When a Leading Financial Service Provider and Bank from the US Encountered Significant Test Data Management Challenges

The product team was developing a new loan origination system to improve loan KYC and KYB experience. The DevOps team that was tasked with the testing and execution of data encountered significant challenges with test data management.

Challenges that Arise in the Absence of Data Masking Automation

Manual Data Provisioning: Test data was manually copied from production databases, leading to inconsistent datasets and delays in provisioning.

Compliance Risks: Sensitive customer data was accidentally exposed in test environments, violating Consumer Financial Protection Bureau (CFPB) data privacy regulations.

Test Quality Issues: Manually copied data often lacked the diversity needed for edge-case testing, resulting in undetected bugs that caused errors in production.

These challenges caused delays in the CI/CD pipeline and increased the risk of deployment failures.

Solution: Integration of Automated Test Data Management and Data Masking Tools

Tool Selection:

Our team helped the bank understand the key features of some of the best data masking tools (e.g., Informatica Data Masking, Delphix, or DataVeil) to support automated workflows, compliance requirements, and integration with CI/CD pipelines.

Workflow Integration:

• The data masking tool was integrated into the bank’s existing TDM workflows and CI/CD pipeline.
• The TDM system triggers the data masking tool when new datasets are required for testing.
• Masking rules and templates are pre-configured based on field types and compliance policies.

Workflow Steps Involving Test Data Management Automation Include the Following:

1. Dataset Request

A tester or developer initiates a request for test data using a self-service TDM portal. For example, they request customer data for testing loan application approval logic.

2. Data Extraction

The TDM system extracts the required dataset from the production database or data warehouse.

3. Automated Masking

The dataset is passed to the data masking tool.

Predefined masking rules are applied:

SSNs: Replaced with format-preserved random numbers.

Account Balances: Shifted by a random percentage to retain realistic ranges.

Names: Replaced with synthetic names from a lookup table.

Transaction IDs: Replaced with unique but unrelated identifiers.

Masking ensures dependencies (e.g., relationships between customer ID and accounts) remain intact.

4. Validation and Provisioning

The masked data is validated to ensure referential integrity and compliance with masking rules.

The sanitized dataset is provisioned to the requested test environment automatically.

5. Data Usage

Testers execute test cases using the masked data. Since it is representative of production data, tests produce accurate and reliable results.

6. Data Cleanup

After testing, the dataset is automatically deleted or refreshed to maintain a clean environment.

Key Features of the Data Masking Tool

1. Role-Based Access Control: Ensures only authorized users can access sensitive data or masking rules.
2. Format-Preserving Masking: Maintains data format and field dependencies for compatibility with the application.
3. Integration with CI/CD Pipelines: Automatically triggers masking workflows as part of the build or release process.
4. Audit Trails: Logs all masking activities to demonstrate compliance with regulatory requirements.

Outcomes we Achieved

a) Regulatory Compliance: The bank ensured compliance with GDPR and CCPA by never exposing raw production data in test environments.

b) Faster Test Cycles: Automating masking reduced the time to provision test data from hours to minutes, accelerating CI/CD pipelines.

c) Improved Data Quality: Masked datasets retained the structure and realism of production data, enabling effective testing.

d) Eliminated Errors: Automated workflows removed the risks of manual errors in data preparation.

e) Time Saved: Data provisioning time reduced from 6 hours to 30 minutes.

f) Error Reduction: Data integrity issues dropped by 90%.

g) Compliance Adherence: Passed external audits with zero findings on test data handling.

The above example illustrates how our integrating of data masking tool with TDM workflows has streamlined processes, enhanced compliance, and improved testing efficiency.

Our Approach and Framework to Ensure the Success of Test Data Management Automation

1. Adoption of Synthetic Data Generation

What it means:

Generate artificial test data that mimics real-world data without using sensitive production data.

How:

Use synthetic data tools that support diverse data modeling.

Define rules to simulate edge cases, anomalies, and large-scale datasets.

Benefits:

Ensures compliance with data privacy regulations like GDPR.

Facilitates comprehensive testing without production data risks.

2. Automation of Data Masking

What:

Anonymize sensitive production data while retaining its format and referential integrity.

How:

Use data masking tools integrated with TDM workflows.

Apply role-based access control to restrict access to sensitive data.

Benefits:

Enables safe use of production-like data for testing.

Reduces legal and compliance risks.

3. Enabling Continuous Data Refresh

What:

Regularly update test environments with refreshed or replenished data.

How:

Implement automated scripts to sync test environments with anonymized production data.

Use incremental updates to reduce downtime.

Benefits:

Prevents data staleness and ensures accurate test results.

Supports long-running test cycles with dynamic data needs.

4. Leveraging Data Virtualization

What:

Provide a virtualized layer of test data without copying or moving large datasets.

How:

Implement virtualization tools to create lightweight virtual datasets.

Use snapshots to simulate various data states and scenarios.

Benefits:

Reduces storage costs and improves data access speed.

Supports parallel testing in multiple environments.

5. Integrating TDM with CI/CD Pipelines

What:

Embed TDM processes into automated CI/CD workflows to provision data during pipeline execution.

How:

Define TDM actions as part of CI/CD stages (e.g., data generation, masking, cleanup).

Use API-driven TDM tools to provision data programmatically.

Benefits:

Enables on-demand data provisioning for automated tests.

Streamlines the testing pipeline for faster releases.

6. Maintaining a Centralized Test Data Repository

What:

Establish a central repository to manage and govern all test data.

How:

Organize datasets by type, use case, and environment.

Implement version control for datasets.

Enforce data governance policies and permissions.

Benefits:

Ensures consistent and reliable test data across teams.

Simplifies data access and reduces duplication.

7. Incorporating Intelligent TDM Tools

What:

Use advanced TDM tools powered by AI and ML to predict data requirements and optimize data provisioning.

How:

Leverage tools to analyze testing patterns and suggest relevant datasets.

Use ML models to identify missing or insufficient test data scenarios.

Benefits:

Improves data coverage and quality for testing.

Reduces manual effort in defining data requirements.

Example Workflow with Improved TDM

Conclusion

Adopting our proven approach ensures efficient, compliant, and high-quality TDM that eliminates bottlenecks of manual test data provisioning. By automating TDM, banks can also accelerate delivery timelines, improve software quality and ensure compliance with data privacy standards.

The use of TDM tools can help generate datasets representing tens of thousands of loan applications within minutes. This allows DevOps teams to identify and fix critical bottlenecks in the loan approval workflow, thereby avoiding potential production issue that can delayed loan processing and cause customer dissatisfaction.