Rule-Based
Synthetic Data

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

Rule-Based

Key benefits of using Rule-Based Synthetic Data

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 Omni user documentation

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Features

Discover our features

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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Subsetting

Create manageable data subsets

Reduce records to create a smaller, representative subset of a relational database while maintaining referential integrity.

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Rule-Based

Why Rule-Based Synthetic Data is more advanced

Data Cleaning and Transformation

Effortlessly clean and reformat data, such as trimming whitespace, changing text casing, or converting date formats.

Statistical Calculations

Perform row-level calculations using logical, mathematical, or conditional formulas to transform data at a granular level.

Logical Operations

Apply logical tests to data to create flags, indicators, or to filter and categorize data based on specific criteria.

Mathematical Operations

Perform row-level mathematical transformations, such as applying formulas for adjustments, scaling, or rounding.

Text and Date Manipulation

Transform text and date fields with functions like trimming, formatting, or extracting parts of a date or text field.

Data simulation

Generate data using predefined generators, enabling customization by applying specific distributions such as uniform or normal.

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