Pattern-Based Synthetic Data Generation

Generate Synthetic Data according to patterns

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Health Plus Ai
KPN
Philips
Nederlandse Spoorwegen
New compliance
SAS
Universiteit Leiden
Lifelines
Centraal Bureau voor de Statistiek
Euris Group
KVK
Leiden University Medical Center
UMCG

Key benefits of using
Pattern-Based Synthetic Data

Explore how to use Pattern-Based Synthetic Data in practice

Future Scenario Generation
Future Scenario Generation
Generate values that follow precise data patterns (e.g. custom IDs, product numbers) using regex or format rules.

This ensures realistic, structurally valid data for testing and system integration.
Future Scenario Generation
Future Scenario Generation
Pattern-Based Synthetic Data can extract existing trends and patterns to generate hypothetical future scenarios.

This allows organizations to generate data for potential future scenarios.
Scenario Testing and Simulation
Scenario Testing and Simulation
Generate rare or hypothetical scenarios not present in existing data.
 
This enables the creation of various scenarios relevant for stress-testing of applications.
Rare Observation Generation
Pattern-Based Synthetic Data can be used to generate underrepresented cases, edge cases, rare events, or unusual scenarios, ensuring that data reflects potential scenarios.

User documentation

Explore the Syntho user documentation

Learn more

Product Demo

Watch the Syntho Engine in action

Data creation in 3 steps

Data creation in <span class="accent-for-white">3 steps</span>
01
Define patterns

Decide the structure or format your data should follow.

• Example: Define a format for the product number
• Tools: Business rules, formulas, or constraints

02
Use Rule-Based Synthetic Data

Set up formulas or rules to automatically generate values according to the defined patterns.

• Example: Product Number: ?????-########:
• ? is replaced by a character from a defined set
• # is replaced by a number
• Note: Many additional pattern-based options are available (see the user documentation for full details)

03
Generate and validate the data

Run the formulas, preview the results, and verify correctness.

• Ensure all rules are applied correctly
• Optionally, tweak or correct patterns as needed

Real data problematic?
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