How Do I Generate Synthetic Data?

There are a number of tools that can help you generate synthetic data. These tools include Twinify and Benerator. These can be used to create test data and ensure referential integrity. They are also cost-effective.

Synthetic data is created by using an AI model to mimic the patterns, correlations and statistics of real-world datasets. This process is often more reliable than a traditional statistical approach.


Synthetic data is a type of computer-generated data that mimics real-world occurrences. This data is used to test and validate machine learning and deep learning models. It can also be used to generate more precise datasets for specific uses. For example, it can be used to rebalance imbalanced datasets or impute missing data points. This data is often used in regulated industries, where it is difficult to obtain genuine or sensitive data.

There are many tools and platforms that can be used to generate synthetic data. Some of these are free, while others offer a premium service. Some of these companies specialize in a specific market or technology. Some offer a comprehensive solution that includes data preparation and visualization.

Synthetic data can be useful for a wide variety of applications, from medical imaging to self-driving cars. It can help reduce the time and cost required to develop a model, and it can also improve the accuracy of the results. It can also be used to test new algorithms or models without compromising privacy.

Gretel AI

Gretel AI lets engineers create anonymized, synthetic data sets based on their own real-world data to use in analytics and machine learning. The company’s platform can be hosted on the cloud or in customers’ own servers. Its data generators are available via web GUI, Software Development Kits (SDKs), and command line. Its customers include leading tech and e-commerce companies.

Synthetic data can be used for many applications, including improving existing algorithms and detecting anomalies. This method can also help companies meet regulatory compliance requirements. For example, a company may be required to delete data after a certain period of time to comply with privacy and security policies. This can be expensive and time-consuming. Synthetic data can be a cost-effective and scalable alternative.

Several startups are offering solutions for synthetic data generation, such as Twinify, which allows users to programmatically generate test data from their own databases. Other companies are focusing on visual data synthesis, including computer vision and face generation.


Creating synthetic data is one of the most popular techniques used to train AI models. This method is also known as “data augmentation.” The goal is to generate random data that is similar to the training datasets, but without the sensitive information. This process is useful for testing AI models and ensuring that they perform well in real-world applications. Conventional methods include using a software tool or partnering with a third party that specializes in this service. However, these options can be expensive and require a dedicated IT resource.

The MOSTLY AI platform makes it easy to generate synthetic data with built-in privacy mechanisms that can be used to improve machine learning performance. It can be used to create confidential data, rebalance imbalanced datasets, or to impute missing data points. This helps organizations to build better and more accurate machine learning models that can handle the sensitivity of their data. It is also useful for generating test data that is more representative of the real world.


There are many ways to Synthetic Data Generation, but the most effective way depends on your specific business needs. For example, if you want to test a new model without compromising sensitive customer data, you can use a Python-based tool like Graphite to generate realistic mock data sets that contextually link and look like real data. The process is fast and easy, and it can help you find potential problems with your model.

The first step in generating synthetic data is to determine which field is most critical to your analysis. Often, this is a time-dependent field such as a date in a time series. For this reason, it’s important to select a dataset that is as complete as possible. A moderate amount of missing values can be easily handled by a statistical machine learning model, but an excessive number of missing values may cause overfitting. Using a synthetic data generation tool like MOSTLY AI can help you avoid this problem.

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