Test metric definitions and source data for freshness, coverage, duplicates, missing values, impossible values, and drift.
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Run this before trusting a KPI, anomaly, cohort, funnel, or experiment result.
Record the source, covered period, extraction time, expected grain, primary or compound key, required fields, field types, allowed ranges, timezone, and known late-arrival window. State the metric formula and population separately from the file schema.
For each issue give the check, expected result, observed result, affected rows and periods, likely metric impact, severity, and owner. Show results before and after any proposed exclusion. Do not drop a bad row silently.
Use blocked when the issue can change the answer enough to affect the stated
decision. Use provisional when the result remains useful with a clear bound.
Never repair values by guess, backfill from an unrelated period, deduplicate on name alone, or hide a failed check. Avoid exposing raw personal or sensitive fields in the report. Treat a pipeline change as a possible cause of a metric move until an independent source confirms the business change.
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Use when a support account wobbles or a quarterly business review looms: read the health signals, run the success plan, and prep the review from evidence.
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Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.
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Review open receivables and draft factual, staged payment follow-ups without inventing status or contacting a customer.
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Write ad variants that each test one idea, within platform limits and supported claims.
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Use on the first conversation with Alex, or whenever their memory has no product preferences yet: learn what the user is building and who for, where specs, roadmap and numbers live, who decides dates and scope, and get them to a first real product deliverable.
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Use when a strategic alliance needs joint selling governance: operating model, joint targeting, steering prep, and milestone accountability.
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