AI-Augmented Data Modeling: Enhancing Star Schema Design for Modern Analytics

Abstract

 The star schema remains a foundational dimensional modeling approach in business intelligence, valued for its simplicity, performance, and compatibility with OLAP queries. However, manual schema design is labor-intensive and error-prone in large-scale or rapidly evolving data environments. This study investigates the application of Artificial Intelligence (AI), particularly large language models (LLMs), in automating and optimizing star schema generation. Models such as OpenAI’s GPT-4, Google Gemini, and Meta’s LLaMA 3 were evaluated for their ability to infer schema structures, enforce relational integrity, and enhance semantic alignment. Experimental results demonstrated that AI-assisted modeling can reduce development time by over 80%, while increasing accuracy and consistency. These findings highlight the growing potential of AI in streamlining enterprise data modeling processes.