Authors: Madison Carter, Jack Murphy, Liam Peterson, Chaitanya Srinivas, Yashwanth kumar
Abstract: Enterprise organizations increasingly rely on high-quality data to support operational efficiency, business intelligence, regulatory compliance, and artificial intelligence-driven decision-making. However, the growing volume, variety, and velocity of enterprise data generated from heterogeneous sources, cloud-native applications, and distributed systems present significant challenges in maintaining data quality. Traditional rule-based data quality management approaches are often reactive, resource-intensive, and inadequate for identifying emerging quality issues in dynamic environments. This paper proposes an AI-enabled predictive framework for enterprise data quality engineering that integrates machine learning, predictive analytics, metadata management, data governance, and intelligent automation into a unified architecture. The proposed framework employs machine learning algorithms to analyze historical and real-time data quality metrics, detect anomalies, predict potential data quality issues, and recommend proactive remediation strategies before errors propagate across enterprise systems. Metadata-driven governance enhances transparency by maintaining comprehensive information regarding data lineage, ownership, transformation rules, quality indicators, and compliance requirements throughout the data lifecycle. The framework further incorporates automated data profiling, validation, cleansing, standardization, quality scoring, continuous monitoring, and intelligent alert generation to improve data reliability and operational efficiency. Integrated governance and security mechanisms strengthen regulatory compliance, audit readiness, policy enforcement, and enterprise accountability while supporting scalable deployment across hybrid cloud environments. By combining predictive intelligence with automated data quality engineering, the proposed framework significantly improves data accuracy, consistency, completeness, and trustworthiness, reduces manual intervention, accelerates anomaly detection, enhances analytical performance, and supports business intelligence, predictive analytics, and AI-driven decision-making. The framework establishes a scalable, resilient, and future-ready foundation for managing complex enterprise information ecosystems, enabling organizations to achieve sustainable digital transformation, operational excellence, and continuous innovation.
