Why use synthetic tabular data?

• Data Augmentation:

  Synthetic data can increase the size and diversity of existing datasets, which can be helpful when real data is scarce or unbalanced. 
• Privacy:

  It allows for the use of data without revealing sensitive information, complying with privacy regulations like GDPR and HIPAA. 
• Testing:

  Synthetic data can be used to test software and algorithms without relying on real-world data. 
• Analytics:

  It can be used to explore patterns, perform simulations, and gain insights without the concerns associated with real-world data. 

How is synthetic tabular data generated?

• Generative models:

  Generative adversarial networks (GANs) are often used to learn the underlying structure and patterns of real data and then generate synthetic samples. 
• Rule-based approaches:

  These methods use predefined rules and logic to create synthetic data. 
• Statistical models:

  Statistical models like Gaussian Mixture Models (GMMs) can be used to capture the distribution of real data and generate samples from it. 
• Other methods:

  Various other techniques like neural networks and simulations can also be used. 

Examples of use cases:

• Healthcare:

  Synthetic data can be used to simulate patient data for developing and testing new treatment algorithms. 
• Finance:

  Synthetic data can be used to study financial markets and test trading strategies. 
• Software testing:

  Synthetic data can be used to test software under various conditions and ensure its reliability. 

Key considerations:

• Data quality:

  Synthetic data should accurately reflect the statistical properties of real data. 
• Privacy:

  Synthetic data should not reveal any sensitive information about the individuals or entities from whom the real data was derived. 
• Model performance:

  The generated synthetic data should be able to be used effectively by machine learning models.