Operationalizing Genie Ontology in Your Data Stack

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Summary

Genie Ontology works on day one, but achieving the highest possible accuracy depends on the underlying foundation. This guide shows you how to build that foundation on your data.

Use the six layers as your progressive maturity path to improve the data foundation, enrich metadata, model critical business semantics, curate trusted assets, govern access, and evaluate and improve.

Roll out one domain at a time rather than trying to boil the ocean. Every resolved entity, documented table, certified metric, and governed dataset improves answer quality, while evaluation keeps the system accurate as the business evolves.

Beyond the semantic model: Building shared business context for AI agents Large language models know how to reason, but they don't know your business. Giving enterprise AI the business context it needs means more than connecting it to data. Agents also need to understand your definitions, relationships, business rules, authoritative sources, and permissions. Genie Ontology closes that gap by combining modeled business semantics with context learned from the governed tables, queries, dashboards, notebooks, and other supported assets your teams already use. Genie ranks that context by authority and relevance, applies permissions, and delivers the most useful context to Genie at answer time. External agents can also access Genie’s intelligence through MCP. A good semantic model provides an authoritative core. Semantic models capture the business concepts you deliberately define; an ontology extends that foundation with the broader relationships, knowledge, and context AI needs to understand how the business actually operates. In Databricks speak, Unity Catalog Semantics combine Metric Views, Pages, and Domains to establish your trusted business definitions. Genie Ontology then builds on that modeled core by incorporating inferred context from your existing assets, giving agents a much broader understanding of the business than a semantic model alone can provide. The key is to model the “head” and let Genie Ontology infer the “tail” . Genie works from what it can automatically learn on day one, while deliberate curation improves the critical definitions and sources that must be right. The following six layers are progressive practices for increasing trust over time, not prerequisites for Genie to begin delivering value.

Let’s look at each layer to get a deeper understanding. Layer 0: Get your data foundation right for agents Before you describe or model anything, the underlying data has to be in a shape an agent can reason over. This layer is about the physical foundation: clean data tables, sound schemas, and one consistent identity per real-world entity. Logical business modeling comes later in Layer 2. This layer is easy to skip and expensive to fix later, because no amount of good metadata or semantic modeling can compensate for a broken physical foundation. Getting your data foundation right means focusing on two key areas: Model the durable gold layer around business processes . This includes identifying facts with a clear grain and reusable, conformed dimensions. A star schema or hybrid model gives you a reliable foundation of facts and dimensions without copying business logic into every downstream table. That does not mean agents should reason over raw dimensional tables. The interface an agent sees can be narrower, and often should be: a Metric View or a purpose-built view that pre-joins the common dimensions for one domain, exposes only the fields that matter, documents the grain, and defines its measures canonically. You will build exactly that in Layer 2. The point is to shape the consumption surface deliberately on top of a sound model, rather than throw an agent at a data dump. A wide table is not the problem. A wide table with mixed grains, duplicated business concepts, and no canonical metric definitions is an invitation to guess. Resolve entities into golden records . If "customer" means active accounts in Sales and every account ever in Support, an agent will not know which definition to trust. If the same customer has three different IDs across systems, it can also be double-counted. Reconcile the same real-world entity across sources so that one customer is one customer.

Layer 1: Enrich your metadata Metadata is the descriptive foundation that helps both the semantic layer and context extraction understand your data. When a table is named fct_ rev_ daily, and a column is named rev_amt, an agent has to guess what they mean. When the same table carries a description that says "daily recognized revenue, net of refunds, by product" and the column has a comment that says "recognized revenue in USD," the agent has something real to reason with. Good descriptions are one of the highest-return, lowest-cost investments you can make, and they improve every downstream tool, not just Genie. Here are three essential steps to take. Add table descriptions and column comments in Unity Catalog . Write for a new analyst who does not know your schema: say what the data represents, what its business purpose is, and flag any known caveats. Concentrate the effort where it pays off, on the curated, business-ready tables that dashboards and agents actually query. Apply tags to classify and organize . Descriptions provide the narrative meaning; tags provide structured signals for classification, discovery, and governance. Use them to capture sensitivity (PII, PHI, PCI), ownership, business function, and other attributes that need to be consistently understood across the data estate. Governed tags let administrators define an approved set of keys and values so classification remains consistent rather than drifting team by team. These signals can also feed into access policies and other governance controls later. Automate the first pass where volume makes manual work impractical . The Databricks Solution Accelerator, dbxmetagen , uses large language models to generate descriptions, detect and tag sensitive data, and propose classifications. Nothing is written to Unity Catalog until a human reviews and approves it, so it accelerates the work rather than replacing the judgment.

Layer 2: Model the business with a semantic layer Metadata explains individual tables. The semantic layer defines the business logic on top, so metrics that matter most mean the same thing...