While enterprise leaders overwhelmingly believe in the promise of synthetic data, adoption lags. As a result, synthetic data has great, untapped potential. The answer? Enterprises need the right, high-quality approach that upholds referential integrity and earns their trust.

Our research shows the biggest barrier to adoption isn’t awareness, compliance, or governance. It’s data quality. Across industries, enterprise leaders increasingly recognize synthetic data’s value in protecting sensitive information, moving at agentic speed, and expanding data access without increasing risk.

However, only 38% of leaders believe synthetic data delivers sufficient realism and just 34% believe it preserves referential integrity, according to recent Perforce Delphix research.

Synthetic data usage within software engineering and testing teams remains surprisingly limited, likely for this reason. As a result, enterprises are missing out on the opportunities that true, high-quality synthetic data can provide. The first step to tapping into these opportunities: Ensuring synthetic data meets your quality standards.

Synthetic data is getting so much attention for good reason. For years, organizations have struggled to balance two competing priorities: providing teams with the data they need while protecting sensitive information.

Traditional approaches often force teams to choose between access and risk. Data is either restricted, creating bottlenecks for development and testing, or distributed broadly, increasing compliance and security concerns.

By generating new, synthetic datasets that reflect production data characteristics without its privacy risks, organizations can increase test data availability while maintaining governance and compliance. More than half of enterprises (51%) already use synthetic data to protect sensitive data, alongside approaches such as static and dynamic masking.

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