RAG-Driven Data Quality Governance for Enterprise ERP Systems

• URL: http://arxiv.org/abs/2511.16700v1
• Date: Tue, 18 Nov 2025 12:08:44 GMT
• Title: RAG-Driven Data Quality Governance for Enterprise ERP Systems
• Authors: Sedat Bin Vedat, Enes Kutay Yarkan, Meftun Akarsu, Recep Kaan Karaman, Arda Sar, Çağrı Çelikbilek, Savaş Saygılı,
• Abstract summary: We present an end-to-end pipeline combining automated data cleaning with LLM-driven query generation.<n>The system is deployed on a production system managing 240,000 employee records over six months.<n>This modular architecture provides a reproducible framework for AI-native enterprise data governance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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