RAG-Driven Data Quality Governance for Enterprise ERP Systems

TL;DR

This paper introduces an AI-driven data quality governance system for enterprise ERP, combining automated cleaning and GPT-4-powered SQL query generation to improve accuracy, speed, and cost-efficiency in managing large employee datasets.

Contribution

It presents a novel end-to-end pipeline integrating multi-stage data cleaning with retrieval-augmented generation for natural language querying in multiple languages.

Findings

92.5% query validity achieved

Query turnaround time reduced from 2.3 days to under 5 seconds

System maintains 99.2% uptime and reduces costs by 68% compared to GPT-3.5

Abstract

Enterprise ERP systems managing hundreds of thousands of employee records face critical data quality challenges when human resources departments perform decentralized manual entry across multiple languages. We present an end-to-end pipeline combining automated data cleaning with LLM-driven SQL query generation, deployed on a production system managing 240,000 employee records over six months. The system operates in two integrated stages: a multi-stage cleaning pipeline that performs translation normalization, spelling correction, and entity deduplication during periodic synchronization from Microsoft SQL Server to PostgreSQL; and a retrieval-augmented generation framework powered by GPT-4o that translates natural-language questions in Turkish, Russian, and English into validated SQL queries. The query engine employs LangChain orchestration, FAISS vector similarity search, and few-shot learning with 500+ validated examples. Our evaluation demonstrates 92.5% query validity, 95.1% schema compliance, and 90.7\% semantic accuracy on 2,847 production queries. The system reduces query turnaround time from 2.3 days to under 5 seconds while maintaining 99.2% uptime, with GPT-4o achieving 46% lower latency and 68% cost reduction versus GPT-3.5. This modular architecture provides a reproducible framework for AI-native enterprise data governance, demonstrating real-world viability at enterprise scale with 4.3/5.0 user satisfaction.