Intelligent Data Analysis: Agent Architecture Combined with Semantic Layer for Product Implementation
This article explores how large‑model technologies can address data analysis challenges by introducing an Agent‑based architecture integrated with a semantic layer, detailing design principles, optimization paths, technical implementation, real‑world retail case studies, product design considerations, and future directions for intelligent analytics.
The presentation begins by outlining the pain points of large‑model adoption in data analysis for management, business, and technical teams, emphasizing the gap between raw data and actionable insight and the difficulty of handling ad‑hoc analytical requests.
It proposes a solution that adds a semantic layer between business terminology and technical data, describing seven core elements for building this layer, and explains why traditional in‑warehouse semantic definitions are insufficient for flexible, user‑driven analysis.
An external (warehouse‑outside) semantic architecture is introduced, allowing business users to define and modify metric semantics without altering ETL pipelines, and enabling large models to translate natural‑language queries into structured JSON that drives semi‑automated SQL generation.
The article contrasts NL2SQL approaches, which suffer from accuracy, performance, and security issues, with the proposed NL2Semantics method that leverages the semantic layer to improve reliability and permission handling.
A detailed retail‑chain case study demonstrates how the semantic layer and Agent architecture empower store managers to obtain insights via natural language, reducing reliance on BI analysts and accelerating decision‑making.
Product design considerations discuss the integration of LUI (low‑code UI) and GUI to lower the interaction barrier for B‑side users, handling ambiguous queries through model‑driven clarification and supporting enterprise‑specific jargon through configurable mappings.
Future outlook envisions data agents evolving from passive listeners to proactive digital employees that automatically generate reports, suggest actions, and integrate conclusions into broader decision‑making workflows.
The Q&A section addresses semantic alignment of colloquial terms, implementation of a clarification mechanism, and decomposition of complex analytical tasks using memory‑augmented large models.
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