Designing Data Governance Structure: An Organizational Perspective (original) (raw)
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One Size Does Not Fit All---A Contingency Approach to Data Governance
Enterprizes need Data Quality Management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have mostly assigned accountabilities for DQM to Information Technology (IT) departments. They have thereby neglected the organizational issues critical to successful DQM. With data governance, however, companies may implement corporate-wide accountabilities for DQM that encompass professionals from business and IT departments. This research aims at starting a scientific discussion on data governance by transferring concepts from IT governance and organizational theory to the previously largely ignored field of data governance. The article presents the first results of a community action research project on data governance comprising six international companies from various industries. It outlines a data governance model that consists of three components (data quality roles, decision areas, and responsibilities), which together form a responsibility assignment matrix. The data governance model documents data quality roles and their type of interaction with DQM activities. In addition, the article describes a data governance contingency model and demonstrates the influence of performance strategy, diversification breadth, organization structure, competitive strategy, degree of process harmonization, degree of market regulation, and decision-making style on data governance. Based on these findings, companies can structure their specific data governance model.
Lecture Notes in Computer Science, 2016
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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Both information systems (IS) researchers and practitioners consider data governance as a promising approach for companies to improve and maintain the quality of corporate data, which is seen as critical for being able to meet strategic business requirements, such as compliance or integrated customer management. Both sides agree that data governance primarily is a matter of organisation. However, hardly any scientific results have been produced so far indicating what actually has to be organised by data governance, and what data governance may look like. The paper aims at closing this gap by developing a morphology of data governance organisation on the basis of a comprehensive analysis of the state of the art both in science and in practice. Epistemologically, the morphology represents an analytic theory, as it serves for structuring the research topic of data governance, which is still quite unexplored. Six mini case studies are used to evaluate the morphology by means of empirical data. Providing a foundation for further research, the morphology contributes to the advancement of the scientific body of knowledge. At the same time, it is beneficial to practitioners, as companies may use it as a guideline when organising data governance.
Data Governance as Success Factor for Data Science
Lecture Notes in Computer Science, 2020
More and more, asset management organizations are introducing data science initiatives to support predictive maintenance and anomaly detection. Asset management organizations are by nature data intensive to manage their assets like bridges, dykes, railways and roads. For this, they often implement data lakes using a variety of architectures and technologies to store big data and facilitate data science initiatives. However, the decision-outcomes of data science models are often highly reliant on the quality of the data. The data in the data lake therefore has to be of sufficient quality to develop trust by decision-makers. Not surprisingly, organizations are increasingly adopting data governance as a means to ensure that the quality of data entering the data lake is and remains of sufficient quality, and to ensure the organization remains legally compliant. The objective of the case study is to understand the role of data governance as success factor for data science. For this, a case study regarding the governance of data in a data lake in the asset management domain is analyzed to test three propositions contributing to the success of using data science. The results show that unambiguous ownership of the data, monitoring the quality of the data entering the data lake, and a controlled overview of standard and specific compliance requirements are important factors for maintaining data quality and compliance and building trust in data science products.
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Lecture Notes in Computer Science
Data is quite popularly considered to be the new oil since it has become a valuable commodity. This has resulted in many entities and businesses that hoard data with the aim of exploiting it. Yet, the 'simple' exploitation of data results in entities who are not obtaining the highest benefits from the data, which as yet is not considered to be a fully-fledged enterprise asset. Such data can exist in a duplicated, fragmented, and isolated form, and the sheer volume of available data further complicates the situation. Issues such as the latter highlight the need for value-based data governance, where the management of data assets is based on the quantification of the data value. This paper has the purpose of creating awareness and further understanding of challenges that result in untapped data value. We identify niches in related work, and through our experience with businesses who use data assets, we here analyse four main context-independent challenges that hinder entities from achieving the full benefits of using their data. This will aid in the advancement of the field of value-driven data governance and therefore directly affect data asset exploitation.