Testing & QA

Test data management is a messy business, especially in complex enterprise environments riddled with decades old components, databases and systems. Today there are basically two approaches for generating test data. Either you use production data, or you make up some rule-based mock data.

Running tests with production data could be a way to get the job done, but it is certainly not a safe practice. Many companies then turn to legacy anonymization techniques - or even worse - simple de-identification. The privacy risks associated with these approaches are well documented today.

Rule-based mock data is no real solution either. Test engineers often don't have knowledge about the exact data schemas and have only a rough idea what the data is supposed to look like. Plus defining all the rules is a time-consuming and tedious task. Although from a privacy perspective this is a safe approach there is one last major disadvantage: mock data carries little statistical insights and can't be used for anything a little bit more sophisticated than simple testing.

But there is an alternative: synthetic test data!

Synthetic test data generation comes with many advantages. MOSTLY AI’s easy to use Platform empowers test engineers to create realistic synthetic copies of customer data. The Platform leverage Generative AI Models to learn structures, correlations, and business rules of production data and then to recreate them. The result looks the same, but none of the new data points match the original. Synthetic test data is fully anonymous data and as such exempt from privacy regulations. It can be freely used for testing in non-production environments and can be even shared outside of the walls of heavily protected institutions, like banks and health insurance providers.

Software testing and development get faster, cheaper, and – most importantly – products come to life with higher quality and fewer bugs.

Synthetic data can play a valuable role in enhancing the product design cycle in various ways:

While synthetic data undoubtedly presents numerous advantages, it's imperative to acknowledge and address its inherent limitations. The effectiveness of synthetic data hinges on its ability to faithfully replicate the intricacies and nuances of real-world datasets, ensuring that the insights drawn are genuinely reflective of actual scenarios. 

At MOSTLY AI, our commitment to providing the most accurate synthetic data available on the market underscores our dedication to overcoming these challenges. We understand the paramount importance of precision in data synthesis, and our cutting-edge technology is designed to meticulously capture the essence of diverse datasets. Experience the unparalleled accuracy of our synthetic data for yourself by starting a free trial today, and witness how MOSTLY AI is reshaping the landscape of data-driven innovation.