Gartner® Market Guide: Synthetic Data Generation

Backed by Gartner’s Market Guide

Modern synthetic test data generation tools go beyond basic synthetic data, combining privacy-preserving techniques, AI-driven generation, and test data provisioning.

Compare synthetic data methods based on:

Synthetic test data generation techniques
Privacy-preserving synthetic data 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

Generate SYNTHETIC DATA

Synthetic data generation for software testing and development

Get the Gartner report

Key capabilities to compare in synthetic test 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 test data generators

Who This Guide Is For

Evaluate synthetic data platforms before shortlisting vendors