The AI Data Analyst: A Framework for Autonomous Data Analytics, Highlighting LLM and AI Agents

Abstract

This work presents an automated data analytics framework that integrates large language model (LLM) and artificial intelligence (AI) agents to perform end-to-end analysis based on Google's six-step methodology: Ask, Prepare, Process, Analyze, Share, and Act without human involvement throughout the analytics process. The system requires only user input containing the objective, data, context, and prior hypotheses. Then, the system autonomously generates prompts and executes tasks through specialized AI agents. AI agents perform the roles of planning tasks and directing the actions of agents, leveraging LLM for generation of ideas and reasoning to inform their plans and actions. Experiments on two datasets across domains, including education and business, show strong performance. Average analytics scores range from 8.6 to 9.4 out of 10, with average execution times varying between 1.9 and 6.6 min, and error rate varying between 0.2 and 1.2 occurrences per run. Additionally, the system can perform complex tasks such as coding, statistical analysis, machine learning modeling, and visualization generation. The results demonstrate the potential of LLM to function as a virtual data analyst, enabling fully automated, domain-independent analytics.

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