Construction and Application of a Metric System: Business, Technical, and Product Perspectives

Background

A metric system is a structured collection of dimensions and indicators that supports business goal implementation, decision making, and stability monitoring.

Business Perspective Pain Points

Trustworthiness: inconsistent business definitions lead to unreliable data.

Understandability: varied terminology causes communication overhead.

Measurability: indicators must accurately reflect business changes.

Traceability: evolving business definitions make historical tracking difficult.

Technical Perspective Pain Points

Data stability: timely and stable data production.

Data quality: accuracy, completeness, and consistency.

Product Perspective Pain Points

Accessibility: ease of data access.

Compliance: secure and compliant usage, preventing data leaks.

Metric System Construction

Three main solutions are proposed: solving business pain points by selecting core business metrics and standardizing the construction process; solving technical pain points by normalizing data pipelines, handling ETL performance, and monitoring data quality; solving product pain points by productizing metadata and providing data as services.

Metric Design

Core business goals are defined, business processes are mapped, departmental objectives are broken down, and specific measurement indicators are derived.

Metric Standardization

Standardized processes include data demand review (involving business, analysis, and data‑warehouse teams) and data‑development standardization (defining dimensions, atomic metrics, and DWD layer structures).

Metadata Management

Manage the metric construction workflow (SOP → productization).

Manage data models (physical and logical).

Manage metric and dimension metadata (business domain, definitions, lineage).

Package data as services and track usage for stability and ROI.

Application and Future Development

Metrics are applied in dashboards, reports, and data products; emerging scenarios include natural‑language data retrieval via AI models, diagnostic attribution, and intelligent operations. Future directions focus on improving NL query accuracy, automated attribution using statistical methods, and secure, customizable AI‑driven operational support.