Often organizations deal with two general types of reconciliation: record count matches and values matches.
Record Count matches: This type of reconciliation deals with matching records in source vs target and alerting when counts goes suddenly high or low.
Value matches: This type of reconciliation deals with matching total value such as revenue, cost, quantity, count of product or material or customer.
With reconciliation still a labor-intensive process, especially when one considers that everything from diverse source systems to complex ETL logic to complex analytics data model, organizations find themselves constantly spending many hours to reconcile data. Not surprisingly, lack of data integrity takes a toll on matching accuracy and the ability to automate reconciliation.
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