Asian Journal of Current Research

Public health systems generate unprecedented volumes of data through electronic health records, disease surveillance networks, mobile health platforms, and environmental sensors, yet this analytical capacity rarely converts into timely policy action. This narrative review examines the structural, technical, institutional, and ethical barriers that separate data analytics from public health policymaking, and identifies opportunities that may narrow this gap. Drawing on literature published between 2018 and the present, the review synthesises evidence across six domains: data governance and interoperability, workforce and institutional capacity, knowledge translation mechanisms, algorithmic bias and equity, visual communication for decision-makers, and the particular constraints faced by low- and middle-income settings. The review finds that technical sophistication in analytics has consistently outpaced the institutional architecture needed to use it, with fragmented data systems, an underprepared informatics workforce, weak knowledge-brokering structures, and uneven regulatory oversight of artificial intelligence each acting as independent and compounding obstacles. Conversely, promising developments in interoperability standards, dashboard design, evidence-to-decision frameworks, and workforce upskilling programmes demonstrate that the gap is narrowing in some jurisdictions, although progress remains uneven across income settings. The review concludes that closing the translational gap requires simultaneous investment in data infrastructure, in the people who interpret it, and in the institutional relationships that connect analysts to decision-makers, rather than further investment in analytic methods alone.