Webinar Description
Key Takeaways
- Examines why poorly generated synthetic data can introduce more errors than using no test data at all
- Demonstrates techniques for generating synthetic data that maintains referential integrity across complex datasets
- Covers data masking strategies for protecting sensitive information while preserving data utility
- Addresses enterprise-scale data provisioning for development, testing and AI workflows
- Aimed at data architects, QA managers, DevOps teams and compliance professionals in regulated industries
About the Event
This virtual webinar, titled “Synthetic & Masked Data Done Right: Why Bad Test Data Is Worse Than None,” explores the technical and compliance challenges of generating test data for software development environments. Perforce Delphix experts lead demonstrations showing how to create synthetic data that remains realistic and referentially intact while masking sensitive information effectively.
The Problem with Poor Synthetic Data
Synthetic data that fails to preserve relationships between database tables and fields can break application logic during testing, leading to undetected defects that surface in production. The webinar addresses how broken data relationships undermine test validity and why maintaining referential integrity is essential for meaningful quality assurance.
Key Topics
- Synthetic data generation with preserved data relationships
- Data masking techniques for sensitive information
- Data virtualization for test environments
- Provisioning data for AI workflows
- Scaling data generation across enterprise environments
Who Should Attend
The webinar targets technical professionals responsible for test data management, including data architects, data engineers, QA and test managers, and DevOps practitioners. It is particularly relevant for organisations in regulated industries such as finance, healthcare and insurance, where compliance requirements govern the handling of sensitive data in non-production environments. Solutions architects and principal engineers working on data infrastructure will also find the content applicable to their responsibilities.