Skip to content

How to evaluate synthetic data quality?

Synthetic data quality metrics PDF report

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! 



Correlation Matrix for Multivariate Data

How to Profile Datasets with a big number of Variables?

As the Data-Centric AI paradigm has come to prove that focusing on data quality will have the most transformative impact in industries across all verticals, more and more companies and organizations worldwide are starting to look for the...

Read More
consortium for Responsible AI

YData in the world’s biggest consortium for Responsible AI

YData, the startup that created the first data-centric platform that accelerates the development of Artificial Intelligence (AI) solutions, announces today its participation in the world’s biggest artificial intelligence consortium for...

Read More
Time-series synthetic data generation

The trade-offs of time-series synthetic data generation

Synthetic data is artificially generated data that is not collected from real-world events and does not match any individual's records. It replicates the statistical components of real data without containing any identifiable information,...

Read More