Why synthetic data is the future of data-driven business

In today’s data-driven economy, access to high-quality information is essential. But working with real-world data often comes with limitations: privacy risks, legal constraints and limited availability. That’s where synthetic data comes in.

This powerful technique is helping businesses accelerate innovation, comply with regulations like the EU AI Act and unlock opportunities that traditional data sources simply can’t offer.
It also allows European companies to strengthen their sovereignty in an increasingly uncertain global landscape.

In this blog post, we’ll explore what synthetic data is, how it works, where it’s used and why it’s quickly becoming an essential part of a modern data strategy.

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What is synthetic data?

Synthetic data is artificially generated information that replicates the statistical patterns of real-world datasets without containing any actual personal or sensitive information. It looks and behaves like real data but isn’t linked to any real individual.

For example, a synthetic dataset might reflect the income distribution, spending behaviour or demographic profile of actual customers. This allows you to analyse, test or model real-world scenarios without exposing private information.
 

Why synthetic data matters

Synthetic data offers a range of powerful benefits that make it an increasingly valuable tool for data-driven teams:

• Privacy and compliance: Because synthetic datasets contain no personal information, they’re typically exempt from regulations like GDPR and HIPAA. That means teams can work faster and more freely without the compliance overhead.
  
  
• Solving data scarcity: In sectors like healthcare or finance, collecting real-world data can be difficult or expensive—especially for rare events. Synthetic data helps fill these gaps by providing large, balanced datasets where real data is limited or costly to obtain.
  
  
• Speed and scalability: Collecting real data takes time and resources. Synthetic data can be generated on-demand, dramatically accelerating development cycles.
  
  
• Built-in annotations: With synthetic data, the “ground truth” is known from the start—for example, what’s in an image or the type of transaction being modelled. This can remove the need for time-consuming manual labelling.
  
  
• Experimentation without risk:  Want to test edge cases or extreme scenarios? Synthetic data lets you explore those safely, without exposing sensitive information or taking on unnecessary risk.

How synthetic data supports the full lifecycle

Synthetic data is not a one-trick pony. It adds value across the entire software and data lifecycle.

The growing adoption of AI products is accelerating the need for high-quality, privacy-safe data throughout this lifecycle. To become truly data- or AI-driven, organisations need access to large, reliable datasets at every stage—data that isn’t always readily available or that may be restricted by privacy regulations. Synthetic data helps close that gap.

How it's generated: SMOTE vs. GANs

The method you use to generate synthetic data depends on your goals.
Two of the most common techniques are:

• SMOTE (Synthetic Minority Over-sampling Technique): SMOTE is widely used to fix class imbalances in structured data. It works by interpolating between existing data points in the minority class to create new, synthetic examples. It’s especially helpful in use cases like fraud detection or churn prediction, where rare events are underrepresented.
  
  
• GANs (Generative Adversarial Networks): GANs are advanced deep learning models that can generate highly realistic synthetic data. A GAN learns from real data and then produces new examples that mimic its patterns—making it a go-to method for generating images, text, or complex tabular relationships.

In short: SMOTE is easy to implement and great for structured tabular data. GANs offer unmatched realism but require more technical expertise and computing power.

Regulatory gold: synthetic data and the EU AI Act

Synthetic data isn’t just a productivity booster—it’s a powerful compliance tool. The upcoming EU AI Act, expected to take effect in 2026, emphasizes transparency, bias mitigation and data privacy in AI systems. Synthetic data helps organisations meet those requirements head-on:

• Bias mitigation: Article 10 of the Act promotes the use of synthetic data to balance datasets and reduce bias in AI models—especially in cases where real-world data is skewed or incomplete.
• Privacy by design: Because synthetic data doesn’t involve real individuals, it sidesteps GDPR concerns. That means teams can train robust models without compromising user privacy.
• Transparency: The AI Act requires clear labelling of AI-generated content. When synthetic data is used in customer-facing tools—like images, audio, or text—it must be explicitly disclosed.

In short: synthetic data is more than a workaround—it’s becoming a regulatory best practice.

Ethical considerations: privacy and fairness

Synthetic data helps reduce many of the risks tied to real data, but it’s not a magic bullet:

• Privacy leaks can still occur if generation methods inadvertently reproduce real data points.
• Bias present in the original dataset can persist—or even worsen—if not properly addressed during the generation process.

That’s why responsible synthetic data generation should include privacy validation, bias audits and interdisciplinary oversight. It’s essential to treat synthetic data with the same ethical care as real data—especially when it drives decisions that affect people.

Getting started with synthetic data: a practical guide

Ready to start using synthetic data? Here’s a straightforward roadmap to help you get going:

Step 1: Identify high-impact use cases

Start by pinpointing areas where data limitations are slowing you down—think privacy concerns, limited sample sizes or risky test environments. Synthetic data works especially well for AI model training, load testing and cross-team data sharing.

Step 2: Plan your generation strategy

Understand your data structure, relationships, and constraints. Decide whether you need basic mock data, rule-based generation, or high-fidelity AI-generated data (like via GANs).

Step 3: Choose your tools

Start small and scale as you learn. Try open-source libraries like SDV (Synthetic Data Vault) or explore commercial platforms like Mostly AI, Syntho, or SAS Data Maker. Begin with a pilot project to prove value before scaling up.

Step 4: Integrate in your ecosystem

Synthetic data generators can be plugged into your ETL pipelines, CI/CD workflows, and MLOps platforms. For example, you can create synthetic datasets in Databricks, store them in Azure Data Lake, and feed them into automated model training workflows—without changing your architecture.

Final thoughts

Synthetic data is no longer a futuristic idea, it’s a practical, powerful asset that’s shaping the next generation of AI development. It helps businesses move faster, innovate safely and stay compliant in an increasingly regulated landscape.
All these advantages translate into measureable business results: faster time-to-market, improved model accuracy, and lower compliance costs.

As access to real-world data becomes more restricted, synthetic data offers a secure, scalable, and ethical alternative.

Whether you’re a startup building your first model or an enterprise navigating GDPR and the EU AI Act, now is the right time to explore how synthetic data can support your goals.

Want to know how synthetic data can empower your organisation?