ENSURING DATA QUALITY AND INTEGRITY DURING CLOUD MIGRATIONS WITH AI - Durga Prasad Katasani (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2025)
Cloud migrations are essential for organizations aiming to modernize their data infrastructure, but they often face significant challenges related to data quality and integrity. Poor data quality can lead to operational disruptions, compliance risks, and increased costs across business operations. This article explores how AI-driven solutions can enhance data quality and integrity throughout the migration process. By leveraging AI techniques such as anomaly detection, automated data cleansing, and real-time validation, organizations can achieve seamless, accurate, and reliable data migrations. The article examines various aspects of AI implementation in data quality management, including automated profiling, self-healing pipelines, edge computing considerations, and explainable AI frameworks. Through case studies and practical frameworks, this article demonstrates the transformative impact of AI on data quality management in cloud migrations, while also addressing the challenges and limitations organizations must consider.