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Quick Guide to Test Data Management Process and Tools

Acknowledging Data Management
          Best Practices with DataOps


What is Test Data ?

Test data is the data used to test the software application. Example: For test the login functionality, username, and passwords required. So the values of username and password are test data.This article will give an overview of Test Data Management. Test data are of two types- static data and transactional data. Static data comprises names, currencies, countries, etc., which are not sensitive. But when it comes to transactional data it involves data like credit/debit card numbers, information about bank accounts or it can be your medical history, there is always a risk of the data getting stolen. Test data management- Test Data management is the method by which we can satisfy the test data requirements of test teams by including high-quality data with the right quantity and format. Efficient management of data utilized for testing is necessary to supplement the testing efforts and to maximize return on investment for the highest levels of success and coverage. You would also love to explore more about Test Driven Development in Golang.

How Test Data Management Works?

Four essential TDM techniques that empower software testing -
  • Exploring the test data.
  • Validating test data.
  • Building test data for Reusability.
  • Automating TDM tasks to accelerate the process.

Exploring the Test Data

Data can be present in different forms and formats, which can be spread across multiple systems as well. The respective team needs to search for the right data sets by their requirements and the test cases. Locating the correct data in the required format inside the time limitations is critical. This increases the demand for a robust Test Management tool that can manage with end-to-end business requirements for testing an application. It is clear that manually locating data and retrieving it is a slow task and might bring down the efficiency of the process. Hence, it is fundamental to bring into play a Test Data Management solution that ensures useful coverage analysis and data visualization.

Validating the test data

In the present scenario where associations are implementing agile methodologies, the data can be sourced even from real users. This data mostly comes through the application, which continued as practice for creating and exploring test data that gets utilized for conducting test cases by QA teams. Thus, the test data must be secured against any break in the development process, where sensitive personal data such as names, contact details, financial information, and addresses must not get uncovered. This test data can be additionally stimulated to generate a real environment, which can further influence the outcomes. Real data is vital for testing applications, which is sourced from production databases and later masked for safeguarding the data. It is crucial that the test information is validated and the resulting test cases give a genuine picture of the production environment when the application goes live.

Building test data for Reusability

Reusability is essential to ensuring cost-effectiveness and maximizing the testing efforts. The objective should be to utilize it as much as possible and optimize the value of work that has done. It should be getting from a central repository. Eventually, no time wasted in resolving any unseen issues with the data. Datasets get put away as reusable assets in the central repository and supplied to the respective teams for further utilize and validation.

Automation can accelerate the process

Test Data Management involves scripting, data generation, data masking, cloning, and provisioning. Automation of all these activities can turn out to be successful. It won't just quicken the procedure yet additionally make it considerably more proficient. During the Management process, the test data gets connected to a specific test, which can feed into an automation tool that confirms that the data given in the expected format at whatever point required. Automating the process assures the quality of the test data during the development and testing process. Like Regression Testing or any common tests, even production of test data automated. It helps in replicating enormous activity and number of users for an application to create a production scenario for testing. It helps save time in the more extended run, reduces efforts, and helps detect any error with the data on an ongoing basis. Eventually, the QA team would be in a better position to streamline and validate test data management efforts.

Benefits of Test Data Management

Benefits of test data management are below -
  • Create better quality software that will perform reliably on deployment.
  • Prevents bug fixes and rollbacks.
  • Creates a more cost-efficient software deployment process.
  • Lowers the organization's compliance and security risks.
  • Customized test data to different kinds of testing - Functional, Integration, Performance, Security, etc.
  • thereby resulting in no overstepping of test data by multiple teams.
  • Traceability of test data to test cases to business requirements helps to understand the test coverage as well as a defect pattern.
  • Assembles relationships and efficiencies by granting insights driven Decision-Making across the entire organization.
  • Reduced data refresh cycle.

Why Test Data Management Matters?

The quality of test data matters. If applications tested against generic data, many problems can arise once the application put into production. Applications must be tested rigorously against data to avoid problems that are as similar as possible to the actual data that will use.

Data and Continuous Delivery

Accurate, relevant, high-quality data is essential to the cornerstones of Continuous Delivery - Test Coverage, Automation, and Continuous Testing. With quality data, can discover defects earlier in the development life cycle for a less expensive fix and less danger of bugs in production. If testing and QA flops due to poor data quality, end-product fails too. The knock on impact is unhappy clients who complain to possibly millions of people on social media and switch their service to another brand, taking friends, family, and followers with them. On the other hand, incredible information cleanliness improved security and streamlined data management, result in an improved Customer Experience (CX), Digital Happiness, customer loyalty, better brand name, and higher income.

Data Regulations

Another obvious standout advantage of getting to grips with data, not just for the test but enterprise-wide. The benefits are mitigating the risk of hefty fines; improving revenue by leveraging quality data, and reduce the risk of security breaches, to drive effective Decision-Making.

How to Adapt TDM?

The critical phases involved in a TDM process are -
  • Planning
  • Analysis
  • Design
  • Build
  • Maintenance
Phase Steps Involved
Planning 1. Assign Test Data Manager (TDM) and define data requirements and templates for data management. 2. Prepare documentation including a list of tests and data landscape reference. 3. Establish a service level agreement and set up the test data management team. 4. Appropriate plans and papers signed off.
Analysis 1. Initial set up and sync exercises involve data profiling for each data store assignment/recording of version numbers for existing data in all environments. 2. Collection/consolidation of data requirements. 3. Update project lists. 4. Analyze data requirements and latest distribution log. 5. Asses for gaps and impact of data modification. 6. Define data security, backup, storage, and access policy. 7. Prepare reports.
Design 1. Decide strategy for data preparation and Identify regions needing data to be loaded/refreshed. 2. Identify appropriate methods and data sources and providers. 3. Identify tools. 4. Data Distribution plans. 5. Coordination/communication plan. 6. Test activities plan. 7. Document for a data plan.
Build 1. Execute plans and masking/de-identification where applicable. 2. Backup data and Update logs.
Maintenance 1. Support change requests, unplanned data needs, problems/incidents. 2. Prioritize requests and Analyze requirements and consider if they can be met from existing/modified current data including data assigned to other projects. 3. Required data modification and back up new data. 4. Assign version markers and log with an appropriate description. 5. Review status of ongoing projects. 6. Data profile exercises. 7. Assess/address gaps. 8. Refresh data where needed. 9. Schedule and communicate maintenance. 10. If necessary, redirect requests. 11. Documentation and reports.

Test Data Management Best Practices

Some Best practices are -
  • Never use excel as a test data source for automation, unless that’s the only option.
  • Externalize test data.
  • Discover and understand the test data.
  • Generate unique prerequisite data through automation for each automation run, wherever possible.
  • Consider all test environments.
  • Localization+Environment combined strategy.
  • Mask or de-identify sensitive test data.

Tools for Test Data Management

Some tools for Test Data Management are -
  • Informatica Test Data Management tool.
  • CA Test Data Manager (Datamaker).
  • Compuware’s Test Data Management.
  • Tarantula
  • InfoSphere Optim Test Data Management.
  • HP Test Data Management.
  • LISA Solutions for Test Data Management.

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