Data Quality & Reconciliation
Numbers you can't trust are worse than no numbers.
The practice of cross-checking your analytics output against the source of truth (Stripe invoices, bank statements). Without reconciliation, dashboard numbers drift quietly until decisions are made on fiction.
Worked example
Dashboard MRR is $48,200; independently normalised Stripe subscription MRR is $48,847 on the same date, currency, discount, and status basis. The $647 gap (about 1.3%) needs reconciliation. Raw invoice totals are not comparable to MRR because annual bills, taxes, and one-off charges differ by design.
Illustrative comparisons
Acceptable driftThese unsourced ranges are illustrative scenarios, not measured industry benchmarks, percentiles or recommended targets. Their qualitative labels are editorial interpretations. Compare your own cohorts using the same segment, period, currency and measurement basis.
Why it matters
Investor due diligence will tie out your MRR to Stripe to the dollar. Forecast accuracy depends on input accuracy. Customer-facing pricing changes ride on the assumption that you know what customers actually pay. Data quality is invisible until it's a crisis — bake it in early.
Common mistakes
- Calculating MRR from CRM data instead of payment data — they always diverge.
- Not reconciling MRR snapshots against Stripe invoices monthly.
- Ignoring proration adjustments and credit notes — small per-event, large in aggregate.
- Treating refunds as churn (they're a revenue adjustment, not a customer event).