Title: A Conceptual Model for Metadata Management and Data Lineage in Data Governance: Ensuring Quality and Consistency
Authors: Naomi Chukwurah, Ekene Cynthia Onukwulu Mercy Odochi Agho Seun Akinfolarin
Volume: 9
Issue: 3
Pages: 201-217
Publication Date: 2025/03/28
Abstract:
In the rapidly evolving data-driven landscape, organizations are increasingly recognizing the critical role of metadata management and data lineage in ensuring data quality and consistency. This abstract introduces a conceptual model designed to enhance data governance through effective metadata management and data lineage tracking. The model integrates key elements of data governance, including metadata repositories, data lineage tools, and governance frameworks, to provide a comprehensive approach to managing data assets. By centralizing metadata and automating data lineage tracking, the model ensures that data is accurate, reliable, and consistently used across the organization. The proposed model emphasizes the importance of metadata as the foundation for understanding, managing, and governing data assets. It incorporates automated tools that capture and visualize data lineage, enabling organizations to trace the flow of data from source to destination. This capability is crucial for identifying and mitigating data quality issues, ensuring compliance with regulatory requirements, and supporting decision-making processes. Furthermore, the model advocates for the integration of metadata management and data lineage with existing data governance frameworks, facilitating the alignment of data management practices with organizational goals and policies. By doing so, organizations can achieve a higher level of data integrity, transparency, and accountability. The conceptual model also addresses the challenges of scalability and complexity in modern data environments. It proposes a flexible architecture that can be adapted to different organizational needs and data landscapes, ensuring that the model remains relevant and effective as data volumes grow and technology evolves. In conclusion, this conceptual model for metadata management and data lineage provides a robust framework for enhancing data governance. By ensuring data quality and consistency, the model supports organizations in leveraging their data assets more effectively, driving better business outcomes, and maintaining compliance with data-related regulations.