Practical Application of a Metric‑Based Management Cockpit in Kuaishou E‑commerce Data Operations

The presentation introduces the concept of a management cockpit—a data‑driven dashboard used to monitor business processes, transform data into actionable products, and uncover new opportunities—within Kuaishou's e‑commerce data operations team.

It is organized around three main topics: (1) the type of data‑content application products, (2) the construction of the management cockpit product, and (3) common problems and solutions for cockpit implementations.

Data‑content applications aim to combine data deeply with business actions, focusing on business‑impacting metrics to provide clear insight and improvement levers. Two primary purposes of indicators are to visualize business health and to supply data‑driven improvement handles for decision‑making.

Designing a management cockpit starts with defining analysis themes and building a corresponding metric system. The system enables monitoring of business funnels, rapid identification of issues, and direct influence on business processes. It can evolve into data products such as strategic dashboards, real‑time screens, or operational tools.

The article distinguishes descriptive (analysis) scenarios—like traditional dashboards, real‑time screens, and the management cockpit—from operational scenarios where users can directly adjust strategies (e.g., user profiling, recommendation systems, matching mechanisms).

Key characteristics of an effective cockpit are comprehensiveness, directionality, systematic structure, intuitiveness, and effectiveness, analogous to a vehicle’s central control panel that integrates internal and external data.

Product construction emphasizes a closed‑loop between data observation and data usage, continuous enrichment of data content, and alignment with business evolution. Goal management follows a “define‑decompose‑track‑inspect” process, ensuring objectives are set, broken down, tracked, and warned when unmet.

Metric system design can adopt the OSM (Objective‑Strategy‑Metric) framework, using a north‑star metric as the core goal, deriving strategy indicators that directly affect the goal, and then defining monitoring indicators for each execution scenario.

Visualization and interaction design must highlight critical data and conclusions, keep content concise, automate textual insights, align layout with analytical flow, and provide clear annotations to enhance readability.

Stability assurance includes rigorous code reviews, multi‑layer content validation, functional testing, and consistent metric definitions across products. Timeliness is ensured through real‑time monitoring of computation pipelines and fallback mechanisms.

Iterative directions focus on enriching data content, deepening diagnostic granularity, and scaling successful patterns from core use cases to broader scenarios, while maintaining data accuracy through multi‑layer governance and automated monitoring.

The session concludes with a Q&A on evaluating cockpit quality, business value, iteration priorities, and preventing decision‑making difficulties caused by poor data presentation.