Bring your business intent into Databricks.
AI in Databricks can only be as right as its understanding of your business. Duodata captures what your numbers mean and deploys that meaning natively to Unity Catalog metric views, so AI/BI Genie answers the question your business is actually asking.
AI can give you a confident answer to the wrong business question.
AI/BI Genie reads tables and columns. It doesn't know which revenue your finance team reports, who owns that definition, or which adjustments it includes — unless someone tells it.
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 AI/BI Genie
Net revenue grew 5.4% from Q2 to Q3.
Definition used: net revenue = gross revenue − discounts − credits (owner: Finance), served through Unity Catalog metric views.
Fictional example
Same data. Different question. Different decision.
Two quarters of fictional revenue show what changes when AI uses the definition your finance team signs off.
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.
Business owners define intent. Your data team owns the platform.
Business owners
- Agree what each measure means, in plain language.
- Name the owner and where it is used.
- Review changes before AI uses them.
Your data team in Databricks
- Maps definitions to tables, columns and relationships.
- Deploys native metric views through its own release process.
- Keeps governance, access and runtime in Databricks.
Databricks combines Unity Catalog metric views, Pages and Domains with context learned by Genie Ontology. Duodata helps your business and engineering teams curate definitions, ownership and implementation mappings for those native capabilities.
Supporting reading: Operationalizing Genie Ontology in your data stack (Databricks)(opens in a new tab)
Existing reports and models are valuable inputs: we start from what your teams already use.
Deploy and maintain semantics.
Bring business meaning, physical schemas and open models into one engineering workflow. The Databricks App deploys Unity Catalog metric views used by AI/BI Genie. Already have Ossie models? Start with the Databricks App, then connect Duodata's business definitions when your team needs shared ownership and meaning.
Build and maintain the layers behind your AI.
Use business definitions, source mappings and engineering standards to build transformations, facts and dimensions, then validate the results from source to output.
- 01
Silver and gold layers
Transformations, facts and dimensions built to your standards.
- 02
Maintained formulas
Definitions and schema changes flow through review, not rework.
- 03
Reconciliation
Source → silver → gold → semantic checks on every change.
Worked examples for Databricks
- AI for finance and planningFinance & FP&AGive AI the meaning behind your financial numbers.“Sales grew 10%. How much did net revenue grow?”Explore the Finance & FP&A example
- Business meaning for insurance analyticsInsuranceGive every claims question a clear business definition.“Premium collections grew. Why did our net claims payment ratio rise?”Explore the Insurance example
Official Databricks documentation
- Unity Catalog metric views(opens in a new tab)
- AI/BI Genie(opens in a new tab)
- Databricks Apps(opens in a new tab)
Running on Snowflake? See Duodata for Snowflake
Bring your use case
Bring one business question, its owner, the data in Databricks and the result you expect. We scope a paid proof of concept that puts that meaning to work in Unity Catalog metric views.
- 01One business question
- 02One owner of the definition
- 03The data it depends on
- 04The result you expect
- Bring your use case