Data holds secrets. Your company’s ability to extract value from its data hinges on unlocking the insights contained within. Test data is a critical but often overlooked subset of data. It has the potential to turbocharge your operations or, if misused, get you in serious regulatory trouble. Understanding the uses for test data, along with best practices and common stumbling blocks, will help to paint the bigger picture of your organization’s data strategy.

Uses for Test Data

Test data is a tool. Like most tools, how you use it matters just as much as what it can do. Here are the primary ways in which companies use test data.

Software Development

Test data is hugely important in software development. We’ve all used a program that didn’t work quite right, indicating that the company didn’t do enough testing or perform the right tests. Unfortunately for businesses, the later they discover the bug, the more it costs to fix. Bugs found during internal testing are between 16 and 160 times cheaper to fix than if they are uncovered after a release has gone live. That represents enormous potential savings for companies that can effectively test their programs, not to mention the improved user experience that will result from a better testing process.

Data Analysis

Test data is also increasingly important to businesses for use in data analysis. Data often contains insights that can help companies optimize pricing, uncover inefficient processes, or reveal new potential product opportunities. Consequently, data analysts need large quantities of data on which they can perform their analysis. Getting your analysts the right data at the right time is key to producing the best insights. Delays in this process could lead to missed opportunities for your business. It’s also important to remember that analysis is only as good as the quality of the underlying data. Consequently, providing high-quality data to your analysts is of utmost importance.

Issues with Using Live Data

Given the importance of data for software development and data analysis, most companies are already using test data in one manner or another. Unfortunately, some companies are using their live, or production, data for these purposes, which has several significant shortcomings.

Speed

One of the biggest issues with using live data is the speed issue. Live databases can have millions and sometimes billions of individual data points. For your average business laptop, dealing with that amount of data can dramatically slow things down. Of course, you could use a service like Google Data Cloud, which lends you resources to speed up the process—but you have to pay for this service, and the more you use it and the more complex your problems, the greater the overall price. The only way to solve the speed and cost problem simultaneously is to reduce the data set size to create a smaller test data sample.

Security

Your live data may include many personal information about your users. With ever-tightening data privacy and security laws worldwide, your company may be noncompliant if running its tests with live data. Improperly handling data could lead to millions of dollars in fines. Plus, running your tests with live data creates an additional risk of user data being abused, leaked, or accessed by an unauthorized party. Consequently, effective test data sets should take steps to help protect user privacy and find ways to protect key personal information. Likewise, test data should always be compliant with the relevant data privacy laws where your business operates.

Specific Testing Needs

Software testing may require specific types of test data that may not be present in your live database. Blank data tests programs for faults if the entered information is absent. For example, if a user skipped over a blank on a form, would the system catch the mistake, or would it cause a fault somewhere down the line? Valid test data is data of the right type and structure for a specific application. A valid data test simulates how the platform would run during normal operation. Invalid data tests, on the other hand, tests how the program responds when invalid data is entered. This helps ensure that user error messages and other user guides have been implemented correctly.

Boundary conditions may also need to be tested. These ensure that all data entered is in the proper range. For example, a user may be asked to rate a service on a scale of one to ten, so the system should identify a score of twenty as outside the boundaries.

Representativeness

If you’re using data for testing or analysis, it’s important that it accurately represents the rest of the data in the data set. For software testing, that means that the test data you use is similar to your live data in type and structure, so the tests you run represent the data that will be moving through the system when it goes live. Any statistical relationships in the data set must be preserved for analysis, even if the data set is altered to address speed, security, or data privacy concerns. This is a considerable challenge for many businesses that can be hard to manage with many modern test data management tools.

Learning More about Test Data

Test data can be a powerful tool for many kinds of businesses. But questions remain: How should companies generate the best test data possible? And, once they have it, how should they manage it? While the answer may seem straightforward at first, generating and managing test data at scale presents its own challenges, especially regarding speed and security. To learn more about the best practices for generating and managing test data, check out our white paper: Test Data Management for Decision Makers .

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