Understanding Test Data Management, its challenges, tools, and techniques.
Several enterprises fail to recognize the need for test data management in their application development initiatives. This happens even when they strive toward achieving seamless test automation and improved application quality. However, without proper data management strategies and the right test data management tools, the QA practices adopted may not yield the right ROI (Return on Investment) over time. Most enterprise technology leaders are unaware of the right data management process in software testing; and this can hinder the process of test data management especially in a large technology landscape.
What Is Test Data Management and why is it needed?
Test data management is the process of seamlessly managing test data in a software testing project to ensure that all test scenarios are given access to the right test data whenever needed. With test data management, test data is provided in the best format required for test activities and in the right volume to meet all unique testing needs. This is primarily used for automated testing, end-to-end automated testing activities.
Different test cases may require data of several types. But there are underlying guarantees that every test data needs to fulfil before it is used for testing: quality, availability, timely access, realistically real-life use data, and compliance to any regulatory or policy framework that governs test data usage.
As more organizations rely on digital channels to run the lion’s share of their business, delivering disruption-free and seamless customer experience at all digital touchpoints is a number one priority. To ensure that the applications offer this guarantee, it is crucial to run strict QA checks. Every single workflow and functionality need to be checked by enterprises; it must emulate real-life operating conditions for the application. With realistic test data, a lot of testing areas can be covered; let us also remember that the test data volume in modern enterprise application development initiatives will be quite high and diverse. This gives rise to the need for robust test data management.
But first, let us understand the challenges:
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Challenges of Test Data Management:
Siloed data and data spread across legacy systems in varied formats end up wasting time while sourcing the same for QA and software engineers. The need to subset test data to achieve full test coverage and it also helps recreate and fix production issues. Data teams can minimize the quantity of test data and its associated hardware and software costs.
- Discovering and protecting customer-sensitive data which must be anonymized in the test environment. DE-identifying sensitive data and PII, while assuring referential integrity of the masked data, can be complex, challenging, and labor-intensive.
- Enforcing referential integrity of masked data; data and schema needs to be consistent across databases and tables.
- Extending test coverage is most efficient only if all the test data needed to fully operate the test cases is given as much importance
- Poorly designed test data causes false positive errors, leading to valuable time and effort wasted in dealing with non-existent software bugs. With insufficient test data the quality and reliability of the software is negatively impacted.
- Reusing the test data is critical for re-running tests to verify software fixes through regression testing.
- Preventing data overrides is crucial in QA teams as it leads to test data getting re-provisioned, and the tests re-run.
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Top test data management techniques
Let us explore some of the key test data management strategies that can help drive maximum ROI -
Analyze Before Finalizing Data
One of the main strategies to follow is to ensure the creation of all data sets needed for test execution. Suppose it is an acceptance testing phase. In that case, the test data management must propagate a data creation initiative that grabs all data types relevant to end-to-end acceptance testing. Through a comprehensive analysis, every data element that will be a part of the test cycle must be identified and recorded in the test data management process.
Mimic the Production Scenario
Adding to the data analysis part, the production environment forms an equally important aspect of data organization. Having a clear idea of the production environment and then checking for missing data elements is vital. Once identified, they must be added to the test data management records.
Foster Automation
Creating test data is not easy, especially with the volume and veracity of requirements in modern digital application testing initiatives. Just like test automation, the creation of test data can also be automated. From a test data management strategy perspective, this is a core activity. Automation reduces the number of errors that usually find their way into test data. By comparing different test results of consecutive test executions in the same test scenario, it will be easier to improve the accuracy of test cases. The best part is that the comparing part itself can be automated for a truly seamless experience.
Protect Confidential and Sensitive Data
Today, many enterprise applications run on the cloud or conform to the cloud-native paradigm. From a cloud-testing perspective, this implies using sensitive and classified data in large volumes in the test environment to check and validate the performance of the cloud-based application.
In such cases, protecting sensitive data from leaks and unauthorized access within test environments is critical. Businesses must formulate a proper strategy to ensure that all participating entities in the test cycle adhere to data protection standards and guarantees.
Maintain a Centralized Test Data Repository
By maintaining a centralized test data repository, enterprises can significantly lower the time for future test initiatives as the data for testing will be readily available. When testing activities detect new data types, they can automatically move to the centralized repository. This setup facilitates better adoption of standards and compliance frameworks because of the centralized distribution of data.
Benefits and choosing the right tool for Test Data Management.
Using test management tools improves the testing process and helps in faster delivery of the product. They also aid in controlling the overall costs of testing by introducing testing automation that helps ensure that this process is seamless. Here are how test management tools benefit test execution workflows:
- Transparent approach of monitoring the system under test, ensuring that the activity is on track and is well coordinated.
- Testing process can be fine-tuned due to active collaboration, communication, and data analysis.
- Progress, defects, and test results are tracked, and the entire process can be simplified as everything is carried out in a single application.
- Test data management tools are scalable and can be used even if testing activities become larger and more complex.
Here are some key features of an ideal test data management tool:
- Attractive user interface and easy-to-use design for testers
- Accommodates multiple projects and user permissions
- Flexible with scheduling and organization
- Traceability of all efforts.
- Provides enough monitoring and metrics.
Conclusion
The failure to devise a seamless test data management strategy can hurt the automation testing ambitions of the business significantly. Enterprises therefore need access to test automation strategies that imbibe the principles of test data management. ACCELQ is a leading state-of-the-art AI driven codeless automation testing platform, that can help enterprises build resilient test data management strategies and implement them for better digital ROI. Get in touch with us for a personalized demo.
Bhuvaneswari Gireesh
Content Manager
She has over ten years of digital content experience and is deeply passionate about technology. Adept at producing different kinds of digital content, she is also a voracious reader. While English language is her first love, fitness comes a close second.
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