Synthetic Data: Complete Guide and Leading Companies

Definition

Synthetic data is artificially generated data that mimics the statistical properties, structure, and relationships of real-world data without being collected directly from real users, sensors, or environments. It’s created through algorithms, simulations, or generative AI models and serves as a substitute for real data in training, validating, and testing AI/ML systems.

Examples:

  • Generating realistic but fake credit card transactions to train fraud detection models
  • Creating 3D synthetic images of pedestrians and traffic scenarios for autonomous vehicle training
  • Producing synthetic medical records for healthcare AI research while maintaining patient privacy
  • Generating synthetic speech data in multiple languages and accents for voice recognition systems

Advantages

Privacy & Compliance: Eliminates risks associated with sensitive data handling (HIPAA, GDPR, CCPA compliance)

Scalability: Enables rapid generation of massive datasets, addressing data scarcity issues

Bias Mitigation & Balance: Allows oversampling of rare events and underrepresented groups to create more balanced datasets

Cost Efficiency: Reduces dependency on expensive and time-consuming real-world data collection

Safe Testing: Enables experimentation with dangerous, rare, or impossible scenarios (e.g., catastrophic failures, extreme weather)

Data Augmentation: Expands existing datasets with variations to improve model robustness

Challenges

Fidelity & Distribution Gaps: Synthetic data may fail to capture real-world complexity, edge cases, or distribution shifts, leading to poor production performance

Bias Amplification: Poorly designed generation processes can replicate or magnify existing biases from training data

Validation Complexity: Requires rigorous statistical validation and domain expertise to ensure quality and representativeness

Stakeholder Acceptance: Organizations may be hesitant to rely on “artificial” data for critical decisions

Computational Overhead: High-quality synthetic data generation, especially for images, videos, and 3D environments, demands significant computational resources

Model Collapse Risk: Using synthetic data to train models that then generate more synthetic data can lead to quality degradation over iterations

Leading Synthetic Data Companies (2024-2025)

Tabular & Structured Data

  1. Mostly AI – European leader in privacy-preserving synthetic data for financial services, telecom, and insurance with strong GDPR focus
  2. Gretel.ai – Developer-friendly API platform for tabular, text, and time-series data generation
  3. Hazy – UK-based specialist in privacy-safe synthetic data for banking and financial services
  4. Tonic.ai – Database subsetting and synthetic data generation for development and testing environments

Computer Vision & Visual Data

  1. Synthesis AI – Photorealistic human-centric synthetic data for computer vision applications
  2. Datagen – Synthetic human data including faces, bodies, and behaviors for AR/VR, retail, and security
  3. Synthetaic – Computer vision training data with focus on satellite imagery and geospatial applications
  4. Cvedia – Simulation-based synthetic data for autonomous vehicles, robotics, and smart city applications

Healthcare & Life Sciences

  1. MDClone – Healthcare synthetic data platform enabling medical research without privacy concerns
  2. Syntegra – Clinical and healthcare synthetic data with focus on maintaining clinical utility

Specialized Applications

  1. Rendered.ai – Physics-based synthetic data generation for aerospace, defense, and industrial applications
  2. AiCure – Synthetic data for pharmaceutical and clinical trial applications

Note: The competitive landscape evolves rapidly, with new entrants and acquisitions regularly reshaping the market.

Best Practices

  • Always validate synthetic data quality against real-world distributions
  • Combine synthetic data with some real data when possible for optimal results
  • Regularly audit synthetic data generation processes for bias
  • Ensure domain experts review synthetic datasets for realism and utility
  • Start with pilot projects to establish trust and demonstrate value before scaling