Metric Store Explained: Centralized Metric Management

A metric store is a centralized system for defining, managing, and serving business metrics across an organization. Rather than metrics being scattered across BI tools, spreadsheets, and code, a metric store provides a single source of truth that all analytics tools consume.

What a Metric Store Does

Centralized Definitions

Every metric is defined once, in one place:

• Calculation formula
• Business rules
• Valid dimensions
• Ownership information

Consistent Serving

All consuming applications query the metric store:

• BI dashboards
• Ad-hoc SQL
• AI assistants
• Embedded analytics
• APIs

Same definition, everywhere.

Governance Integration

The metric store supports governance workflows:

• Certification status tracking
• Change management
• Version history
• Access control

Discovery and Documentation

Users can find and understand metrics:

• Searchable catalog
• Definitions and descriptions
• Usage examples
• Related metrics

How Metric Stores Work

Define

Metric definitions are created in the store:

metric:
  name: Monthly Recurring Revenue
  description: Sum of normalized monthly subscription values
  calculation: |
    SUM(
      CASE
        WHEN billing_period = 'monthly' THEN amount
        WHEN billing_period = 'annual' THEN amount / 12
      END
    )
  filters:
    - subscription_status = 'active'
  dimensions: [customer_segment, product, region]
  owner: finance_team
  certified: true

Query

Applications query metrics through APIs:

GET /metrics/mrr?dimensions=region&filter=time:last_quarter

The metric store translates this into database queries and returns results.

Govern

Changes follow defined processes:

• Proposed changes are reviewed
• Approved changes are versioned
• Users are notified of changes
• History is maintained

Benefits of a Metric Store

Consistency

Everyone uses the same definitions. Revenue on the board deck matches revenue in the CRM dashboard.

Efficiency

Define once, use everywhere. No more recreating the same calculations in multiple tools.

Governance

Centralized definitions are easier to govern than scattered implementations.

AI Readiness

AI systems can query the metric store for explicit definitions, reducing hallucination risk.

Self-Service

Business users can explore governed metrics without understanding underlying data complexity.

Metric Store Architecture

A typical metric store includes:

Definition layer: Where metrics are defined (YAML, code, UI) Computation engine: Translates definitions into database queries Query interface: APIs and integrations for consuming applications Governance layer: Certification, change management, access control Discovery layer: Catalog, search, documentation

Implementing a Metric Store

Option 1: Dedicated Platform

Purpose-built metric store platforms (sometimes called metrics layers or headless BI) provide full functionality out of the box.

Option 2: BI Tool Native

Some BI tools have metric store capabilities (Looker's metrics layer, dbt metrics). These work well within that ecosystem but may not extend to other tools.

Option 3: Build Custom

Building a custom metric store requires significant engineering investment and ongoing maintenance. Generally not recommended unless requirements are highly specialized.

Getting Started

1. Inventory existing metrics: Document where metrics are currently defined
2. Identify pain points: Where does inconsistency cause problems?
3. Prioritize metrics: Start with high-value, high-pain metrics
4. Select platform: Choose a metric store that fits your stack
5. Implement incrementally: Start small, prove value, expand

A metric store is foundational infrastructure for trustworthy analytics - worth the investment for any organization serious about data-driven decisions.