Discover why enterprises rely on synthetic test data generation to test their applications – at lower risk and higher scale – without endangering privacy.
Table of Contents
Synthetic Data is a Good Start but Not Enough
Synthetic Data Challenges
Benefits of Using Production Data for Testing Apps
New Approach to Using Production Data for Testing
Synthetic test data generation is the way artificial data is created for software testing and quality assurance. Because synthetic data mimics real data but is not derived from real-world sources, it complies with data privacy laws, while also ensuring greater data diversity.
Enterprises generate synthetic data to test software applications without the risk of exposing Personally Identifiable Information (PII). Synthetic test data not only assures compliance with data protection regulations, it also covers a wider range of scenarios than real data and is available consistently, which facilitates more comprehensive testing and reduces reliance on scarce real-world data. Synthetic test data generation can be controlled and manipulated precisely, enabling testing of specific conditions, and enhancing quality assurance. And since it’s inherently scalable, synthetic test data can be generated in large volumes to evaluate system performance under various loads.
Created via generative AI models, a roles engine, cloning, masking and more, synthetic data generation is a best practice for data privacy protection, as well as application testing and reliability.
Synthetic data generation tools are employed in software development, data analysis, and Machine Learning (ML). Not only do they safeguard sensitive data and privacy by allowing testing without exposing real user data, but also enable more comprehensive testing by creating diverse data scenarios, including edge cases and anomalies, which can be hard to obtain from actual data.
Moreover, synthetic data generation offers researchers and data stakeholders a consistent and readily available source of data, reducing their dependency on restricted real-world data. It also improves testing accuracy by enabling users to control specific test conditions at scale by generating large data volumes.
There are 4 main ways to generate synthetic test data:
Generative AI
Generative AI synthetic data techniques leverage ML models, such as Generative Pre-trained Transformer (GPT), which "learn" from production data to create synthetic data that closely resembles real data.
Rules engine
A rules engine generates test data via user-defined business rules. Additional intelligence can be added by referencing the relationships between the data elements, to ensure relational integrity across all systems.
Entity cloning
Entity cloning aggregates the data for a particular business entity (e.g., a single customer) from underlying sources – cloning and masking it – on demand. It's used to assemble the massive amounts of data required for load or performance testing, because unique identifiers are generated for each cloned entity.
Data masking
Data masking protects PII, while retaining the characteristics and properties of the actual data. It substitutes altered values (or pseudonyms) for any sensitive information, while preserving data utility.
Other methods include:
Bootstrapping, that resamples test data from an existing dataset with replacement samples, preserving key statistical properties.
Data randomization, that introduces randomness within defined constraints to imitate real-world variability.
Data transformation, that applies different mathematical or logical operations to existing data to create synthetic test data variations.
Interpolation and extrapolation, that fill in missing values based on existing data trends.
Markov chains, that build a sequence of events based on the probability of transitions between states.
Monte Carlo simulations, that employ random sampling and mathematical models to ensure data realism.
Parameterized models, that use statistical models with predefined parameters that conforms to specific distributions.
Time series forecasting, that combines historical data with advanced forecasting techniques to estimate synthetic data points in the future.
Synthetic test data generation has its challenges, such as the resultant data being:
Calculated fields
Generating fields which are calculated based on other fields, e.g., total amount outstanding, which sums payments against all invoices for a customer
Complex data relationships
The synthetic test data may not be able to simulate complicated data relationships and structures found in real data.
Lack of rare events and edge cases
Rare events or outliers in real data might not be well-represented in synthetic data
Negative testing
Real data can’t be used to test how a system behaves when subjected to invalid data
Such challenges illustrate why using production data for testing – in combination with synthetic data – is a better bet.
Enterprises are shifting to advanced entity-based synthetic test data generation tools to create more compliant and realistic synthetic test data, more easily. The most advanced synthetic test data generation solutions leverage business entities (customers, for example), which are automatically modeled based on metadata from the original datasets.
Entity-based synthetic test data generation solutions use a variety of different data generation techniques, alone or together, including:
K2view is one of the only synthetic test data generation solutions to support all of these techniques.
Learn more about K2view entity-based synthetic data generation tools.