What is data reconciliation?

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Data reconciliation is the process of comparing data from two or more sources to ensure it is consistent, accurate, and complete. In finance, this typically means verifying that the same transaction or balance is recorded correctly across different systems — the ERP, the bank, a subledger, a point solution, or an external data source.

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It is distinct from account reconciliation (which focuses on a specific balance sheet account) in that it focuses on the integrity of data flows between systems, not just the accuracy of a reported balance.

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Where data reconciliation is needed

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Data reconciliation is required anywhere financial data moves between systems: ERP to bank statement, ERP to payment gateway, CRM to billing system, billing system to revenue recognition platform, subsidiary to group consolidation. Each handoff is a point where data can break, lag, or diverge.

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In enterprises running multiple ERPs, multiple entities, and multiple point solutions, the number of data reconciliation points can be very large — and each one is a potential source of error in the financial close.

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Why it's manual by default

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Most finance teams handle data reconciliation through a combination of scheduled exports, VLOOKUP-heavy spreadsheets, and manual review. The process is time-consuming, error-prone, and opaque — when a break is found, tracing it back to the source can take hours.

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Automated data reconciliation platforms change this by matching data in real time, flagging breaks as they occur, and providing drill-down into the source records on both sides.

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Related: Account Reconciliation · ERP Reconciliation · Recon Automation · Data Quality in Finance