Webinar: Why Multiple Synthetic Data Generation Approaches Are Essential for Production Use Cases

Webinars

August 17, 2026

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Date: 10 september 2026
Time: 16:00 (CET)
Duration: 1 Hour
Location: Virtually via Zoom

Why Multiple Synthetic Data Generation Approaches Are Essential for Production Use Cases

There is no single synthetic data generation technique that works best for every use case.

Many organizations struggle to bring synthetic data for databases into production because they try to apply the wrong technique to the use case at hand.

AI-generated synthetic data can provide powerful statistical fidelity, but can become challenging when dealing with complex multi-table databases, referential integrity, and proving accuracy. By bringing multiple synthetic data generation techniques together in one platform, organizations can combine the strengths of AI-generated synthetic data with techniques such as synthetic data masking — applying the right approach, or combination of approaches, to each use case.

By bringing multiple synthetic data generation techniques together in one platform, organizations can combine the strengths of AI-generated synthetic data with techniques such as synthetic data masking — applying the right approach, or combination of approaches, to each use case.

The real opportunity lies in combining different synthetic data approaches and applying the right technique to the right use case.

What you’ll learn in this webinar

In this webinar, we’ll explore how to build a pragmatic synthetic data strategy for production environments.

You’ll learn:

Why one synthetic data generation approach cannot optimally solve every use case
How to determine which synthetic data technique fits which use case
Where AI-generated synthetic data delivers the greatest value — and where it can introduce complexity
Why multi-table databases and referential integrity can be challenging for AI-based generation
How to think about accuracy and validation of synthetic data
How synthetic data masking can help preserve complex database structures while protecting sensitive information
How AI generation, synthetic data masking, and other techniques can complement rather than compete with each other
How to identify low-hanging-fruit synthetic data use cases instead of beginning with the most complex scenario
How to move from synthetic data experimentation toward scalable production adoption