*By Ana Claudia Donner Abreu
This article explores how the data economy and Artificial Intelligence (AI) support the formulation of Evidence-Based Public Policies (EBPs). It was formulated based on a systematic and integrative literature review conducted in the Scopus and Web of Science databases. The results indicate that the integration of large volumes of data, analyzed in real time by AI, offers significant potential for the elaboration of more accurate and efficient public policies. However, there are challenges, such as technological infrastructure, privacy, data security and professional training, that need to be overcome. In addition, it highlights the need for an ethical and regulated approach to avoid deepening existing inequalities and to ensure the responsible use of data in the formulation of public policies.
The data economy emerges as a multifaceted economic paradigm that goes beyond the simple collection and analysis of information. In this context, data are strategic assets comparable to capital and labor, but with distinctive characteristics, such as non-rivalry, which allows their simultaneous use by multiple economic agents (Börner et al., 2018; Cong, Xie & Zhang, 2021). This model is based on the application of AI, Big Data and Business Intelligence, enabling the optimization of decision-making processes in organizations and governments (Bodislav et al., 2018). On the other hand, there are regulatory and ethical challenges, such as surveillance capitalism and the social impacts of data monetization (White & Boatwright, 2020; Snell, Tarkkal & Tupasela, 2021). From another perspective, the systemic characteristic of this economy highlights the need for regulation capable of balancing commercial interests and the protection of individual rights (Tang, Plasek & Bates, 2018).
AI enhances the data economy by expanding the analysis of large volumes of information, identifying patterns and trends to support strategic decisions (Chang et al., 2024). Automation provided by algorithms allows for faster and more accurate responses in sectors such as health, finance, and public safety (Wamba et al., 2018; Börner et al., 2018). The analysis of large volumes of data, collected in different ways today, makes it possible to identify patterns, anticipate demands, and customize government actions, making public management more agile and responsive. In addition, interoperability between different levels of government enables the creation of integrated information systems, favoring transparency, social participation, and innovation in public policies (OECD, 2023).
The increasing integration of AI in this scenario strengthens the capacity to analyze and use large volumes of data, promoting more accurate and adaptable public policies. Countries such as Canada and members of the European Union are already using digital strategies to support policies in real time, ensuring greater alignment with population needs (World Economic Forum, 2023; Government of Canada, 2018). The potential of this approach includes promoting transparency, strengthening citizen participation, and mitigating partisan political biases (Mayer-Schönberger & Cukier, 2013).
As an application, the data economy and artificial intelligence (AI) have enabled the formulation of evidence-based public policies (EBPs), driving more informed and effective decision-making, as they are based on a more complete analysis of data to optimize resources and increase the social impact of public policies. This approach, which was driven by technological advances and the New Public Management (NPM), reinforces the importance of decision-making based on empirical evidence (Fogaça et al., 2023). Examples occur in public health, with immunization and neonatal screening programs (Bronstein et al., 2019; Dabanch et al., 2019). In the education sector, data helps to personalize. The multidisciplinary integration of evidence and the disconnect between academic research and policymaking are highlighted as challenges (Loader & Sparks, 2014).
The implementation of PPBEs faces ethical, social, and technological challenges. Protecting people's data privacy suggests strict data security policies to prevent abuse and leaks (Elvy, 2017). Furthermore, algorithmic bias can reinforce inequalities and compromise social justice, which indicates the need for clearer and fairer algorithms (Holm & Ploug, 2017). In this sense, Murtagh et al. (2022) reinforce that transparency in AI processes is essential to building public trust and social control over the policies formulated.
Furthermore, inequality in access to technology compromises data representativeness, requiring digital inclusion initiatives to ensure more equitable public policies (Sestino et al., 2023). Technical barriers, such as the interoperability of government systems, hinder data integration, demanding common standards to improve administrative efficiency (Zech, 2016).
The research concluded that the integration of AI and the data economy has transformative potential for PPBE, providing more informed and adaptive policy decisions (Börner et al., 2018). The personalization of policies enables interventions tailored to the needs of different population segments, promoting social inclusion (Lammi & Pantzar, 2019; Börner et al., 2018). This fusion promotes greater efficiency and responsiveness in public management and improves real-time monitoring of implemented policies (Tang et al., 2019).
Given this scenario, the consolidation of Evidence-Based Public Policies (EBPs) in the era of the data economy and Artificial Intelligence requires a continuous effort to balance technological innovation and responsible governance. The advancement of data analysis tools enables more effective and adaptable policies, but requires investments in infrastructure, regulation and professional training so that their benefits are widely accessible. Thus, the future of EBPs will depend not only on technological evolution, but also on the ability of governments to create regulatory environments that guarantee transparency, ethics and digital inclusion, ensuring that the use of data and AI contributes to more efficient and equitable public management.
*Ana Claudia Donner Abreu – Researcher at Think Tank. She holds a Bachelor’s and Master’s degree in Administration from the Federal University of Santa Catarina (UFSC).
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
Article originally published on the Connected Smart Cities website https://portal.connectedsmartcities.com.br/2025/04/11/a-economia-de-dados-e-a-inteligencia-artificial-como-elementos-para-formulacao-de-politicas-publicas-baseadas-em-evidencias-ppbe/