Data Quality and Integration: The AI-Driven Evolution

Abstract

This article examines the transformative impact of artificial intelligence on data quality and integration practices across modern enterprises. Traditional rule-based approaches to data validation and integration are increasingly insufficient for addressing the complexity, volume, and velocity of contemporary data ecosystems. The emergence of AI-driven techniques—including automated anomaly detection, intelligent data profiling, adaptive schema mapping, and natural language processing for metadata management—represents a paradigm shift in how organizations ensure data integrity and seamless information flow. The article demonstrates how machine learning approaches offer superior adaptability and accuracy compared to conventional methods. Industry case studies across healthcare, finance, and manufacturing illustrate the practical benefits of AI-enhanced data management, including reduced integration times, improved quality metrics, and enhanced decision support capabilities. The article identifies key challenges in semantic consistency, scalability across heterogeneous environments, and ethical governance of increasingly autonomous data systems. Looking forward, the potential for self-healing data frameworks and federated approaches to cross-organizational quality management suggests a future where data infrastructure becomes not merely a passive repository but an intelligent, adaptive foundation for organizational knowledge and decision-making.