Product metrics: Implementation and data quality

Specifies collection, identity, storage, validation, privacy, monitoring, and debugging for product metrics. This reference fixes the adoption boundary, failure behavior, and observable evidence while leaving environment-specific limits configurable.

Package status: reference context ready for human review. The contract and test scenarios are complete, but no claim is made that an adopting implementation has passed them.

Decision

For Product metrics, adopt a versioned event and metric dictionary owned with the product decision. Implement the behavior through one authoritative write path, typed interfaces, explicit state, and idempotent side-effect handling. This package is a reference contract: the adopting team must choose numeric limits, providers, jurisdictions, and operational owners from evidence in its own environment.

Scope

• The Product metrics actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes described by this implementation block.
• Primary and alternate interfaces, background work, caches, integrations, support paths, and evidence that can exercise the same boundary.
• Adoption-specific configuration, rollout, recovery, and verification responsibilities needed to apply the reference safely.

Outside this block

• Choosing a universal vendor, framework, jurisdiction, numeric threshold, retention period, or service-level objective for every adopter.
• Claiming that packaged scenarios have run against a downstream implementation or that this reference grants legal, security, or accessibility certification.

Contract

• Give every event a semantic owner, schema version, trigger, required properties, and examples of events that must not be emitted.
• Keep account, device, anonymous, and session identities distinct; merge only through an auditable deterministic rule.
• Compute decision metrics from defined populations and windows, including late events, exclusions, and missing-data behavior.
• Apply consent and collection controls before emission, and keep sensitive values out of free-form properties and dimensions.
• Validate inputs once at the boundary and preserve the normalized representation used by downstream logic.
• Persist the business transition before or atomically with an outbox record; consumers deduplicate by durable operation identity.
• Define each product metric with decision owner, population, event sources, numerator, denominator, window, exclusions, identity, freshness, and expected direction.
• Pair outcome metrics with quality and harm guardrails, segment material cohorts, and prevent a proxy or easily manipulated activity count from becoming the sole success authority.

Implementation guidance

• Ship additive storage and interface changes first, backfill observably, then retire old paths after compatibility evidence.
• Model Product metrics inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
• Store the applicable Product metrics contract or policy version with material state so migrations, replay, and support decisions remain attributable.
• Introduce the path behind controlled rollout, compare expected and observed outcomes, and keep a tested recovery route until adoption evidence is complete.

Failure handling and safeguards

• Schema violations enter a visible quarantine or rejection path; they do not silently alter a metric denominator.
• Invalid or contradictory Product metrics state is rejected or quarantined; the implementation never guesses a value that broadens authority or duplicates an effect.
• Retries are bounded and use durable identity; after exhaustion, work reaches an inspectable terminal state with an accountable owner.

Verification and operations

• Inject dependency failure before and after persistence and prove that retry converges on one externally visible outcome.
• Monitor Product metrics success, denial, validation failure, dependency failure, retry exhaustion, and recovery by contract version without sensitive payload dimensions.
• Re-run paired positive and negative fixtures after policy, schema, dependency, migration, or boundary changes and preserve the resulting evidence.

Adoption assumptions

• Names and values in the example are a concrete fixture, not universal defaults; adopters replace them through documented evidence and ownership.
• The adopting system can provide authenticated identity, durable operation or record versions, bounded telemetry, and a controlled path for change.

The executable-looking examples in this package are fixtures and acceptance contracts. Run the collection validator to check structure and metadata, then translate and execute the scenarios in the target repository before recording implementation evidence.