Data reconciliation

Data reconciliation is the process of comparing data from two or more sources, identifying differences, and resolving them so the records are consistent, explainable, or fit for a defined business purpose. In manufacturing and regulated operations, it commonly refers to checking whether transactions, quantities, statuses, timestamps, or quality records match across systems such as MES, ERP, LIMS, historians, SCADA, or paper and electronic records.

The term includes both the detection of mismatches and the follow-up work needed to explain or correct them. That may involve confirming the source of truth, tracing missing transactions, correcting master data, reprocessing integrations, or documenting why a difference is expected. It does not mean changing data until systems agree without review. A proper reconciliation process distinguishes between valid operational differences, timing differences, and actual data errors.

How it appears in operations

Data reconciliation often appears where information moves between systems or must support traceability, reporting, inventory control, batch review, or quality records. Common examples include:

• comparing ERP inventory balances with MES consumption and production reporting

• matching batch or lot records between execution systems and quality systems

• verifying that machine, historian, or SCADA counts align with production declarations

• checking that released, shipped, scrapped, or reworked quantities are consistent across records

• reviewing interface failures, duplicate transactions, or missing timestamps after system integration events

Reconciliation may be manual, system-assisted, or automated with exception reporting. In regulated environments, the result is often an evidence trail showing what was compared, what differed, and how the difference was handled.

What it includes and excludes

Data reconciliation commonly includes record matching, variance detection, exception review, root-cause tracing for mismatches, and documented correction or justification. It may use rules, tolerances, or workflow approvals depending on the process.

It is not the same as data migration, data cleansing, or routine synchronization, although those activities can reduce reconciliation issues. It is also not the same as financial account reconciliation only. In industrial settings, the term is broader and may apply to production, quality, inventory, maintenance, and traceability data.

Common confusion

Data reconciliation is often confused with data validation and data synchronization. Validation checks whether data meets defined rules or formats. Synchronization moves updates between systems. Reconciliation compares records across sources to confirm consistency and explain differences.

It can also be confused with traceability. Traceability tracks lineage, relationships, and history across materials, lots, or processes. Reconciliation checks whether related records agree and whether discrepancies can be explained.