What are some benefits and drawbacks of synthetic data?

Benefits of privacy-focused synthetic data (or synthetic data from Gretel):

1. Safe to share due to built-in privacy protection measures.
2. Maintains the statistical properties and insights of the original data.
3. Can solve problems that the original data cannot, such as correcting imbalances or biases by generating records with underrepresented attributes (e.g., specific genders, ages, races, or ethnicities).

Drawbacks of synthetic data:

1. Requires complex configuration of compute environments.
2. May require knowledge of advanced machine learning.
3. Difficult to assess the privacy and efficacy of the generated synthetic data.

Gretel addresses these drawbacks by:

1. Automatically managing systems in the cloud, eliminating the need for in-depth machine learning knowledge.
2. Providing a "report card" with each synthetic dataset, outlining its usability and privacy.
3. Keeping the core technology open-source while offering these solutions as part of the product.