Design of University Data Governance Process System Under the Big Data Environment
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Data governance
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Data governance is growing from a nice-to-have approach to becoming mandatory in all types of organisations. However, there is a lack of research addressing the implementation of formal data governance in the public sector. This research aims to review the state of the art in research on government data governance frameworks. Firstly, we reviewed the existing frameworks through a systematic literature review to identify, analyse, and assess the published and peer-reviewed data governance frameworks, with a particular interest in the public sector. The main purpose is to find the key aspects that have been researched and identify the shortcomings of the existing frameworks. Government data governance has set itself apart from other data governance frameworks that depend on corporate firms' perspectives to generate business value. The creation of public value is the primary driver of government data governance, which has different organisational scopes and governance purposes. As a result of functional differences, reconfiguring the existing frameworks is fundamental for governments. This reconfiguration created new dimensions referred to as organisational scope and data scope, in addition to the structural, procedural, and relational governance mechanisms.
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There has been a great deal of confusion around the term information governance (IG), and how it is distinct from other similar industry terms such as information technology (IT) governance and data governance. This chapter discusses the differences and includes examples in hopes of clarifying what the meaning of each is, and how they are related. It delves into more detailed definitions and a comparison of the three. Data governance is a newer, hybrid quality control discipline that includes elements of data quality, data management, IG policy development, business process improvement, and compliance and risk management. Data governance can be seen as part of IT governance, which is also a part of a broader program of information governance. There are several IT governance frameworks that can be used as a guide to implementing an IT governance program. IT governance seeks to align business objectives with IT strategy to deliver business value.
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Data Governance and Data Management need to work hand-in-hand. Data Governance provides the oversight, measurement, and communication while Data Management provides the tactical operations to achieve desired outcomes. It is important to understand how Data Governance and Data Management align in support of larger business goals. It is important to understand how Data Governance and Data Management align in support of larger business goals. A great first step to gaining this understanding is to examine an overall framework of a program and its components parts. They can be broken down into Data Governance, Data Management, Data Stewardship, Business Drivers, Solutions, and Methods. The SAS Data Management Framework breaks down each of these components. Data architecture policies include statements about data models, data movement, data sharing, data integration, data standards, ETL standards, data access, and service level agreements. Data life cycle policies will pertain to the management of data from its creation to its eventual destruction.
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More and more data is becoming available and is being combined which results in a need for data governance - the exercise of authority, control, and shared decision making over the management of data assets. Data governance provides organizations with the ability to ensure that data and information are managed appropriately, providing the right people with the right information at the right time. Despite its importance for achieving data quality, data governance has received scant attention by the scientific community. Research has focused on data governance structures and there has been only limited attention given to the underlying principles. This paper fills this gap and advances the knowledge base of data governance through a systematic review of literature and derives four principles for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance strategy and approach.
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