How do I get AI to understand my company data?

Summary

• AI understands your data when you give it business context, not just table access. Encode what your data means and how your company defines its metrics, then point your AI interface at that context.
• Start with rich metadata in your catalog: clear table and column descriptions are what let a natural-language tool map a question to the right data.
• Define your metrics once in a governed semantic layer so "revenue" or "active customer" resolves to one agreed calculation everywhere, instead of each team's version.
• Give the AI example queries, a business glossary, and declared table relationships. These teach it your organization's logic, language, and joins.
• On Databricks, Unity Catalog metadata, generally available metric views, and AI/BI Genie provide this foundation; Genie Ontology (in Public Preview) adds a context layer that infers definitions from your existing assets.

How do I get AI to understand my company data?

AI understands your company data when you give it business context, not just access to tables. A model won't infer that "active customer" excludes churned accounts, or that your fiscal year doesn't start in January, unless you encode those definitions somewhere it can read. The practical work is three things: describe your data in a catalog, define your metrics once in a governed semantic layer, and configure your natural-language interface with example queries and a glossary of your terms. Do that and questions map to the right tables with the right logic. Skip it and even simple questions come back confidently wrong.

What does it mean for AI to "understand" your data?

Understanding here means the system can translate a business question into the correct query against the correct data, using your definitions. Raw tables and column names rarely carry that meaning on their own: a column named "rev_amt_net" tells a model nothing about whether it includes refunds. A semantic layer supplies the missing meaning, the entities, metrics, relationships, and rules that describe how your business actually works. A semantic layer captures the concepts you deliberately define; an ontology extends that with the broader relationships and context an agent needs to reason over your data.

Key capabilities to evaluate

Capability	Why it matters
Metadata and descriptions	Table and column descriptions let the system map a question to the right source. Without them, answers drift.
Governed semantic/metric layer	Metrics defined once, as measures and dimensions, so every tool and team uses the same calculation.
Business glossary	Shared definitions for terms like "active customer" so ambiguous words resolve consistently.
Declared relationships	Primary and foreign keys tell the system how tables join, which reduces join errors.
Instructions and example queries	Sample SQL and text guidance teach the tool your logic and phrasing.
Permission-aware context	The AI should use only the context and data the asking user is allowed to see.

How Databricks approaches this

Databricks builds this context in Unity Catalog and surfaces it through AI/BI Genie. You start with metadata: add descriptions to tables and columns so Genie can interpret questions and map them to the right data. You then define metrics in Unity Catalog metric views, which are generally available. Metric views declare business metrics, dimensions, measures, and synonyms as governed SQL objects, so "revenue" means the same thing whether it's asked in a dashboard or a Genie Agent. Because they inherit Unity Catalog permissions, lineage, and audit, the same governance covers AI access. Point Genie at metric views rather than raw tables and answers rest on agreed definitions.
Next, teach Genie your organization. Configure Genie Agents with example SQL queries, sample datasets, and text instructions that show how you calculate key metrics and phrase questions. Use the Unity Catalog business glossary for shared terms, and declare primary and foreign keys so the system knows how tables relate.
Genie Ontology, currently in Public Preview, ties this together as a context layer. It combines the context you model and govern in Unity Catalog (metric views, domains, and glossary pages) with inferred context: snippets it extracts from your existing metric views, dashboards, SQL queries, and Genie Agents, such as metric definitions, authoritative sources, and business rules. Each snippet carries an authority score, and every snippet is gated by Unity Catalog permissions, so Genie uses only context from assets a user can already see. The guiding pattern is to model the definitions that must be right and let the ontology infer the rest.

How to build it in practice

Databricks describes a three-step approach:

1. Build on a strong foundation. Curate the data Genie will use so answers stay accurate and consistent.
2. Teach Genie your organization. Add metadata, declare joins, and supply SQL patterns and a glossary so it learns your logic and language.
3. Test, refine, and maintain. Use benchmarks and feedback to keep answers accurate as data and usage change.

Semantics are the foundation of AI accuracy. Models can't be expected to guess your business definitions, so agreeing on them and encoding them is the work that makes AI reliable on your data.

FAQs

Why does AI give wrong answers about my data even when the data is correct?

Usually because it lacks business context. If metrics and terms aren't defined, the model guesses how to calculate them. Defining them in a semantic layer and glossary fixes most of these errors.

What's the difference between a semantic layer and an ontology?

A semantic layer captures the metrics and concepts you deliberately define. An ontology extends that with the broader relationships, rules, and context that describe how the business operates, so agents can reason across it.

Do I have to define everything up front?

No. Model the definitions that must be right, then let inferred context fill in the rest. Genie Ontology, in Public Preview, learns additional context from your existing dashboards, queries, and metric views.

How does this stay secure?

Context and data are governed by Unity Catalog. Genie uses only context extracted from assets the requesting user has permission to see, and metric views inherit Unity Catalog permissions, lineage, and audit.

Where do metric views fit?

Metric views are the governed, reusable definitions of your business metrics. Pointing Genie at metric views rather than raw tables is what keeps its answers consistent with the rest of your BI.