Best Practices of Test Data Management
Abstract
Almost everybody experiences "data issues" when performing application testing for software. Teams of software developers don't normally have adequate access to test data to viably carry out test data management (TDM).
Generally, application teams manufactured data needed for testing and development in an unstructured manner. Because of the improvements in the application tasks, a few big organizations have taken advantage of this to utilize centralization to gain economies of scale. Because of such initiatives to accomplish huge advantages of efficiency gains, the scope for test data management has been expanded by IT Team members by including tools for generating synthetic data, sub-setting and as of late, masking for manipulation of production data.
The prerequisites for the TDM have been explained here and the process has been scrutinized, best practices and strategies that are pertinent to test data management, the usefulness, functionalities, and highlights of the TDM tools that are available. The risks, challenges, and benefits of the TDM that is fundamental for organizations that continually handle data are likewise examined here.
Test Data Management
Test data management process includes designing, storing, planning, and managing software testing methodologies and processes.
It offers an extra avenue for the testing and quality assurance team to completely control files, data, rules, and the policies that are produced all through the entire testing life cycle of the software.
Test data management allows the separation of test and production data, keeping tested software versions, monitoring bugs, and performing other software testing measures.
Test Data Management Requirements
Now, let’s evaluate the requirements for test data management.
- Appropriate Data Availability
Businesses are changing business goals rapidly. The targets of the businesses are to cater to the requirements of the end-user, so, it's significant that the appropriate test data is extremely important to determine the testing quality.
- Workgroup co-ordination for data creation/refresh
- Often, if the product that is being shipped involves many workgroup teams in a large organization, a great degree of coordination between these workgroups is required to create and refresh test data.
- Data Storage
Even though different data types are needed to be created by the test groups to make sure of adequate testing, organizations should likewise consider that a repository is needed to store each data type.
- Data Maintenance
Having a repository is an absolute necessity, but the storage of superfluous and unused data won't just occupy huge extra space, it will likewise become difficult in retrieving accurate data during a specific testing process, particularly when the repository doesn't have any maintenance version or archives.
Strategies for Test Data Management
- Data Analysis
The Products involve management controller applications. Database application and the middleware application act as a team with one another. A thorough analysis of data types must be made for successful management.
- Data setup
New data creation or modification is based on knowledge of the production scenarios and the accessibility of the information in the current test environment.
- Data Cleanup
The altered or modified test data during release spanning a long period, probably won't be relevant at once, but essential later on. So, the proper time for the cleanup process of test data should be considered.
- Sensitive Data Identification
To test applications appropriately, a lot of sensitive data might be required. For example, a cloud-based test system is usually a good choice as it renders on-demand product testing of various products, which makes sensitive data identification very important.
- Protecting sensitive data
The identified sensitive data must be protected. We must identify the mechanism to protect sensitive data, especially in cases in which the user environment has to be replicated. The used test data volume primarily governs the process.
- Automation
Automation is beneficial in identifying errors that will undoubtedly appear during data testing. A strategy for doing this is by contrasting the results from the data results by utilizing a set of data generated from consecutive test runs.
Benefits of Test Data Management
- Effective and Improved software quality with dependable performance when launched.
- Prevention of bug fixes & rollbacks.
- Develops a software deployment process that is affordable
- Reduces the organization’s security risks and compliance.
- Test data that are customized for various kinds of testing, for example, integration, functional, security, performance, etc.
- Above point brings about no violating of test data by a few teams.
- Test data traceability for the test of cases to business requirements to gain insight into the test coverage along with the defects.
- Assembles efficiencies and relationships by giving comprehensive insights and understanding of Decision making all through the organization.
- Reusing test data artifacts will decrease the cycle time frame.
- Limited IT spending plan coordinated toward the non-production environment
The benefits are considerable compared to investment done on a TDM team, which will eventually build the profitability and efficiency of the organization using streamlined TDM and data provisioning.
Risks and Challenges Related To Test Data Management
Risks and challenges that must be taken into consideration when building up a strategy for TDM are the potential impacts on:
- Time availability
This is the time available for data discovery, management, and generation.
- Parallel data utilization
An organization’s operation is in a distributed environment, one which is a multi-supplier and outsourced across SDLC and the usage of your data by various users at multiple locations simultaneously.
- Vulnerable to a data breach
It, SMACT, legacy & new databases, and all structured and unstructured data they provide generate a dynamic data landscape with numerous data types stored in different environments, making it difficult to access data breaches and increasing the chances of a data breach.
- Different data, organizing/managing
For the different test types, from performance testing, user acceptance, etc., various types of data are needed. You need to properly organize your data from the beginning to comply with test time limits and prevent spiraling costs.
- Data Security
Data protection is critical, therefore requiring new safety procedures and a cultural transition to make sure that sensitive data should not be copied into an e-mail, and that the use of live production data in test environments is not compromised.
- Data privacy
And lastly, compliance with data privacy policies that include the new GDPR.
Test Data Management: Best Practices
Previously, application testing was simple. You just required one mainframe, limited data set, in-house data, and some insights into security and privacy. But presently, many other factors must be considered before carrying out the test data management.
Having the right test data is crucial for your business to avoid production risks, lawsuits, and compliance fines.
- Data generation and extraction.
The information needed by most applications normally cuts across databases, frameworks, and LPARs. A couple of these are usually generated in external data. Your specifications require a clear definition that can take a lot of time while paying regard to:
- Data types needed by the test teams.
- Executing the Central Repository together with Version Control
- The skillset needed to grasp a few databases and schemas for data access. Knowing the production data can be hard for test teams.
- Frequent data extraction refresh (how and when) to ensure it’s current.
- Executing seamless data update.
- Automating future data refresh process
- Protection of sensitive data.
Concerning the large storage of data, companies maintain a large amount of data for their production framework. To comply with stringent regulations on data privacy, teams are required to introduce data scrambling, encryption, and masking as well as when ensure that there is appropriate production data management. These policies are required for outsourcing.
- Planning
All data cannot be utilized. It is suggested that a subset is selected that best represents by mimicking the production in every way. Planning involves ways in which to standardize the testing in all groups to restrict the overhead of test environment management.
- Maintenance
This is needed for the storage, maintenance, and updating of all test data regularly. Storage maintenance expenses can become very high. Therefore, it is essential to look for an affordable option.
- Auditing
Appropriate auditing of test data should be performed to ascertain an appropriate representation of the workload that will be executed during production. The maintenance of referential integrity is a must.
Process of a test data management tool currently available in the market.
Typically, data are stored in diverse formats, locations, and types, in a system. There are different guidelines where its management requires these procedures.
Test data management tools find the appropriate test data from the available ones during a testing process.
Collects data subset from the generated data from numerous sources.
Test tools use masking for sensitive data such as customers’ details.
It compares the baseline data and actual data for ascertaining the application’s data accuracy.
Test data is regularly refreshed for increased efficiency of the application.
Conclusion
Advanced IT companies are evolving in test data management with the use of advanced data masking and distribution. Not only is virtual data offered in real-time by admins to application teams in a matter of minutes, but repeated masking algorithms are additionally being constantly executed to simplify launch from Ops to development.
Better handling of test data management promotes quicker app delivery and therefore you can achieve your business objectives faster. However, business organizations that neglect to invest in test data management will subsequently start to lag.
Make the most of test data management to move your business forward.