The AI revolution that we’re currently living through is a direct result of the explosion in the amount of data that’s available to be mined and analyzed for insights.
However, collecting data from the real world can be challenging. Storing and working with personal data creates privacy and security challenges, and other types of data can be expensive or even dangerous.
So why not generate artificial data that’s close enough to real-world data that it can be used for many of the same purposes at a fraction of the cost in terms of time, money and risk? That’s the promise of synthetic data — another field where generative AI is quickly becoming a valuable tool.
Here’s my roundup of some of the most useful, interesting or unique generative AI tools designed to create synthetic data, including both free and paid-for tools:
Mostly, 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. Highlighted as a Cool Vendor by Gartner, it stands out by enabling the creation of datasets that guarantee privacy and compliance with data protection regulations such as GDPR and CCPA. Its user interface is built around natural language, meaning the data that it creates can be queried in the same way as you would chat to a bot like ChatGPT. It also includes guardrails to protect against the introduction of bias into the synthetic data it creates.
Gretel makes it easy for just about anyone to create tabular, unstructured and time-series data for use in any type of analytics or machine-learning workflow. It’s designed to be simple to use, allowing synthetic data to be created with little coding experience. A large number of connectors and API integrations make it compatible with most cloud and data warehouse infrastructures, and an active user community is available for help and support.
Synthea is a free-to-use, open-source tool specifically designed to create synthetic patients for use in healthcare analytics. It can create entire medical records of patients who may not exist but nevertheless could hold clues to solving challenging healthcare problems. This means medical researchers can carry out their work without having to worry about privacy or the ethical considerations of working with real patient data.
A comprehensive platform for developing realistic, compliant and secure synthetic data, Tonic is built primarily for software and AI development. In addition to synthetic data generation, it offers de-identification for the anonymization of real-world data. It can be deployed on-premises or accessed in a cloud environment and is designed to integrate with all commonly used databases.
Faker is a library available for Python and JavaScript, as well as several other languages, rather than a standalone tool, so it requires some coding knowledge. However, it is a popular tool with users who want to create fake data ranging 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 compromising privacy that comes with using real data.
More Generative AI Tools For Synthetic Data
In addition to the five tools outlined above, here are others that are worth checking out:
Allows the creation of very technical and complex datasets.
Simplifies data masking and anonymization with synthetic data generation for business.
Computer vision and video analytics powered by synthetic data.
Create datasets with dynamic validation tools to ensure they are as realistic as possible.
Create labeled synthetic data as a service.
Dynamic data generation with enterprise scalability, targeted at data generation for software testing.
Recently relaunched as the world’s first synthetic data marketplace.
Generates data for the purpose of training machine learning models.
No-code data augmentation designed to enhance privacy and improve the performance of neural networks.
Synthetic data aimed at healthcare professionals.
Synthetic training data generator for computer vision applications.
Billed as a “data amplifier,” it mimics real datasets by matching the characteristics and correlations of existing data.
Open-source machine learning model for generating high-volume synthetic data.
Self-service data generation for insights and decision-making.
Automated synthetic data generation to enhance productivity and AI model performance.
