What is Synthetic Data?
Synthetic data is artificially generated data that mimics the statistical properties and patterns of real data. It's used for training machine learning models, software testing, and sharing data while preserving privacy.
Generation Methods
Statistical Methods
- Distribution sampling
- Monte Carlo simulation
- Bootstrap methods
Machine Learning
- GANs (Generative Adversarial Networks)
- VAEs (Variational Autoencoders)
- Diffusion models
Rule-Based
- Business rules
- Data schemas
- Domain knowledge
Use Cases
ML Training
- Augment real data
- Address class imbalance
- Increase dataset size
Privacy
- Share data safely
- Regulatory compliance
- Research collaboration
Testing
- Software testing
- Performance testing
- Edge case generation
Development
- Mock data
- Demo environments
- CI/CD pipelines
Benefits
- Privacy preservation
- Unlimited volume
- Controlled characteristics
- Cost effective
- No collection needed
Challenges
- Quality validation
- Bias reproduction
- Distribution coverage
- Complexity of real patterns
- Evaluation metrics
Evaluation
- Statistical similarity
- ML model performance
- Privacy guarantees
- Utility metrics
Tools
- Synthetic Data Vault
- Gretel.ai
- Mostly AI
- Tonic.ai