ANALYTICS GOVERNANCE

Make KPI reliability a control, not a hope

A reusable validation layer for analytics data covering schema, missingness, duplicates, business rules, referential integrity, reconciliation, and explicit PASS/REVIEW/FAIL states.

Open technical evidence ↗
SchemaStructure and type checks
CompletenessMissingness and duplicates
Business rulesRanges and identities
ReconciliationSource-to-report controls

Problem

A polished dashboard can still be wrong when its underlying data contains structural defects. This framework converts common analytical failure modes into repeatable pre-publication controls.

Workflow

Decision context → Profile → Validate → Reconcile → PASS / REVIEW / FAIL → KPI reliability.

Controls

  • Schema validation.
  • Missingness and duplicate detection.
  • Range and business-rule checks.
  • Referential-integrity checks.
  • Source-to-report reconciliation.
  • Check-level classification.

Why it matters

Analytics errors often enter through definitions, joins, missing records, duplicated facts, impossible values, or broken metric identities before they appear as obvious visual defects. Quality checks make those risks visible before publication.

The fixture is synthetic and intentionally imperfect. Production deployment would require source-specific rules, ownership, alerting, and ongoing monitoring.

Portfolio role

This is supporting evidence for an analyst who treats data quality as part of analytical work rather than a separate afterthought.