Paying a test data manager service provider to assist a company is an investment in the quality of the process it will use. It's important, however, to understand what TDM is and why you may want to seek out a test data management service.
What Is TDM?
Suppose you have a system that you wish to test, and it requires a great deal of data for validation. To conduct the tests, you'll need to use dummy data. Managing this data involves a level of rigor that many organizations aren't capable of. It's important to weed out biases in the processes, and the best way to accomplish that is usually to get independent input from a test data management service firm.
Can't You Use Existing Data?
The data may have been used during the initial creation of a model or process, and this almost always runs the risk of feeding bias into your tests. Your goal during a test is always to get results that aren't skewed, and that means you may have to build a testing set that mimics the existing data.
It's also worth noting that your existing dataset may not be large enough to produce meaningful test results. A small retailer, for example, might not have enough current sales to project what its future will look like. Using test data may make it possible to scale up to the point you can achieve statistically relevant results.
Consider what a company building a stock market modeling application might have to do. Using existing data can lead to overfitting, and the test results from an overfitted model will look impressive. Worse, testing against the real-world market might not shake out problems for weeks, months, or even years with an overfitted model. You might think you have it made in the shade only to discover there's a fundamental flaw.
Many industries are subject to privacy regulations like GDPR, CCPA, and HIPAA. It can be difficult to properly anonymize data to the point it avoids exposing user protected information. Rather than employ anonymization methods, it might be simpler to create a mimicked dataset that uses generated info.
You can't just use randomly generated data to conduct a test. The generated data for the test has to look and behave like your real data. In the stock market example, that means it has to experience natural swings and volatility. Similarly, the data should integrate seamlessly enough that a fully automated system won't run into errors or anomalies while using it.Share
27 July 2020
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