Testing with production data. To test or not to test?

When it comes to testing, a critical question for QA leaders is deciding what data to test against: should they use production test data for their test cases or should they reach for synthetically generated test data? Utilizing a synthetic test data platform may offer a viable alternative to traditional methods and serve as an effective self-service test data solution.

Using production data for testing

As we can see, using production test data is still a common practice in many companies. We've decided to review the benefits and disadvantages of using production data for testing and help you decide whether these are the most suitable type of data for your test scenarios. (And perhaps guide you towards embracing the use of synthetic test data.)

Advantages

Expediency
Leveraging production test data provides a notable advantage in terms of speed. Testers can rapidly access authentic, real-world data, expediting the assessment of the application's performance, functionality, and identification of potential issues.

Realistic scenarios
Production test data faithfully mirrors actual user behavior, offering a rich source of authentic scenarios for more meaningful and realistic testing. By replicating the conditions and interactions users experience in the real world, testing becomes more comprehensive and reflective of the application's performance under genuine usage conditions.

Large dataset
Production databases are known for housing vast amounts of data. This abundance allows testers to conduct thorough examinations across various facets of the application. The availability of a diverse and extensive dataset ensures comprehensive testing, covering a wide range of scenarios, inputs, and conditions.

Challenges and Risks

Relying solely on production test data introduces notable challenges and risks. From our experience, using production data brings on more disadvantages than benefits. Here, synthetic test data platforms and self-service test data solutions can offer significant advantages.

Privacy concerns
The utilization of production test data raises critical privacy concerns as this data may encompass sensitive information. To adhere to privacy regulations, a meticulous process of anonymization or pseudonymization becomes imperative. This ensures that personally identifiable information (PII) and other sensitive data are adequately protected during testing. For effective test data anonymization alternatives, consider using advanced techniques and tools.

Incomplete test coverage
The production database might lack comprehensive permutations, resulting in incomplete test coverage and potential oversight of issues. It underscores the importance of considering additional data sources or methodologies, such as synthetic test data platforms, to ensure a more exhaustive testing regimen.

Format compatibility issues
Introducing new features may render existing production data incompatible. This can result in inaccuracies during testing, as the data may no longer align with the expected formats. Ensuring compatibility is crucial for obtaining reliable and representative test outcomes, particularly in dynamic development environments.

Unsupported elements
Outdated or unsupported elements in production test data can mislead test results, especially if they are no longer valid for the current application version.

Data restoration challenges
Managing massive production databases poses difficulties in frequent data restoration and refresh, potentially causing testing against outdated or irrelevant information. A synthetic test data platform can help mitigate these challenges by providing up-to-date data on demand, and many of these platforms offer self-service test data options.

Data volatility
The concept of data volatility underscores the dynamic nature of production test data, introducing a level of inconsistency and unpredictability into the testing environment. This volatility arises from the constant changes in the production database over time, and as a result, data available today may be unavailable tomorrow.

What should you consider if you test with production data?

To ensure the test environment aligns as closely as possible with production, many QA teams copy production data. However, several considerations arise:

Privacy-sensitive information
If your production test data includes privacy-sensitive information, adhering to regulatory compliance becomes paramount. This involves implementing measures such as masking, filtering, or outright removal of sensitive data to ensure that privacy regulations are meticulously followed during testing. Utilizing test data anonymization alternatives can be a crucial part of this process.

Handling data volume
Assessing the capacity of your test environment to handle the volume of production test data is crucial. If your test environment proves insufficient, strategic decisions such as filtering or selecting specific cases become necessary to manage and optimize data volume effectively. Using a synthetic test data platform might help manage large data volumes more effectively.

Refreshing data
When obtaining a new copy of production test data that potentially overwrites previous changes, it's imperative to contemplate the repercussions on ongoing tests. Implementing a refresh option becomes a key strategy to maintain the relevancy and accuracy of your testing environment. Self-service test data options can also simplify this process.

Data dependencies
Understanding and testing all possible circumstances or settings involving dependencies between data is essential. Identifying these interdependencies ensures a thorough evaluation of your application's behavior under various conditions, contributing to a more comprehensive testing approach.

Verdict? To test or not to test against production data?

As you navigate the complexities of testing with production test data, addressing these considerations will not only enhance the accuracy of your test results but also contribute to the overall effectiveness of your testing processes. Exploring synthetic test data options and synthetic test data platforms might offer valuable alternatives to conventional methods and address many of the challenges associated with production data.

Still wondering whether production test data is suitable for your testing scenario? Explore other types of test data in our in-depth article that helps to bring more insight into this topic: Common threats to test data in software development