Synthetic Data

Synthetic data is artificially generated data that mimics the statistical properties and patterns of real-world data, without being collected from actual events, people, or transactions. Instead of gathering data from the real world — which can be expensive, slow, or legally complicated — you use AI or statistical methods to create data that looks and behaves like the real thing.

How synthetic data is generated

Several approaches can create synthetic data:

• Statistical methods: Algorithms analyse real data to understand its distributions, correlations, and patterns, then generate new data points that follow the same statistical rules. The synthetic data looks realistic but does not correspond to any real individual or event.
• Generative AI models: Large language models can generate realistic text data (customer reviews, support tickets, emails). Image generation models can create realistic photos. These approaches are increasingly common because they can produce highly realistic output.
• Rule-based generation: For structured data (transactions, sensor readings, user events), rules and constraints can generate data that follows known business logic. "Generate 10,000 e-commerce transactions where 3% are fraudulent and follow these patterns."
• Simulation: Physical or mathematical simulations generate data that represents real-world scenarios — weather patterns, traffic flows, manufacturing processes.

Use cases

Synthetic data addresses several common business challenges:

• Training AI models: You need thousands or millions of examples to train a model, but collecting that much real data is impractical. Synthetic data fills the gap. This is especially common in healthcare, where patient data is heavily regulated, and in fraud detection, where real fraud examples are rare.
• Testing software systems: Before launching a new application, you need realistic test data that covers edge cases. Synthetic data lets you create specific scenarios — unusual transactions, system failures, unusual user behaviour — without waiting for them to occur naturally.
• Privacy protection: Real customer data cannot be freely shared between teams or with external partners due to privacy regulations (GDPR, HIPAA). Synthetic data that captures the same statistical patterns without corresponding to real individuals can be shared more freely.
• Addressing data imbalance: If your training data has very few examples of a rare event (a specific type of fraud, a rare medical condition), synthetic data can generate additional examples to help the model learn to recognise them.
• Prototyping and development: When building a new AI application, teams need data to work with immediately. Synthetic data lets development start before real data collection is complete.

Advantages

• Speed: Generate as much data as you need, on demand, without collection infrastructure.
• Cost: No data collection, cleaning, or labelling costs for the synthetic portion.
• Privacy: No real personal data means fewer regulatory constraints.
• Control: You can specify exact characteristics — generate data with specific distributions, edge cases, or scenarios that would be rare in real data.
• Scalability: Need ten times more training examples? Generate them.

Risks and limitations

Synthetic data is not a free lunch. Key risks include:

• Distribution shift: If the synthetic data does not accurately capture the patterns in real data, models trained on it will perform poorly in the real world. The model learns an approximation of reality, not reality itself.
• Inherited bias: If the real data used to inform synthetic generation contains biases, the synthetic data will reproduce those biases — and may amplify them.
• Overconfidence: Large volumes of synthetic data can give a false sense that a model has been thoroughly trained, when in fact it has been trained on a simulation that may miss important real-world nuances.
• Connection to model collapse: When AI-generated content (including synthetic data) is used to train new AI models, it can contribute to model collapse — the gradual loss of diversity and quality across model generations.

Best practices

• Always validate synthetic data against real data samples before using it for training.
• Use synthetic data to supplement real data, not replace it entirely.
• Document what proportion of your training data is synthetic and how it was generated.
• Regularly test models trained on synthetic data against real-world performance benchmarks.