📚Semantic Model

NEW

Also known as: SDM

A business-centric abstraction that defines entities, metrics, and relationships independent of physical storage and implementation details.

DESCRIPTION

A semantic model captures business meaning—concepts, measures, and relationships—separate from technical schemas. It supports consistent analytics, governance, and interoperability across tools and platforms. BI and semantic layers expose curated calculations and dimensions for trusted reporting. Semantic models often leverage ontologies, RDF, or tabular datasets with measures, hierarchies, and relationships.

ALTERNATIVE DEFINITIONS

Wikipedia

A semantic data model is a data model that views data as existing within a universe of discourse; it is intended to capture more of meaning than a relational model.

Contrasts semantic richness with traditional relational models.

Source

Microsoft Learn

Semantic models in Power BI are datasets that define relationships, measures, and hierarchies to support reporting and analytics.

Emphasizes practical BI semantics: relationships, measures, hierarchies.

Source

RELATED TERMS

Knowledge Representation Semantic Layer Knowledge Graph RDF Kimball Methodology Metric Layer Data Glossary Star Schema

COMPETING TERMS

Physical Data Model

RELATIONS

• Broader: Model

KNOWLEDGE AREAS

Semantic & Knowledge Modeling Data Modeling Data Warehousing & BI Data Architecture

TARGET AUDIENCE

Data Modeler BI Specialist Analytics Engineer Data Architect Data Steward

EXTERNAL SOURCES

• Wikipedia - Semantic data model
• Microsoft Learn - Power BI semantic model overview
• W3C - RDF concepts for semantic modeling
• dbt Labs - dbt semantic layer overview

TOP QUESTIONS

Why is a Semantic Model crucial for analytics consistency?

It standardizes metrics and relationships, preventing conflicting definitions across dashboards and teams.

Category: Relevancy

How does a Semantic Model differ from a Physical Data Model?

Semantic models focus on business meaning; physical models define storage structures and technical implementations.

Category: Distinction

What technologies implement Semantic Models?

BI semantic layers, RDF/OWL ontologies, dbt semantic layers, and tabular models in Power BI.

Category: Tools

How is governance applied to Semantic Models?

Stewards define canonical metrics, approve changes, and document lineage and usage constraints.

Category: Management

What is a common mistake with Semantic Models?

Encoding tool-specific logic instead of business semantics, which reduces portability and trust.

Category: Mistakes

How do Semantic Models support interoperability?

They serve as shared contracts so multiple tools can consume definitions consistently.

Category: Best practices

What’s an analogy for a Semantic Model?

It’s like a dictionary and rulebook for your data—defining words and how they relate.

Category: Example

Who maintains a Semantic Model in organizations?

Data Modelers and Stewards collaborate with domain experts to curate and evolve semantics.

Category: Roles

How to start building a Semantic Model?

Identify core entities and metrics, define relationships, and publish governance-backed documentation for consumers.

Category: Getting started