Defines questions, entities, events, properties, metrics, segments, and interpretation rules for data quality. 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 Data quality, adopt a versioned event and metric dictionary owned with the product decision. Specify the decision question, population, events, identity, metric formula, guardrails, and interpretation before instrumenting it. 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 Data quality actors, inputs, outputs, states, policy or schema versions, and externally visible outcomes described by this analytics 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.
- Version numerator, denominator, attribution, exclusion, and time-window rules together with the experiment or dashboard.
- Do not promote a result when sample-ratio mismatch, missing events, identity drift, or a guardrail breach remains unresolved.
- Measure completeness, validity, uniqueness, timeliness, and reconciliation by schema version and producer, not only at aggregate table level.
- Quarantine invalid records with bounded payload evidence and prevent them from silently entering decision metrics.
Implementation guidance
- Replay the metric over a frozen fixture containing late, duplicate, anonymous, merged, excluded, and missing events.
- Model Data quality inputs, outputs, actors, states, invariants, side effects, and evidence before selecting framework or vendor details.
- Store the applicable Data quality 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 Data quality 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
- Recompute the result independently from raw fixtures and confirm segmentation cannot leak restricted or identifying data.
- Monitor Data quality 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.