Data governance
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Data Governance: A Comprehensive Overview
Introduction to Data Governance
Data governance refers to the exercise of authority and control over the management of data, aiming to increase its value while minimizing associated costs and risks. Despite its growing importance, data governance remains underdeveloped and under-researched, necessitating further exploration to deepen both academic understanding and practical application .
Challenges in Data Governance
Complexity and Cost
Data management solutions are becoming increasingly expensive and unable to cope with the complexity of modern data environments. Businesses have evolved in their data usage, driving new demands that require innovative governance strategies. The rise of Big, Open, and Linked Data (BOLD) further complicates data governance, as these systems must manage varied, dynamic, and real-time data streams.
Organizational and Regulatory Issues
Historically, data governance efforts have been fragmented and driven by IT departments, leading to rigid processes and a lack of organizational support. This has resulted in informal governance practices with ambiguous regulations, often siloed within specific enterprise repositories. Additionally, the regulatory and ethical requirements for data governance, especially in systems involving artificial intelligence, add another layer of complexity.
Frameworks and Models
Conceptual Frameworks
A holistic view of data governance is essential for guiding both practitioners and researchers. A structured literature review identified six major dimensions of data governance, providing a conceptual framework that includes antecedents, scoping parameters, and governance mechanisms. This framework helps organizations approach data governance in a structured manner, addressing both theoretical and practical aspects.
Government Data Governance
A new framework for government data governance, based on the concept of a data middle platform, has been developed to meet the digital service needs of governments. This framework adapts to organizational operations and data, improves data auditing and oversight, and facilitates communication, thereby realizing the value of data assets.
Emerging Models
Four emerging models of data governance have been identified: data sharing pools, data cooperatives, public data trusts, and personal data sovereignty. These models highlight the roles of various stakeholders, including small businesses, public bodies, and civic society, in democratizing data governance and redistributing the value produced through data.
Data Governance Activities
Defining, Implementing, and Monitoring
A comprehensive analysis of data governance activities identified 120 distinct actions across various decision domains. The analysis revealed that practice-oriented publications focus more on implementing and monitoring activities, while scientific publications emphasize defining activities . This discrepancy highlights the need for a balanced approach that incorporates both theoretical definitions and practical implementations.
Collective Action Problem
Governing data as an organizational asset presents a collective action problem, particularly in local government organizations. Challenges such as value, collaboration, capabilities, overview, practices, and politics must be addressed to develop viable data governance solutions.
Technological Approaches
Ontological-Based Models
An ontological-based model for data governance has been proposed to reduce the complexity of managing big data environments. This model uses semantic techniques and automatic ontology-based reasoning to control decision-making and responsibilities for data management processes. A prototype implemented for Telefonica's global video service demonstrated the feasibility of this approach.
Conclusion
Data governance is a critical yet complex field that requires a structured and holistic approach. By understanding the challenges, frameworks, and activities involved, organizations can develop effective data governance strategies that enhance data value while minimizing risks. Emerging models and technological advancements offer promising solutions to the evolving demands of data governance, paving the way for more robust and democratic data management practices.
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