Proper test data management helps businesses create better products that perform more reliably on deployment. But creating test data, in the right amount and with the right kinds of relationships, can be a much-more-challenging process than one would think. Getting the most out of test data requires more than simply having a tool for generating or subsetting data; it requires having a clear test data management strategy.

Test data management might not be the first area that comes to mind when thinking about corporate strategy. But testing generally holds just as much potential as any other area to damage your business if handled incorrectly—or to propel you to further success if handled well.

An upshot of this is that it can help you find the best test data management tools as well. After all, if the creator of a tool understands what is involved in a test data management strategy, you can rest assured their tools will actually be designed to make those strategic goals a reality.

Here, then, are the elements for a successful and secure test data management strategy.

The Core Elements of Test Data Management Strategies

Creating a secure test data management strategy starts with having a plan that makes your goals explicit, as well as the steps for getting there. After all, it doesn’t matter how secure your strategy is, if you don’t achieve the outcomes you’re looking for. All effective test data management strategies rely on the following four pillars.

Understanding Your Data

First, it’s essential that you understand your data. Good testing data is typically composed of data points of radically different types sourced from a different database. Understanding what that data is and where it comes from is necessary to determine if it will produce a test result that reflects what your live service offers. Companies must also consider the specific test they’re running and alter the data they choose to produce the most accurate results possible.

De-Identifying Data

Second, producing realistic test results requires using realistic data. However, companies that are cavalier with their use of customer data in their tests put themselves at greater risk of leaks and breaches and may also run afoul of data privacy laws.

There are many different methods for de-identifying data. Masking permanently replaces live data with dummy data with a similar structure. Tokenization replaces live data with values of a similar structure that appear real and can be reversed later. Encryption uses an algorithm to scramble information so it can’t be read without a decryption key. Whichever approach you use, ensure your personally identifiable information is protected and used per your privacy policy.

Aggregating and Subsetting Data

Third, your company may hold hundreds or billions of data points. Trying to use all of them for testing would be extremely inefficient. Subsetting, or creating a sample of your data that reflects the whole, is one proven method for efficient testing. Generally, data must also be aggregated from multiple different sources to provide all the types of data that your tests require.

Refreshing and Automating Test Data in Real Time

Finally, your company is not static. It changes and grows, and as it does, the data you hold can shift dramatically. If your test data is static, it will quickly become a poor representation of your company’s live environment and cause tests to miss critical errors. Consequently, test data must be regularly refreshed to ensure it reflects your company in the present moment. The best way to accomplish that task is to leverage automation to refresh your data regularly.

What Makes a Test Data Management Strategy Secure?

The reality of using test data is that, if improperly handled, it multiplies the preexisting security issues that your company already has. For example, if you take one insecure dataset and create five testing datasets, you end up with at least six times as much risk.

When your data isn’t secure to begin with, securing your test data won’t make a meaningful impact on your overall security posture. At the same time, creating test data comes with its own risks. Data will be stored in new locations, accessed by more people than usual, and used in ways that it might not be during the normal course of business. That means you need to pay special attention to your test data to keep it secure.

The following framework provides a way to think through the new risks that test data create.

Who?

First is the who. In addition to the people assembling the test datasets, other people (such as back- and front-end developers, or data analysts) will come in contact with the test data. While it’s tempting to provide all of them with the same data, the reality is that the data they need to do their job will vary from role to role. Your experienced lead developer will need a higher level of insight for troubleshooting than a junior developer on their first day on the job. To maximize your security around this data, you need a tool that can help you make these kinds of nuanced decisions about access.

What?

Knowing what data you’re using matters. With an ever-growing number of data privacy laws around the world, businesses must be able to detail how they’re using data in their operations. Using data that’s not covered by your privacy policy or using data in a manner that isn’t covered could result in serious regulatory action, possibly in multiple countries at once. Companies increasingly need to be able to prove they’re in compliance, which is most easily accomplished with robust audit logging.

Where?

An increasing number of countries are penalizing companies for offshoring data, especially if it isn’t declared in the enterprise’s privacy policy. Even with that in mind, running your data analysis in other countries may still make financial sense. In that situation, companies should evaluate whether masked or entirely synthetic datasets would suffice to reduce the risk of regulatory action or leaks that come with moving data across borders.

How?

The growing complexity of securing your test data management process means that it’s no longer possible for humans to oversee every part of the process. A good policy starts with your human workers setting the rules, but then a technological solution is needed to handle the process at the scale required for modern business applications.

Overall, test data management strategies will vary from company to company. However, by following the principles in this article, companies can develop an approach that meets their testing needs while ensuring that data is kept secure.

How Mage Can Help with Test Data Management

While it would be dramatic to suggest that a poor test data management strategy could doom a business, it’s not an exaggeration to suggest that a poor strategy drives up costs in a measurable way. Poor testing can easily lead to a buggier product that takes more time and costs more money to fix. And a worse product could lose customers, even as the expanded fixes hurt your bottom line. The good news is that companies don’t have to develop their test data management strategy on their own. Mage’s suite of test data management tools provides everything businesses need to build their test data pipeline while having the customization they need to make it their own. Schedule a demo today to see Mage in action.

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