Rule-Based Synthetic Data

Generate synthetic data to mimic real-world or targeted scenarios using predefined rules and constraints

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Key benefits of using
Rule-Based Synthetic Data

Generate high-quality synthetic data that is tailored to your specific rules and constraints

Generate Data from
scratch

In cases where data is either limited or where you do not have data at all, the need for representative data becomes crucial when developing new functionalities.

Rule-based synthetic data enables the generation of data from scratch, providing essential test data for testers and developers.

Enrich data

Rule based synthetic data could enrich data by generating extended rows and/or columns. It can be used to produce extra rows to create larger datasets easy and efficiently.

Additionally, Rule based synthetic data can be used to extend data and generate additional new columns potentially dependent on existing columns.

Flexibility and customization

The rule-based approach provides flexibility and customization to adapt to diverse data formats and structures, enabling the full tailoring of synthetic data according to specific needs.

One can design rules to simulate various scenarios, making it a flexible method for generating data.

User documentation

Explore the Syntho user documentation

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Discover our features

Explore features to protect sensitive information and maintain data integrity.

Formula-Based Synthetic Data Generation

Generate Synthetic Data according to defined formulas

Synthetic data can be tailored to match specific business rules, simulate rare or edge case scenarios, and enrich datasets by generating data based on formulas. This enables new data generation and edge case generation based on formulas.

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Pattern-Based Synthetic Data Generation

Generate Synthetic Data according to patterns

Pattern-based synthetic data enables organizations to generate hypothetical future scenarios, data that follows patterns and simulate rare or future events.

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