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Abstract: The research provides a holistic view of data governance, developed a conceptual framework for data governance, synthesized literature and provided research agenda on data governance.
Introduction: Data governance is the exercise of authority over the management of data. Its main aim in an organization is for maximizing the value of data assets and managing of data-related risks. The advent of regulatory laws (such as GDPR) is forcing organizations to pay close attention to what data is stored, and where and how these data are being used. These organizations are also being made to resolve the challenges of incomplete or inaccurate data.
Definition: According to the authors’ Data governance specifies a cross-functional framework for managing data as a strategic enterprise asset. Therefore, data governance specifies decision rights and accountabilities for an organization’s decision-making about its data. Also, data governance formalizes data policies, standards, and procedures and monitors compliance.
The above definition is divided into six (6) parts;
1. Data governance is a cross-functional effort, meaning it allows for collaboration across functional boundaries and the data subject areas.
2. Data governance is a framework that provides structure and formalization for the management of data.
3. Data governance focuses on data as a strategic enterprise asset.
4. Data governance specifies decision rights and accountabilities for an organisation’s decision-making about its data. That is, determines the decisions to be made about data, how these decisions are made and who makes these decisions on behalf of the organisation.
5. Data governance helps develop data policies, standards and procedures
6. Data governance monitors compliance in the organisation.
Method: The method used is a structured literature review of publications between 2001 – 2019 (145 publications).
The conceptual framework developed in the research on data governance encompasses six (6) dimensions:
1. Governance Mechanism: comprises formal structures connecting business, IT, and data management functions, formal processes and procedures for decision-making and monitoring, and practices supporting the active participation of and collaboration among stakeholders.
2. Organisational Scope: represents the expansiveness of data governance and it corresponds to the unit of analysis. It can be classified into two groups, intra-organisational and inter-organisational.
3. Data Scope: data is the representation of facts in various forms such as text, numbers, images sound or video; thus, all data governance programs must specify which type of data it is focusing on. Data can thus be categorised into two – traditional data and big data.
4. Domain Scope: is identified based on data decision domains. Further analysis classified it into (a) data quality (b) data security (c) data architecture (d) data life cycle (e) metadata (f) data storage and infrastructure.
5. Antecedents: these are the external and internal factors that precede or predict the adoption of data governance, they have an impact on the implementation and level of adoption of data governance.
6. Consequences: is the outcome of data governance in an organisation, there are two (2) types – intermediate performance effect and risk management.
Research agenda & outlook: The five research agendas identified for future research on data governance are;
1. Governance mechanisms
2. Scope of data governance
3. Antecedents of data governance
4. Consequences of data governance
5. Generalizability and replicability of findings
Results: It identified major building blocks of data governance in six (6) dimensions. It also identified five (5) research areas and provided fifteen (15) research questions. It designed a conceptual framework with an overview of antecedents, scoping parameters & governance mechanisms.
Future research:
1. Conduct expert interviews or case studies to ascertain which data governance concepts are applied in practice.
2. Conduct a quantitative study to identify the correlations between antecedents, the scoping parameters and data governance mechanisms.
3. Call for more research on data governance.
References:
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