Test data is a key ingredient in engineering workflows. We all know that when we build applications, launch features, or architect to get out of tech debt, we need to test our code. But sometimes, our test data fails us and we launch with bugs. Here are the 7 most common ways that test data can fail us.
This is data that doesn’t match the current state of production environments. Perhaps you changed the schema or business logic. Or perhaps there are obsolete behaviors or the actions of newly released features are missing. Test data is not a one and done endeavor, and using outdated test data can lead to significant discrepancies between test outcomes and actual performance, resulting in the release of bug-ridden software.
If accessing or generating test data is too slow, it adds significant lag and lead time to the testing process. This delay would be further compounded by a CI/CD pipeline. At that point, it doesn’t matter if the data is high quality. This is actively hurting your development lifecycle. Many organizations hack around this by reducing their testing frequency or refresh cadence, which leads to stale test data as described above. Ultimately, this gives developers less feedback while developing and less stable deployments.
The scale of test data matters significantly. On one hand, overly large datasets can cause tests to run inefficiently, while on the other, too little data fails to simulate real-world usage, leading to poorly scaled applications. Because of the complexity of getting good test data, organizations will often create a single test dataset that gets used across all potential use cases; however, your database needs are very different when you’re doing scale testing vs bug hunting.
Oftentimes, especially with home-grown solutions, the test data will only reflect what the designer can anticipate. It’s very easy to miss structural variants, especially those that are uncommon. Relying heavily on manually-created test data can introduce human errors and biases.
There’s an old joke where a programmer builds a bar with a bartender. The QA will test a wide range of orders: 1 drink, 9999999 drinks, -20 drinks, an iguana, and “qwerty” drinks . After all that testing, the end user comes and asks where the bathroom is and the bar goes up in flames. Ultimately, the real world is a lot more complicated than we can predict.
Our test data should try to best capture some of those real world eccentricities. This can be particularly impactful when the software is intended for a diverse user base or when it needs to handle a wide range of inputs. Without sufficient variety in your test data, certain bugs or issues may go undetected until after deployment.
Getting compliant test data is easy. You can fully redact or even scramble your data into nonsense. However, getting useful and compliant data is much harder. Which is why some 50% of organizations still use production data. But using real user data without proper de-identification or failing to comply with data protection regulations (such as GDPR or HIPAA) can lead to legal and security issues. Furthermore, if your test data is not securely handled, it can be exposed to unauthorized access, leading to data breaches and other security incidents.
One of the pain points we’ve seen working with large organizations is that they sometimes have a Frankenstack. Legacy databases like DB2 LUW on one side of the house and Snowflake or even GraphQL at another part of the organization. Because test data is usually provisioned per tool, there is no referential integrity. This makes it really hard to test end-to-end where you can connect your application environment to various product tools. Regardless of what’s on the backend with the engineers, your end users have 1 Bank of America login—whether they’re using your product for a line of credit, a savings account, or CDs.
When not managed efficiently, test data can be very costly. For on-prem solutions, this can be the result of needing to set up reserve servers and infrastructure. Even cloud native organizations will buy a large RDS container and populate databases because it becomes too cumbersome to regularly spin up and spin down ephemeral data environments, or even worse, spinning up a test database and leaving it on forever. Useful test data that doesn’t balloon infrastructure cost is important.
To mitigate all of these issues, it's essential to implement a robust test data management strategy that ensures test data is diverse, well-organized, representative of production environments, and kept up-to-date with application changes. Additionally, automating the creation and maintenance of test datasets can help to alleviate some of these challenges, making the testing process more efficient and effective. Here at Tonic.ai we help make this possible with a full suite of products and features. Curious to learn more? Let’s chat.
