Synthetic Data

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What is Synthetic Data?

• Unlimited scale: Can be produced in any quantity to satisfy data-hungry AI models.
• Privacy-preserving: Contains no personally identifiable information, supporting compliance with regulations such as GDPR.
• Bias control: Enables developers to curate balanced datasets and intentionally minimize unwanted biases.
• Customizability: Facilitates the design of novel or rare scenarios not present in the real world. ^[1gk7r3]

Potential Synthetic Data Providers

• Mostly AI
• Synthesis AI
• DataGen
• Hazy
• Gretel.ai
• Sky Engine AI

Why Organizations Adopt Synthetic Data Approaches

• Data abundance: AI models need vast, high-quality, labeled data to perform well. Synthetic data eliminates limitations due to data scarcity. ^[1gk7r3]
• Cost and speed: Generating synthetic datasets is faster and, over time, less expensive than collecting and cleaning real-world data.
• Enhanced privacy: Safeguards sensitive customer and proprietary information, enabling innovation without compromising confidentiality or breaching data protection laws.
• Bias reduction: Synthetic datasets can be engineered to be statistically balanced, reducing the propagation of societal biases into AI models. ^[1gk7r3]
• Edge case testing: Enables simulation and training for unusual, rare, or dangerous situations (e.g., self-driving car near-misses) that are difficult to gather via real-world collection. ^[1gk7r3]
• Leveling the playing field: Smaller companies or initiatives with limited access to proprietary data can produce competitive AI solutions by leveraging synthetic data. ^[1gk7r3]

What Can Be Done With Synthetic Data?

• Training, validating, and benchmarking AI/ML models when real data is unavailable, incomplete, or confidential.
• Simulation and scenario analysis for product design, safety testing, and what-if experimentation.
• Bias and fairness audits enabling controlled studies on how algorithms behave with or without specific features.
• Personalization and recommendation systems that rely on privacy-preserving, large-scale simulated datasets.
• Autonomous systems (e.g., robotics, vehicles) requiring millions of unique yet realistic inputs for robust performance before real-world deployment.

Citations