Test Data Management (TDM) is a complicated and critical process for agile DevOps. Testing applications is a multi-step process, each of which needs to be handled carefully. To meet the required test coverage and results, avoiding mistakes in each step is critical. However, avoiding mistakes can also add to the tasks if the root cause of the mistakes is not determined and addressed. Discover how TDM ensures a smooth testing process with better test coverage across multiple environments and scenarios.
Test Data Management, as a process, can be broken down into several basic undertakings:
- Test Data Golden Copy Creation and Management
- Test Data Virtualization and Sub-set Creation
- Test Data Provisioning and Self Service
- Synthetic Test Data Generation
- Securing Test Data
The overall process of TDM is much more complicated but generalizing it similarly can help identify mistakes in the technical and operational aspects of test data management. The most common mistakes are a combination of technical and operational errors coming together or piling up in the data repository. So, let’s look at test data management’s most common and costly mistakes.
Lack of TDM Planning and On-The-Go Test Data Creation
Planning TDM-related processes well in advance can be one of the most critical solutions to avoid mistakes in test data management and application testing. Test data requirements are directly correlated to the development projects planned or being executed in any given period. Identifying the key test data requirement frequency, priority, and connections can help create an optimal golden copy for any project and effectively virtualize the test data. Efficient planning can help you strategize test data management for the whole project whilst saving you time in test data provisioning and other time-consuming processes to acquire test data.
TDM planning should be an integral part of project planning and management from day one to succeed in efficient execution. In a CI/CD DevOps cycle, test data management planning should be executed in parallel and as early as possible. Planning must ensure that test data availability, quality and quantity, and access to the right teams.
Not Testing the Test Data for Validity and Consistency
One of the biggest problems testers faces is the lack of clean and error-free data. While testing also uses ‘incorrect’ forms of test data, it is limited to the nature of the test. However, any errors or discrepancies in test data can lead to deficiencies in testing and irrelevant test results. Thus, it is important to correct the test data using reliable methods that do not generate new discrepancies. Manual test data handling for cleanup, sub-setting, or any other modification can lead to such errors. These errors can exist in the available data already too. The domino effect of using defective test data can go very far into the product.
Using automation and smart technologies can be the best way to eliminate and rectify such discrepancies in test data. You can also test the provisioned data with known versions of the product and compare results, but that may not be applicable for new features or if the discrepancies existed while testing older versions. There can be multiple ways of testing or analyzing the test data itself to make sure such discrepancies do not exist.
Lack of Version Control and Updates to Test Data
The most optimal test data management provides reusability, scalability, and rapid profiling of test data. However, this requires a standardized system of maintaining test data versions and updates. Without updates, test data will become irrelevant in time and without version management, it will become difficult to provision the right test data.
SLK has a wide array of experience developing agile and efficient systems for various clients, including Fortune 500 companies. Our solutions encompass all possibilities and deliver systems enabled by scalable automation and management practices. Our TDM practices have accelerated DevOps for many of our clients by holistically reducing errors and standardizing processes.
The mistakes mentioned in the article comprehensively cover the most deep-rooted challenges with test data management. You might encounter many more errors and problems with application testing, test automation, and test data management. However, these can be reduced significantly by using the right approach and extensive automation and machine learning systems. As time passes, other operational and technical mistakes can be identified by thoroughly analyzing every aspect of the testing process. Efficiency is not a destination, but a path of improvement paved with careful considerations!