Synthetic Data Services

The Synthetic Data Generation Service in the Health-X Innovation Sandbox provides a secure and compliant platform to generate synthetic or test data to support innovation experimentation closer to real-world, production-like environments. This service is especially useful for project teams that require massive data for machine learning model training, software testing, behavioral simulations, and building product demos — without compromising data privacy.

Last updated 15 July 2026

Why Synthetic Data?

• Safe Experimentation: Enables project teams and external collaborators to test their ideas without exposing real personal data.
• Compliance Ready: Mimics the statistical structure of real data while ensuring compliance with privacy regulations.
• High Volume & Fast: Allows teams to generate large volumes of test data quickly and efficiently.
• Production-Like Testing: Simulates realistic datasets to validate solutions in environments closer to production.

Key Features

• Structured Tabular Data (Phase 1): Generation of high-quality, synthetic tabular datasets that resemble real production data while preserving data privacy.
• Pre-Loaded Datasets: Synthetic datasets can be pre-loaded from open sources (e.g., Synthea) for faster prototyping and testing.
• Customizable Coverage: Configure datasets to suit your test scenarios, such as specific demographics or use cases.
• Collaboration-Friendly: Teams can download relevant dataset copies from the HXIS Data Hub, preserving dataset integrity by avoiding overwrite conflicts.

Project Objectives

• Quality: Produce synthetic data that is balanced and unbiased.
• Productivity: Speed up the testing cycle with quickly generated datasets.
• Volume: Supplement limited or non-existent datasets.
• Coverage: Enable targeted, scenario-specific testing.
• Compliance: Reduce risks related to non-compliance and sensitive data exposure.

Innovation Impact

• Accelerates Prototyping: Especially valuable for projects where production data doesn’t exist yet (e.g., new applications).
• Enhanced Privacy: Avoids the use of actual PII while retaining the statistical integrity of the data.
• Reuse for Multiple Scenarios: Synthetic datasets can be reused across different test cases and projects, increasing testing efficiency.
• Data Governance Alignment: Follows governance best practices, including data sourcing from clusters/agencies with appropriate oversight.

Future Plans

Synapxe aims to expand Synthetic Data Services to support more complex data types, including:
• Unstructured data such as clinical notes
• Images and video for use in computer vision and AI model development

Synthetic Data Generation Process Flow

A detailed flowchart for synthetic data generation and downloading process involving multiple teams and steps.

Get Started

Access the service via the SynData Service and start building realistic, privacy-preserving datasets for your next innovation project.