Is GPT-4 a Good Data Analyst?

TL;DR

This study evaluates GPT-4's effectiveness as a data analyst by comparing its performance to human analysts across various data tasks, using a systematic framework and specific metrics.

Contribution

The paper introduces a comprehensive framework and evaluation metrics for assessing GPT-4's data analysis capabilities compared to professional humans.

Findings

GPT-4 achieves performance comparable to human data analysts.

The study provides insights into GPT-4's strengths and limitations in data analysis.

Results suggest potential for GPT-4 to assist or augment data analysis tasks.

Abstract

As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by artificial intelligence (AI). This controversial topic has drawn great attention in public. However, we are still at a stage of divergent opinions without any definitive conclusion. Motivated by this, we raise the research question of "is GPT-4 a good data analyst?" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. We propose a framework to tackle the problems by carefully designing the prompts for GPT-4 to conduct experiments. We also design several task-specific evaluation metrics to systematically compare the performance between several professional human data analysts and GPT-4. Experimental results show that GPT-4 can achieve comparable performance to humans. We also provide in-depth discussions about our results to shed light on further studies before reaching the conclusion that GPT-4 can replace data analysts.