Gartner® Market Guide: Data Masking vs Anonymization vs Synthetic Data

Compare key data privacy methods

Benchmark masking, data anonymization, tokenization, and synthetic data across use cases.

Understand masking strengths and limits

See how the different approaches impact protection, compliance, complexity, and overall data utility.

Balance privacy, realism, and utility

Maintain data fidelity and usability across non-prod environments without risking compliance.

Evaluate data masking vendors and market fit

Review Gartner’s review of best tools, key capabilities, and criteria for evaluating available solutions.

Data masking used to be the default starting point for evaluating privacy tools. Modern requirements demand a more balanced approach that considers PII discovery, PII masking, database masking, and synthetic data generation.

Testing and development require realistic non-prod data
Analytics and AI demand greater statistical integrity
Evolving privacy regulations must be strictly enforced

As a result, leading teams now compare multiple methods to find their ideal blend before selecting a test data masking vendor.