Building an LLM‑Driven Metric Platform for Data Democratization

The article introduces the practice of building a metric platform in the era of large models, aiming to achieve data democratization—enabling non‑technical users such as frontline staff and managers to access and use data effectively.

1. LLM opens the data democratization era – LLM agents can automate data request workflows, reducing the long hand‑off between business users, analysts, product managers, and engineers, and allowing conversational data retrieval.

2. Paths to data democratization – Two common implementations are NL2SQL (natural language to SQL) and NL2API (natural language to data‑analysis API). Both suffer from hallucination, requiring a semantic layer to annotate data semantics for accurate query translation.

The semantic layer can be built inside the warehouse (internal semantics) or outside (external semantics). Internal semantics embed annotations in ODS/DWD/ADS layers, while external semantics create a separate model layer, often realized as an indicator platform.

3. Internal semantics – Placing the semantic layer within the warehouse enables NL2SQL pipelines but faces challenges such as lower accuracy (60‑70%), high cost, performance variability, limited handling of custom operators/UDFs, and data‑security gaps.

4. External semantics – Moving the semantic layer out of the warehouse creates a model‑driven indicator platform (NL2Semantic2API). This approach improves accuracy (>95% with SwiftAgent), reduces training cost, enhances performance via API‑level acceleration, ensures fine‑grained RBAC security, and supports custom operators.

5. Innovation points of the indicator platform – The SwiftMetrics platform provides unified business semantics, accelerated computation via a Hyper Metrics Engine (HME), multi‑source heterogeneous data support, interactive dialogue QA, and continuous self‑learning from usage history.

HME accelerates queries by pre‑computing aggregates, automatic partition pruning, and caching, achieving up to 100× speedup (e.g., reducing a 30‑second query to 0.3 seconds).

6. Case studies – In a leading tea‑chain, SwiftAgent enables managers and store staff to obtain real‑time metrics via WeChat dialogue, cutting decision latency. In a major city bank, the platform reduced daily data‑request tickets by ~50%, lowered manual costs, and improved data‑asset utilization.

7. Future outlook – The vision is to let LLM agents orchestrate multiple business systems end‑to‑end, allowing operational staff to issue high‑level natural‑language goals (e.g., “find male customers with holdings > 1 M and > 10 trades in the last 30 days”) and have the system automatically retrieve, analyze, and trigger actions.

Overall, the platform demonstrates how LLM‑driven AI agents, combined with a robust semantic layer and accelerated query engine, can realize true data democratization across enterprises.