Abstract
The discourse on the importance of data governance in the public sector has found traction, given the focus on the centrality of data for the global development agenda. Whilst global partnerships and government-wide initiatives have shaped the recognition of data as an asset, a role is emerging for National Statistical Organisations (NSOs) to take the lead in data governance in the public sector. For the government, the strategic use of data is pivotal in addressing policy agendas and providing development and service delivery results affecting societies.
Research and knowledge gaps exist in the discipline of data governance and in data governance as a practice in the public sector. There are many definitions and thematic understandings of data governance and different data governance models in the literature and the public sector across countries. There are no universally acceptable elements of data governance that can be considered the most critical core elements. Furthermore, selective implementation with no holistic approach to how elements of data governance models are implemented.
Globally, there is a responsiveness from national governments to data governance, and certain elements have been defined to direct data governance as a discipline. In the case of South Africa, there is an intent by key national departments to lead data governance. At a sub-national level, the Western Cape Government (WCG) institutionalised Province-wide data governance (PWDG) in 2015 to deliver technical outputs toward the initial data governance capability.
The thesis developed a public sector data governance model at a sub-national government level to increase the use of data and enable data-driven decision-making that results in societal change. The thesis addressed the above research and practice gaps by identifying desired minimum core elements, the associated data governance practices, and the institutional collaboration across government to institutionalise data governance. The WCG, a sub-national government of the national government of South Africa, was used as a case study.
The research design was centred around a theory building process with applied research methods where theory was generated to build the conceptual data governance model logically. The thesis combined primary and secondary research methodologies that employed descriptive and exploratory methods underpinned by phenomenological experiences. The theory building was grounded in secondary research, including literature reviews, archival research, and primary research using mixed methods and a case study. The conceptual theory building included qualitative methods for a construct analysis, the initial conceptual model building and analysis, and participant feedback. The research plan took on a two-phased approach where Phase 1 included a development study, and a case study, and Phase 2 included testing the conceptual data governance model.
The data governance model developed at a sub-national level affirmed the value of the theory generated and that the data governance model has the potential to be used for government-wide application. The conceptual data governance model is presented as a province-wide model that provides a viable approach for applying data governance at a sub-national level towards realising the value of data as a strategic asset in the public sector. The composition of the data governance model is confined to the boundary of the public sector. In line with this, a key finding is that the data governance model accounts for eight units with distinct relationships and propositions to each other. Twelve core elements have been defined that emanate from an integrated index of 25 concepts and/or practices established as a list of desired core elements for a viable data governance model.
Data Governance in the Public Sector: A Data Governance Model for the Strategic Use of Data at the Sub-National Level
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Seventeen core themes defined the data governance mechanisms towards establishing, optimising and having a desired state of data governance practices.
The thesis makes recommendations detailing the policy implications for data governance and data for development. It broadly highlights data initiatives and research opportunities for public sector data governance and, more specifically, to sustain the data governance model for institutionalisation.