ARTIFICIAL INTELLIGENCE

“Synthetic data is artificially generated information that computationally or algorithmically mimics the statistical properties, patterns, and structure of real-world data without containing any actual observations or sensitive personal details.” – Synthetic data

What is Synthetic Data?

Synthetic data is artificially generated information that computationally or algorithmically mimics the statistical properties, patterns, and structure of real-world data without containing any actual observations or sensitive personal details. It is created using advanced generative AI models or statistical methods trained on real datasets, producing new records that are statistically identical to the originals but free from personally identifiable information (PII).

This approach enables privacy-preserving data use for analytics, AI training, software testing, and research, addressing challenges like data scarcity, high costs, and compliance with regulations such as GDPR.

Key Characteristics and Generation Methods

• Privacy Protection: No one-to-one relationships exist between synthetic records and real individuals, eliminating re-identification risks.^1,3
• Utility Preservation: Retains correlations, distributions, and insights from source data, serving as a perfect proxy for real datasets.^1,2
• Flexibility: Easily modifiable for bias correction, scaling, or scenario testing without compliance issues.¹

Synthetic data is generated through methods including:

• Statistical Distribution: Analysing real data to identify distributions (e.g., normal or exponential) and sampling new data from them.⁴
• Model-Based: Training machine learning models, such as generative adversarial networks (GANs), to replicate data characteristics.^1,4
• Simulation: Using computer models for domains like physical simulations or AI environments.⁷

Types of Synthetic Data

Applications and Benefits

• AI and Machine Learning: Trains models efficiently when real data is scarce or sensitive, accelerating development in fields like autonomous systems and medical imaging.^2,7
• Software Testing: Simulates user behaviour and edge cases without real data risks.²
• Data Sharing: Enables collaboration while complying with privacy laws; Gartner predicts most AI data will be synthetic by 2030.¹

Best Related Strategy Theorist: Kalyan Veeramachaneni

Kalyan Veeramachaneni, a principal research scientist at MIT’s Schwarzman College of Computing, is a leading figure in synthetic data strategies, particularly for scalable, privacy-focused data generation in AI.

Born in India, Veeramachaneni earned his PhD in computer science from the University of Mainz, Germany, focusing on machine learning and data privacy. He joined MIT in 2011 after postdoctoral work at the University of Illinois. His research bridges AI, data science, and privacy engineering, pioneering automated machine learning (AutoML) and synthetic data techniques.

Veeramachaneni’s relationship to synthetic data stems from his development of generative models that create datasets with identical mathematical properties to real ones, adding ‘noise’ to mask originals. This innovation, detailed in MIT Sloan publications, supports competitive advantages through secure data sharing and algorithm development. His work has influenced enterprise AI strategies, emphasising synthetic data’s role in overcoming real-data limitations while preserving utility.