How to evaluate synthetic data quality?

Generating synthetic data lays a crucial role in addressing the problematic aspects of data in Data Science, such as balancing classes, expanding small datasets, and securely sharing sensitive information like bank transactions while protecting privacy. YData's Fabric generates reliable and secure synthetic data, which we
assess by comparing our advanced generative models to three essential standards: utility, fidelity, and privacy.

The synthetic data quality report from Fabric, provides a set of interpretable metrics that answer the following questions:

• How can we ensure that the synthetic data retain the same statistical information, correlations, and properties as the original data?
• How can we ensure that synthetic data can replace real data for applications such as analytics or Machine Learning (ML)?
• How can we ensure the generated data can't be reversed-engineered to disclose sensitive information?

Download this white-paper to learn more about:

• The mechanisms integrated into the synthetic data process to avoid overfitting
• The importance of measuring fidelity, utility and privacy
  
• The different metrics and statistics used to computed the scores

Unlock the potential of your data with Fabric Community Version – Generate synthetic data effortlessly and ensure alignment with your business goals using our comprehensive synthetic data quality PDF report!