Business ontology vs data dictionary
Both describe your data, but at very different levels of depth. A data dictionary documents columns and types. A business ontology defines concepts, metrics, relationships, and their mappings to the database. Here is how they compare, when each is the right choice, and how to evolve from one to the other.
Side-by-side comparison
| Dimension | Data Dictionary | Business Ontology |
|---|---|---|
| Scope | Tables, columns, data types | Business concepts, metrics, dimensions, relationships |
| Relationships | Foreign keys between tables | Formal semantic relationships: subclass, part-of, measured-by |
| Metric definitions | Not supported | First-class: expression, aggregation, time dimension, dependencies |
| AI-readability | Low — column names are ambiguous | High — machine-readable OWL/RDF with typed semantics |
| Versioning | Rarely versioned; often a static wiki or spreadsheet | Git-native: diff, branch, review, merge |
| Traceability | Column-level documentation | Full path: concept → metric → table → column |
| Alternative labels | Not supported | SKOS altLabel: synonyms, legacy names, report aliases |
| Standards | Vendor-specific or ad-hoc | W3C OWL, RDF/Turtle, RDFS, SKOS |
When a data dictionary is enough
A data dictionary is the right tool when your primary need is column-level documentation. If your team is small, your schema is straightforward, and everybody already agrees on how metrics are calculated, a well-maintained data dictionary covers the basics: what each column contains, its type, and whether it can be null.
Data dictionaries also shine during migrations and onboarding. When a new analyst joins, they can look up what crt_dt means or which table holds customer addresses. The dictionary acts as a reference card for the physical schema.
Where data dictionaries fall short is at the business level. They do not capture how concepts relate to each other, how a metric should be computed, or what synonyms should resolve to the same entity. If your analytics grow beyond a handful of dashboards or you introduce AI-powered queries, a data dictionary alone will leave gaps.
When you need an ontology
You need a business ontology when metric consistency matters across teams and tools. If different departments define “revenue” differently, or if a dashboard number does not match a spreadsheet number, the root cause is usually a missing shared definition. An ontology solves this by defining each metric once with its formula, grain, filters, and source table.
You also need an ontology when you want AI to understand your data, not just guess from column names. Natural-language analytics work best when the AI has structured context: formal definitions, relationships between concepts, and explicit mappings. An ontology provides this context in a machine-readable format.
Finally, an ontology is the right choice when governance needs to scale without slowing teams down. Because the ontology is a Turtle text file in Git, every change goes through pull requests, code review, and commit history. You get an auditable record of every definition change without a heavyweight catalog tool or an approval committee.
How to evolve from a data dictionary to an ontology
You do not need to discard your existing documentation. The evolution from a data dictionary to a business ontology is incremental and can start with the metrics and concepts that matter most.
Start with your existing column documentation
Connect your warehouse to Magnowlia and import the technical layer automatically. Your existing column descriptions become the foundation for the business layer.
Add business classes and relationships
Group columns into business concepts (Customer, Order, Product) and define how they relate to each other. The AI assistant can suggest classes based on your schema structure.
Define metrics with mappings
For each key metric (revenue, churn, conversion), add a formal definition: the SQL expression, the time dimension, filters, and the source table. These go beyond what a data dictionary can express and are what enable consistent, AI-powered analytics.
Explore more
What is a business ontology?
Definitions, why it matters, and how it works in practice.
Learn more →Benefits of an ontology
Consistent metrics, traceability, governance, and AI accuracy.
Learn more →Ontology examples
Real-world Turtle snippets for e-commerce, SaaS, and marketing.
View examples →Ontology and semantic layer
How an ontology underpins a semantic layer for BI and AI.
Learn more →Ready to go beyond a data dictionary?
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