Practical Application of a Metric System Management Dashboard in Kuaishou E‑commerce Data Operations

The sharing session, titled "Metric System Management Dashboard Scenario Application Practice," introduced how Kuaishou's e‑commerce data operations team applies a management cockpit to visualize business, monitor data, and provide actionable insights for business improvement.

The talk was organized around three main points: the type of data‑content application products, the construction of the management cockpit product, and common cockpit issues with their solutions.

It explained the business‑driven logic of data content, emphasizing that indicators serve two purposes: clarifying business status and offering data levers for improvement, thereby supporting decision‑making.

Designing an indicator system starts with defining analysis themes, then building a metric hierarchy that can monitor business funnels and pinpoint problems for direct remediation.

Data content can evolve into data products such as management cockpits for business analysis, marketing activities, or supply‑side operations, enabling new business opportunities through user and product profiling.

The presentation distinguished between business analysis (strategic, long‑term) and operation analysis (tactical, process‑level), highlighting that management cockpits are primarily used for the former.

Key characteristics of an effective management cockpit were identified as comprehensiveness, directionality, systematic structure, intuitiveness, and effectiveness, akin to a vehicle's central control panel.

Product construction emphasizes a continuous lifecycle: aligning data richness and insight depth, establishing goal management (set‑track‑trace‑inspect), and using the OSM framework to decompose core metrics into strategy and execution layers.

Visualization and interaction design focus on highlighting critical data, simplifying content, automating rule‑based textual conclusions, and ensuring the layout mirrors analytical workflows for seamless presentation.

Stability assurance requires strict data accuracy checks, multi‑layer validation (code review, content verification, functional testing), consistent metric definitions across products, real‑time monitoring, and rapid response mechanisms for latency or anomalies.

Iterative directions include enhancing data richness, deepening diagnostic granularity, scaling successful patterns from core use‑cases to broader scenarios, and maintaining governance across data warehouse, content, and application layers.

Common Q&A covered how to evaluate cockpit quality (coverage of user/management concerns), measure business value (speed of issue identification), guide product iteration (horizontal‑vertical expansion), avoid decision‑making difficulties (effective indicator and visualization design), and ensure content accuracy through multi‑layer governance and online monitoring.

Finally, the speaker thanked the audience for their attention.