iceDQ Success Story: One Bank 14 Use Cases

Challenge: Automating manual ETL testing and reconciliation across multiple environments.

Automated Testing: Implemented automated testing by replicating test cases.

Full Data Coverage: Ensured comprehensive testing by eliminating sampling.

Automated Data Reconciliation: Ensured accurate data comparison across databases.

Advanced Data Validation: Performed in-depth transformation checks using expressions.

Improved Productivity: Saved time, resources, and costs while improving data quality.

Challenge: Initial testing relied on sampling, which hindered full data validation.

Pre-built Rule Templates: Accelerated the rule generation process.

Automated Testing: Full-scale testing with automated test case replication across all environments, from DEV to QA to PROD.

Expanded Testing Scope: Increased the number of tables tested from 10 to 200.

Optimized Timeline: Significantly reduced project duration.

Resource Efficiency: Decreased the number of QA resources required.

Enhanced Coverage: Increased the number of tested tables substantially.

Challenge: Post-cutover, there was a need to ensure all applications connected correctly to the new active Snowflake instance.

Sync Issue Detection: Identified data synchronization issues.

DR Certification: Certified the successful transition between active and DR sites.

Data Reconciliation: Compared and reconciled data between active and DR environments.

Comprehensive Validation: Performed full count and data validation tests.

Challenge: Manual monitoring using scripts was inefficient and required frequent updates due to changing business requirements. Previous checks were count-based only.

Beyond Count Validation: Not only counts but also validated the data values.

Handling Complex Transformations: Managed complex data transformations using built-in expressions.

Adaptable Rules: Implemented changes to business rules on-the-fly.

Country-Specific Adaptation: Adjusted testing for different countries and codes.

Reference Data Validation: Identified and resolved issues with reference data.

Comprehensive Testing: Monitored process execution and validated reference and mapping data.

Challenge: No dedicated tool for regression testing, with previous efforts relying on manual methods or scripts.

Complete Automation: Automated the regression testing.

Post-Release Testing: Ran suites after releases to ensure the production is not impacted.

Efficient Parameter Handling: Easily updated values across 100s of rules using parameters.

Business Rule Flexibility: Made rule changes efficiently via iceDQ’s expression handling.

Challenge: Initial data testing was manual, relied on sampling of API responses, and lacked comprehensive coverage.

Vendor API Querying: Retrieved and compared data from multiple vendor APIs with the bank’s environment.

Bulk Data Retrieval: Efficiently pulled large volumes of data from APIs.

Full Automation: Eliminated manual validation and reconciliation of data.

Challenge: Manual validation of Kafka messages was time-consuming and lacked scope.

Native Salesforce Integration: Connected seamlessly with Salesforce and other data sources.

Data Reconciliation: Compared data across environments like Salesforce, SQL Server, Data Lake, and Snowflake.

Automated Testing: Performed thorough data checks, ensuring data accuracy.

Challenge: Manual review of vendor files and determination of data priority were inefficient.

Automated Data Validation: Scripted validation process for efficiency.

Priority-Based Reverse Lookup: Utilized mapping tables to perform reverse lookups on vendor files.

Data Reconciliation: Validated and reconciled the security master file against vendor data.

Challenge: To ensure 100% automated end-to-end Salesforce Testing of the migration from legacy to the unified Salesforce platform.

Automated Data Validation: Scripted validation process for efficiency.

Priority-Based Reverse Lookup: Utilized mapping tables to perform reverse lookups on vendor files.

Data Reconciliation: Validated and reconciled the security master file against vendor data.

Challenge: API testing was manual and required code changes, causing delays in the testing process.

Leveraging JMeter Data: Reused existing JMeter data for reconciliation.

Streamlined Process: Used iceDQ rules for automated data comparison and validation.

Simplified Data Creation: Easily generated source data and stored API responses for comparison.

Dynamic Rule Updates: Quickly modified rules using iceDQ’s low-code/no-code UI.

Challenge: Testing a complex data flow across multiple systems and large volumes of data while ensuring accuracy and compliance.

Data Validation and Reconciliation: Ensured data accuracy throughout the flow from source systems to final risk rating.

Transformation Logic Checks: Used UDFs to validate complex transformations.

Daily Large Volume Testing: Handled the testing of 10,000-12,000 accounts daily.

Continuous Monitoring: Automated validation on a scheduled basis.

Challenge: Changes in data attributes within JSON feeds caused data flow issues and integrity problems.

Native JSON Support: Validated JSON files seamlessly.

Real-time Monitoring: Halted data flow when errors or attribute changes occurred.

Granular Error Detection: Identified specific records causing validation issues.

Challenge: Migrating and testing 300+ employer groups, 1.5 million individual accounts, and $10+ billion in assets under management (AUM).

Native Connectivity: Connected and compared fixed-width files across databases and data lakes.

Compliance Proof: Generated testing proof required for regulatory compliance.

Scalability: Handled increasing data volumes without performance loss.

Low-Code/No-Code Automation: Enabled automated test creation and execution.

Challenge: Monitoring and reconciling data across different systems and sources efficiently.

One-to-Many Reconciliation: Utilized built-in UDFs to reconcile data across various sources.

Real-time Monitoring: Tracked daily row counts and generated exception reports to detect anomalies.

Null Value Monitoring: Tracked null counts and reported significant changes.