Synthetic Data Generation: Definition, Types, Techniques, and Tools

Synthetic data generation techniques.


  • Synthetic Data Generation: Definition, Types, Techniques, and Tools


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Frequently Asked Questions

The synthetic data will remove the requirement for real-world data in the near future. It is proven from research that synthetic data will completely overshadow real data in AI models.

Data generation tools are also known as data generators. These tools will generate data as per some patterns instead of reading the data which already exists in a database. A transformation will be defined to generate the data. It will use the CTL template for data generator or implement a record generate interface.

To create synthetic data, the data scientist needs to create a robust model replicating a real-world dataset. Based on the probabilities, certain data points that happen to be real dataset might generate realistic data points.

The advantages of synthetic data will be cost reduction, agility, higher speeds, cutting-edge privacy, and intelligence. When you transform test data generation into AI governance, the synthetic data will deliver high-value use cases across businesses.

Synthetic test data is dummy data that you use during the development and testing phase of any application. It will not be based on real-world data and will be artificially created with the help of algorithms or models.

It is vital controlling the random processes which will generate data based on the statistical distributions or generative models. It will ensure that the results are sufficiently diverse and seem real. Synthetic data should also be customizable so it can be altered as per customer requirements.

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