Definition
Artificially generated information that mimics real-world data. It's created using algorithms and models to replicate the patterns and characteristics found in real datasets. This allows for data-driven applications while addressing privacy concerns and data limitations.
It can be created using statistical methods or artificial intelligence techniques such as deep learning and generative models. Despite being artificial, it retains the statistical properties, relationships and distributions of the original data. This allows synthetic datasets to serve as a reliable tool for training machine learning models when large, labelled datasets are needed.
Why synthetic data is used
Synthetic data addresses challenges around data quality, representativeness, access and privacy. It is particularly useful when:
- Real-world data is scarce, such as rare medical conditions or extreme weather events.
- Data privacy needs to be protected, especially in healthcare, finance or sensitive research.
- High-quality labelled data is needed quickly, as synthetic datasets are often pre-labelled and ready for use.
It also enables researchers and organisations to simulate scenarios that are difficult or costly to capture with real data.
Types of synthetic data
Synthetic data can be classified by format or by the level of synthesis:
- Format: Text for natural language processing, tabular data for databases, multimedia (images, video) for computer vision tasks.
- Level of synthesis:
Fully synthetic: Entirely artificial, does not contain any real-world records. Useful, for example, in fraud detection where real fraudulent data is limited.
Partially synthetic: Derived from real data but replaces sensitive fields to protect privacy, commonly used in clinical research.
Hybrid: Combines real and synthetic records, allowing analysis without revealing individual-level information.
How synthetic data is generated
Several methods are used to generate synthetic data, each suited to different use cases:
Statistical methods: Sampling from known distributions, interpolation or extrapolation for time series and correlated data.
Generative adversarial networks (GANs): A generator produces synthetic data while a discriminator evaluates its realism; both improve iteratively.
Transformer models: Use encoders and decoders to capture patterns in text or tabular data and generate realistic artificial outputs.
Variational autoencoders (VAEs): Compress data into lower-dimensional representations and then reconstruct new variations.
Agent-based modelling: Simulates individual agents and their interactions in complex systems, such as epidemiology or traffic flow, to produce synthetic data.
Organisations can generate their own synthetic data or use prebuilt datasets and open-source tools.
Benefits of synthetic data
Synthetic data offers multiple advantages:
- Privacy protection: Reduces exposure of personal data and supports data protection by design.
- Customisation: Datasets can be tailored to the needs of a specific model or scenario.
- Efficiency: Accelerates data preparation and labelling, saving time in model development.
- Richness: Fills gaps in underrepresented groups or rare events, improving fairness and model generalisation.
- Scenario simulation: Enables controlled testing and “what-if” analysis for situations difficult to capture.
Challenges and risks
Despite its benefits, synthetic data carries risks that must be managed:
- Bias amplification: Synthetic datasets may reproduce or worsen biases present in source data.
- Model collapse: Repeated training on AI-generated data can reduce performance.
- Accuracy versus privacy trade-offs: Increasing fidelity can risk reidentification; prioritising privacy may reduce accuracy.
- Limited outlier coverage: Rare but important data points may be missing, affecting model robustness.
- Trust issues: Highly realistic synthetic data can blur the line between real and artificial, increasing risks such as deepfakes.
Governance and responsible use
To use synthetic data safely, organisations must prioritise robust governance:
- Maintain a mix of real and synthetic data to prevent model collapse and ensure accuracy.
- Document generation methods, assumptions, and parameters to maintain traceability and accountability.
- Use fairness validation and distribution similarity metrics to prevent bias and measure quality.
- Treat synthetic data governance as a strategic priority, involving developers, policy makers and organisational leaders.
Key takeaways
- Synthetic data complements real-world data, enabling model training where data is scarce or sensitive.
- It can be fully synthetic, partially synthetic or hybrid, and include text, tabular or multimedia formats.
- Common generation methods include statistical sampling, GANs, transformers, VAEs and agent-based modelling.
- Benefits include privacy protection, efficiency, customisation, inclusion of underrepresented cases and scenario simulation.
- Risks include bias amplification, model collapse, missing outliers, trade-offs between accuracy and privacy, and erosion of trust.
- Rigorous testing, validation, documentation and mixing real with synthetic data are essential for reliable AI performance.
- Strong governance, transparency and collaboration across stakeholders maximise the safe and effective use of synthetic data.