Synthetic data is a key component in many machine-learning models. Its advantages include cost and time efficiency.
Companies use synthetic data to test new applications and train models while protecting privacy. Swiss insurance company La Mobiliere used a synthetic tabular dataset to build an effective churn prediction model while preserving customer privacy.
Test Data Speed
There’s a long-standing notion that faulty input leads to faulty output, which is especially true with test data. Creating realistic synthetic test data is the fastest way to get real-time testing results for software quality assurance, and it can be done on demand.
The quality of synthetic data can be high or low, depending on the use case. High-quality data can be simulated from theoretical distributions, or it can be generated from existing data. This is sometimes called “background knowledge.” It might be knowledge of a stock market’s behavior from textbook descriptions or a statistical distribution of customer traffic in a store based on years of experience.
Sometimes the real data doesn’t exist, or collecting it would be cost-prohibitive or unethical. Synthetic data can also be used to cover edge or rare cases in the dataset, which are harder or impossible to model without real-world data. This is a common use case for image recognition and text/audio/video analysis models.
Test Data Quality
Using synthetic data is ideal for situations where access to real-world data is limited. This is especially true for healthcare, where access to patient records can be difficult and expensive. In addition, medical journals are now strongly encouraging researchers to make their data publicly available to improve research and innovation, but this can create problems for privacy.
For example, removing identifiers from public datasets is not always enough to safeguard privacy, and unique values may remain that can be mapped back to individuals. This trade-off between accuracy and privacy must be optimized on a case-by-case basis using imputation methods or differential noise techniques.
Getting realistic test data for software testing (and performance benchmarking) is another common use case for synthetic data. This includes ensuring that software applications meet their functional and performance requirements and covering edge or rare cases that would be difficult, impractical, or unethical to collect in the real world. Using synthetic data in this way reduces the noncompliance and security risks associated with using actual personal data in test environments.
EU GDPR is a significant change in the way companies handle personal data. The new regulations mandate that businesses incorporate privacy safeguards into every step of the data process. Non-compliance comes with hefty fines.
To comply with the GDPR, developers need to use only test data that doesn’t include real, identifiable personal information. It’s not enough to simply mask data or remove identifiers, because attackers can still link anonymized datasets to the original data.
To address this challenge, many organizations turn to synthetic data. It’s cheaper and faster to generate than real data, and it can be stored in a fully controlled environment. In addition, the models used to synthesize data can be trained on-premise, where the actual data resides, preventing the need to transfer it across the organization’s network. This helps keep sensitive data secure and maintains the integrity of the tested system.
Enterprise Test Data Generator
An enterprise test data management solution that enables teams to self-provision synthetic data doesn’t require access to production systems and can save time. This is a critical consideration, particularly in an agile environment.
One classic use case for synthetic data is testing software applications, especially during the development phase. This can include functional and performance testing, as well as stress tests that push the software to its limits.
Other times, real data doesn’t exist or is cost-prohibitive to collect. This can be true for edge or rare cases that are too expensive, impractical, or unethical to capture in the real world.
The best way to solve these challenges is with a high-quality, AI-powered, data generation platform that’s capable of synthesizing complex database structures while maintaining referential integrity. And it’s important to choose a solution that provides built-in privacy checks to ensure your customers’ data isn’t exposed to external parties. This is especially crucial when it comes to data used for security testing purposes.