A Multi-layered Data Preparation Model for Health Information in Sudan

Abstract

Data quality is a major challenge in almost every data project in today’s world, especially when the required data has a national or global look and feel; however, data preparation activities dominate the efforts, cost, and time consumption. Nowadays, many data collection approaches are continuing to evolve in the era of big data to accommodate revolutionary data flows, especially in the health sector, which contains many different levels of data types, formats, and structures; however, the lack of qualified and reliable data models is still an ongoing challenge. These issues are even magnified in developing countries where there is a struggle to make advances in health systems with limited resources environments, and to adopt the advantages of ICT to minimize the gaps in health information systems. This article introduces a geo-political multi-layered model for data collection and preparation, combined with distributed quality measures approach to minimize the effort, cost, and time consumption challenges in data projects. The currently used data collection method in Sudan was analysed and gaps were identified, with respect to geo-political structure of the country. The result of the model provides structured datasets framed by time and geographical spaces that can be used to enrich analytical projects and decision-making in the health sector.