AI-generated synthetic data is the meaning of the original source data without any of the sensitive information. The patterns, the correlations, the insights all remain the same, yet none of the datapoints match those in the original dataset used to train the synthetic data generator. 

These synthetic datasets contain all the value of the data without any privacy risk. A bit like a rich cake without calories.
Synthetic data gives a true representation of the real world. As a result, it can be used as a drop-in placement for real data. What's more, synthetic data generation allows for data augmentation processes to mold the dataset to fit certain criteria, like size or fairness. 

Historical data is like a mirror of the world we live in. This means that it's full of bias, discrimination, and injustice. AI systems trained on raw historical data will pick these bad patterns up and amplify them at scale. MOSTLY AI's team has been at the forefront of fairness research since day one. We've been working on methods to remove these embedded biases via synthetization. Fair synthetic data generation introduces constraints based on a mathematical definition of fairness.  

AI-generated synthetic datasets are flexible, safe to hold, share and discard. The process of data synthesis is suitable for subsetting and augmenting the original data. As a result, synthetic data serves exceptionally well as test data and AI training data. It really is better than real.

Gartner recommends to start your synthetic data journey with tabular data and to include synthetic data in your overall data strategy. We at MOSTLY AI can help you every step of the way!

If you would like to explore what benefits synthetic data can bring to your organization, consider the following steps: