How effective is an LLM-based Data Analysis Automation Tool? A Case Study with ChatGPT's Data Analyst

Artificial Intelligence (AI) tools are increasingly becoming integral to analytical processes. This paper evaluates the potential of Large Language Models (LLMs), specifically OpenAI's ChatGPT’s Data Analyst, in data analysis. We conducted a structured experiment employing this tool in 36 questions spanning descriptive, diagnostic, predictive, and prescriptive analyses to assess its effectiveness. The study revealed an overall efficiency rate of 86.11%, with robust performance in the descriptive and diagnostic categories but reduced efficacy in the more complex predictive and prescriptive tasks. By discussing the strengths and limitations of a state-of-the-art LLM-based tool in aiding data scientists, this study aims to mark a critical milestone for future developments in the field, particularly as a reference for the open-source community.

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Published
2024-10-14
DOI
https://doi.org/10.5753/sbbd.2024.240841
Citations
2
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.69
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article

How effective is an LLM-based Data Analysis Automation Tool? A Case Study with ChatGPT's Data Analyst

Cláudio E. C. Campelo, Beatriz A. de Miranda
2 citations
Privacy-Preserving Technologies in Data
0.69
article

How effective is an LLM-based Data Analysis Automation Tool? A Case Study with ChatGPT's Data Analyst

Cláudio E. C. Campelo, Beatriz A. de Miranda
article en
2 citations

Abstract

Artificial Intelligence (AI) tools are increasingly becoming integral to analytical processes. This paper evaluates the potential of Large Language Models (LLMs), specifically OpenAI's ChatGPT’s Data Analyst, in data analysis. We conducted a structured experiment employing this tool in 36 questions spanning descriptive, diagnostic, predictive, and prescriptive analyses to assess its effectiveness. The study revealed an overall efficiency rate of 86.11%, with robust performance in the descriptive and diagnostic categories but reduced efficacy in the more complex predictive and prescriptive tasks. By discussing the strengths and limitations of a state-of-the-art LLM-based tool in aiding data scientists, this study aims to mark a critical milestone for future developments in the field, particularly as a reference for the open-source community.

Universidade Federal de Campina Grande (BR)
Openalex Percentile: Top 24%
Privacy-Preserving Technologies in Data
0.69
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