Syntho Engine connectors

PII, PHI, and other direct identifiers are sensitive and can be spotted manually or automatically with our PII scanner to save time and minimize manual work. Then, one can apply Mockers to substitute real values with mock values to de-identify data and enhance privacy.

• First name
• Last name
• Phone number
• Social Security Number, SSN
• Bank number, etc.

PII stands for Personal Identifiable Information. PHI stands for Personal Health Information and is an extended version of PII dedicated to health information. Both PII and PHI are identifiers and relate to any information that can be used to distinguish or trace an individual’s identity directly. Here, with identifiers, only one person shares this trait.

Production data is privacy-sensitive
Testing and development with representative test data is essential to deliver state-of-the-art software solutions. Using original production data seems obvious, but is not allowed due to (privacy) regulations according to the GDPR and the Dutch Data Protection Authority. This introduces challenges for many organizations in getting the test data right.

Production data does not cover all test scenarios
Test data management is essential because production data often lacks the diversity required for comprehensive testing (or does not (yet) exist at all), leaving out edge cases and potential future scenarios. By creating and managing diverse test data sets, it ensures thorough testing coverage and helps identify potential issues before deployment, mitigating risks and bugs in production to enhance software quality.

Optimize testing and development
Let your testers and developers focus on testing and development, instead of test data creation. Test data management optimizes testing and development by maintaining and updating test data, saving developers and testers time typically spent on data preparation. Automation of test data provisioning and refreshing ensures data relevance and accuracy, allowing teams to focus on analyzing results and enhancing software quality efficiently. This streamlined process improves overall testing speed, agility, and productivity in the development lifecycle.