Test data management is fast becoming the primary focus of software companies. Behind this growth in interest are two significant reasons.
First, tightening compliance and regulatory standards has forced organizations to look deeper into their test data management practices. Second, monetary losses due to poor testing, post-release defects, and regulatory fines also play a role.
If you’re also looking to implement a robust test data management strategy in your company, here’s all you need to know.
Questions to ask when planning your test data management strategy
During the planning phase of test data management implementation, ask these questions to understand your requirements.
● How much data is needed?
Using production data introduces added costs of extensive data storage and maintenance. Testing can also become inefficient and unfocused with too much data at hand.
Too little data to work with can make testing hollow, leading to defects.
● What data is needed?
Understanding each data point’s exact business requirement and relevance is essential. Your data priorities won’t be straight without a clear understanding of relevance.
● When is the data needed?
Based on the testing cycle and schedule, data needs to be appropriated to the stakeholders. You need to know when the data will be required. And will it be on-demand or based on a strict schedule?
● Where is the data needed?
This question is concerned with the testing solution and environment you’re working with.
● How is the data being protected?
A fundamental question to answer: Many intelligent data management solutions are configurable based on changing compliance standards. By documenting the security protocols in place, you’re not just avoiding data breaches but also massive regulatory fines.
● What are the test and system dependencies?
Document all your cross-system integrations and application-specific requirements to maintain referential integrity.
● Who will need the data?
The GDPR requires you to have an assigned employee to manage testing data. Moreover, you must record all the teams and people accessing the data.
● What type of testing is being conducted?
The type of testing involved impacts the type and size of data being used. Additionally, if test automation is in place, the data needs to be highly stable and predictable.
● How will the data be managed and maintained?
Data reusability is one of the most significant benefits of test automation and test data management. But to reuse data, it needs to be appropriately maintained. This requires added documentation of all the protocols to follow and the person responsible for it.
How to build a strategy for test data management?
While each company needs to build customized strategies based on its unique requirements, you should follow a few key steps. These steps ensure that high-quality data is within reach of the right stakeholders whenever needed.
Define the system you’re working with
The entire point of TDM is to access any data for any situation without defining the parameters of the data itself. This leaves QA and testing teams responsible for determining the data management system, not the data. You can do this manually or by using intelligent test data management solutions.
Extract as and when required
A successful TDM process must enable testers to request data as and when required without preparing anything. For this to work, all the test data should either be stored in a single repository or test data management solution that integrates all the fragmented data silos.
Reusability and adaptability
Testing doesn’t necessarily end with one result. Often, tests are meant to be repeated over and over again. When repeating older tests, new ongoing tests shouldn’t be impacted. Your TDM strategy needs to make accommodations for the process of refreshing test data. Your test data management solution must be adaptable and easy to sync while providing the data granularity required.
Data protection and privacy
The biggest reason companies are turning towards test data management is compliance. Being non-compliant can hamper business in significant ways. Depending on your requirements, your strategy must account for the steps needed to meet data protection standards. This can either be done through obfuscation or by using representative data.
Synthesize artificial data
Many times, extracting test data from production data isn’t a viable option. That’s when you require a solution that can generate artificial data based on the required parameters. Your test data management strategy should include the means to create synthetic data that fulfills all requirements while maintaining the referential integrity of the underlying systems.
Provisioning data
This is the final step in your strategy. Once data has been sourced, synthesized, and obfuscated, testers need to transfer it to different testing environments. Your test data management solution needs to seamlessly integrate with all systems and environments while giving you the freedom to manipulate the data as and when required.
Your test data management strategy is incomplete without a robust management solution. Avo’s Intelligent Test Data Management is one such solution that provides representative data to work with while meeting compliance and quality standards. It empowers the QA and development teams with easy access to the right data, expediting time-to-market and reducing costs.
Schedule a demo today to learn more about Avo’s Intelligent Test Data Management and how it can power your testing process.
