Glossary

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Glossary / Evaluation and implementation guide

Data Reconciliation

Data reconciliation is the operational process of detecting and resolving differences between two systems that should hold the same data.

It is a recurring task, not a one-time setup: counts drift, fields diverge, and deletes fail to propagate.

Reconciliation answers "what differs and why," then applies a fix — manually, by rule, or by re-running a full comparison.

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A practical example

Example: a monthly audit compares contact counts between the CRM and the email platform, finds 400 records present in one and missing in the other, and traces the gap to failed sync batches during an API outage.

What to evaluate before investing

  • Ask whether the vendor offers a comparison or diff report between its platform and connected systems, and how granular it is.
  • Test whether you can re-trigger a sync for a specific record or date range without a full re-import.
  • Check if reconciliation can run on a schedule with alerts, rather than only when someone notices a problem.

Limitations and tradeoffs

Reconciliation is detective work after divergence happens; it does not prevent the divergence. Pair it with strong conflict rules and monitoring, or you will reconcile the same gaps every month.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.

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