Synthetic Data Generation with Kubeflow Pipelines

Synthetic Data Generation - Why and How? Key Benefits of Using Synthetic Data Frameworks for Creating Synthetic Data The Synthetic Data Vault (SDV) Evaluation Criteria for Synthetic Data Our On-Premise Analytics Platform: ARCUS Needed environment to create synthetic data Exploring the Creation and Usefulness of Synthetic Data Using Synthetic Data Generators to Enable Multiple Environments without Data Transfer Summary Synthetic Data Generation - Why and How? Key Benefits of Using Synthetic Data Frameworks for Creating Synthetic Data The Synthetic Data Vault (SDV) Evaluation Criteria for Synthetic Data Our On-Premise Analytics Platform: ARCUS Needed environment to create synthetic data Parallelism needed Parallelism needed Exploring the Creation and Usefulness of Synthetic Data Using Synthetic Data Generators to Enable Multiple Environments without Data Transfer On-premise Cloud On-premise On-premise Cloud On-premise Summary When creating insights, decisions, and actions from data, the best results come from real data. But accessing real data often requires lengthy security and legal processes. The data may also be incomplete, biased, or too small, and during early exploration, we may not even know if it’s worth pursuing. While real data is essential for proper evaluation, gaps or limited access frequently hinder progress until the formal process is complete. To address these challenges, synthetic data provides an alternative. It mimics real data’s statistical properties while preserving privacy and accessibility. Synthetic data generators (synthesizers) are models trained on real data to generate new datasets that follow the same statistical distributions and relationships but do not contain real records. This allows for accelerated development, improved data availability, and enhanced privacy. Depending on the technique used, synthetic data not only mirrors statistical base properties of real data but also preserves correlations between features. These synthesizers — such as those based on Gaussian Copulas, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) — enable the creation of high-fidelity synthetic datasets. See more description of these techniques below. While the above focuses on speed of development in general, and augmentation of data to improve performance of analytical modes, there are more motivations for creating (synthetic) data: Enhanced Privacy and Security Mimics real datasets without containing sensitive or personally identifiable information, mitigating privacy risks and ensuring compliance with regulations like GDPR.