Synthetic Data Generation is no longer experimental — it’s becoming foundational to modern test data management. According to the 2025 Perforce Delphix State of Data Compliance and Security Report, 63% of global enterprises already use synthetic data to protect sensitive information in non-production environments.
But what exactly is synthetic data?
Synthetic data is artificially created data designed to resemble real production datasets. Unlike data masking, which modifies existing data, synthetic data generation creates entirely new datasets from scratch — often using AI, statistical modeling, or rules-based logic.
Why Enterprises Are Adopting Synthetic Data Generation
Customization:
Teams can generate data tailored to specific testing scenarios — including rare edge cases that production data may not contain.
Efficiency:
On-demand data generation removes bottlenecks in development and accelerates release cycles.
Improved Data Privacy:
Because synthetic data isn’t tied to real individuals, it reduces breach risk and limits exposure in non-production environments.
Higher Test Quality:
Synthetic datasets help fill gaps in edge cases, reducing false positives and negatives.
When to Use It — And When Not To
Synthetic data generation is powerful, but not universal. Real or masked production data is still better suited for debugging and functional testing where exact replication is required.
Best practice?
Pair synthetic data generation with data masking for a balanced, secure, and high-coverage test data strategy.
Methods of Synthetic Data Generation
- Generative AI models
- Rules-based logic
- Randomized structured data
- Entity cloning
With AI accelerating adoption, enterprises are now able to generate high-fidelity synthetic datasets at scale — without compromising security or speed.
By implementing Perforce Delphix, Express Scripts fundamentally changed how data was delivered across engineering and data science teams. What once took weeks was reduced to near-instant access—delivering HIPAA Compliant Data overnight through automated data virtualization and integrated masking.
The impact was immediate. Teams gained faster access to fresh, secure data, removed development bottlenecks, and accelerated delivery of new healthcare solutions. Beyond speed and efficiency, the shift also improved job satisfaction and unlocked new innovation opportunities across the organisation.
This case study highlights how modern data platforms enable healthcare leaders to balance compliance, velocity, and innovation—without compromise.
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Perforce Delphix is an Insight Partner of the CxO Institute event in Palo Alto on April 8, 2026. Join us to explore how synthetic data generation and AI-driven data governance are reshaping enterprise IT leadership.
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