Authors: Henry C. Ward, Associate Professor Samantha N. Price, Professor Patrick J. King, Sarah T. Morris, Chaitanya Srinivas, Sai Nishil
Abstract: Data quality has become a critical success factor for enterprise data warehouses as organizations increasingly rely on data-driven decision-making, business intelligence, and advanced analytics to achieve strategic objectives. The effectiveness of analytical systems depends on the accuracy, completeness, consistency, timeliness, and reliability of the underlying data. This research pre-sents a comprehensive data quality engineering framework for enterprise data warehouses that emphasizes strategic approaches to data profiling, validation, and governance throughout the data lifecycle. The study examines engineering methodologies for identifying data anomalies, detecting inconsistencies, validating business rules, and implementing automated quality assurance mecha-nisms within enterprise data integration processes. It further explores the role of metadata man-agement, master data management, Extract-Transform-Load (ETL) pipelines, and continuous monitoring in maintaining high-quality data across heterogeneous enterprise environments. The proposed framework integrates data governance principles with scalable engineering practices to improve regulatory compliance, operational efficiency, and analytical accuracy while reducing risks associated with poor-quality information. In addition, the research discusses the adoption of cloud-based data warehousing, artificial intelligence, machine learning, and real-time validation technologies to strengthen enterprise data quality management in modern digital ecosystems. By establishing standardized profiling techniques, validation frameworks, and governance policies, organizations can enhance data integrity, optimize business intelligence performance, and support reliable decision-making across large-scale enterprise information systems. The findings demon-strate that strategic data quality engineering serves as a foundational element for sustainable enter-prise data management, enabling organizations to maximize the value of their data assets while supporting digital transformation, organizational agility, and long-term business competitiveness.
