What is data reconciliation?

Data reconciliation is the process of comparing two or more datasets to reveal discrepancies.

Data reconciliation is performed for a specific function – like reconciliation of invoices to the general ledger – but can apply to many processes across your business. Specific reconciliation procedures and tools can differ depending on the team and use case, or they can be standardized across the enterprise.

How does it work?

Data reconciliation can be performed on data at rest (in databases or file systems), or in motion (as it moves between systems and processes). When data in motion is reconciled, it’s validated between steps to ensure no data is lost or transformed incorrectly as it moves between locations.

Data quality checks are often performed prior to a reconciliation process to ensure that the data used is valid.

Data reconciliation is often performed on financial data, but it has a variety of other uses. For example, you can reconcile a customer list sent from a third party with your internal list of customers.

Increase accuracy, operational efficiency, and mitigate risk with automated data reconciliation


Why is data reconciliation important?

What does data reconciliation mean for your business? What are the benefits? 

Data reconciliation helps ensure the accuracy and completeness of your data across sources. With the right solutions, you experience benefits like improved operational efficiency, stronger regulatory compliance, and reduced risk.

Software solutions can provide a consistent method for reconciliation across the enterprise.  They should balance and reconcile data at the individual field level, in addition to the transaction level. That way you’re alerted if a transaction is missing, but also if a field that was originally $10,000 was transformed to $1,000,000, or if a policy ID was transposed.  

Any of these issues can cause major problems downstream when the amount on the transaction is used to make a payment, or policy documents are sent to an insured individual.

All of this means that data reconciliation solutions need to include a unified method to route and manage exceptions that are generated from the reconciliation process. There will always be transactions that aren’t exact matches, and when this does happen, there needs to be a simple way to resolve the issue.

How can Precisely help you with data reconciliation?

We’re here to help protect your organization with Data360 DQ+ a first-of-its-kind solution that boosts the quality of your data in motion and at rest with enhanced monitoring, visualization, remediation, and reconciliation. It provides a data reconciliation process that’s automated and auditable, reducing your risk of compliance violations.