k2view-logo-1
Gartner_logo.svg
path-3345

MARKET RESEARCH: SYNTHETIC DATA GENERATION

Gartner® Market Guide for Synthetic Data Generation

Learn how organizations generate synthetic data, compare synthetic data generation tools, and evaluate leading synthetic data vendors.

Get it straight to your inbox:

Stacked pages of the Gartner Market Guide for Synthetic Data Generation report.

Inside the report: Synthetic data strategies and tools

vector

Unlock the potential of synthetic data

Enhance data privacy compliance while accelerating software testing and innovation.

vector

Choose the right techniques

Guidance on selecting rule-based, AI-driven, or other tabular synthetic data generation methods.
vector

Compare synthetic data vs masking

Understand the advantages and disadvantages of each per use case.
vector

Identify leading vendors

Evaluate leading synthetic data generation tools, critical capabilities, and vendors.
Send me the Gartner report

inside the report

Synthetic data generation
strategies and tools

  • Unlock the potential of synthetic data generation
    Improve data privacy and accelerate software testing with modern synthetic data techniques.
  • Choose the right techniques
    Guidance on selecting rule-based, AI-driven, and tabular synthetic data generation methods for testing and analytics.
  • Compare synthetic data vs masking
    Understand when to use synthetic data vs data masking based on privacy risk, data utility, and testing requirements.
  • Identify leading vendors
    Evaluate leading synthetic data generation tools and vendors, including key capabilities, use cases, and market coverage.
Table of leading synthetic data generation vendors and their solution names featured in the Gartner Market Guide.
A sample of synthetic data generation and masking vendors mentioned in the report

MARKET CONTEXT

How to generate synthetic data:
methods and tools


Synthetic data generation is the process of creating artificial data that mimics real-world patterns without exposing sensitive records. Organizations generate synthetic data to support software testing, analytics, and ML training when production data cannot be used or shared safely.

Synthetic data generation methods


Teams generate synthetic data using methods such as rule-based generation, AI-driven generation, and tabular synthetic data techniques. The right synthetic data generation method depends on the use case, data type, and required level of realism.

Chart showing utility requirements by use case for synthetic data compared to original data, from the Gartner Market Guide.

Synthetic data vs data masking


Synthetic data and data masking address different privacy and testing needs. Many teams compare synthetic data vs data masking based on privacy risk, data utility, and how well each approach supports software testing and ML training.

What to look for in synthetic data generation tools


When evaluating synthetic data generation tools, organizations typically compare:

•    Privacy controls and regulatory safeguards
•    Data quality validation and realism testing
•    Scalability for enterprise and production workloads
•    Support for test data generation and ML model training

Gartner example comparing original data and masked data to demonstrate data masking.

MARKET RESEARCH: SYNTHETIC DATA GENERATION

Get the Gartner report

Group 91433-2

© Copyright 2026 K2VIEW

Privacy Policy

Security

Terms of Use

Cookie policy