One major bottleneck is identifying sensitive data across large, complex databases. To address this, we developed an AI-powered PII scanner that detects sensitive data automatically, even when it’s not obvious from column names. It also suggests how to replace this data and apply the right generators instantly, streamlining the entire process which can save a lot of time. We also provide a wide variety of generators, which means no matter the scenario, there’s always an efficient path forward.
We also see that a lot of the clients we work with have struggled with older, scripting-based solutions. In those setups, even small changes require developer involvement and take weeks to roll out. With our platform, that kind of bottleneck doesn’t really exist as you can move fast without waiting on a dev team to write or update scripts every time.
Connecting to databases and generating the actual data are the two most-used features. We offer a lot of connectors, so it’s easy for users to plug into their systems and get going.
When it comes to the generators, usage really depends on the use case. AI-powered generation is especially useful when statistical realism matters. For example, in analytics or model training scenarios. But for standard test data needs, format-specific generators (like for social security numbers) are used often. We also see a lot of teams relying on features that help maintain consistency across multiple systems, which is really important in enterprise environments.
We’ve actually put together a full deck that outlines the top 16 test data use cases we see most often with our clients. It covers not just what the use case is, but also the pain points teams are dealing with today and how we solve them.
The platform is used for all the expected things like regression testing, API testing, security testing, performance and load testing, but also for creating demo environments with production-like data. That’s especially important when showcasing a solution or feature. When it comes to training AI and machine learning models, having clean, realistic test data is critical, and we support that too.
A lot of organizations know they need to get serious about compliance and want to speed up innovation, but they don’t know where to start. That’s exactly why we created the use case deck, to help teams get going quickly, with real clarity on how to move forward.
If you’d like to get a hold of this deck, feel free to reach out to stephan@syntho.ai
Create and manage high-quality test data efficiently
Enhancing data privacy and compliance
Reduce manual effort in test data generation
Accelerate development and testing
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