Synthetic data is information that is created artificially via computer simulations rather than gathered from real-world events. Synthetic data has been traditionally used to validate mathematical models and as a stand-in for operational or production data.
However, synthetic data is becoming more prevalent in AI training because it can be used without privacy restrictions, can simulate nearly any condition, and is often immune to statistical problems such as item nonresponse and other logical constraints.
Synthetic data is part of the Alternative AI Training Datasets meta trend.
Collecting real-life data to train AI is often expensive and time-consuming.
Additionally, much of this real-world data has collection and accuracy issues.
Which is why AI developers are increasingly turning to alternative AI training data (such as synthetic data).
In fact, Gartner forecasts that synthetic data will become the primary data source used to train AI models by 2030.