If your business is engaged in testing or needs valuable user insights, one of its most vital pieces of kit is a high-quality synthetic data generation tool. By simulating “real” data, these tools enable safe testing, development, and machine learning model training without putting sensitive information at risk. Synthetic data can be generated using statistical models, rule-based systems, or advanced techniques such as generative adversarial networks (GANs). A suite of synthetic data generation tools can accelerate innovation and time-to-launch, while ensuring your business stays on the right side of data privacy regulations.
The Benefits of Using Synthetic Generation Tools
There are a plethora of benefits that come with using effective synthetic data generation tools. These include:
- The creation of a safe testing environment that allows developers to test applications using realistic data without putting sensitive information at risk.
- Compliance with privacy regulations through the elimination of personal identifiers. This helps ensure businesses don’t fall foul of regulations such as GDPR and PCI DSS.
- Accelerated development, with teams able to avoid delays by generating data on demand.
- Enhanced data variety and distributions to improve model robustness.
- Cost efficiency: synthetic data generation tools reduce the need for storage, data acquisition, and manual anonymization processes.
- Bias mitigation, as synthetic data can be engineered to support fairer, more balanced artificial intelligence outcomes.
- Scalability: organizations can easily generate large volumes of data to support load balancing, big data analytics, and better performance testing.
- Boosted security, limiting the risk of data breaches by removing the dependency on production data.
- Promotion of innovation by making it easier for testers to experiment with new architectures, algorithms, and analytical techniques.
How Do Synthetic Data Generation Tools Work?
Synthetic data generation tools replicate the statistical patterns and structure of the original data, creating new samples that remove identifiable information while retaining high usability. These tools are essential for compliant and secure data-driven development and testing, often learning from existing datasets and incorporating a range of advanced features.
Synthetic data generation can also involve data augmentation to enhance existing datasets and tools for simulation-based generation. The latter models complex systems to create synthetic data that reflects real-world dynamics.
Top 5 Synthetic Data Generation Tools
K2view Synthetic Generation Tool
The K2view Synthetic Data Generation suite is an all-in-one solution for managing the full synthetic data lifecycle—from extracting and subsetting source data to building pipelines and generating synthetic datasets. It creates realistic, privacy-compliant data ideal for software testing and machine learning. Using patented entity-based technology, it maintains referential integrity and ensures both consistency and accuracy across datasets. Thanks to its innovation and performance, K2view’s product earned recognition as a Visionary in Gartner’s 2024 Magic Quadrant for Data Integration, establishing the tool as a trusted choice for organizations focused on secure, high-quality test data.
MOSTLY AI Synthetic Data Generation Suite
This option from MOSTLY AI offers a customer-centric approach and advanced privacy technology. The incorporated tools specialize in creating highly realistic synthetic data that mirrors customer behaviors, demographics, and preferences, making it ideal for analytics, testing, and AI training. Features include bias elimination, fairness optimization, and scalable solutions, helping organizations unlock deeper insights while preserving privacy. MOSTLY AI aims to enhance customer experiences and drive smarter, data-informed decisions without compromising compliance or data integrity.
Synthea Open Source Synthetic Data Generation Tool
Synthea is an open-source synthetic data generation tool built specifically for healthcare organizations. The tool provides a customizable framework to suit a wide range of research needs. Its healthcare-centric design makes it ideal for both building AI solutions and testing healthcare-related applications. Widely adopted in the medical field, Synthea could be a good choice for organizations seeking accurate synthetic data provisioning to improve patient outcomes without putting personal information at risk.
Gretal Synthetic Data Generation
With features for converting, generating, and masking datasets for machine learning and artificial intelligence applications and testing scenarios, Gretel offers advanced privacy controls to ensure regulatory compliance and business peace of mind. The tools are highly usable for data scientists and developers, and support the generation of sequential and time series data for even very complex use cases. This choice could be a good one for start-ups and small organizations seeking a relatively low-cost solution.
Hazy Synthetic Data Generation Software
Synthetic data software Hazy (recently acquired by AI company SAS) is designed for enterprise-level organizations operating in highly regulated sectors such as finance and healthcare. Its features allow users to generate privacy-protecting data for analytics and AI environments, ensuring compliance with regulations, including CCPA and GDPR. Hazy empowers developers and analysts to create highly customizable datasets to meet the unique needs of individual organizations requiring tailored solutions. The software’s focus on integration and compliance makes it an ideal choice for businesses that handle highly sensitive data.
How to Choose the Right Synthetic Data Generation Tool?
With several great options on the market, it can be tricky to identify the best one for your business’s or team’s needs. To ensure you choose the right option, consider these things:
- Does the tool fully meet your team’s needs, and is it relevant for the sector in which your organization operates?
- Will the tool help ensure consistent compliance with the relevant data protection and privacy regulations?
- Will the tool preserve the statistical properties of the original source data?
- Can it easily handle large datasets?
- Can the tool easily scale and expand as your business needs change?
- Does the tool offer a wide range of customizable features?
- What is their customer support like? In the event of a problem, will you be able to contact customer support via several means and 24/7?
- Does the price of the tool suit your budget? Is a free trial period on offer?
By carefully considering all of these things, you’ll have the best chance of choosing the very best synthetic data generation tool for your team.
Protect Your Sensitive Data with a Best-in-Class Synthetic Data Generation Tool in 2025
There are so many benefits to deploying a high-quality synthetic data generation tool, from protecting sensitive data and ensuring regulatory compliance to enhancing the entire testing and development cycle. Check out our best-in-class options above to identify the best for your business or team’s needs, and prepare to experience a boost in productivity, accelerated time to launch, and improved peace of mind.
