Synthetic Data Generation: Methodology, Use Cases, and What You Need to Know

Many companies have integrated synthetic data into their overall test data management process. Of the 500+ enterprise leaders we surveyed for our 2026 Test Data Management Report for AI-Ready Enterprises, 51% said their organization is using synthetic data.

Whether you’re currently using or considering using synthetic data generation (also known as artificial data generation), it’s important to understand its best practices, benefits, and risk considerations. Here’s what you need to know.

What is Synthetic Data?

Synthetic data is artificial data, made to resemble real production data. It’s often made using statistical methods or generated by AI.

Synthetic data differs from data masking because it’s completely new. While some forms of masking will replace real data with fictitious data, synthetic data generation creates entirely new data from scratch — enabling teams to generate purpose-built datasets for new applications, features, and testing scenarios.

Use Cases for Synthetic Data Generation

While synthetic data is often useful, it does not fit every scenario. Here’s a look at a few use cases for synthetic data generation:

Benefits of Synthetic Data Generation

Synthetic data can be fit for a variety of use cases because of its various benefits, such as customization and efficiency. Some of the synthetic data benefits include:

• Customization: You can tailor synthetic data to your exact testing needs, matching required formats, distributions, and relationships while intentionally dialing up rare conditions. Teams can generate domain-specific datasets (including new-feature scenarios and edge cases) on demand, without being constrained by what production happens to contain.
• Efficiency: Synthetic data generation gets teams data they need when they need it, speeding up application and new feature development. According to our Perforce Delphix 2026 Test Data Management Report for AI-Ready Enterprises, 99% of organizations are waiting longer than one business day for a fresh full production copy of test data, with 42% waiting weeks or months. Synthetic data generation can eliminate waiting and test data delivery bottlenecks by delivering purpose-built datasets on demand, without depending on production copy cycles.
• Increased Data Privacy: Data breach risk and consequences are both reduced with synthetic data, as it’s not attributable to actual people. Synthetic data helps minimize sensitive data sprawl and the amount of production data present in non-production environments.
• Richer Data: Real data can be scarce, and missing test results can lead to false positives or negatives. The 2026 Test Data Management Report for AI-Ready Enterprises found that data quality is the top test data management priority for enterprises. This is why synthetic data is gaining traction: It helps teams generate reliable, fit-for-purpose datasets that support faster development and testing while reducing unnecessary exposure of sensitive production data. Synthetic data generation can also help fill gaps for edge and corner cases, enabling more comprehensive testing coverage and preventing any impact on release quality. 

Synthetic Data Generation Concerns

Even with all the benefits that synthetic data offers, it’s important to note what challenges and concerns to look out for. If you’re leveraging synthetic data generation, be sure to:

• Use synthetic data purposefully and only for select use cases. Real, masked data is better suited for functional testing and debugging.
• Evaluate the quality and realism of your synthetic data. Check that the data does not have errors or nonsensical information, such as bad formats or ranges like ZIP codes with letters or negative account balances when they’re not allowed. Ensure the referential integrity of data across datasets and systems.
• Pair your synthetic data with data masking. To get the most out of your test data management strategy, use both masked and synthetic data. Doing so will reduce your data security risk and increase the amount of data at your disposal while balancing realism and flexibility across environments.

How to Generate Synthetic Data  

There are many ways to generate synthetic data, including:

• Generative AI Generation: Artificial intelligence uses algorithms trained on data samples to create new, synthetic data.  
• Rules-Based Generation: Defined logic, constraints, or business rules, established by the user, helps generate synthetic data.
• Random Data Generation: This method generates data in a way that mimics a data structure but may not reflect real-world data.
• Entity Cloning: Different from the others, this method makes exact copies of an existing entity.

However, with the boom in AI, it has become a go-to method for generating synthetic data. Delphix, for example, uses this method. We use AI to generate customized, high-fidelity synthetic data, which you can use to ensure data security and optimize your teams’ test data management strategy at enterprise speed — enabling faster development and testing across applications and AI workflows.