Gartner® Market Guide

The technical guide to synthetic data generation: methods, tradeoffs, and tools

Access the Gartner® evaluation of synthetic data vendors, tools, and platforms. Learn how to move beyond basic de-identification to provision high-utility, privacy-safe synthetic data for software testing, AI/ML training, and enterprise analytics. 

Gartner Market Guide for Data Masking and Synthetic Data

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Inside the report

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Evaluate synthetic data methods

Explore synthetic data approaches and where each fits across your use cases. 

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Balance privacy, realism, and data utility

Analyze the tradeoffs between privacy and the realism required for software testing and AI.

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Accelerate test data delivery

Maintain referential integrity at scale while bypassing the bottlenecks of traditional masking.

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Navigate the vendor landscape

Compare 30+ representative synthetic data vendors and providers, including K2view, to identify the best-fit for your needs.

Why this report matters now

Modern engineering is stalled by legacy masking and rigid privacy constraints. As software testing and AI training scale, provisioning realistic synthetic data instantly is no longer a luxury, it’s a competitive requirement. The report provides a strategic framework to: 

  •  Unblock testing: Deliver structurally consistent,  production-like test data on demand, for dev, test, and QA, without legacy masking delays. 
  • Fuel AI models:  Generate high-fidelity synthetic data that preserves data utility for accurate AI, LLM, and machine learning model training. 
  • Scale privacy: Implement "privacy-by-design" architectures that automate compliance as global regulations tighten. 

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

PII masking blended with synthetic data and privacy techniques

Who should read this report

  • Infosec: Modernize PII protection via privacy-by-design architectures  
  • Data engineering: Automate high-fidelity data delivery at scale
  • Analytics & AI: Fuel AI models with high-utility data 
  • Quality engineering: Utilize synthetic test data and realistic non-production data without delays 
Compliant data supporting infosec, data engineering, analytics and AI, and quality engineering teams
Gartner Market Guide for Data Masking and Synthetic Data

Avoid the pitfalls of synthetic data generation

Access the Gartner framework to compare 30+ synthetic data vendors and identify the right technical methods for your testing and AI strategy. 

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