Governance
Different metrics need different rigor. ClariLayer enforces tier-based governance so experimental work can move quickly while operational and financial definitions keep validation, approval when required, and release evidence attached.
Start FreeTwo VPs bring two different numbers for the same metric. The meeting derails from strategy into “auditing the slide.” Once trust breaks, executives revert to gut-feel — the million-dollar data stack becomes useless.
Different departments have legitimate reasons for different views. The problem is not disagreement — it is invisible, ungoverned disagreement. ClariLayer makes variants transparent, intentional, and safe.
Not every metric needs the same rigor. ClariLayer enforces the right level of governance for the right level of stakes.
Tier 0
Any user can define. Self-serve release. Fast-track for internal experiments and exploratory analysis. AI-generated SQL accepted with standard validation.
Tier 1
Any user can define. Warehouse validation required before release. Owner approval can be required by policy. For day-to-day team metrics.
Tier 2
Role-gated: only designated users can define. Multi-approver review required. Template-generated SQL gets a lighter review path. For board-level reporting.
Release Control
ClariLayer treats metric governance as an execution workflow, not a policy PDF. A release candidate carries the proposed definition, validation report, dependency context, owner, and approval state. If the required evidence is missing or stale, the metric should not become the trusted version consumers rely on.
That matters for both people and systems. An analytics engineer reviewing a pull request needs to know what changed and why. An AI agent querying the Contract API for governed metric context needs to know whether the metric is approved, deprecated, experimental, or approved for financial reporting.
Approved definitions produce immutable artifacts with contract metadata, SQL, validation reports, and reviewer context. Iteration creates a new version instead of rewriting the old one.
Finance, marketing, and sales operations may need different views of a metric. ClariLayer keeps the shared core visible while documenting which variant is current for which purpose.
Direct deploy and rollback are available for released Tier 0 Experimental metrics as warehouse views; higher-risk tiers remain release-bundle/handoff-first.
Catalog browse, validation probes, direct deploy, and rollback are shared across Databricks, Snowflake, and BigQuery. Observe/query-history ingestion remains Databricks-only today.
Experimental metrics can move quickly. Operational metrics need validation and can add owner review. Financial metrics require stronger review. The rigor matches the stakes.
The "Single Version of the Truth" is a myth that creates bottlenecks. ClariLayer supports a shared core with intentional, transparent deltas — governed, versioned, and auditable.
Once a metric version is released, it is never changed. Iteration means creating a new version with its own contract metadata, validation report, and review history.
The full AI conversation behind every definition is preserved. "Why did we exclude refunds from MRR?" — the actual reasoning chain, not just a changelog entry.
Approved metrics produce release-bundle artifacts for engineering review and handoff: contract YAML, SQL artifacts, validation evidence, and reviewer context.
The conversation audit trail is institutional knowledge that cannot be recreated. After 12 months with 50+ governed metrics, migrating means losing the reasoning behind every definition.
“Why did we exclude refunds from MRR?” — the actual reasoning chain, not a changelog entry.
“Who approved this churn definition?” — the full approval chain with timestamps.
“When was this last validated?” — validation evidence attached to every version.
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