Best Practices for Building an International Ride‑Hailing Data Metric System at Didi
This article outlines Didi’s best‑practice approach to constructing a global ride‑hailing data metric system, covering business scenarios, metric categories, pain points such as definition and technical challenges, and a comprehensive solution involving organizational structure, processes, model design, tooling, timezone handling, and governance.
International ride‑hailing business spans five continents and multiple time zones, requiring localized data views.
Didi classifies metrics into decision, process, and observation categories, each supporting strategic, operational, and analytical needs.
Key challenges include metric definition across diverse markets, technical complexities of multi‑timezone data production, management fragmentation, evaluation difficulty, and high assurance costs.
The proposed solution addresses five dimensions: establishing dedicated metric‑production organizations, standardized end‑to‑end processes, layered model architecture (source, fact, core, thematic, application), comprehensive metric‑management tools, and robust governance with tiered accuracy, timeliness, and historical completeness controls.
Special attention is given to timezone handling via a unified SDK, country grouping, and hour‑to‑day conversion, enabling a single data pipeline to serve all regions.
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