The term "synthetic data generation" refers to the computer-based creation of artificial yet realistic data that does not relate to real individuals or events. Synthetic data is used when real data is unavailable, restricted due to privacy regulations, or insufficient for training, testing, or simulations. It enables organizations to carry out data-driven processes such as machine learning, software testing, or simulations efficiently and in compliance with data protection requirements.
Dataset Simulation: Creation of artificial data structures (e.g., customer data, transactions) that mirror real-world scenarios.
Anonymization and Masking: Replacing or transforming real data with synthetic alternatives to ensure data privacy.
AI-based Data Generation: Using machine learning (e.g., GANs) to generate complex, realistic synthetic datasets.
Data Variation and Manipulation: Controlled generation of data with specific characteristics, distributions, or anomalies.
Test Data Management: Creation and management of synthetic data for automated software testing or quality assurance.
Domain-specific Templates: Predefined structures for quickly generating industry-specific synthetic datasets (e.g., healthcare, finance).
Pipeline Integration: Automated embedding of data generation into existing data science, DevOps, or testing workflows.
A fraud detection AI model is trained with synthetic transaction data to learn rare patterns.
A developer uses synthetic customer data to test a CRM module without exposing real personal data.
A medical technology company generates synthetic patient records to train a diagnosis system without revealing sensitive data.
A financial institution creates synthetic accounting data to validate a new accounting software solution.