Synthetic Data refers to artificially generated data that is created to replicate the structure and patterns of real world data without containing actual personal or sensitive details. Synthetic data is designed to behave like real life data which in turn makes it more useful for testing, analysis, training and development purposes whilst reducing privacy and security risks.
Organisations often use synthetic data in areas such as testing, cybersecurity, machine learning and research where realistic datasets are needed but using real life customer or even employee information could create compliance or confidentiality concerns. An example would be that a company may generate synthetic customer records to test a new system instead of exposing genuine PII.
As this does not directly identify real individuals, synthetic data can help organisations improve innovation and collaboration while supporting data protection requirements such as GDPR. However synthetic data still needs to be carefully created and managed to ensure it accurately reflects real world scenarios and does not unintentionally reveal sensitive information.
Organisations often use synthetic data in areas such as testing, cybersecurity, machine learning and research where realistic datasets are needed but using real life customer or even employee information could create compliance or confidentiality concerns. An example would be that a company may generate synthetic customer records to test a new system instead of exposing genuine PII.
As this does not directly identify real individuals, synthetic data can help organisations improve innovation and collaboration while supporting data protection requirements such as GDPR. However synthetic data still needs to be carefully created and managed to ensure it accurately reflects real world scenarios and does not unintentionally reveal sensitive information.
