The Future of Test Data Management: Why Synthetic Data is Your Competitive Edge

PII, or Personally Identifiable Information, refers to sensitive data linked to individuals. Privacy regulations make it challenging to use personal data for testing purposes, so it is essential to protect this data accordingly.

The PII scanner detects all PII attributes and identifiers. While a birthdate alone may not uniquely identify an individual, you can customize the scanner to include attributes like birthdate and other variables as needed. Then, our PII scanner can also detect non-identifiers such as the birthdate.

The scanner offers both “shallow” and “deep” scans: a shallow scan reviews metadata, such as column names and data types, while a deep scan leverages advanced entity recognition to analyze actual data in depth. This flexibility allows you to specify which PII types to detect.

Syntho supports handling Blob data, both by duplication and exclusion of such columns. Details can be found in our User Documentation. We can deepdive further into this with you, if desired.

Yes, Syntho can detect and adapt PII data as configured during setup and as demonstrated during the webinar.

More information about our PII scanner can be found here.

More information about our mockers to adapt PII can be found here.

Syntho offers over 150 mock data generators that accurately mimic real-world data characteristics. Rule-based synthetic data can also be customized to suit specific requirements.

Syntho’s Test Data Management solutions are designed to mask and de-identify sensitive data at scale, including complex relational datasets. Syntho’s consistent mapping feature is important to realize preserving consistency and referential integrity for complex relational datasets and works across tables, across databases, across systems and even over time.

It is both viewable in the tool, as well as there is an option to export it as text.

Yes, Syntho’s AI-powered generation automatically captures patterns and complex relationships between columns, reproducing them in the generated synthetic data.

Additionally, Syntho offers rule-based synthetic data methods, including calculated columns, to model business rules from scratch, e.g. for cases where you don’t have any data yet.

Yes, we facilitate on-premise deployments and all features are available on-premise.

Yes, Syntho has a PII text scanner that can identify and mask PII in unstructured text data. For example, it can detect and replace PII in text fields, such as doctor’s notes, by tagging and obfuscating sensitive information like names, dates, and SSNs, while creating mock replacements.

More information can be found on this page under the “Introducing the PII text scanner” section.