Synthetic Data

Synthetic data is artificially generated information that replicates real-world data patterns without using actual user data. This approach offers several key advantages:

1. Privacy Protection: By generating artificial data instead of collecting real user information, you maintain privacy and avoid copyright issues.
2. Pattern Replication: The generated data accurately reflects real-world distributions, edge cases, and constraints.
3. Noise Reduction: Unlike real-world data, synthetic data eliminates unnecessary noise while maintaining essential patterns.

This makes synthetic data particularly valuable for:

• Machine learning model training
• System testing and validation
• Product development workflows
• AI model refinement

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Key Characteristics:

• Schema-Driven: You are asked to define the columns, types and the description for the datapoints you want to generate
• Realistic Distribution: The data that is generated follows rules, patterns and are maintaining a distribution, they aren’t random values
• Safety: Data can be completely harmless such that it doesn’t have personally identifiable information, or toxicity etc.

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When to use Synthetic Data

• When real data is unavailable, incomplete, or too sensitive to use
• When testing systems at scale or simulating rare scenarios
• When training AI models that require balanced or diverse inputs

Whether you’re building prototypes, augmenting training sets, or simulating uncommon scenarios, synthetic data empowers you to move faster and with greater flexibility.

Learn more about how to create synthetic data in your workflow in minutes.