Webinar Description
Key Takeaways
- Live demonstration of AI-driven synthetic data generation using Perforce Delphix
- Explores the integration of synthetic data with data masking for comprehensive test data management
- Addresses compliance challenges in regulated industries including financial services and healthcare
- Designed for engineering leaders, QA teams, DevOps professionals and compliance officers
- Focuses on eliminating data bottlenecks that slow development and testing cycles
Introduction
Development and testing teams frequently encounter a fundamental tension: they need realistic, high-quality data to build and validate applications effectively, yet accessing production data raises significant privacy and compliance concerns. This webinar from Perforce Delphix demonstrates how synthetic data generation addresses this challenge, offering engineering and data leaders a practical look at AI-driven approaches to test data management.
The session arrives at a time when data privacy regulations continue to tighten across jurisdictions, making traditional approaches to test data increasingly untenable. Organisations in financial services, healthcare and other regulated sectors face particular pressure to maintain development velocity without compromising on data protection obligations.
About This Event
This virtual demonstration, led by Senior Product Manager Mayank Ahluwalia, provides a technical walkthrough of the synthetic data capabilities within Perforce Delphix. The session combines educational content with a live product demonstration, allowing attendees to observe how the platform generates realistic synthetic datasets in practice.
Beyond the technical demonstration, the webinar covers practical strategies for integrating synthetic and masked data into existing test data management workflows. This dual approach—combining data masking with synthetic generation—reflects an emerging pattern in enterprise data management where organisations layer multiple techniques to achieve both realism and compliance.
The Test Data Challenge in Modern Development
Software teams have long struggled with what might be called the test data paradox. Effective testing requires data that accurately reflects production scenarios, including edge cases and realistic distributions. However, using actual production data exposes organisations to privacy risks and potential regulatory violations, particularly when that data contains personally identifiable information or sensitive business records.
Traditional solutions have included data subsetting, where teams extract portions of production databases, and data masking, which obscures sensitive fields while preserving data structure. Each approach carries limitations. Subsetted data may not capture the full range of production scenarios, while masked data can sometimes be reverse-engineered or may lose referential integrity across related tables.
Synthetic data generation represents a different approach entirely. Rather than transforming existing records, synthetic systems create entirely new datasets that maintain the statistical properties and relationships of production data without containing any actual sensitive information. When combined with masking techniques, organisations can deploy whichever method best suits each specific use case.
AI-Driven Generation and Compliance Considerations
The Delphix approach employs artificial intelligence to analyse source data patterns and generate synthetic records that preserve realistic distributions, correlations and business logic. This matters because poorly generated synthetic data—random values that ignore real-world constraints—proves largely useless for meaningful testing.
From a compliance perspective, synthetic data offers distinct advantages. Because the generated records never existed as real customer or business information, they fall outside the scope of many data protection regulations. This can simplify data governance considerably, particularly for organisations operating across multiple regulatory jurisdictions with varying requirements around data handling, retention and cross-border transfer.
Who Should Attend
The session is particularly relevant for professionals responsible for balancing development speed against data governance requirements. This includes data engineers tasked with provisioning test environments, QA leads seeking more representative test datasets, and DevOps engineers working to streamline delivery pipelines. Compliance managers and product managers in regulated industries will also find value in understanding how synthetic data fits within broader data protection strategies.
Conclusion
As organisations face mounting pressure to accelerate software delivery while strengthening data privacy practices, synthetic data generation has moved from experimental technique to practical necessity. This demonstration offers a concrete look at how one platform approaches the challenge, providing attendees with both technical understanding and strategic context for evaluating synthetic data within their own environments.