What Is Synthetic Data?

5 Min Read

An Intro to Future Workflows

When production data is sensitive, expensive, otherwise inaccessible, or simply nonexistent, synthetic data is the statistical equivalent - relevant, abundant, and infinitely valuable. Unhindered by the barriers and regulations of actual data, developers can work at a real-time pace - powering application prototypes, training machine learning models, extending dataset properties, and applying other custom modifications as needed or anticipated.

The Real Value of Synthetic Data

A synthetic dataset is a proxy for a real dataset. It retains the critical properties of the real dataset while allowing the user to extend or anonymize its contents to meet operational requirements.

A good analogy is put forth in the MIT article, "The real promise of synthetic data":

"Synthetic Data is a bit like diet soda. To be effective, it has to resemble the 'real thing' in certain ways. Diet soda should look, taste, and fizz like regular soda."

By comparing real and synthetic data to regular and diet soda, the author emphasizes the retention of critical properties. With synthesization, not only does diet soda retain the essential look, taste, and fizz properties, but it also offers additional benefits like zero calories! Similarly, a synthetic dataset's added value extends beyond the essential properties of the real dataset to include the benefits of flexibility, speed, and scale.

Below we list the varying levels of data synthesization:

Levels of Synthetic Data

Practical Use Cases

If real data is the new oil, is synthetic data the renewable energy?

  • The reduction of laborious and expensive processes in obtaining real data, including dependence, survey collection, access, and consolidation.

  • The increase of data efficiency by shifting focus from gathering data to building software. Data efficiency is essential in greenfield projects and ground-breaking research, where the priority is building experimental models rather than collecting perfect datasets.

  • The scaling of real datasets to capture edge cases, fill in missing data, or modify fields based on continual change.

  • The enablement of research and analysis reproducibility with fewer regulatory and intellectual property concerns.


Synthetic data is powerful. It allows developers, scientists, analysts, and consultants to create and validate models with greater efficiency, scalability, and reproducibility. Ultimately, synthetic data improves accessibility and security, thereby driving technological innovation through rapid prototyping and happy workflows.

At Overseed, we are focused on providing the configuration options needed to create synthetic data that's virtually indistinguishable from real data.

Look out for future blog posts on the subject and how we aim to overcome the challenges in this space. And as always, we look forward to your feedback and suggestions at support@overseed.io.

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