In the test data management service world, there are three kinds of data commonly used on projects. These are standard, erroneous, and extreme data. This article explores what each one is and how a test data management firm will employ each.
When creating a dataset for a project, standard data form the mainline. Standard data is correct in the sense that it reflects what the real-world data set shows. This sort of data is useful for tasks like training machine learning systems for projects. If you needed to build a fraud-detection program for a bank, for example, you'd need standard data to show what normal transactions look like.
Standard data is also regularly used in testing systems. Generally, a TDM provider will separate the data into pools for training, validation, and testing. You can then use the results from the testing phase to verify that your systems are working properly.
It's not enough to use standard data. You also need data that has quirks, bugs, and dissimilarities compared to the main set.
This serves several purposes. Foremost, it can allow you to detect outliers and anomalies. Secondly, by testing false assumptions, it can determine if a system is being too lax. Thirdly, highly buggy data can crash systems, helping you to spot where a program might need to be fixed. Finally, erroneous data can be used to confirm or throw out validation tests by flagging problems.
Some data exists within the correct set but at the periphery. This sort of testing data is invaluable because it helps you to determine just where the precision and accuracy levels are for a program. If a lot of extreme data points are being detected properly, you know you're getting very good results. Conversely, a system that isn't catching most of the extreme data points might need a bit of tightening.
Why It Matters?
It's very easy to deploy computerized solutions and assume the machines are doing great work. This is a dangerous assumption in even the best of circumstances. Many programs will look like they're working fine right up until a perfect set of conditions appears. Especially with mission-critical applications, you don't want to discover these kinds of issues only once a system has made it into production.
Test data management allows you to apply a scientific approach to figuring out potential problems with a system. You will also be able to assemble reports and show your work to other stakeholders.
To learn more, contact a test data management service.