Entity type #
Each option in the drop-down Entity Type represents a classification or categorization of data entities based on their role and purpose in the data model. Here’s a detailed explanation of each option, along with its connection to data governance methodologies:
Aggregate #
An aggregate represents a collection of data that are logically grouped together. Aggregates often summarize or consolidate data for reporting or analysis.
- Supports data quality by ensuring consistency in aggregated calculations (e.g., total sales or averages).
- Aligns with data stewardship as aggregates are often used in decision-making and should be verified for accuracy.
Auxiliary #
Auxiliary entities provide supporting or additional context to primary data entities. These are not central to the core process but enrich the data or provide reference values.
- Useful for data enrichment and ensuring contextual accuracy.
- Plays a role in metadata management, as auxiliary data often describes or enhances other data elements.
Composition #
A composition entity defines the structure or makeup of another entity. For example, a product may have a composition of various components.
- Vital for master data management (MDM) to maintain relationships between entities.
- Facilitates data modeling by defining hierarchical relationships between entities.
Fundamental #
Fundamental entities are the core or foundational building blocks of your data model. Examples include customers, products, or employees.
- Central to data governance policies, as they are the primary focus for data quality and standardization.
- Form the base for data stewardship and data accountability frameworks.
- Align with regulatory compliance requirements by ensuring these entities adhere to industry standards.
Metadata #
These entities describe other entities, essentially being “data about data.” Examples include descriptions of database tables, columns, or data formats.
- A cornerstone of metadata management.
- Supports data discoverability and understanding across the organization.
- Ensures proper data cataloging and documentation, which are critical for transparency and compliance.
- Describe a status of data from operational point of view
Relation #
A relation entity defines the connections or associations between two or more data entities. For instance, a customer purchasing a product forms a relationship.
- Critical for data modeling and building relational databases.
- Enhances data integration by establishing clear linkages between entities.
- Supports data lineage by tracking how entities interact over time.
Snapshot #
A snapshot entity captures the state of data at a specific point in time. These are often used for historical analysis or auditing.
- Essential for data auditing and ensuring regulatory compliance.
- Facilitates data lineage and historical tracking.
- Supports data retention policies, especially for time-sensitive data.
Specialisation #
A specialization entity represents a more specific version of a broader category. For instance, a “vehicle” entity could have specializations such as “car” or “truck.”
- Promotes clarity in data classification and taxonomy.
- Supports hierarchical data modeling.
- Aids in enforcing data policies for different categories or levels of specialization.
Transaction #
Transaction entities capture individual events or actions, such as a sales transaction, a payment, or an inventory update.
- Integral to data accuracy and traceability.
- Supports compliance with regulations that require transaction-level detail (e.g., GDPR, financial reporting standards).
- Aligns with data quality management, as transactional data often requires rigorous validation.