Definition · data infrastructure
Ontology
Ontology is a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support inference, distinct from the analytic semantic layer. For ontology, a useful definition states a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support, who owns it, and which decision it supports.
Also known as data ontology, business ontology
Why it matters
Understanding ontology matters because leaders need a shared, source-backed meaning before they can compare results, explain performance, or decide what to do next. Pluvo's ontology is a versioned, immutable graph of a company's financial entities and the relationships between them across systems, the structure that lets it trace a variance in one place to its cause in another.
In practice
Operating example
Ontology is useful when teams need a shared interpretation of a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support inference, distinct from the analytic semantic layer. The definition should make source data, timing, ownership, and the decision it supports explicit.
Pluvo example
Pluvo's ontology is a versioned, immutable graph of a company's financial entities and the relationships between them across systems, the structure that lets it trace a variance in one place to its cause in another.
In practice, teams should define ontology with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding ontology matters because leaders need a shared, source-backed meaning before they can compare results, explain performance, or decide what to do next. Pluvo's ontology is a versioned, immutable graph of a company's financial entities and the relationships between them across systems, the structure that lets it trace a variance in one place to its cause in another.
A strong workflow for ontology separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Pluvo's ontology is a versioned, immutable graph of a company's financial entities and the relationships between them across systems, the structure that lets it trace a variance in one place to its cause in another.
FAQ
What is an ontology in data and AI?
Ontology is a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support inference, distinct from the analytic semantic layer. For ontology, a useful definition states a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support, who owns it, and which decision it supports.
What is the difference between an ontology and a semantic layer?
The boundary for ontology differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support inference, distinct from the analytic semantic layer, so teams should compare those boundaries before using it in reporting or planning.
What is the difference between an ontology and a knowledge graph?
The boundary for ontology differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support inference, distinct from the analytic semantic layer, so teams should compare those boundaries before using it in reporting or planning. For ontology, the practical boundary is a formal model of the concepts, entities, and relationships in a domain, including hierarchies and constraints that support inference, distinct from the analytic semantic layer.