A Governance-Driven Data Quality Engineering Framework for Enterprise Data Warehouses

8 Aug
Posted on Author

Authors: Caroline Morris, Brian Edwards, Eric Nelson, Rachel King, Chaitanya Srinivas, Aneesha Raj

Abstract: Data quality has become a strategic priority for organizations that rely on enterprise data warehouses to support business intelligence, advanced analytics, regulatory compliance, and data-driven decision-making. As enterprise ecosystems continue to expand across cloud platforms, distributed databases, and heterogeneous data sources, maintaining consistent, accurate, complete, and trustworthy data presents significant challenges. This paper proposes a governance-driven data quality engineering framework for enterprise data warehouses that integrates data governance principles with automated data profiling, validation, cleansing, metadata management, master data management, and continuous quality monitoring. The proposed framework establishes standardized policies, quality metrics, data stewardship responsibilities, and validation mechanisms to ensure the reliability and integrity of enterprise data throughout its lifecycle. It incorporates scalable engineering practices that support data integration, schema evolution, lineage tracking, anomaly detection, and compliance with organizational and regulatory requirements. Furthermore, the framework leverages artificial intelligence and machine learning techniques to automate data quality assessment, identify inconsistencies, recommend corrective actions, and improve operational efficiency. By combining governance policies with intelligent quality engineering processes, the framework enhances data consistency, interoperability, analytical accuracy, and enterprise-wide information management while reducing manual intervention and operational risks. The proposed approach provides organizations with a practical and scalable foundation for building resilient, high-quality enterprise data warehouses capable of supporting digital transformation initiatives, real-time analytics, and sustainable business growth.

DOI: http://doi.org/10.5281/zenodo.21850338