Test Data Management has become an integral part of software development in recent times. It deals with the management of simulated data or test data which act as a substitute for real-life data during software development. This enables software developers to run adequate and thorough test runs in order to identify potential fallacies and fix apparent issues – ensuring a reliable and efficient deployment of the finished product for their clients.
Additionally, test data management also benefits the developer by making development more efficient, reducing deployment times all at a lower cost – allowing them to remain more competitive in the market. However, quality test data must be prepared first and managed efficiently in order to keep the development pipeline flowing.
What are the potential challenges with TDM?
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- Slow provisioning of test data: Test data creation times are a function of the people directly involved. Developers create preliminary test data, which is a slow, manual process. Post creation, the data then needs to be copied to be provided to testers which consumes more time. The creation of updated data that can be fed into a test environment can take weeks since conception. Data masking is done in order to protect sensitive information with the possibility of a data breach in mind. This is a necessary security measure and requires time to be implemented – also contributing to developmental delays.
- Delayed procurement of the latest data: During the development process, there are times when an updated version of a specific data set may be required by the developer for better results. However, this is quite complex to procure in a specific environment and often developers have to make do with what they have at hand. This leads to a significant chunk of time being spent resolving issues arising from the same.
- The storage factor: Storage can get quite expensive given the sheer volume of data companies deal with. The test data often needs to be shared across multiple teams involved in development which may demand storage space adequate to accommodate multiple copies of such a large data set. Better coordination among teams prevents issues such as creation and storage of duplicates or obsolete versions of test data.
How to prepare test data better?
- Efficient creation and sharing of data – Integration of a better data toolset combined with the implementation of automation helps eliminate two of the major problems mentioned above.
A better toolset provides access to more up-to-date, useful data and offers additional features such as data masking by default. Data automation reduces manual steps like identification of target data and configuring it for test data creation and also reduces time required to share created test data.
- Procurement of quality data – Access to the latest data in an environment is often complex and timely procurement becomes difficult during the developmental process. A faster refresh of data should be prioritized in order to prevent late-stage developmental errors owing to stale data. Multiple data sets should be made available at a certain point in time for more accurate and efficient data collection.
- Efficient subsetting – Common data should be shared when possible in order to reduce creation of multiple subsets of test data. This prevents errors arising due to mismatch among subsets and also allows for creation of complete data copies to be created occupying less space than that of multiple subsets.
What is the ideal course of action?
An efficient TDM algorithm has the following stages:
- Identification of basis of data collection – The first step is to create a set of criteria to facilitate proper data collection. This gets impacted by the planned characteristics of the end product – things such as the nature of business, the volume of data that needs to be fed, and so on. Once the criteria are finalized subsetting can be initiated to allow a more catalogued input of data.
- Extraction of data – Based on pre-decided criteria, test data material needs to be extracted across a variety of sources available to make the test data more comprehensive. This is aided with the deployment of better data tools that can better identify and extract relevant data with more efficiency and integrate them with the test environment.
- Quick rolling back of test scenarios – Multiple copies of test data are in developmental status simultaneously, each with its own specific user. More often than not the test data in one of such scenarios need to be rolled back to the default or last available version deemed to be stable – due to the identification and rectification of bugs.
- Masking of sensitive information – This can be achieved by storing core data values in a centralized system with adequate security measures in place to prevent unauthorized access.
- Synthesis – The generation of synthetic data may be required in situations where the available data pools do not have adequate data which can completely satisfy the requirements of the test data being created.
- Integration in Test Environments – Post collection or creation of synthetic data, test data tools are employed to find a suitable Test Environment while testers proceed to integrate data into them.
In conclusion, TDM is required for efficient and affordable software development. Loads simulating realistic use-case scenarios are run in order to fine-tune the feature set and eliminate developmental errors.
Identifying and creating a relevant criterion for test data collection allows quality data to be collected or artificially synthesized which is then tested rigorously in a secure environment before being implemented in the test environment for detailed analysis and adjustments.
Software development without proper TDM makes work harder, slower, and more prone to bugs – things to be specifically avoided in this competitive IT market.