🤓What is synthetic data and why is it used?
Synthetic data is artificial data that mimics observations of the real world and is used to prepare machine learning models when obtaining real data is not possible due to complexity or cost. Synthesized data can be used for almost any project that requires computer simulation to predict or analyze real events. There are many reasons why a business might consider using synthetic data. Here are some of them:
1. Efficiency of financial and time costs. If a suitable dataset is not available, generating synthetic data can be much cheaper than collecting real world event data. The same applies to the time factor: synthesis can take a matter of days, while collecting and processing real data sometimes takes weeks, months or even years.
2. Research of rare data. In some cases, the data is rare or there is danger in collecting it. An example of sparse data would be a set of unusual fraud cases. An example of dangerous real-world data is traffic accidents, which self-driving cars must learn to respond to. In this case, they can be replaced by synthetic accidents.
3. Eliminate privacy issues. When it is necessary to process or transfer sensitive data to third parties, confidentiality issues should be taken into account. Unlike anonymization, synthetic data generation removes any trace of real data identity, creating new valid datasets without sacrificing privacy.
4. Ease of layout and control. From a technical point of view, fully synthetic data simplifies markup. For example, if an image of a park is generated, it is easy to automatically label trees, people, and animals. You don't have to hire people to manually lay out these objects. In addition, fully synthesized data is easy to control and modify.