Mock vs. Synthetic Data
Mirage currently supports both Mock Data Generation and Synthetic Data Generation. Mock and Synthetic data are alternative forms of data that you can consider when real-world data is unavailable, restricted or unsuitable for certain use cases. While both serve as substitutes for real data, they differ in how they are generated and their characteristics.
| Mock Data | Synthetic Data |
|---|---|
Available today on Cloak (API) and Mirage (Web UI, API coming soon)
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Available on Mirage (Web UI) |
| Created using rule-based approaches, designed to mimic the structure and format of real-world data, with support for SG-specific entities. Unlike SDG, MDG does not learn from real-world data patterns. | Generated using statistical models and generative AI techniques that learn patterns and relationships in real-world data. |
| Suitable for scenarios requiring predictable and repeatable datasets that do not have to be highly realistic. For example, it can be used for software testing/ development and basic validation tasks. | Suitable for scenarios where statistical patterns in an existing dataset need to be retained, or if you require a more realistic dataset. For example, it can be used for AI/ML training, sharing of sensitive data and software testing. |
Sample use cases:
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Sample use cases:
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To optimise your data generation process, consider a hybrid approach:
- Use mock data generation for columns that don’t require high fidelity to the original dataset. This is suitable for fields with simple rules or distributions.
- Reserve synthetic data generation for columns that need to closely mimic the patterns and relationships in your real data. Read more in the Prepare Dataset guide.
