Determining the enabling factors for implementing cloud data governance in the Saudi public sector by structural equation modelling

Abstract

Businesses have grown more sophisticated in their use of data, which drives new demands that require different ways to handle this data. Data management solutions on their own are becoming very expensive and unable to cope with the reality of everlasting data complexity. Forward-thinking organisations believe that the only way to solve the data problem will be the implementation of effective data governance. Attempts to govern data failed before, as they were driven by information technology (IT), and affected by rigid processes and fragmented activities carried out on a system-by-system basis. Until very recently, governance remained mostly informal, with very ambiguous and generic regulations in silos around specific enterprise repositories, lacking structure and the wider support of the organisation. Despite its highly recognised importance, the area of data governance is still underdeveloped and under-researched. In the cloud computing context, the cloud brings new issues of data governance, where the loss of governance is one of the top risks of cloud computing. Thus, before adopting the cloud, the organisations should develop a cloud data governance programme. It is important, therefore, to understand the enabling factors for successful implementation of cloud data governance in organisations. However, as every organisation has its own constraints and requirements, the phrase ‘no one size fits all’ applies in this case. This study focuses on the case of the public sector in the Kingdom of Saudi Arabia. Therefore, the aim of this research is to identify the enabling factors in adopting and implementing cloud data governance in the Saudi public sector. The study’s sample covered the largest and most important Saudi public sector organisations, with questionnaires distributed to relevant employees. The results of the study were based on 206 respondents, and structural equation modelling (SEM) was used to evaluate these results.