Synthetic data is artificially generated data that mimics the structure and statistical properties of data gathered from real-life events.
It's possible to produce partially synthetic data, where only some synthetic data points complement an existing dataset. A fully synthetic dataset doesn't contain any original data.
Structured and unstructured datasets can serve as a basis for generating synthetic tabular or image data.
Finally, privacy mechanisms can be added during the generation process to protect sensitive information in the original data.
Protecting privacy and utility: Data access is a strategic topic for all businesses. With the GDPR coming into force, privacy also became a cornerstone of data strategies as organizations followed new compliance regulations and security requirements.
To competitively fuel the development of AI applications, build personalized customer experiences and develop competitive products, organizations must answer the modern data challenges posed by compliance and security constraints, data silos, and innovation needs.
Contrary to real-world data, your teams can access anonymized synthetic data without going through lengthy validation processes. Your organization’s data becomes more agile with synthetic data.
We’ve built auxiliary protection mechanisms and privacy tools that let you document the anonymity of privacy-preserving synthetic data. You avoid the financial risks of non-compliance and the security risks related to data re-identification.
Personal data processing imposes security and compliance restrictions that don't apply to anonymized synthetic data so that you can unlock data applications, from data retention to external sharing, without compromising privacy.
