Specifies storage, interfaces, validation, access, freshness, monitoring, and failure behavior for analytics result provenance, so adopters can let every number, chart, and narrative statement identify the metric, scope, data snapshot, transformation, and q...
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 Analytics result provenance, implement the semantic contract through owned, typed boundaries so adopters can let every number, chart, and narrative statement identify the metric, scope, data snapshot, transformation, and query evidence that produced it. Provenance supports reproduction and review; it does not expose restricted SQL, credentials, raw personal data, or internal schema to the model. This is a reference contract: database products, numeric budgets, jurisdictions, retention, organizational defaults, and accountable owners remain explicit adoption choices.
Scope
- The Analytics result provenance actors, inputs, outputs, states, versions, and externally visible outcomes needed to let every number, chart, and narrative statement identify the metric, scope, data snapshot, transformation, and query evidence that produced it.
- The role-specific focus of this block: implement the semantic contract through owned, typed boundaries, including primary, cached, asynchronous, export, support, and recovery paths where applicable.
- Adoption-specific configuration, ownership, rollout, evidence retention, and review responsibilities needed to use the contract safely.
Outside this block
- Provenance supports reproduction and review; it does not expose restricted SQL, credentials, raw personal data, or internal schema to the model.
- Choosing a universal database, model, renderer, vendor, numeric threshold, retention period, timezone, jurisdiction, or service-level objective.
- Claiming that packaged scenarios ran against a downstream implementation or that this reference grants security, privacy, accessibility, analytical, or legal approval.
Contract
- The implemented Analytics result provenance boundary enforces this rule: Each result carries durable request and result IDs plus the exact metric version, normalized scope, filters, intervals, grain, dimensions, and as-of time.
- The implemented Analytics result provenance boundary enforces this rule: The evidence records dataset snapshot, ingestion watermark, connector version, query-plan digest, and quality checks without returning executable SQL to the model.
- The implemented Analytics result provenance boundary enforces this rule: Every chart series, KPI card, and narrative claim references one or more result IDs; derived claims name their input result IDs and transformation.
- The implemented Analytics result provenance boundary enforces this rule: Authorization scope and access trimming are part of provenance so two users can understand why apparently similar requests produced different totals.
- The implemented Analytics result provenance boundary enforces this rule: Cached results retain original computation evidence and add cache age; a cache hit never pretends to be a newly executed query.
- The implemented Analytics result provenance boundary enforces this rule: Logs use correlation-safe identifiers and exclude credentials, raw prompts containing sensitive data, unrestricted filters, and returned row payloads.
- The implemented Analytics result provenance boundary enforces this rule: Exports preserve metric descriptions, periods, filters, units, freshness, quality warnings, and result IDs alongside values.
- The implemented Analytics result provenance boundary enforces this rule: Retention of reproducibility evidence follows classification and audit policy independently from retention of detailed analytical rows.
Implementation guidance
- Store the versioned semantic object separately from database-specific compiled artifacts and presentation-specific documents.
- Validate at ingestion, registry, query, result, and rendering boundaries and keep one normalized representation through downstream steps.
- Roll out additively, compare old and new evidence over frozen fixtures, and keep a reversible migration until consumers adopt the new version.
- Instrument success, rejection, staleness, partial results, version conflicts, and repair without recording sensitive payloads.
Failure handling and safeguards
- For Analytics result provenance, A result missing required lineage is marked unverifiable and cannot support an automated executive conclusion.
- For Analytics result provenance, If a source snapshot expires, the system retains the prior provenance record and states that exact recomputation is no longer available.
- For Analytics result provenance, A mismatch between displayed values and their result IDs invalidates the complete dashboard response rather than only the affected tooltip.
Verification and operations
- For the implementation evidence of Analytics result provenance, select random chart points and trace each one to a metric version, normalized request, data watermark, and connector execution record.
- For the implementation evidence of Analytics result provenance, reproduce retained fixtures from provenance and compare values, labels, warnings, and authorization scope with the original response.
- For the implementation evidence of Analytics result provenance, inspect telemetry and exported artifacts to confirm that evidence remains useful without leaking row payloads or credentials.
Adoption assumptions
- The adopting product has authenticated identity, a versioned authorization policy, owned metric definitions, bounded telemetry, and a controlled path for change.
- Names and values in the example are fictional adoption fixtures, not universal defaults, production credentials, performance promises, or business targets.
- Referenced specifications constrain protocol, security, accessibility, or vendor behavior; the adopting team must confirm current applicability before promotion.
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.