Building and Managing an Indicator System in a Data Warehouse: Practices from the Dongchedi Business
This article explains how the Dongchedi team designed, implemented, and monitored a comprehensive indicator system within a petabyte‑scale data warehouse, covering standards, metadata management, model construction, quality monitoring, and diverse application scenarios to improve data reliability and business insight.
The indicator system is crucial for data analysis and application; this article shares how the Dongchedi business built and operationalized such a system from a data‑warehouse development perspective.
It outlines six discussion points: establishing indicator standards, convergence of indicator model construction, quality monitoring strategies, full‑stack application scenarios, metadata management standards, and future outlook.
Dongchedi is a one‑stop automotive information platform with massive data volume (hundreds of PB) and diverse real‑time and offline workloads, requiring a robust data‑warehouse to serve both C‑end and B‑end scenarios.
The DataLeap platform serves as a unified metadata hub, providing standardized naming, modeling, and service capabilities, and supports downstream BI tools such as the Indicator Observation Platform.
The indicator‑system framework consists of three layers: basic capability construction (metadata standards, model mounting, lineage, service quality), indicator service capability (routing, fault‑tolerance), and application capability (visual query, BI platforms, commercial services).
Metadata standards cover naming conventions, business definitions, hierarchical levels (primary to fourth‑level), and catalog management, ensuring consistency and preventing ambiguity.
Model construction follows a layered approach (detail/DWD, light aggregation/DWA, data‑mart/DM) to address different business stability and real‑time requirements, with guidelines to avoid duplication, inconsistent granularity, and maintenance overhead.
Quality monitoring includes three parts: indicator‑system specification monitoring, query‑service monitoring (consistency, slow queries, SLA, data drift), and governance monitoring, supported by a visual monitoring dashboard.
Comprehensive application scenarios range from internal management dashboards to self‑service BI platforms and commercial operation tools, all leveraging the unified indicator definitions and services.
Future plans involve integrating Balanced Scorecard (BSC) for strategic alignment, expanding unified data‑service layers beyond indicators, and exploring large‑model‑driven natural‑language data interaction.
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