Understanding synthetic data
Synthetic data is artificially created data that, if done right, contains all the characteristics of production data.
The reason it’s called synthetic data is that it doesn’t have a physical existence – that is, it doesn’t come from real-life observations or experiments that we create to gather data that we subsequently use to run analysis or build machine learning models on.
A foundational principle of machine learning is that you need a lot of data, ranging from thousands to billions of observations. The amount you need depends on your model.
As we have outlined many times already, when the required volume of data is difficult to come by, one approach is to improve the signal in your data to make it possible to produce accurate and relevant outputs, even on smaller datasets.
Another option is to create synthetic data to cover the gaps. A major benefit of synthetic data is its scalability. Real training data is collected...
