Preserve referential integrity in an entire relational data ecosystem
Ensure data consistency across your data ecosystem by preserving referential integrity.
Data Integrity
Consistent mapping ensures that relationships and dependencies between different data fields remain intact, preserving the logical flow and structure of the dataset during testing.
Accurate Testing
It allows for realistic and reliable test environments, as masked data maintains its contextual relevance, leading to more accurate test results and system validation.
Reduced Risk of Errors
Consistent mapping reduces the risk of introducing errors or inconsistencies, ensuring smooth system performance.
Simplified Debugging
When masking is consistently applied, it becomes easier to track and identify issues during testing, as the masked data behaves in the same way as the original data.
Explore the Syntho user documentation
Discover how consistent mapping enhances data accuracy and reliability beyond traditional methods
Integration testing and end-to-end testing
In complex systems, different modules or components may rely on each other through database relationships, potentially across different systems. Referential integrity is crucial during integration testing to ensure that these dependencies are properly maintained, and the integrated components work together as expected.
Realistic testing scenarios
Testing environments should mirror the production environment as closely as possible to ensure that the testing scenarios are realistic. If referential integrity is not maintained, the behavior of the system may differ from what is expected in a production setting, leading to inaccurate test results.
Data quality
Non-production environments are not exempt from the need for high-quality data. Maintaining referential integrity ensures that the data used for testing and development accurately reflects the relationships between entities in the system. This is essential for producing reliable results and making informed decisions during the development process.
Advanced mockers
Advanced mockers are configurable mockers that enable users to fine-tune data according to their specific needs. Examples include the custom text mocker, which generates customizable strings containing letters, numbers, and symbols, and the Uniform Distribution Mocker, which allows users to set minimum.
Select the columns that should be consistently mapped to new mock values.
Preview the values and enable the consistent mapping feature to ensure original values will be linked across different generation jobs.
Preview and confirm the values to ensure that the original values have been consistently replaced with mocked values. This consistency will be maintained across different data generation jobs and tables, preserving data integrity.
Explore other features that we provide
Test Data Management
De-Identification & Synthetization
Comprehensive Testing with Representative Date.
Rule-Based Synthetic Data
Simulate Real-World Scenarios.
Subsetting
Create Manageable Date Subsets.
Smart De-Identification
PII Scanner
Identify PII automatically with our AI-powered PII Scanner.
Synthetic Mock Data
Substitute sensitive PII, PHI, and other identifiers.
Consistent Mapping
Preserve referential integrity in an entire relational data ecosystem.
AI Generated Synthetic Data
Quality Assurance Report
Assess generated synthetic data on accuracy, privacy, and speed.
Time Series Synthetic Data
Synthesize time-series data accurately with Syntho.
Upsampling
Increase the number of data samples in a dataset.
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Substitute sensitive PII, PHI, and other identifiers with representative Synthetic Mock Data that follow business logic and patterns.
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.
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.
Unlock data access, accelerate development, and enhance data privacy.
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