Broadcom Test Data Manager enables effective test data processes with tools for synthetic data generation, data masking, and data subsetting. It enhances automation and ensures data privacy and compliance, catering to diverse testing environments.
Broadcom Test Data Manager provides centralized data management with flexible functionalities like cubing and test matching, simplifying the creation and handling of test data. It supports both relational and non-relational data, optimizing test processes for effective results. Users find its intuitive UI and self-service portal time-saving. However, enhancements in programmatic capabilities, non-relational data handling, automation, and integration speed are needed. A unified web-based interface, better API usability, and expanded data source support could improve user experience. Mainframe functionality, data reservation, and web-based UI transitions also require focus for stability and scalability improvements.
What are its key features?
- Synthetic Data Generation: Create realistic test data without affecting actual records.
- Data Masking: Protect sensitive data through advanced masking techniques.
- Data Subsetting: Efficiently manage data volumes by extracting relevant data portions.
- Centralized Management: Simplifies data operations through a single management point.
- Flexible Functionalities: Offers cubing concepts and test matching for precise data targeting.
- Comprehensive Data Support: Handles both relational and non-relational data seamlessly.
What ROI benefits should you consider?
- Enhanced Automation: Streamlines test data processes, reducing manual effort and errors.
- Time Savings: Intuitive interfaces and self-service portals increase efficiency.
- Compliance Assurance: Adheres to data privacy norms, safeguarding sensitive information.
- Reduced Disk Usage: Efficient data subsetting decreases storage requirements.
- DevOps Integration: Seamlessly integrates with pipelines, accelerating testing phases.
Broadcom Test Data Manager is widely used in industries like healthcare and finance for data masking and test management. It aids in creating synthetic data, managing subsetting, and anonymizing information, particularly valuable in handling regulated data. Its alignment with DevOps and comprehensive data capabilities are key in supporting secure, efficient testing workflows.
