So what Is Synthetic Test Data? Synthetic test data refers to a dataset artificially generated to replicate production data. While synthetic data doesn't mirror actual objects, events, or individuals, it can possess statistical or mathematical realism. The integration of synthetic test data expedites test data provisioning—a frequent bottleneck for DevOps and software testing teams. The main benefits of synthetic test data are:

Privacy and Compliance: Banks deal with sensitive customer information, making data privacy and compliance critical. Synthetic test data helps in safeguarding this confidential information while ensuring regulatory compliance, as it doesn't contain real customer data.

Enhanced Security: Using synthetic data minimizes the risk of exposing real customer information to potential breaches or unauthorized access during testing procedures. It enhances production data in cases of sparse information, like when a specific sample necessitates real-life data from 1,000 customers, but only data from 10 customers is accessible.

Testing Scenarios: It allows the creation of diverse and complex testing scenarios that might not be possible with limited real data, enabling more comprehensive testing for different scenarios, including edge cases.

Efficiency, Speed, Dependability and Reliability: Synthetic data streamlines the testing process by providing ample data swiftly, reducing the time spent waiting for real data to test different functionalities or systems.

Data Quality Improvement: Synthetic data can be generated to simulate various scenarios, helping to enhance data quality by identifying and rectifying anomalies or inconsistencies in the systems' responses.

Cost Savings: Using synthetic test data reduces the need for large-scale storage and management of actual customer data for testing, thereby cutting down infrastructure and operational costs.

In certain scenarios, synthetic test data is preferred over production data due to security and privacy compliance considerations. For instance, synthetic data creation proves invaluable when relevant or complete production data is lacking. Moreover, synthetic datasets mitigate cybersecurity and noncompliance risks associated with using real sensitive data in testing environments. It proves particularly beneficial for testing new applications without existing production data, enabling testers to match their needs with the closest available "look-alike" profiles. To test data management teams, the authenticity of the data is more significant than whether it's real or synthetic.

There are several reasons to opt for synthetic test data over production test data. The use of synthetic test data offers a secure, compliant, and efficient alternative to production test data, mitigating the risks associated with handling actual customer data in testing environments while fostering enhanced data optimization and enrichment, such as:

Increased scalability: Real production datasets frequently lack the volume of reliable, complete, and high-quality data necessary for meaningful software testing. Synthetic test data, where parameters are easier to define than rules-based test data, provides greater scalability and flexibility for testing teams.

Stronger data protection: While data masking tools secure private data in testing environments, synthetic data creation completely safeguards sensitive or personal information.

Enhanced Data Quality: Synthetic data enables simulation of different scenarios, improves data quality by identifying and rectifying errors, inaccuracies, and biases that can undermine testing reliability

Here are three key criteria when determining the use of production test data versus synthetic test data:

Efficiency and Speed: Synthetic test data expedites the testing process by quickly supplying substantial data volumes, minimizing the waiting time for real data to evaluate various functions or systems. Time limitations frequently determine the preferable test data type. Obtaining production test data might require days or weeks without adequate test data management tools, while generating synthetic test data can be achieved within minutes, notably reducing the time necessary for data masking.

Cost: Banks need to assess the reasonable cost of preparing, managing, and archiving test data. Synthetic data testing capabilities provide a more versatile and cost-effective approach compared to maintaining and customizing systems for managing production test data. This reduces the necessity for extensive storage and management of actual customer data for testing, thereby cutting down infrastructure and operational costs.

Regulatory Compliance: It helps in adhering to strict regulatory guidelines by avoiding the use of real customer data in non-production environments, mitigating potential legal and ethical issues.

Enhance Testing Security

The absence of proper test data is a major reason why nearly two thirds of business applications reach production without being properly tested. Validata’s ‘super-app’ uses #AI to rapidly generate large sets of synthetic test data to eliminate the risk of data breaches by creating production-like data but without the sensitive content. It leverages Virtual and Synthetic Data to mitigate the risk of using production data in Dev/Test environments. Read more on how ConnectIQ can mitigate the risks of test data on our ebook.