LLM-Augmented Enterprise Search And Knowledge Discovery In Master Data Management Systems

Enterprise organizations increasingly rely on Master Data Management (MDM) systems to maintain consistent, accurate, and authoritative representations of core business entities such as customers, products, suppliers, and locations, forming the backbone of operational, analytical, and regulatory processes. While traditional MDM platforms excel at data governance, entity resolution, stewardship workflows, and lifecycle management, they are largely optimized for structured access patterns and predefined matching rules, which limits their ability to support flexible semantic search, exploratory querying, and cross-domain knowledge discovery over heterogeneous enterprise data landscapes that include structured records, metadata, documents, and contextual signals. Recent advances in Large Language Models (LLMs), particularly when combined with retrieval-augmented architectures, offer a promising pathway to address these limitations by enabling natural-language interaction, semantic reasoning, and context-aware synthesis grounded in authoritative enterprise data. By integrating dense retrieval techniques for semantic matching, generative reasoning for synthesis and explanation, and non-parametric enterprise corpora such as governed master data repositories and knowledge graphs, LLM-augmented enterprise search systems can transform MDM from a primarily administrative capability into an intelligent knowledge access layer. Drawing on foundational research in information retrieval, retrieval-augmented generation (RAG), and enterprise knowledge graphs, this article proposes a reference architecture for LLM-enabled MDM search, examines critical design considerations such as grounding, access control, and auditability, and discusses the broader implications for data quality, governance, trust, and explainability in enterprise environments.