The business meaning behind every answer.
An ontology is a set of connected concepts and relationships your business agrees on. Duodata keeps a deliberately lightweight metrics model — one your people can own — and puts it to work in your platform.
What does revenue mean here?
Ask how much revenue grew and an assistant answers fluently. Whether it used the revenue your finance team reports is another matter.
Question
“How much did revenue grow?”
AI answer without the business definition
Revenue grew 10%.
Gross revenue: $10.0m → $11.0m. The gross-revenue answer is 4.6 percentage points above the growth Finance reports.
Gross revenue, Q2 vs Q3 2026
Growth: 10.0%
| Component | Q2 | Q3 |
|---|---|---|
| Gross revenue | $10.0m | $11.0m |
| Discounts | $0.5m | $0.8m |
| Credits | $0.2m | $0.4m |
| Net revenue | $9.3m | $9.8m |
Fictional example in USD. Not customer data.
A metrics model people can own.
Each measure carries its plain-language definition, its owner, the dimensions it is cut by and the reports that use it — deliberately small, deliberately business-owned.
- Metric
- Net revenue
- Definition
- Gross revenue less discounts and credits
- Synonyms
- Net sales
- Unit
- USD
- Owner
- Finance
- Dimensions
- Quarter · Customer segment
- Reports
- Quarterly review
The story is simple: business meaning becomes a semantic implementation, and the semantic implementation runs in your platform, where AI assistants use it. The model stays small enough for the people who own the numbers to own the model.
Q1 · How much did revenue grow?
10% on gross — the column an assistant finds first.
Q2 · Show the business definition.
Net revenue grew 5.4%: gross − discounts − credits, owned by Finance.
Q3 · Why did discounts grow faster?
The grounded answer opens the next business question — and any missing definition returns to business review.
Meaning, connection, data.
The semantic layer explains agreed meaning as queryable measures, breakdowns and relationships. Duodata captures and maintains the business model and its connection to that implementation.
From business meaning to platform execution
Defined by the business
Net revenue
= gross revenue − discounts − credits
- Owner
- Finance
- Used for
- Quarterly reporting
Duodata model
Your business meaning, ready for AI
One agreed definition with an owner, a time grain and the words people use to ask for it.
Choose your platform
Your platform executes the calculation. Pick one; you don't need both.
Answer in Cortex Analyst
Net revenue grew 5.4% from Q2 to Q3.
Definition used: net revenue = gross revenue − discounts − credits (owner: Finance), served through Semantic Views.
Fictional example
Reviewed SQL sums the amounts per calendar quarter into the persisted Gold table snapshot.
Calculation, report usage and influence are different things.
Solid edges compute the number. A dashed edge records an asserted business influence for review — it is not proved causation and not part of the reconciliation.
- Gross revenue
- Discounts
- Credits
Net revenue
Owner: Finance · Quarterly
An asserted business relationship, recorded for review. It is not a calculation, not proved causation, and not part of the numeric reconciliation.
Meaning changes and implementation changes are reviewed differently.
A definition change is a business policy review. A schema change is a mapping review. Both are human decisions, recorded and carried through — never silently auto-fixed.
Meaning change
The business revises a policy or definition — for example, how conditional inspections count. Review is a human, business-owned decision; the change is recorded with its owner and date.
Implementation change
A column or schema changes in the source. The impacted mapping surfaces for review; nothing is silently re-pointed. There is no universal auto-fix — a person confirms the new mapping before it deploys.
Mapping and native output match the observed schema.
1 · Observed schema
DEMO.GOLD.REVENUE_BY_QUARTER
Snowflake database DEMO, schema GOLD, table REVENUE_BY_QUARTER
- CREDITS NUMBER(18,2)
- DISCOUNTS NUMBER(18,2)
- GROSS_REVENUE NUMBER(18,2)
- QUARTER DATE
2 · Mapping
| Logical | Physical |
|---|---|
| gross_revenue | GROSS_REVENUE |
| discounts | DISCOUNTS |
| credits | CREDITS |
Business definition unchanged: net revenue = gross revenue − discounts − credits.
3 · Totals
- Q2 net revenue
- $9.3m
- Q3 net revenue
- $9.8m
Mapping current
format: schema.yaml
version: "1.0"
schemas:
- source:
database: DEMO
schema: GOLD
type: snowflake
tables:
REVENUE_BY_QUARTER:
columns:
CREDITS: {type: "NUMBER(18,2)", is: [notnull]}
DISCOUNTS: {type: "NUMBER(18,2)", is: [notnull]}
GROSS_REVENUE: {type: "NUMBER(18,2)", is: [notnull]}
QUARTER: {type: DATE, is: [notnull]}name: revenue_semantics
description: Fictional quarterly revenue example.
tables:
- name: revenue
base_table:
database: DEMO
schema: GOLD
table: REVENUE_BY_QUARTER
time_dimensions:
- name: quarter
expr: QUARTER
data_type: DATE
facts:
- name: gross_revenue
expr: GROSS_REVENUE
data_type: NUMBER
- name: discounts
expr: DISCOUNTS
data_type: NUMBER
- name: credits
expr: CREDITS
data_type: NUMBER
metrics:
- name: net_revenue
expr: SUM(gross_revenue) - SUM(discounts) - SUM(credits)Fictional example. Nothing here runs against a warehouse.
Metrics ontology, semantic layer and data catalog are complementary.
Catalogs already store terms, owners, relationships and policies, and platforms ship native semantics. The value Duodata adds is focused business participation and maintained meaning and mappings — not universal sync or connector promises.
Metrics ontology
What measures mean, who owns them, how they relate.
Duodata: Duodata's home: business participation, meaning and mappings, review and ongoing maintenance.
Semantic layer
How measures compute against data, natively in the platform.
Duodata: Duodata deploys into it: Unity Catalog metric views, Snowflake semantic views.
Data catalog
What data exists, where it lives, who can use it. Catalogs already store terms, owners, relationships and policies.
Duodata: Duodata contributes governed definitions and maintained mappings the catalog can reference.
Business metric ownership, alongside your platform.
- Business ownership
Give KPI owners a shared place to define meaning with the data team.
- Review and approval
Review proposed definition changes with the people accountable for the KPI.
- Calculations and drivers
Make calculation dependencies and business driver relationships explicit.
- Metric suggestions
Review suggestions when new questions expose a missing definition.
- Cross-system meaning
Keep a business definition explicit as its implementations change across tools.
On Databricks, this sits alongside Unity Catalog Semantics metric views, Pages and Domains, and the modeled and inferred context of Genie Ontology — Duodata curates definitions and mappings for those capabilities and does not control Genie's ranking. Supporting reading(opens in a new tab)
Start with measures; contribute to something wider.
A focused metrics model is not a rung on a maturity ladder. Scientific, clinical and broad domain models serve different needs — and a focused model can contribute to a wider enterprise ontology program through open formats, across an explicit boundary.
Duodata scope · focused business model
Measures, their definitions, owners, dimensions and reports — plus the business entities and entity relationships they refer to. Small enough for business participation.
Analytics interchange · Apache Ossie (incubating)
The open semantic-model interchange carries the agreed model into platform-native semantic assets — its verified role.
Alignment with your enterprise ontology
Broader programs stay customer-owned — wider domain models, vocabularies and reasoning serve different needs. The focused model contributes its agreed concepts and metric definitions to them; it does not replace them.
Ontology-guided engineering, layer by layer.
Definitions drive agent and review work into native semantics, gold facts and dimensions, and silver preparation — with bronze source validation underneath. Select a layer to see its input, output and check.
Defined once by the business
Duodata Metrics Ontology
- Definitions & formulas
- Dimensions & relationships
- Governance & ownership
Guided work
Agents & review
Create, test and maintain each layer — with human review before deploy.
- Input
- Reviewed mappings and the declared business definitions.
- Output
- A Unity Catalog metric view or Snowflake semantic view, ready for Genie or Cortex Analyst.
- Check
- Native validation — the semantic asset compiles and answers with the agreed definition.
Primary sources.
Jessica Talisman's writing informs how we distinguish shared business language, practical semantic models and formal ontology programs. The platform references are the official documentation.
- Jessica Talisman — The Semantics of Semantics(opens in a new tab)
- Jessica Talisman — Language is the Bridge(opens in a new tab)
- Jessica Talisman — Ontologies and Other Myths(opens in a new tab)
- Databricks — Operationalizing Genie Ontology(opens in a new tab)
- Databricks — Metric views YAML reference(opens in a new tab)
- Snowflake — Semantic views overview(opens in a new tab)
- Snowflake — Semantic view YAML specification(opens in a new tab)
- Apache Incubator — Ossie(opens in a new tab)
Built for Snowflake and Databricks.
Databricks
Business meaning deploys as Unity Catalog metric views, answered by AI/BI Genie.
Explore Duodata for DatabricksSnowflake
Business meaning deploys as Semantic Views, answered by Cortex Analyst.
Explore Duodata for SnowflakeStart a free pilot
We work through one business question with you: agree its meaning, map it to the relevant data and review how it works on Snowflake or Databricks. We agree the scope together before we start.
- 01One business question
- 02One owner of the definition
- 03The data it depends on
- 04A review on your platform
- Start a free pilotWatch demo