Gartner® Market Guide: Synthetic Data Generation

Backed by Gartner’s Market Guide

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

Compare synthetic data methods based on:

Synthetic data generation techniques
Privacy-preserving and compliance approaches
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 synthesize data across use cases

Review privacy-preserving capabilities

Understand how tools protect sensitive data for 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

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Not sure how synthetic data works?

See how synthetic data generation helps teams create compliant, realistic data without exposing sensitive information

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Synthetic data generation tools: Compare top platforms and features

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Key capabilities to compare in synthetic data software

Compare synthetic data generation techniques

Review privacy-preserving capabilities

Balance data realism and utility

Evaluate synthetic data tools and vendors

Why teams are evaluating synthetic data generators

Who This Guide Is For

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Not sure how synthetic data works?

Evaluate synthetic data platforms before shortlisting vendors