A Review of Implementing AI-Powered Data Warehouse Solutions to Optimize Big Data Management and Utilization

This review examines the implementation of AI-powered data warehouse solutions to optimize big data management and utilization, analyzing 25 peer-reviewed articles published over the last decade. As organizations increasingly rely on vast amounts of data for strategic decision-making, traditional data warehousing techniques have struggled to keep pace with the volume, variety, and velocity of modern data. The integration of artificial intelligence (AI) into data warehousing processes has emerged as a critical advancement, enhancing data processing efficiency, accuracy, and scalability. This study synthesizes findings from the literature to highlight key benefits such as automated data extraction, transformation, and loading (ETL) processes, real-time analytics, and improved data quality through advanced cleansing and anomaly detection. Additionally, it identifies significant challenges including data security risks, integration complexities, and the need for specialized skills and substantial investments. The review concludes with recommendations for future research and practical applications, emphasizing the importance of strategic planning and robust security measures to fully leverage AI's potential in revolutionizing data warehousing.