Managing and improving data quality through the adoption of data warehouse in the public sector

The increasing use of technology in organizations has resulted in burgeoning sizes of data, a wider variety of data sources and diverse data structure, all calling for better storage, retrieval and analytics. The increasing demands that data make on organizations, if not handled with care, could lead to a compromise of data quality. In this context, there is an urgent need for the comprehensive capturing, measure and control of data. This need has necessitated the adoption of business intelligence tools like data warehousing (DW) to ensure quality and the segregation of analytical operations from mainframe transaction processing. However, the benefits of DW adoption have been primarily enjoyed by the private sector and little effort has been made to adopt business intelligence tools in the public sector. This is reinforced by the literature, which shows that there is little empirical research on developed frameworks to inform DW adoption in the public sector. Data for the current study was collected from the Department of Rural Development and Land Reform (DRDLR) in South Africa and analyzed using Structural Equation Modelling. The results indicate that performance expectancy, effort expectancy, behavioural intention, and environmental and technological characteristics are key antecedents for DW adoption. This study’s finding could be used to extend research on technology adoption in the public sector, as well as to give insights into the foundations of data quality management.