Data-Driven Decision-Making and Public Sector Performance in Sierra Leone: The Role of Digital Analytics in Improving Governance
TL;DR
The findings show that Sierra Leone’s difficulty is less a shortage of data than a shortage of the governance arrangements, skills and incentives needed to convert data into decisions.
Abstract
Sierra Leone has committed itself, in successive national development plans and in its National Digital Transformation Strategy, to governing on the basis of evidence rather than intuition. Yet the distance between that ambition and the daily reality of public administration remains wide. This article examines how digital analytics and data-driven decision-making shape governance quality, institutional effectiveness, accountability, transparency and public sector performance in Sierra Leone. The problem it addresses is twofold: the country has invested in an expanding set of digital systems, including the Integrated Financial Management Information System, the Integrated Tax Administration System, the District Health Information Software platform and a biometric civil register, while the analytical use of the data these systems generate remains thin, fragmented and weakly institutionalised. The study pursues four objectives: to map the digital governance landscape; to assess how far data currently informs decisions across the budget cycle, service delivery and oversight; to diagnose the institutional, technological, legal and political constraints on analytical government; and to derive transferable lessons from Estonia, Singapore, South Korea, the United Kingdom, Rwanda, Kenya, China, Ghana, Botswana and the United Arab Emirates. Methodologically, the article rests on qualitative document analysis, comparative policy analysis and embedded case studies, drawing on government publications, international assessments and peer-reviewed scholarship from 2019 to 2026. The findings show that Sierra Leone’s difficulty is less a shortage of data than a shortage of the governance arrangements, skills and incentives needed to convert data into decisions. The article contributes an integrated conceptual framework linking analytics capability, data quality and institutional capacity to public sector performance, and a sequenced implementation roadmap suited to a low-income, aid-dependent state. Its policy implications extend to comparable Sub-Saharan African administrations.