Using AI to Transform Modern Data Platforms: Bridging the Gap between Data and Business Users

Abstract

The integration of artificial intelligence in modern data platforms has fundamentally transformed how organizations interact with their data assets. This transformation encompasses several key innovations: natural language interfaces that enable direct SQL query generation, AI-powered business catalogs that automate metadata management, and conversational analytics systems that facilitate intuitive data exploration. These advancements have democratized data access across organizational hierarchies, reducing dependency on specialized technical teams while enhancing operational efficiency. The evolution from traditional rule-based systems to sophisticated neural network architectures has enabled more accurate query processing, improved schema mapping, and context-aware interactions. Additionally, the implementation of active metadata management and automated governance frameworks has strengthened data quality and compliance measures. As these technologies continue to mature, organizations face both opportunities and challenges in scaling their AI implementations while maintaining security, privacy, and model explainability.