Audit Data and Ground Truth

Determine what the data actually represents and whether its labels can support the intended claim. The outcome is an evidence-backed readiness judgment, not a generic profile report.

Anchor the audit

State the intended decision, entity, row grain, target, prediction moment, outcome window, and population. If these are not known, surface that uncertainty before interpreting columns or labels.

Preserve source data unchanged. Work from read-only inputs or documented snapshots, and keep every correction, exclusion, or derived label traceable to its source.

Inspect in risk order

1. Source and lineage: Identify origin, owner, extraction time, filters, transformations, versions, and competing sources of truth.
2. Structure: Measure row and entity counts, schema, types, key uniqueness, duplicates, missingness, ranges, and category cardinality. Report both counts and rates where scale matters.
3. Meaning: Confirm units, code sets, null semantics, sentinel values, event meanings, and whether one row means what the analysis assumes.
4. Relationships: Test key uniqueness on each side before joining. State expected and observed cardinality, unmatched rates, row multiplication, and aggregation effects.
5. Time: Separate event time, record time, update time, and availability time. Check impossible orderings, late arrivals, backfills, and future knowledge.
6. Ground truth: Trace each label from operational event to encoded value. Verify annotator or system provenance, adjudication, label maturity, class definitions, coverage, disagreement, and missing-label behavior.
7. Reconciliation: Compare raw records with independent records or a stratified sample. Do not let agreement with a downstream table validate the upstream process that created both.