DataTerminology.comNEW
Also known as: Master Data Modeling, Reference Data Modeling
Create conceptual, logical, and physical representations that define entities, attributes, relationships, and constraints for master and reference data.
DESCRIPTION
Data modeling clarifies meaning, keys, and constraints for shared data. ER diagrams capture entities and relationships; normalization reduces redundancy; canonical models unify semantics across systems. Reference datasets use code lists, hierarchies, and controlled vocabularies. Modeling underpins governance, contracts, and quality rules. Good models accelerate integration, analytics, and maintainability. Versioning and documentation ensure models evolve safely over time.
ALTERNATIVE DEFINITIONS
Wikipedia
Data modeling is the process of creating a data model for the data to be stored in a database.
Focuses on the modeling process for storage and structure.
Oracle
Data modeling defines and organizes data elements and their relationships to standardize how data is stored and accessed.
Emphasizes standardization for storage and access.
IBM
Data modeling is the practice of describing data structures, relationships, and rules to support business requirements and technical implementation.
Connects business requirements to technical design.
RELATED TERMS
Entity-Relationship Model Canonical Data Model Knowledge Representation Class Hierarchy Feature Scaling Schema Registry Business Glossary Master Data Reference Data
COMPETING TERMS
Kimball Methodology Data Vault
RELATIONS
- Broader: Data
KNOWLEDGE AREAS
Data Modeling Semantic & Knowledge Modeling Reference & Master Data Data Architecture
TARGET AUDIENCE
Data Modeler Data Architect Information Analyst Analytics Engineer Data Steward
EXTERNAL SOURCES
- Wikipedia - Data modeling
- W3C - Ontology modeling for semantics
- Data Vault Alliance - Alternative modeling approach
- Martin Fowler - Domain modeling perspective
TOP QUESTIONS
Why is Model Data essential for master and reference domains?
It defines meaning, keys, and relationships critical for shared understanding. Proper models reduce integration friction and preserve data quality across systems.
Category: Relevancy
What’s the difference between conceptual, logical, and physical in Model Data?
Conceptual captures business concepts; logical adds attributes and constraints; physical maps to specific technologies and storage patterns.
Category: Comparison
How do ontologies support Model Data?
Ontologies formalize semantics and relationships, enabling interoperability and reasoning across domains and tools.
Category: Best practices
What common mistakes occur in Model Data?
Overfitting to a single use case, excessive denormalization, and poor naming conventions lead to ambiguity and brittle integrations.
Category: Mistakes
Who participates in Model Data activities?
Data modelers lead; architects and stewards validate standards; analysts verify business meaning; engineers confirm implementability.
Category: Management
How does versioning impact Model Data?
Versioning preserves history and compatibility, enabling controlled evolution and safe consumer adoption of changes.
Category: Operations
What is the role of canonical models in Model Data?
They unify semantics across systems, reducing translation overhead and misunderstandings between producers and consumers.
Category: Best practices
How does Model Data relate to Dimensional Modeling?
Dimensional models optimize analytics; master/reference models ensure consistent entities and codes. Both must align on keys and definitions.
Category: Opinion
Where to start learning Model Data?
Begin with ER modeling basics, normalization, and business glossary practices, then explore domain-driven design and semantic modeling.
Category: Learning