of Urban Management, 12(1), 74–87. DOI: https://doi.org/10.1016/j.jum.2023.01.003.

Van Der Aalst, W., & van der Aalst, W. (2016). Data science in action. Springer.

Downloads

Published

2025-05-26

How to Cite

Macedo, J. A. F., Andrade, R. M. de C., Magalhães, R. P., Cruz, L. A., Neto, J. F. Q., Chavez, S. B., Filho, M. F. B. M., Escola, J. J., Sousa, A., Oliveira, P. A. M., Ribeiro, D. S., & Fabrício, P. V. A. (2025). BEPP-DS: Building Evidence-Based Public Policies with Data Science. Conference on Digital Government Research, 26. https://doi.org/10.59490/dgo.2025.1045

Conference Proceedings Volume

Vol. 26 (2025): Annual International Conference on Digital Government Research

Section

Practical reports

License

Copyright (c) 2025 José Antonio F. Macedo, Rossana Maria de Castro Andrade, Regis P. Magalhães, Livia A. Cruz, José Florêncio Q. Neto, Samir B. Chavez, Mauricio Feijo B. M. Filho, Joaquim José Escola, Amanda Sousa, Pedro Almir M. Oliveira, Davyson S. Ribeiro, Paulo V. A. Fabrício

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.