Architectural Framework and Standardization of LLM-Enhanced Intelligent Data Analysis Systems

In the era of data as a pivotal resource, analyzing and mining data have become critical to enterprises' digital and intelligent transformation. However, with the continuous growth of data types and volumes, traditional Business Intelligence (BI) tools face challenges such as slow processing speeds, inability to handle complex queries, and limitations in multimodal data integration. The rise of Large Language Model (LLM) technology has drawn widespread attention to its potential to empower the field of data analysis. This paper proposes an intelligent data analysis system that integrates LLMs with BI tools, leveraging the capabilities of large models in multimodal data processing, natural language understanding, automated code generation, and intelligent data insights. The system simplifies data analysis workflows and enhances both efficiency and accuracy. From the perspectives of system architecture design, core technological capabilities, standardization requirements, and evaluations of multiple products, this paper aims to explore the current state and future trends of intelligent data analysis systems powered by large language models.