Data Reconciliation for Accurate Supply Chain Reporting

By improving data integration and building reconciliation logic across a complex Azure-based data ecosystem, we helped the client eliminate reporting discrepancies, restoring trust in their inventory data and supporting seamless scaling of new warehouse operations.

In just 1.5 months, we supported our client in fixing critical data discrepancies between their Datawarehouse and centralized data lake. Our team built custom reconciliation jobs and improved data integration workflows using Azure technologies, restoring trust in reporting and ensuring operational accuracy across all facilities.

Challenge

The company has a standalone application for its customers, with a reporting module that uses data from a customer Datawarehouse (DWH). The customer’s WMS is one of the primary source systems that uses the data lake with a database wrapping. All new facilities are onboarded using this application stack, including deleting and recreating orders and receipts.   

However, as the data lake was centralized any deleted customer entities would also be removed from the data lake, while corresponding entries remain in DWH, leading to incorrect reporting of inventory transactions and balance. 

Solution

• Our team improved integration processes and created custom reconciliation jobs, which compare data deltas.  

• The build enabled a system that marked entities/records and excluded from reporting when deleted from the source system. Although the reporting was cleansed all source data remains in a decentralized DWH for process analysis purposes.