Why synthetic data is not the same as data masking

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗳 𝘆𝗼𝘂𝗿 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗵𝗮𝘀 𝘁𝗼 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝟮𝟬𝟬 𝗚𝗕 𝗼𝗳 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮 𝗷𝘂𝘀𝘁 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝟮𝟬𝟬 𝗚𝗕 𝗼𝗳 𝘁𝗲𝘀𝘁 𝗱𝗮𝘁𝗮?

Some data provisioning and synthetic data platforms require significant infrastructure, complex workflows, and long-running pipelines. As data volumes grow, those pipelines become slower, more expensive, and more fragile.

Why?

Because they're fundamentally running a large ETL pipeline that transforms production data rather than learning a generative model.

Typically, those pipelines copy production data, anonymize or mask sensitive values (either in-flight or in a staging environment), and then apply transformation rules to modify the data.

That architecture creates several limitations.

If you need 200 GB of test data, you generally have to extract and process 200 GB of production data.

You either maintain a shadow copy of production—which means additional storage, security reviews, and governance—or perform masking in-flight, which becomes increasingly slow and difficult to scale.

As a result, many organizations refresh test data infrequently, leaving teams working with stale datasets and reducing the effectiveness of testing.

AI model-based synthetic data generation works differently.

Instead of repeatedly transforming production data, you train a generative model on a representative sample. For example, the ING team trained a model using just 10,000 transactions that captured the statistical patterns of a much larger dataset.

Once trained:
 • The model size depends on its parameters, not on the size of the original database.
 • You can generate as much synthetic data as you need, on demand.
 • Since the model—not the generated data—is the primary artifact, datasets can be regenerated whenever needed instead of being stored and versioned indefinitely.

A simple question can often reveal which architecture a platform uses:
𝗜𝗳 𝗜 𝗱𝗼𝘂𝗯𝗹𝗲 𝘁𝗵𝗲 𝗮𝗺𝗼𝘂𝗻𝘁 𝗼𝗳 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮 𝗜 𝗻𝗲𝗲𝗱, 𝗱𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗿𝗼𝘂𝗴𝗵𝗹𝘆 𝘁𝘄𝗶𝗰𝗲 𝗮𝘀 𝗺𝘂𝗰𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮?

If the answer is yes, you're likely looking at an ETL-based transformation pipeline rather than an AI model that generates synthetic data.

Our latest blog explores the architectural differences between ETL-based data transformation and AI model-based synthetic data generation—and why that distinction matters at enterprise scale.