Semantic Layers: The Catalog Owner Playbook

TL;DR: Owning the semantic layer has become a named data-leadership job, not a side task. This semantic layers catalog owner data leader playbook covers the three jobs that role does: pick a deliberate semantic layer architecture, govern a shared business vocabulary as versioned code, and keep the data catalog as the control plane for discovery, lineage, and policy. The payoff is analytics and AI that agree on what “Revenue” means.

Every vendor now claims to sell “semantics,” an “ontology,” or an “AI-ready knowledge layer.” That puts the burden on you to figure out what a semantic layer actually is, which flavor you are buying, and how it fits your catalog and governance strategy (Coalesce, 2025). If you own or influence the enterprise data catalog, the semantic layer is now your problem to define and defend. This post lays out the playbook for that role.

Why is owning the semantic layer becoming a defined role?

A semantic layer is a business-friendly abstraction between your warehouse or lake and the tools on top of it. It maps raw tables and columns into named entities, metrics, relationships, and policies so people and machines can query data in consistent business terms (Coalesce, 2025).

The reason this needs a clear owner is that three pressures now converge on one place. Catalog owners and data leaders sit at the intersection of multi-BI sprawl (Tableau plus Power BI plus notebooks plus AI tools), data mesh with domain ownership, and AI/BI convergence where copilots and agents generate SQL (Coalesce, 2025). All three depend on the same thing: definitions of entities, metrics, joins, and policies written once and reused everywhere.

Ownership is also where most programs quietly fail. Atlan’s research on context-layer ownership is blunt about it: when no single person or team is named as accountable, coverage falls below usable thresholds within the first year (Atlan, 2026). A shared vocabulary with no owner decays the same way.

What does the catalog owner actually own?

The cleanest way to scope the role is to separate two things the catalog owner is responsible for: the active layer that runs at query time, and the index that makes everything findable.

• The semantic layer is active. Every query for a governed metric passes through it, and it applies business logic and row- or column-level access at execution time. Remove it and you are querying raw physical tables with no business rules (Dremio, 2026).
• The catalog is the control plane. It records what data and semantics exist, who owns them, how they are used, and what policies apply. Remove the catalog and queries still run, but no one can find or trust the data (Dremio, 2026).

A useful one-liner from Dremio: analysts use the catalog to find data and the semantic layer to query it correctly (Dremio, 2026). The catalog owner’s job is to keep both in sync, so a glossary term, its metric definition, its lineage, and its access policy all point at each other.

One more distinction protects you from buying hype. An analytics semantic layer (metrics, joins, filters, policies) is not the same as an ontology or knowledge graph (formal concepts, constraints, inference, and global IDs in RDF/OWL/SHACL). They are complementary, but they solve different problems and often live in different systems (Coalesce, 2025). Knowing which one a vendor is selling is half the battle.

How do you choose a semantic layer architecture?

Architecture should be a decision, not the accidental result of whichever BI tool you adopted first. The 2025 playbook narrows it to three patterns (Coalesce, 2025):

1. BI-native — semantics live inside one dominant tool (Looker’s LookML, Power BI’s Tabular/DAX, Tableau Semantics). Simple and fast when one BI tool drives 90%+ of usage, but it locks you in and does not reuse cleanly across other tools.
2. Platform-native — semantics live in the data platform itself (Snowflake Semantic Views with Cortex Analyst, Databricks Metric Views with LakehouseIQ). Governance and lineage sit next to the data, which suits regulated, single-platform shops.
3. Universal / headless — a tool-agnostic engine (Cube, AtScale, GoodData) serves the same metrics to many “heads” via SQL, REST, GraphQL, and MDX. Best for multi-BI, data mesh, and exposing metrics to applications and AI agents.

Pick your center of gravity from your real constraints: how many BI tools you run, your primary platform, your governance pressure, and your AI ambitions (Coalesce, 2025). Whichever you choose, favor options that publish and subscribe metadata, including tags, lineage, and row-level security, back into the catalog so it stays the control plane.

How mature is your semantic foundation?

The playbook offers an L0–L5 maturity model so you can be honest about where you are and what the next investment is (Coalesce, 2025):

• L0 — siloed reports. No semantic layer. “Revenue” is defined five different ways depending on the department.
• L1 — BI-native metrics. Definitions are centralized inside one BI tool, so they are consistent only there.
• L2 — shared layer across tools. A universal or headless layer lets Tableau, Power BI, notebooks, and AI agents query the same definitions.
• L3 — platform-native semantics with governance. Definitions move next to the platform with policy and lineage attached.
• L4 — enterprise ontology/graph mapped to the warehouse. Concepts get global identifiers and constraints, connecting analytic semantics to conceptual knowledge.
• L5 — reasoning-aware agents. Copilots query with awareness of both knowledge semantics and analytic semantics at once.

Most teams are honestly at L1 or L2. The value of the ladder is sequencing: you do not jump to reasoning-aware agents before metrics agree across two BI tools.

What does a 90-day rollout look like?

The catalog owner’s first quarter has a concrete shape (Coalesce, 2025):

• Weeks 0–2: inventory. Build a minimum glossary, identify roughly 25 core entities, and prioritize about 50 key metrics with their formulas, grain, and time logic. The explicit advice is not to boil the ocean: start with the 20–50 metrics that drive most executive discussions, like Revenue, Gross Margin %, Active Users, and Churn.
• Weeks 3–6: pilot. Stand up one domain, such as Sales & Revenue, in your chosen architecture and prove it across three to five downstream tools.
• Weeks 7–10: harden governance. Centralize row-level security, validate end-to-end lineage from source to metric, and sync definitions into the catalog.
• Weeks 11–13: automate. Treat semantics as code with Git, peer review, CI tests, and AI assistants pointed at the semantic models rather than raw schemas.

By day 90 you should have a trusted glossary, your top 50 metrics defined once, at least one domain live across 3+ tools, centralized policy enforcement, and lineage flowing into the catalog (Coalesce, 2025).

A concrete example: Vantage Health

Vantage Health, a mid-size health insurer, ran Tableau for operations dashboards, Power BI for finance, and a growing set of notebooks for actuarial work. “Active Members” meant three slightly different things across those tools, and every quarterly board pack triggered the same argument about whose number was right.

The newly appointed analytics engineering lead took ownership of the semantic layer as an explicit mandate. Following the playbook, she started with 40 metrics that showed up in executive meetings, defined them once with clear grain and time logic, and wired them into all three tools. Row-level security, so regional managers saw only their own rows, was centralized rather than re-implemented per tool (Coalesce, 2025).

Because Vantage was multi-BI and had a copilot pilot underway, she chose a universal layer and kept the catalog as the control plane for ownership, lineage, and policy. She adopted a federated ownership model, with domain stewards maintaining their own metrics under a governance council, matching Atlan’s guidance that federated ownership is effectively required once an organization crosses five or more domains (Atlan, 2026).

This is also where a knowledge-graph approach earns its place. A unified semantic layer like SemanticOS connects the fragmented tools, documents, and definitions across an organization into one graph, so a person or an AI agent can ask a question and traverse the relationships between metrics, owners, source tables, and policies instead of guessing. For Vantage, that meant the board pack number and the copilot’s answer finally came from the same definition.

Key takeaways

• Owning the semantic layer has become a defined data-leadership role, held by the catalog owner or analytics engineering lead, with its own playbook for governance and shared vocabulary (Coalesce, 2025).
• The semantic layer runs at query time; the catalog is the control plane for discovery, lineage, and policy. Keep them in sync (Dremio, 2026).
• Choose architecture deliberately, BI-native, platform-native, or universal/headless, based on your BI count, platform, governance pressure, and AI plans (Coalesce, 2025).
• Use the L0–L5 maturity model to sequence investment, and run a 90-day plan that starts with the 20–50 metrics that matter most.
• Name an owner. Without named accountability, shared-vocabulary coverage decays below usable levels within a year (Atlan, 2026).