What is Synthetic Data Generation?

What Is Synthetic Data Generation?

Synthetic Data Generation is the process of creating artificial data that mimics the statistical properties and patterns of real-world data without containing any actual records from real individuals, transactions, or events. Instead of collecting, cleaning, and labeling real data -- which is often expensive, time-consuming, and constrained by privacy regulations -- organizations can generate synthetic datasets that serve many of the same purposes.

Think of it as the difference between filming on location versus building a movie set. The set is not the real location, but it is designed to look, feel, and function like it for the purpose of production. Similarly, synthetic data is not real customer data, but it captures the same statistical patterns, distributions, and relationships that make it useful for training AI models and testing systems.

How Synthetic Data Generation Works

There are several approaches to generating synthetic data, each suited to different business needs:

• Statistical methods: Algorithms analyze the distributions and correlations in real data and generate new data points that follow the same patterns
• Generative AI models: Large language models and other generative AI systems create realistic synthetic records based on descriptions of the desired data characteristics
• Simulation-based generation: Computer simulations model real-world processes (like customer behavior or supply chain operations) and produce data from the simulated environment
• Differential privacy techniques: Real data is transformed with carefully calibrated noise to create a synthetic version that preserves statistical properties while making it mathematically impossible to identify individuals

The quality of synthetic data is measured by how well it preserves the statistical relationships in the original data while ensuring that no individual real record can be reconstructed from the synthetic output.

Why Synthetic Data Matters for Business

Overcoming data scarcity Many businesses, especially SMBs in emerging markets across Southeast Asia, do not have the massive datasets that AI models typically require for training. A new fintech startup in Jakarta or a healthcare company in Bangkok may have excellent AI ideas but limited historical data. Synthetic data generation can expand small real datasets into larger training sets that enable AI development.

Privacy compliance by design Data protection regulations across ASEAN -- Singapore's PDPA, Indonesia's PDP Law, Thailand's PDPA, and others -- impose strict requirements on how personal data can be used. Synthetic data sidesteps many of these constraints because it contains no real personal information. A bank can train fraud detection models on synthetic transaction data without exposing actual customer financial records.

Faster AI development cycles Waiting for real data collection and labeling often creates bottlenecks in AI projects. Synthetic data can be generated on demand, enabling development teams to start building and testing AI models immediately while real data collection proceeds in parallel.

Testing edge cases and rare events Real-world datasets may contain very few examples of important rare events -- fraudulent transactions, equipment failures, or unusual customer behaviors. Synthetic data can artificially increase the representation of these rare events, enabling AI models to learn patterns they would otherwise miss.

Safe software testing Development and testing environments need realistic data, but using real customer data in test environments creates security risks. Synthetic data provides realistic test data without any privacy exposure.

Key Examples and Use Cases

Financial services: Banks in Singapore and across ASEAN use synthetic transaction data to train fraud detection models, stress-test risk systems, and develop new financial products without exposing actual customer account information.

Healthcare: Hospitals and health-tech companies in Thailand and Vietnam generate synthetic patient records to develop diagnostic AI tools, enabling medical AI advancement while fully complying with patient data protection requirements.

E-commerce: Companies like Shopee and Lazada can generate synthetic user behavior data to test recommendation algorithms, pricing models, and inventory management systems before deploying with real customer interactions.

Manufacturing: Factories across Indonesia and Malaysia generate synthetic quality inspection data, including artificially created examples of rare defect types, to train computer vision systems that detect manufacturing flaws.

Autonomous vehicles: Companies testing self-driving technology generate synthetic driving scenarios, including dangerous edge cases that would be unsafe or impossible to collect from real-world driving.

Getting Started

1. Identify your data bottlenecks: Determine where limited data availability, privacy restrictions, or slow data collection are holding back your AI initiatives
2. Evaluate synthetic data tools: Several platforms offer synthetic data generation as a service, including Mostly AI, Gretel, and Hazy, as well as open-source libraries for technical teams
3. Start with augmentation, not replacement: Begin by using synthetic data to supplement real data, not replace it entirely, and measure whether the synthetic additions improve your AI model performance
4. Validate synthetic data quality: Test that your synthetic data preserves the important statistical properties of real data by comparing distributions, correlations, and model performance metrics
5. Consult your legal team: While synthetic data generally has fewer regulatory constraints than real data, your compliance and legal teams should review your synthetic data approach to confirm it meets local regulatory requirements