Utilize our best-practice solutions to generate test data that reflects production data for comprehensive testing and development in representative scenarios
Testing and development with representative test data is essential to deliver state-of-the-art solutions. Using original production data seems obvious, but is often challenging as it cannot simply be used because it:
This introduces challenges for many organizations in getting the test data right. Hence, Syntho supports all best practice solutions to establish your test data right.
Follow best practices to protect sensitive data while ensuring it remains useful for analysis and testing.
Identify PII automatically with our AI-powered PII Scanner
Mitigate manual work and utilize our PII scanner to identify columns in your database containing direct Personally Identifiable Information (PII) with the power of AI.
Substitute sensitive PII, PHI, and other identifiers
Substitute sensitive PII, PHI, and other identifiers with representative Synthetic Mock Data that follow business logic and patterns.
Preserve referential integrity in an entire relational data ecosystem
Preserve referential integrity with consistent mapping in an entire data ecosystem to match data across synthetic data jobs, databases, and systems.
Explore the Syntho user documentation
Create synthetic data based on pre-defined rules and constraints
Mimic statistical patterns of original data in synthetic data with the power of artificial intelligence
Scan PII automatically with our PII Scanner via the “PII” tab or identify columns that you would like to mock via the “Job Configuration” tab.
Confirm the by our PII scanner suggested mocker automatically or configure mockers on column level.
Confirm to apply the selected mocker to a column via the PII or Job Configuration tab. This allows users the flexibility to spot columns and apply mockers accordingly.
De-identification is a process used to protect sensitive information by removing or modifying personally identifiable information (PII) from a dataset or database.
De-identification is often used when production data is available as a starting point. De-identification is applied to remove or modify (privacy) sensitive information from the dataset or database to comply with data privacy regulations, as the use of personal data is not allowed according to privacy regulations (such as the GDPR).
Synthetisation aims to create synthetic data that is generated artificially and serves as an alternative to real-world data.
Synthetisation is often used when production data is limited, scarce, misses data or does not exist at all as a starting point. New data is artificially generated and serves as an alternative to real-world data.
Unlock data access, accelerate development, and enhance data privacy.
Keep up to date with synthetic data news