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Common questions about synthetic data and our platform
Synthetic data is artificially generated data that mimics the statistical properties and patterns of real data without containing any actual personal information. It's created using advanced AI models like GANs, VAEs, and other machine learning techniques that learn the underlying structure of your original dataset and generate new, statistically similar records.
Our synthetic data generation process employs multiple privacy-preserving techniques including differential privacy, k-anonymity, and advanced generative models that prevent any possibility of reverse-engineering original records. We conduct rigorous privacy audits and testing to ensure zero re-identification risk.
We support a wide range of data formats including CSV, JSON, Parquet, database tables (MySQL, PostgreSQL, etc.), and cloud storage formats. Our platform can handle structured, semi-structured, and some unstructured data types across various industries and use cases.
Our synthetic data maintains 95-99% statistical similarity to the original dataset, depending on complexity and requirements. We measure accuracy through various metrics including correlation preservation, distribution matching, and utility preservation for machine learning tasks.
Yes! We provide comprehensive APIs, SDKs for popular programming languages (Python, JavaScript, Java, Go), and pre-built integrations with popular platforms like Snowflake, Databricks, and cloud services. Our technical team can assist with custom integrations.
Common use cases include ML model training, software testing, analytics and BI, regulatory compliance (GDPR, HIPAA), data sharing between organizations, and research. Industries using synthetic data include healthcare, finance, e-commerce, manufacturing, and technology.
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