Advances in Data Warehousing: Integrating AI for Intelligent Data Mining and Decision Support Systems

Abstract

Artificial Intelligence (AI) is transforming data warehousing by enhancing its design, operation, and analytical capabilities. AI addresses key challenges in data warehousing, including performance, governance, and usability, leading to more intelligent data management1. AI algorithms can analyze usage patterns to suggest effective data models and indexing strategies, which speeds up information retrieval and ensures agile data handling when scaling or integrating new data sources. AI also plays a crucial role in automating data integration, cleaning, and transformation, allowing data engineers to focus on higher-level tasks such as designing data models and creating data visualizations. AI-powered ETL tools automate repetitive tasks, optimize performance, and reduce human error. Furthermore, AI improves the automation of performance tuning and governance through automated tagging, documentation, and natural language search. The integration of AI and machine learning (ML) into data warehousing automates data processing, reduces preparation time, enhances predictive analytics, and enables better prediction of customer behavior and market trends. Generative AI algorithms can analyze existing data structures and recommend optimized schemas, improving the overall architecture of data warehouses. This combination enhances data management processes and reveals deeper analytical capabilities, positioning businesses at the forefront of data-driven decision-making