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Data Quality Assurance & Testing – Investment Bank

This case study showcases how iceDQ’s data testing and monitoring solution helped a leading investment bank successfully complete a complex data warehouse migration and integration project. The project involved migrating Fixed Income data to a new system while integrating it with existing Equities data.

Key Highlights:

1. Project Scope: Over 400 people involved, impacting multiple business areas and operations across regions.
2. Challenge: Testing hundreds of new data feeds and thousands of existing ETL processes while monitoring progress effectively.
3. Solution: Implementation of iceDQ’s Data Testing and Monitoring Platform.
4. Scale: Approximately 5,000 data testing rules implemented over nine months.
5. Global Collaboration: Web-based interface enabled efficient teamwork across locations.

Essential Metrics:

1. Time Savings: Project completed on schedule.
2. Resource Efficiency: Up to 20% fewer resources required for testing over nine months.
3. Cost Reduction: 33% direct cost savings through utilization of offshore resources.
4. Testing Scope: Ability to test complete datasets instead of restricted samples.
5. Issue Detection: Thousands of critical and non-critical data quality defects discovered and resolved.

Key Benefits:

1. Enhanced visibility and unified strategy for all stakeholders.
2. Support for test-driven development in a data-centric project.
3. Continuous, consistent, and automated testing of all data passing through ETL processes.
4. Rapid feedback and drill-down capabilities for efficient problem-solving.
5. Successful sign-off on data quality, crucial for downstream trading operations.

This case study demonstrates how implementing a robust data quality assurance framework can significantly improve the efficiency, accuracy, and cost-effectiveness of large-scale data migration projects in the financial sector.

Download the full case study to learn more about:

1. The specific data testing challenges faced by the bank.
2. How iceDQ’s framework and functionalities addressed these challenges.
3. The quantifiable benefits achieved by the bank through iceDQ implementation.
4. How iceDQ can help you achieve a successful data warehouse migration.