Headless BI

In depth

Traditional BI bundles data modelling, metrics, and charts in one stack. This slows teams down because every dashboard rebuilds the logic and every app keeps its own copy of the truth. Headless BI separates these elements–definitions live in a central metric layer and are retrieved for charts, apps, and workflows using an API.

A headless setup has a few core pieces: A governed metric catalog with clear names and calculations, a semantic model that maps business terms to data, and a query service that exposes metrics through REST, GraphQL, SQL, or a metrics‑aware language. Features, like roles, certification, and version history ensure controlled access to trustworthy data.

Front ends request metrics, not tables. For example, if you ask for “Revenue” grouped by month and region with a filter for product line, the metric layer resolves the definition, applies time logic and dimensional joins, and returns consistent results.

Evolving standards and trends. With Model Context Protocol (MCP), a metric layer can register as a server that lists metrics and runs queries on request. Rich knowledge graphs capture lineage, ownership, and relationships so AI assistants and humans can ask better questions while staying within certified definitions.

Pro tip

Treat metric definitions like code. Include version control, naming conventions, tests for edge cases, and a review process. Ensure consistency with aligned time granularity settings.

Why Headless BI matters

• One source of truth: Everyone references the same definitions, which reduces conflicting numbers in meetings.
• Smoother, faster transitions: Change visualization tools without rewriting business logic.
• Easier governance: Certification, roles, and tags help you control who can create, edit, or publish metrics.
• Better embeds: Product teams can expose trusted metrics inside customer‑facing apps with less custom code.

Headless BI - In practice

• Startup building a customer portal: Product exposes usage and billing metrics inside the app by calling the metric API, so customers and internal teams see the same definitions.
• RevOps standardizing KPIs: Publish a catalog for revenue, pipeline, and churn. Sales dashboards, finance views, and executive reports all pull from the same data set.
• Marketing team switching tools: Move from one charting library to another. With headless BI metrics, the charts change, the definitions don’t.

Headless BI and PowerMetrics

PowerMetrics supports headless BI with a governed metric catalog, certification and tagging, access control for users, groups, and roles, and a metrics‑aware query layer (PMQL). Define metrics once, investigate them with the built-in Explorer, set goals and notifications, embed trusted views, and query metrics from external apps. A rich knowledge graph improves discovery and context. As MCP adoption grows, the metric layer can act as an MCP server so assistants and apps can call metrics through governed endpoints.