Synthetic Test Data vs. Test Data Masking: Why You Need Both

As agentic development continues, scaling enterprises are looking for a data approach that balances compliance with speed. Synthetic test data is seen as the faster choice for getting data when you need it, while test data masking will meet stringent compliance requirements. Some may feel inclined to pick and choose, but both approaches have a lot to offer. 

In this blog, we’ll break down the use cases for synthetic test data vs. test data masking and help you make the right choice for your organization — which could be using synthetic data and masking together.  

Synthetically generated values are useful when:  

• No real data exists that matches schemas. 

• Compliance regulations restrict production data access. 

• You need a way to deliver deidentified, realistic data faster than existing masking techniques. 

Unlike masked or anonymized data, synthetic test data is not a transformation of production data. It is entirely artificial, and unless there are capabilities available to fine-tune synthetic data generators, the data may not have the statistical and referential fidelity. Synthetic data with referential integrity isn’t the standard, but certain solutions (including Perforce Delphix Synthetic Data) can uphold it. 

Because it can act as a substitute for sensitive data, synthetic data generation can mitigate security risk while greatly expanding test scenario coverage. For example, you can use it to test against broader sets of data that may not be available in your environment, such as edge cases, negative scenarios, new markets, and changes to business procedures.  

Generating large volumes of synthetic test data is quick and easy, which can help accelerate development speed. 

Rule-based synthetic data is generated according to predefined business and technical rules. Unlike sample data, which may simply populate fields with plausible values, rule-based data intentionally satisfies validation requirements, geographic constraints, industry regulations, and application-specific logic. This enables teams to create highly structured datasets that reliably exercise specific test scenarios, edge cases, and business processes. 

Say a healthcare provider is preparing to test patient registration, eligibility verification, and claims processing workflows. Rather than using real patient information, the organization can generate rule-based synthetic data resembling patient records. The dataset could include a representative demographic distribution consisting of 20% pediatric patients, 60% adults, and 20% seniors. Patient names can also reflect multiple languages and cultural backgrounds.  

This is just one way that rule-based synthetic data can be used — maintaining utility while also securing privacy for regulations like HIPAA. 

Agentic data is data that enables an AI agent to reason, decide, and act — which is different from traditional test data that typically only requires raw records. It provides additional components for AI to act on, including operational, context, relationship, policy, state, and feedback data. This allows you to move at AI speed with confidence. 

Here’s an at-a-glance comparison of the two: 

By masking production data for testing, you’ll create a safe and compliant copy where sensitive values are replaced with fictious (yet realistic) alternatives. 

In contrast to synthetic data generation, masking one’s existing production data source(s) maintains the real-world statistical fidelity of your data, while providing the deidentification required for non-production environments. 

By using synthetic data, you ensure developers get the data they need when they need it, enabling faster data delivery and development. You can even generate data for edge cases and negative scenarios that don't exist in production.  

Like data masking, synthetic data generation can help prevent sensitive data sprawl. While masking transforms and protects data before it’s used in non-production environments, using synthetic data means that sensitive data doesn’t leave production in the first place.  

With Delphix, you generate purpose-built, realistic data that has referential integrity within and across sources. Using natural language, you can tailor and fine-tune the data for a particular use case, including new functionality, edge cases, and negative scenarios. This helps teams improve testing quality and get the data they need on demand. 

Testing no longer has to wait for a major release to be completed. It can be done at the feature level earlier in the development lifecycle, even before real data is available. This flexibility also extends to testing unusual or niche scenarios where QA teams can find potential issues that might be difficult to replicate with real data alone. 

Masking production data for testing safeguards sensitive information for non-production environments in a repeatable, deterministic way. Meanwhile, synthetic test data generation fills gaps and allows targeted testing of specific scenarios. 

Synthetic test data for software testing can complement masking, and vice versa. With Delphix, you can get both in a single platform.  

The intelligent Delphix DevOps Data Platform unifies data masking, virtualization, and synthetic data, so that teams can validate, govern, and release software at AI speed. Using AI-first synthetic data generation and data masking, Delphix delivers realistic, governed test data on demand, helping teams accelerate development and testing while protecting sensitive data.