Data Decoded

What Is Synthetic Data?

In today’s data-driven landscape, the demand for high-quality, privacy-compliant, and diverse datasets is outpacing supply. Enter synthetic data—artificially generated information that mimics real-world data without exposing sensitive or personally identifiable information (PII). As organisations look to accelerate analytics, AI development, and data sharing, synthetic data is emerging as a powerful and scalable solution.

What is Synthetic Data?

Synthetic data is data that is generated algorithmically rather than collected from real-world events or user activity. It can replicate the statistical properties, structure, and relationships found in actual datasets, making it highly useful for training machine learning models, performing simulations, or testing software systems.

There are three main types of synthetic data:

  • Fully synthetic: No original data is used; it’s generated from statistical or generative models.

  • Partially synthetic: Some real data is retained, with sensitive fields replaced by synthetic values.

  • Hybrid synthetic: A mix of synthetic and real data used to balance realism and privacy.

Why Synthetic Data Is Gaining Traction

  1. Privacy and Compliance With growing concerns over data privacy laws like GDPR, HIPAA, and CCPA, synthetic data offers a risk-free alternative. Since it doesn’t contain real user information, it can often bypass restrictions around PII.

  2. Data Availability Organizations frequently face limited access to production-quality data, especially during early-stage development or when working across departments or third-party vendors. Synthetic data fills these gaps.

  3. Bias Reduction and Fairness By deliberately generating balanced datasets, teams can mitigate historical bias in real-world data and improve model fairness across gender, ethnicity, or geography.

  4. Faster AI and ML Development High-quality synthetic data can significantly accelerate the training and validation of machine learning models by offering a near-infinite supply of labeled, consistent, and clean data.

  5. Cost Efficiency Generating synthetic data at scale can be more affordable than collecting, storing, and managing massive volumes of real data—particularly when labeling is required.

Use Cases for Synthetic Data

  • AI model training and validation (e.g., self-driving cars, NLP, fraud detection)

  • Software testing in environments where real data is restricted

  • Data augmentation to increase the size and diversity of training datasets

  • Healthcare research, enabling access to representative patient data without privacy risks

  • Finance and banking, where customer anonymity is critical

Final Thoughts

As synthetic data tools mature, they’re transforming how organizations innovate with data—enabling faster development, enhanced privacy, and greater accessibility. While it may not replace real-world data entirely, synthetic data is a strategic asset that can bridge gaps, enhance compliance, and fuel AI progress.

For data scientists, engineers, and business leaders alike, understanding and embracing synthetic data in 2025 isn’t just forward-thinking—it’s becoming essential to modern data strategy.