Use Case
Automation Turbocharges Data Reconciliation Process
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Challenges
- Inefficient process: Manual Testing for data reconciliation was limited to data samples and didn’t provide adequate test coverage
- Time-consuming: Manual test process involving Excel sheets with extracted data sets wasted a lot of time
- Error-prone: There was a high-risk of defects and issues slipping through to other environments and production
Solutions
- The ADROSONIC Quality Engineering team conducted an analysis of the scope of data reconciliation, including source and target systems’ data size and table information. Then, a Python based automation utility was created to automate the test process. The new automated reconciliation process proved to be highly efficient, time-saving and error-free.
Benefits
- 100k records validated per minute
- 170m records reconciled (100%) in 3.5 hours
The Shipowners’ Club is a mutual marine liability insurer providing protection and indemnity (P&I) insurance for over 33,000 vessels globally. It is also one of the 12 P&I clubs that are members of the International Group (IG), collectively insuring over 90% of the world’s tonnage.
Being an over 165-year-old firm, The Shipowners’ Club had extensive data stored in its legacy systems. To support their business with more enriched and cleansed data, the Club set up a new strategic Data & Analytics Platform on Microsoft Azure Cloud, migrating data drawn from various legacy systems. However, the Club faced the challenge of ensuring consistency, accuracy, and quality of the migrated data on the cloud platform.
When the insurer approached ADROSONIC for automation of its data reconciliation process, our experts came up with an innovative solution. The ADROSONIC Quality Engineering team built a utility in Python to automatically reconcile the data across systems with 100% accuracy.
An error-free and agile reconciliation process meant the firm could rely on the cloud data for better monitoring, analysis and informed decision-making on strategic and operational business metrics.
- Microsoft SQL Server
- Python
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