20 Generative AI Tools for Creating Synthetic Data
Adobe Stock
The AI revolution we are currently experiencing is a direct result of the explosion in the amount of data that can be mined and analyzed for insights.
But collecting data from the real world can be difficult: storing and working with personal data raises privacy and security challenges, and other types of data can be expensive or dangerous.
So why not generate artificial data that is close enough to real-world data to be used for many of the same purposes, but at a fraction of the cost in terms of time, money, and risk? This is the potential of synthetic data. This is another area where generative AI is quickly becoming a valuable tool.
Here we round up some of the most useful, interesting, or unique generative AI tools designed to create synthetic data, including both free and paid tools.
generally
Essentially, it is a well-established synthetic data platform for generating data that closely mimics the real world. It is used in industries such as finance, retail, telecommunications, and healthcare. Featured as a Cool Vendor by Gartner, the platform stands out by ensuring privacy and enabling the creation of datasets that comply with data protection regulations such as GDPR and CCPA. The user interface is built around natural language, so you can query the created data in the same way you would chat with a bot such as ChatGPT. It also includes guardrails to protect against biases creeping into the synthetic data created.
Gretel
Gretel makes it easy for almost anyone to create tabular, unstructured, and time-series data for use in any kind of analytics or machine learning workflow. Designed to be easy to use, it allows you to create synthetic data with little to no coding experience. Numerous connectors and API integrations make it compatible with most cloud and data warehouse infrastructures, and an active user community provides help and support.
Sincere
Synthea is a free-to-use, open-source tool specifically designed to create artificial patients for use in medical analysis. It allows you to create entire medical records of patients who may not exist but may hold clues to solving difficult medical problems. This means medical researchers can conduct their research without worrying about the privacy and ethical considerations of working with real patient data.
tonic
Tonic, a comprehensive platform for developing realistic, compliant and safe synthetic data, is primarily built for software and AI development. In addition to generating synthetic data, it also provides de-identification capabilities to de-identify real-world data. It can be deployed on-premise or accessed in cloud environments and is designed to integrate with all commonly used databases.
Faker
Faker is not a standalone tool but rather a library available for Python, JavaScript, and several other languages, so some coding knowledge is required. However, it is a popular tool for users who want to create fake data for a variety of things, from e-commerce buying habits to financial transactions. This data can then be used to train anything from recommendation engines to fraud detection algorithms, without the risk of privacy violations that come with using real data.
More generative AI tools for synthetic data
In addition to the five tools mentioned above, here are some others worth checking out:
Broadcom CTA Test Manager
It allows for the creation of highly technical and complex datasets.
Biz Data X
Simplify data masking and anonymization with synthetic data generation for business.
Tvedia
Leverage synthetic data for computer vision and video analytics.
Date Mys
Use dynamic validation tools to create your dataset and make it as realistic as possible.
Edge cases
Create labeled synthetic data as a service.
JenRocket
Enterprise-scalable dynamic data generation for generating data for software testing.
Blurred
It was recently relaunched as the world's first synthetic data marketplace.
K2 View
Generate data for training machine learning models.
Copycut
No-code data augmentation designed to enhance privacy and improve neural network performance.
MD Clone
Synthetic data targeted at medical professionals.
Schimmerse
A synthetic training data generator for computer vision applications.
Sogety
Described as a “data amplifier,” the tool mimics real-world datasets by matching the characteristics and correlations of existing data.
Synthetic Data Vault
An open-source machine learning model that generates large amounts of synthetic data.
Shinso
Self-service data generation for insights and decision making.
Y Data
Automated synthetic data generation to improve productivity and AI model performance.
