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Data is a vital enterprise resource for decision-making, policy creation, and productivity enhancement. Although Uganda’s public sector has made significant investments in digitising services, data governance has received less attention. Moreover, most existing studies on data governance focus on single organisations, leaving a gap in multi-institutional perspectives. This study, therefore, investigated data governance practices in a multi-institutional setting within the Ugandan public sector.
Based on the pragmatist philosophy, the research employed an exploratory sequential mixed-methods design. In the first phase, qualitative data were collected through purposive sampling from six officials representing six different government ministries and analysed using content analysis. In the second phase, quantitative data were gathered from 102 officials across 22 ministries, selected through stratified sampling, and analysed using the Statistical Package for the Social Sciences (SPSS).
The four research questions of this study were responded to by the qualitative stage findings, where it was established that i) in addition to the antecedents of governance of data, as posited by Abraham et al.’s (2019) cross-functional framework, the requirements for effective data governance include data governance principles; ii) the organisational provisions identified included data scope management and governance mechanisms; iii) key challenges identified included a) insufficient policy and regulatory instruments, b) low ICT adoption, c) Infrastructure inadequacies, d) financial constraints, and e) human resource shortages. Analysis of the quantitative data using Statistical Package for the Social Sciences (SPSS) was used to test three hypotheses, where the results revealed The results of the regression analysis conducted to test the three hypotheses revealed the following: i) Data Governance Antecedents significantly explained 15.2% of the variance in Data Governance Principles; ii) Data Governance Antecedents accounted for 14.2% of the variance in Data Governance Mechanisms; and iii) Organisational Culture explained only 3.1% of the variance in Data Quality, a relationship that was not statistically significant. This study contributes to the literature by advancing an understanding of data governance from a multi-institutional perspective in a developing country context, highlighting systemic challenges and requirements that inform both practice and policy.