Synthetic Data

Vibrant digital streams with binary code flowing through a futuristic tunnel, representing the dynamic generation and use of synthetic data.

Synthetic data is data that’s artificially manufactured rather than generated by real-world events. Artificial intelligence (AI) generates synthetic data, replacing actual data for use in training machine learning (ML) models and predicting outcomes. Analytical outcomes are identical using synthetic data as they would be using real data because the data is structurally and statistically the same.

Why is Synthetic Data Important?

Synthetic data is used to validate mathematical models and to train ML models. It can be generated from a sample of real data. Its volume can be adjusted to the required level to meet the needs of the analytics or testing application. If actual data does not exist in the real world, an ML model can be developed to generate representative data to test applications before real users become available.

This type of data is altered to simulate possible scenarios and estimate how they affect the results. For instance, a scenario can try invalid or uncommon inputs or paths in testing applications. Developers usually stick to traditional use cases because they want their applications to function as designed. On the other hand, quality assurance (QA) teams look for potential problems because their role is to improve an application by exploring use cases that developers may not have considered.

Often, regulations such as Personally Identifiable Information (PII) mandate that real data cannot be retained in order to protect the privacy of individuals. In this case, synthetic data can replace the actual data. This reduces the organization’s exposure to accidental release of data but still provides much-needed trend analysis, which can be used to make data-driven decisions.

Synthetic Data Challenges

No data model is 100% faithful to actual data, but it shares characteristics of the real dataset. Synthetic data commonly requires additional validation, such as comparing generated results with human-annotated, real-world information. If the real data sample is too small, it will be reflected in the accuracy of the generated data. Many applications must use synthetic data because the actual data is unobtainable or does not exist. In this case, it is generated using assumptions that may invalidate the analysis because it is not based on empirical data.

Examples of Synthetic Data Applications

Below are examples that demonstrate the usefulness of synthetic data:

  • Financial companies create this type of data containing activity patterns that could result from fraudulent banking or credit card transactions. This data is used to develop more robust fraud detection algorithms.
  • Sharing real data outside of a business or national borders can be restricted due to privacy regulations. Synthetic data is free from such restrictions, allowing datasets to be shared outside an organization or across borders.
  • In insurance, false claims can be profiled. Fraudsters who successfully use an approach will try the same exploit against other insurers. Synthetic data can be generated by the impacted insurer and shared across the industry to improve detection of potential claims fraud.
  • Self-driving cars generate sensor data, which synthetic data can augment to train self-driving algorithms to improve the detection of potential hazards with greater accuracy. Google Waymo driverless taxi service uses this approach with success.
  • Natural language applications such as Amazon Alexa use synthetic data to improve cognition without the privacy risk of sharing real-world conversations.
  • Quality assurance staff in software development teams use generated synthetic data to test the functionality of applications. The generated data can be used to test for valid and invalid application usage to ensure exception handling is coded and working as expected. The same test data can be used for regression testing future application iterations to ensure fixes don’t break what is currently working.
  • Offshoring QA testing in remote locations such as India, for example, is a common practice. Using synthetic data based on actual data gathered from US users helps with QA in other locations.
  • Synthetic data based on real data with human-verified content can be used to help reduce bias in ML models.


Benefits of using synthetic data include:

  • Reduce compliance risk for cross-border data sharing because regulations such as the General Data Protection Regulation (GDPR) only apply to real user data. Traditional approaches that carry more risk are anonymized, or the data is obfuscated. Generated synthetic data eliminates privacy risks.
  • Reduce bias in machine learning using higher volumes of representative generated data.
  • Increase the accuracy of ML models with more training data.
  • Reduce cyber risk by replacing actual data with synthetic data.
  • Assess changes. Synthetic data can be modified to alter outcomes based on simulated environmental changes applied to the ML model. When a business considers changing a product, such as updating a camera in an autonomous vehicle, its impact can be initially assessed using synthetic test data.

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