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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 shows $48,200. Stripe invoices for the month total $48,847. Gap = $647 (1.3%). Within tolerance? Define your threshold before you need it.

Benchmark

Acceptable drift

Tight

< 1%

Acceptable

1–3%

Untrustworthy

> 5%

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).

Tracked in FlowMRR

Data Health → Stripe reconciliation, duplicate detection and multi-currency warnings.

Audit Data Quality in FlowMRR