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This paper addresses the shortcomings of generic retrieval-augmented generation (RAG) systems in enterprise data analytics by introducing a two-layer solution that significantly improves asset discovery in a commercial advertising data warehouse. By implementing a three-tier knowledge base and a Graph-Guided Retriever (GGR) that leverages a 2,859-node knowledge graph, the authors achieve a remarkable increase in retrieval accuracy, with Hit@10 rising from 19.1% to 96.6%. Additionally, the Scene-Aware Ranker (SAR) enhances knowledge coverage and retrieval efficiency, demonstrating the effectiveness of structured knowledge infrastructures in overcoming common data asset retrieval challenges.
A two-layer knowledge infrastructure boosts retrieval accuracy from 19.1% to 96.6%, transforming enterprise data analytics effectiveness.
Enterprise data analytics agents face two structural failures: generic RAG retrieves the wrong asset (Hit@10=19.1%) and delivers no usage knowledge to prevent metric misinterpretation---stemming from four root causes (C1--C4) ranging from semantic gap and entity ambiguity to schema drift and asset-usage gap. We present a two-layer solution deployed in the commercial advertising data warehouse at Xiaohongshu (5,300+ Hive tables, 14 domains). A three-tier dual-purpose knowledge base (179 documents, eight-section annotation template) serves both retrieval and generation, with a closed-loop refresh pipeline maintaining day-level freshness (one yes/no approval, 30s hot-reload). The Graph-Guided Retriever (GGR) uses a 2,859-node knowledge graph as a candidate gate with intent routing to deliver 71.6x token reduction. The Scene-Aware Ranker (SAR) applies 19-class entity recognition and explicit scenario annotations; negative knowledge alone contributes 25 percentage points of Hit@10 gain. On two 100-question benchmarks, Hit@10 rises from 19.1% to 96.6% (+77.5pp) and knowledge coverage from 56% to 77%, at 4.84--5.33s end-to-end latency.