Metamodel
What is a Metamodel?
A metamodel defines the structure, rules, and semantics of other models. It acts as a blueprint that guides how data models should be built and interpreted. In the context of metadata and governance, a metamodel outlines which elements can appear in a model and how they should relate to each other.
For instance, a metamodel might define a generic concept like “Application.” Users can then populate that structure with specific values, such as “CRM System” or “Billing Platform,” in their own models. This structure ensures consistent use of metadata across systems and teams.
Why They Matter
As organizations scale their data operations, they need a consistent framework for describing and managing metadata. Metamodels provide that foundation. They promote alignment by defining what should be modeled and how.
With a well-designed metamodel, teams can:
- Ensure consistency across tools and platforms.
- Encourage interoperability between systems.
- Automate governance processes.
- Reuse modeling patterns across use cases.
- Meet compliance requirements more effectively.
By adopting a metamodel-driven approach, organizations reduce ambiguity and accelerate their ability to manage data with confidence.
Metamodel vs. Model
Understanding the distinction between a metamodel and a model is essential. A model represents specific data or systems, while a metamodel defines the structure that such models must follow.
- Model: A concrete instance, like a diagram of a particular data pipeline.
- Metamodel: The framework that defines allowable elements and how they relate.
Because it governs how models are constructed, a metamodel enables scalability and standardization across different modeling efforts.
Where They are Used
Metamodels appear in a variety of data and design contexts. Here are some common examples:
- Metadata repositories that classify and connect data assets.
- Enterprise architecture frameworks defining relationships between business units, applications, and infrastructure.
- Data governance platforms managing policies, terms, and roles.
- Knowledge graphs that rely on clearly structured concepts.
- Modeling tools such as UML or BPMN that require predefined schema.
Each of these use cases benefits from the structure and clarity a metamodel provides.
Actian and Metamodels
Actian Data Intelligence Platform uses metamodels to structure and connect metadata across the data environment. Instead of collecting disconnected metadata, Actian organizes it into meaningful relationships between data products, business terms, policies, ownership, and more.
By building on a shared metamodel, Actian allows teams to:
- Discover related data assets and metadata in context.
- Apply consistent governance rules across domains.
- Support lineage, policy enforcement, and search.
- Align technical and business users with a shared structure.
This metamodel-driven approach helps organizations create clarity, support automation, and scale data governance with confidence.
FAQ
A metamodel is a model about models. It defines the structure, categories, and relationships that data models must follow, providing consistency across systems.
A data model describes specific data, such as a table or a report. A metamodel defines what types of elements can appear in those models and how they are organized.
Metamodels provide the structure needed to consistently describe and control metadata. This makes it easier to enforce policies, manage quality, and support reuse.
Metamodels are used in metadata catalogs, enterprise architecture, modeling tools, and governance platforms; anywhere consistent modeling is needed at scale.
Actian uses a metamodel to structure metadata across the Data Intelligence Platform, enabling connected data products, lineage, policies, and ownership to be managed consistently and governed efficiently.
