Organizations face challenges in sharing and analyzing sensitive data due to privacy regulations and the risk of exposing confidential information. Traditional data anonymization techniques can reduce data utility, limiting the effectiveness of analytics and machine learning models. Sharing data externally introduces risks of data proliferation and potential misuse.

Datamynd offers a synthetic data generator that enables data teams to create high-quality, privacy-preserving synthetic datasets directly within their Snowflake environment. The application employs a range of machine learning models, including GaussianCopula, CopulaGAN, TVAE, and CTGAN, to generate synthetic data that retains the statistical properties and correlations of the original data. Users can configure column anonymization options and adjust learning parameters to optimize the accuracy and utility of the synthetic data. The generated data can be used for machine learning, data sharing, and analytics without exposing sensitive information.

Native Snowflake application that runs entirely within the user's account, eliminating the need for external API calls

Support for multiple synthetic data generation models, including GaussianCopula, CopulaGAN, TVAE, and CTGAN

Column-level configuration options for anonymization and data type selection

Adjustable learning parameters to fine-tune the accuracy and performance of the synthetic data

Built-in data explorer for visualizing and comparing synthetic data with the original data

Histogram comparison tool to validate the statistical similarity between synthetic and real data distributions

Project-based organization for managing and iterating on synthetic data generation workflows

Datamynd's primary customers are data scientists, data engineers, and data analysts in industries such as healthcare, financial services, and software development who require privacy-safe data for analytics, machine learning, and data sharing.

Datamynd generates revenue through the Snowflake Marketplace.