Synthetic data is the AI-generated version of real data. AI algorithms learn the patterns and dimensions of data. Once they were trained, they can generate infinite amounts of synthetic data that is statistically representative of the original training data.
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.
Real vs synthetic home addresses for insurance pricing
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.
How to generate synthetic data?
MOSTLY AI’s synthetic data platform is easy to use. Through an intuitive interface, and you can generate synthetic data without coding. The free online version of our synthetic data generator offers limited functionality, but allows you to experience synthetic data generation first hand. If you would like to use the enterprise version of MOSTLY AI’s synthetic data platform, please contact us!