From PDFs to Structured Data: Utilizing LLM Analysis in Sports Database Management

TL;DR

This paper explores using Large Language Models like GPT-4 to automate converting semi-structured PDF data into structured formats, demonstrating high accuracy and efficiency in sports database management.

Contribution

It introduces an AI-assisted method utilizing LLMs for processing semi-structured data, showing practical application in sports data management with high success rates.

Findings

90% success rate in automated data processing

Handled over 7,900 data rows from 72 reports

Potential to reduce processing time by 90%

Abstract

This study investigates the effectiveness of Large Language Models (LLMs) in processing semi-structured data from PDF documents into structured formats, specifically examining their application in updating the Finnish Sports Clubs Database. Through action research methodology, we developed and evaluated an AI-assisted approach utilizing OpenAI's GPT-4 and Anthropic's Claude 3 Opus models to process data from 72 sports federation membership reports. The system achieved a 90% success rate in automated processing, successfully handling 65 of 72 files without errors and converting over 7,900 rows of data. While the initial development time was comparable to traditional manual processing (three months), the implemented system shows potential for reducing future processing time by approximately 90%. Key challenges included handling multilingual content, processing multi-page datasets, and managing extraneous information. The findings suggest that while LLMs demonstrate significant potential for automating semi-structured data processing tasks, optimal results are achieved through a hybrid approach combining AI automation with selective human oversight. This research contributes to the growing body of literature on practical LLM applications in organizational data management and provides insights into the transformation of traditional data processing workflows.