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Synthetic Data
The definitive guide to synthetic data

What is synthetic data?

Synthetic data is generated based real-world data. The resulting synthetic data looks, feels and means the same as the original. The synthetic dataset is a perfect proxy for the orignal, since it contains the same insights and correlations.
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Good quality synthetic datasets can be used in a variety of tasks in a privacy-safe, agile manner. None of the synthetic datapoints can be traced back to a datapoint in the original set. This makes synthetic data privacy safe and compliant with the strictest privacy laws and regulations. What’s more, the process of synthesization can be used to augment the original data: fix embedded biases, upsample rare events or generate edge cases not present in the original.

Synthetic data is better than real data

Thanks to the flexibility of the synthetization process, synthetic data can be tailored to suit use cases and protect data privacy simultaneously. Synthetic data is the must-have ingredient for successful data projects throughout organizations. 

Synthetic data generation accelerates the analytics development cycle, lessens regulatory concerns and lowers the cost of data acquisition. 
(Gartner)

What are the use cases for synthetic data?

Good quality synthetic data is an accurate representation of the original data. As a result, it can be used as a drop-in placement for sensitive production data in non-production environments, such as AI training, analytics, software testing and development. Synthetic data is also compliant with all data privacy laws, like GDPR, HIPAA and CCPA and CPA. Synthetic data is the perfect tool to safely unlock sensitive datasets. Businesses use them to extract knowledge and insight in a privacy-compliant way. The most common synthetic data use cases are:
AI training
Synthetic data is increasingly used for AI training. The synthetization process can also augment the data. By upsampling rare events and patterns, AI algorithms can learn more effectively. Synthetic training data perform 15% better than real data. According to Gartner, high-quality, high-value AI models cannot be built without synthetic data.
AI governance
The process of synthetization can remove biases embedded in the original data. You can also use synthetic data for stress testing AI models with data points unlikely to occur naturally. Synthetic data is also a key component of explainable AI, providing insight into the behavior of models.
Synthetic test data
As opposed to rule-based test data, synthetic test data is easy to generate. It is highly realistic and flexibly sized. Synthetic test data is a crucial ingredient for data-driven software development and testing
The synthetic data landscape is continuously expanding. More and more high value use cases are popping up every day.

How good is the quality of synthetic data?

A key question for any synthetic data generator is how accurate its output is. The data synthesis is therefore usually accompanied by an automated quality assurance process. The QA checks if the synthetic data can be trusted to faithfully represent the real world. Each batch of synthetic data generated by MOSTLY AI comes with an automated privacy and accuracy report. We also developed an open source synthetic data benchmarking tool, the Virtual Data Lab. Feel free to use it to gain insights into the quality of the synthetic data our software generates for you!

How was synthetic data invented?

Before generative AI became a reality, the term synthetic data was used for all kinds of fake or mock data, such as:
  • Random data
  • Rule-based data.
Data generation methods reached a new level with AI-powered deep generative models. They can create an unlimited amount of highly realistic, completely safe synthetic data. MOSTLY AI pioneered data synthesis for structured, tabular data.  Today, MOSTLY AI is the expert in generating behavioral and transactional synthetic data.
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How does synthetic data work?

Not all synthetic data is created equal. Modern day synthetic data generators are sophisticated AI algorithms. Some are better than others. MOSTLY AI's category-leading deep neural network models extract patterns from a provided dataset. Once trained on real data, our synthetic data platform can generate completely new synthetic data. This data mimics the characteristics of the original, to the extent that it is nearly indistinguishable from it. Still, as it bears no direct relationship to the actual data, synthetic data is absolutely safe to use and collaborate on.
Companies prefer to use synthetic data in cases where it is not necessary to link back data points to individual records. Good quality synthetic data is private by design. For maximum security, MOSTLY AI synthetic data platform comes with built-in privacy checks. As a result, our synthetic data is compliant with the world’s strictest privacy laws, such as GDPR, HIPAA and CCPA.

How does synthetic data compare to other data anonymization tools?

Legacy data anonymization technologies not only endanger privacy, but also destroy the utility of the data. Synthetic data is the best technology to use when datapoints don’t need to be linked back to originals. We see a lot of companies using pseudonymization as anonymization. But from a legal perspective, pseudonymised data is still personal data. And it needs to be treated and protected as just that. A pseudonymized dataset still includes so-called direct identifiers. Other tools, like generalization, perform well on the privacy front, but fail to preserve data utility.
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