Data has become a hot topic of discussion, but many of these conversations oversimplify what it covers. Those outside of data science operations refer to data as a singular resource or group when there are actually many different forms of data that serve various purposes. One such distinction is test data versus live data.
These two groups may seem the same to those outside the industry or those who’ve just started in it. While there is substantial overlap between these categories, they’re not exactly the same thing. Here’s a closer look.
What Is Test Data?
Test data is, as the name suggests, data for testing. More specifically, it’s input that confirms that software works correctly, either by producing expected results or measuring how the product handles unusual information. Testers spend between 30 and 60% of their time generating, managing, and maintaining this data, so it’s an important part of the data science process.
Despite common preconceptions, companies can often explore potential opportunities before production, thanks to test data. These inputs help reveal where a product could improve, if it’s ready for production, or if it features any bugs. This final check is crucial as developers look to capitalize on increasingly complex data operations.
Since it plays such a critical role, test data must be accurate, precise, and complete. If it’s not, it won’t be sufficient for highlighting potential issues in a program or algorithm.
What form this data takes can vary depending on the end-use of the product in question. Similarly, it can come from various sources, from automated generation tools to legacy real-world datasets to manually produced data.
What Is Live Data?
Live data, by contrast, is the input that a product handles in the real world. The software or algorithm in question will, ideally, handle this information the same way it does test data, but it serves a different purpose. When live data is in the equation, the product is beyond testing. It’s in use.
Like test data, live data should also be accurate, precise, and complete. Otherwise, any results or predictions that come from this information may not reflect reality. Live data also carries another requirement in most cases: it must be timely.
Since live data comes from or is for end-use, it needs to be as current as possible. Outdated information could produce results that are no longer accurate.
Live data can take many forms, all depending on the program’s end-use, but where it comes from differs from test data. It must come from real-world, often real-time, data gathering operations, not from older databases or random generators.
Where the Two Overlap
While test and live data serve different purposes, they share a lot of common ground. Test data must be close to live data in both format and type to provide reliable test results. Because of that, test and live data often look almost indistinguishable from each other.
In fact, since data scientists can spend so much time gathering test data, many often use old live data for testing. This information isn’t technically live since it’s outdated, but it once was. In that case, the same data can work as both test and live data.
Why the Distinction Matters
While test and live data overlap in many areas, you’ll rarely see someone using recent, unedited live data for testing. You want this information to be similar, but it’s important to distinguish between them for security reasons.
Test data must adhere to privacy laws like the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA). Regulations like this require data science operations to ensure they don’t accidentally leak customer information in testing. As a result, this data that was once live requires a few extra steps.
If you use live data for testing, you must remove any parts of it that could leak and reveal sensitive information. Typically, this involves using randomly generated credit card numbers instead of real ones, removing real names, and obfuscating addresses and phone numbers. Without these steps, using live data for testing can be illegal as well as risky.
Learn the Different Types of Data
Understanding the differences between these two types of data can help use them correctly. If you don’t know the distinction, you may accidentally overlook crucial security steps or test a program with irrelevant data.
Data science is often more complex than it seems on the surface. Diving into the technicalities can help ensure you don’t make any missteps in these complicated processes.
