Gartner® Market Guide: Synthetic Data Generation

Backed by Gartner’s Market Guide for Data Masking and Synthetic Data

How teams evaluate synthetic data tools
Modern synthetic data tools go beyond basic data generation—combining privacy-preserving techniques, AI-driven generation, and platform capabilities to support testing, analytics, and AI use cases.

Compare synthetic data tools based on:

Synthetic data generation methods and approaches
Privacy-preserving and compliance techniques
Data realism, accuracy, and statistical integrity
Scalability for testing, analytics, and AI workloads
Vendor capabilities and platform fit

Compare synthetic data generation techniques

Evaluate different approaches to generating synthetic data across use cases

Evaluate privacy-preserving capabilities

Understand how tools protect sensitive data and ensure compliance

Balance data realism and utility

Maintain accuracy and usability for testing, analytics, and AI

Evaluate synthetic data tools and vendors

Compare platforms, capabilities, and vendor fit

Modern synthetic data tools must go beyond basic generation. Teams now evaluate platforms based on privacy-preserving techniques, data realism, scalability, and support for testing and AI use cases.

Testing and development deliver production-like synthetic data on demand
Analytics and AI maintain high-fidelity data for model training and analytics
Privacy & compliance Enforce privacy-by-design across environments

As a result, teams now compare multiple synthetic data tools and approaches before selecting a platform.

SYNTHETIC DATA GENERATION TOOLS

Synthetic data tools: Compare top platforms and features

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Key capabilities to compare in synthetic data tools

Compare synthetic data generation techniques

Evaluate privacy-preserving capabilities

Balance data realism and utility

Evaluate synthetic data tools and vendors

Why teams are re-evaluating synthetic data tools

Who This Guide Is For

Evaluate synthetic data platforms before shortlisting vendors