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Big data analytics (BDA) enhances knowledge and decision-making. Despite its importance, the connection between technical progress and political change is neglected in the administrative process. Most studies focus on e-government, e-governance, and how technology can improve existing operations of the bureaucracy. However, this article aims to explore the potential of BDA for public policy systems and provide a linkage for the transformation toward digital and smart governance using preferred reported items for systematic review and meta-analysis (PRISMA) approach to reveal the relevant documents and narrative review approach to interpret the application of BDA at each step of the public policy system. In addition, this study identifies several common public policy-related big data sources and techniques that could be used at the various stages of the public policy process. This study argues that BDA has the potential to be used for policy formulation in the four main phases—planning, design, service delivery, and evaluation. Most studies confirm its potential in the policy process for taxation, health, education, transportation, law, economy, and social system. This study reveals that it is also suitable for public policy execution stages, such as public supervision, public regulation, service delivery, and policy feedback. Previous studies have indicated that the application of BDA can transform traditional or manual governance systems into digital and smart governance. We contend that the policy cycle should be seen as a dynamic and iterative process characterized by continuous evolution. Though each step of transformation has its unique challenges in handling BDA and maintaining the Information and Communication Technology (ICT) infrastructure, it can ensure an accurate, prompt, and context-oriented public policy system. These insights provide a novel outlook on effectively managing the interplay between innovation and traditional approaches in the realm of public policy development.
Hossin et al. (Sun,) studied this question.