What is Synthetic Data?
Turkish: Sentetik Veri
Synthetic data is generated to mimic real data's statistical properties for testing, analysis, and machine learning training.
What is Synthetic Data?
Synthetic data is data that is not collected directly from real users or operations, but generated through rules, simulations, or generative models. The goal is to mimic the structure, distribution, or edge cases of real data so it can be used in development and analysis environments.
For example, a bank may not want to use real customer information in a test environment, so it can generate account transactions with the same field structure but no link to real people. Autonomous vehicle simulations, medical imaging research, call center text, and fraud scenarios can also use synthetic data.
Opportunities and Risks
Synthetic data can make testing and machine learning work easier in domains with privacy restrictions. It can also increase rare cases, reduce data imbalance, or help test a system before production data exists.
The risk is that synthetic data does not contain all the complexity of the real world. If generated poorly, it can mislead a model, hide bias, or make performance look better than it is. Before it supports production decisions, it should be validated against real samples and managed under data governance rules.