Synthetic data is artificially generated data used as a substitute for real data in product testing, model validation and a wide range of other applications. The main types of synthetic data include text, media such as images or video, and tabular data.
Why do we need it?
Synthetic data supports the development of new products and solutions when privacy requirements restrict the use of real data, or when real data is not yet available. It also allows teams to simulate scenarios that have not been encountered before. In addition, synthetic data is often more cost-effective to produce and manage than collecting and processing large volumes of real data.
The limitations of synthetic data
Despite its benefits, synthetic data is not always a perfect solution. It can only imitate real data, which means it may fail to capture rare but important outliers. Its quality also depends on the data used to generate it, so any biases or inconsistencies in the source data will be reflected in the synthetic output.
Synthetic data is becoming increasingly important in machine learning, as training algorithms requires vast amounts of data. For example, developers of self-driving cars use synthetic data to create large-scale simulations. This helps to avoid safety risks and reduce costs, and it also makes it easier to model complex or rare scenarios that would be difficult to capture in the real world.
