Derived Metrics
Derived metrics are metrics calculated from other base metrics or dimensions rather than directly from raw fact tables, enabling metric composition and reducing calculation redundancy.
A derived metric is built from existing metrics or transforms them. Examples include: profit margin (revenue minus cost, then divided by revenue), growth rate (current period metric minus previous period divided by previous), or blended customer value (sum of customer purchases plus estimated lifetime interactions). Rather than recalculating from source tables, derived metrics reference other metrics and apply transformations.
Derived metrics solve scalability and maintainability challenges. If every analytics question required a new query from raw tables, the number of metrics would explode. By building metrics from metrics, the system is more efficient: changes to foundational metrics (revenue, cost) automatically cascade to derived ones. They also reduce calculation complexity: analysts can think in business terms (profit margin) without managing underlying table joins or aggregation logic.
Derived metrics require a metric platform that supports metric relationships and composition. The system must handle metric dependencies: if a base metric is unavailable or out of date, downstream derived metrics inherit that status. Derived metrics also require careful handling of dimensions: not all dimensions apply to all derived metrics, so the metric system must enforce valid combinations.
Key Characteristics
- ▶Calculated from base metrics via arithmetic or logical operations
- ▶Specify metric dependencies and composition rules
- ▶Inherit freshness from base metrics
- ▶Support conditional logic and dimension filtering
- ▶Reduce custom SQL and calculation redundancy
- ▶Enable metric reuse across multiple derived forms
Why It Matters
- ▶Efficiency: Reuse base metrics rather than re-querying raw tables
- ▶Maintainability: Change base metric once, all derived metrics update
- ▶Simplicity: Express complex analytics as metric compositions
- ▶Consistency: Ensure derived metrics use governed base definitions
- ▶Performance: Cache base metrics, compute derived quickly
Example
Monthly active users (base metric) and monthly paying active users (filtered base metric) combine into paying user percentage (derived): (monthly paying active users / monthly active users) * 100. When the base metric definition changes, the derived metric automatically reflects it.
Coginiti Perspective
SMDL supports derived metrics through the custom aggregation type and hidden measures. A hidden measure can compute an intermediate value (such as total cost) that another measure references to calculate a derived metric (such as cost per unit). These compositions are resolved at query time by the Semantic SQL engine, so derived metrics always reflect current base measure definitions. CoginitiScript blocks can also encapsulate derived metric logic as reusable SQL that feeds into publication pipelines.
More in Semantic Layer & Metrics
Business Logic Layer
A business logic layer is the component of a semantic layer or data system that encodes business rules, calculations, and transformations, making them reusable and enforced across analytics.
Data Abstraction Layer
A data abstraction layer is a software or architectural component that sits between raw data sources and analytics consumers, providing unified access and hiding implementation complexity.
Data Semantics
Data semantics refers to the documented meaning, business context, and valid usage of data elements, including definitions, relationships, constraints, and governance rules.
Dimension
A dimension is a categorical or descriptive attribute used to slice, filter, and organize metrics, such as product, region, customer segment, or date.
Governed Metrics
Governed metrics are business metrics with centrally defined calculations, owners, approval workflows, and enforced standards that ensure consistency and trustworthiness across all analytics consumers.
Hierarchy
A hierarchy is an ordered, multi-level classification of dimension values that enables drill-down navigation and meaningful aggregation across levels, such as day-month-quarter-year or product-category-brand.
See Semantic Intelligence in Action
Coginiti operationalizes business meaning across your entire data estate.