Synthetic data is artificially created data that is used as a simulation or a theoretical value in product testing, model validation, and various other functions. The main types of such data are text, media (such as video or image) and tabular data.
Why do we need it?
Synthetic data enables the development of new products and solutions when privacy terms limit the usage of real data or the latter is not (yet) available. It also enables us to simulate not yet encountered situations. Moreover, it often comes at a lower cost.
The limitations of synthetic data
Despite its benefits, artificial data is not always a perfect solution. It can only mimic real data, so it might not cover some important outliers. Also, its quality depends on the source data. This means that the biases or inconsistencies of the input data reflect in the imitation data.
Synthetic data is becoming increasingly important in machine learning as training algorithms requires vast amounts of data. For example, self-driving car development uses it for creating simulations. This helps to avoid safety issues and extensive costs and makes simulating complex scenarios easier.
