When it comes to preparing test data for software development, two main methods come into play: data masking and synthetic data generation. Both approaches offer ways to avoid using sensitive production data in testing, but their cost implications can vary significantly over time. In this article, we’ll break down the initial, scalability, and long-term costs of synthetic data vs. data masking, helping you choose the best approach for your organization.

Initial implementation costs

At first glance, the initial investment for both data masking and synthetic data generation seems similar. Both approaches require purchasing tools, learning the systems, and setting up the processes. For data masking, the upfront costs include acquiring the right tools and setting up a process to mask sensitive data fields properly.

With synthetic data, the initial expense also involves purchasing a synthetic test data platform and understanding how to generate synthetic test data. However, an added benefit is that this process helps testers deepen their understanding of the data environment, which can significantly aid in future testing efforts.

Scalability costs

When scaling test environments, the costs of masking data increase in direct proportion to the volume of data. The more data you need to mask, the more resources you’ll expend on managing it. In contrast, synthetic data offers more scalability: once you’ve set up your synthetic test data generator, the marginal costs decrease as the amount of generated data grows.

Long-term ROI

Over time, data masking may offer diminishing returns. Since testers often use the same masked datasets repeatedly, the potential for discovering new edge cases or improving test coverage is limited. On the other hand, synthetic data offers long-term advantages. As your synthetic test data platform evolves and improves, it can generate fresh, varied datasets that provide greater test coverage.

Hidden costs

Data masking can come with some hidden costs. For example, testers might need to write additional scripts to extract the right configurations of masked data from larger datasets. This adds to the overall time and resource expenditure. In contrast, synthetic test data tends to avoid these hidden costs, as it is generated directly to match specific testing scenarios, reducing the need for additional customization.

The final verdict

Although both data masking and synthetic data generation come with similar initial costs, synthetic data proves to be more cost-effective in the long run. This is particularly true in large-scale or dynamic data environments, where the need for accurate, varied test data continues to grow. A robust synthetic test data platform provides the flexibility and scalability necessary for modern testing needs, ultimately delivering greater long-term value than traditional masking methods.

Key takeaway

If your testing process involves large datasets or frequent changes, synthetic data is a more scalable and cost-effective solution. It not only addresses the immediate need for secure test data but also offers a strong alternative to traditional test data anonymization and test data masking techniques.