One area where the industry is continually looking for methods to improve testing is test data management (TDM). It's significant since the quality of test data is the primary determinant of testing completeness and coverage. Testing assurance, on the other hand, is generally recognized to be impossible to obtain without high-quality data. Leaving TDM steps out of the testing life cycle, on the other hand, typically results in TDM ignorance on the part of the software development team. In production, the best data can be found; these are the real records that the application uses. When working with production data, it's always a good idea to create a subset.This reduces the time and effort needed to prepare and execute tests, as well as helping in optimization.

What Is Test Data Management?

What Is Test Data Management? Test data management is the process of designing, planning, storing, and managing software quality testing procedures and methodologies.

It delegated control over the data, files, rules, and policies generated during the software testing life cycle to the software quality and testing team.

Test data management is also known as software test data management.

In a word, Test Data is the information that we provide in order to test our application. Every project or company records or stores test data in excel sheets so that quality assurance teams can utilize it to run manual or automated tests, which will be useful for future reference. With the help of an Image to Excel converter, you can even extract data from images and seamlessly integrate it into your test data repository, enhancing the versatility of your testing resources.

Benefits Of Test Data Management You Must Know

#1 Customer Satisfaction

The TDM method has a number of benefits, the most prominent of which are good data quality and broad coverage. Bugs can be discovered early when these features are present during the testing phase. As a result, the app is reliable and high-quality, with few production faults. When a consumer sees such appealing benefits from adopting a TDM technique, his or her trust in the company rises, resulting in considerably higher customer satisfaction.

#2 Saves Cost

The most beneficial feature of the TDM is that it can be reused, resulting in cost savings. The reusable data is selected and preserved for future use in a centralized repository. The testers can turn to the archived data when they need reusable data. Because there is more test data traceability and coverage, the picture becomes clearer at an earlier stage, assisting in early defect discovery and lowering the cost of production repairs.

#3 Data Regulation

Another evident benefit of mastering data is that it benefits the entire firm, not just the test. The benefits include reducing the risk of large fines, generating revenue by utilizing high-quality data, and reducing the risk of security breaches in order to facilitate effective decision-making.

With data privacy standards like the GDPR exam, data management becomes even more important since it helps businesses comply with regulations by performing compliance analysis and implementing data masking tactics.

#4 Data is managed efficiently

Because data is controlled in a single area, a TDM process stands out. The same repository can be used to provide data for many types of testing, such as functional, integration, and performance testing.

Effective test data management helps companies avoid storing too many copies of test data. As a result, data management becomes less complex.

#5 Better Data Coverage

Test Data Management makes it easier to link test data to test cases and, ultimately, requirements. This gives you a bird's-eye view of test data coverage and defect patterns.

Test Data Management Challenges

#01 Lack of Integration

The great majority of tools were built to support waterfall methodologies and do not work well with regular integration and deployment technologies, which are required for Agile projects. Testing organizations are currently experiencing problems integrating test data management with automation, service virtualization, and performance testing frameworks due to a lack of integration support via APIs or Plugins.

#02 Complexity and lack of expertise

The bulk of testing technologies on the market require specialized training and knowledgeable personnel.The problem is exacerbated by the lack of test data management competence among software testers.

#03 Centralized Test Data Management Approach

In many organizations, the test data administration function is handled by a centralized team separate from the Agile Sprint and DevOps teams. The integrated team must cater to the needs of various sprint teams due to the large volume of test data requests, which frequently result in longer data provisioning processes. As a result, DevOps and Agile teams are unable to fully exploit the benefits of continuous integration and testing.

#04 Heterogeneous Data Sources

Advancements in technology architectures have necessitated the provision of data masking or synthetic data synthesis of organized and unstructured test data. For operations such as end-to-end testing, teams must also have a mechanism in place to assure the referential integrity of data delivered to testing environments. In today's world, businesses want to reduce time to market and provide developers with more timely feedback on the quality of their applications. A simple test data management approach and the right mix of technologies are required to realize the benefits of Agile/DevOps, satisfy regulatory standards, and improve the overall user experience.

#05 Time Wasted

Rather than testing, the testing team spends a significant amount of time conversing with solution architects, database administrators, and business analysts.

Test Data Management Techniques

#01 Validating your test data

Data can even be acquired from actual users in today's world, when organizations adopt agile methods. This information is mostly gathered through the application, which is used to generate and analyze test data before being used by QA teams to run test cases. As a result, we must protect the test data from any development process breaches in order to prevent the exposure of sensitive personal data such as names, addresses, financial information, and contact information. This test data can then be replicated to create a realistic environment, which can affect the final results. For testing apps, accurate data is required, which is taken from production databases and then masked to secure the information. When the app goes live, it's critical that the test data is validated and that the created test cases accurately reflect the production environment.

#02 Exploring the Test Data

Data comes in a variety of shapes and sizes, and it might be scattered across several systems. Depending on their requirements and test scenarios, individual teams must hunt for acceptable data sets. As a result, it's vital to find the right data in a timely manner and in an acceptable format. This stresses the importance of a good test management solution capable of handling end-to-end business requirements for application testing. Obviously, manually searching for and obtaining data is a time-consuming task that might affect the efficiency of the operation. As a result, implementing a test data management solution that allows for practical coverage analysis and data visualization is difficult. Exploring and analyzing the data sets in greater depth is critical for developing an effective Test Data Management strategy.

#03 Building reusable Test Data

For ensuring cost-effectiveness and optimizing testing efforts, reusability is essential. We must build and segment test data in order to make it more reusable. It should be accessible through a central repository, with the purpose of maximizing the value of earlier efforts as much as feasible. It is vital to remove bottlenecks and difficulties from the data in order to make it reusable. Finally, no effort is spared in resolving any unknown data issues. Data sets are saved as reusable assets in a single repository and disseminated to the appropriate teams for use and validation. As a result, the test data is easily available for the quick and convenient generation of test cases.

#04 Automation will enhance the process

Scripting, data masking, data generation, cloning, and other aspects of test data management are all aspects of test data management. All of these actions could be incredibly effective if they were automated. It will not only expedite the process but also make it more efficient. Test-data is associated with a specific test throughout the Data Management process, and this data can then be fed into an automation tool, which assures that the data is delivered in the right format whenever it is required. Automating the development and testing processes ensures that the test data is of high quality. Test data generation can be automated, just as regression testing or any other sort of periodic test. It helps create a production environment for testing by simulating high traffic and a large number of users for an application. It saves time in the long run, reduces effort, and aids in the continual disclosure of data mistakes. To save time and resources in the testing process, data extraction software may greatly help by automating the document processing workflow. This type of software validates the extracted data. This is especially useful in test data management as it ensures that the data used for testing is accurate and reliable. In addition, it can be easily integrated with the client's ecosystem, whether it is cloud, on-premises, or hybrid.

#05 Encryption/Decryption

Data is encrypted with special characters, and formatting is removed, rendering the database unreadable. Decryption keys allow data to be read again.

Test Data Management Strategies

#01 Identify sensitive data and protect it

In order to properly test apps, a large amount of highly sensitive data is frequently required. A common option is a cloud-based test environment, which enables for on-demand testing of diverse commodities. Even something as basic as preserving user privacy in the cloud, however, is cause for concern. As a result, we must establish a technique to hide sensitive data, particularly in situations where we will need to duplicate the user experience. The amount of test data used has a big impact on the mechanism.

#02 Analysis of data

In general, test data is generated based on the test cases that are run. For example, in a system testing team, the end-to-end test scenario must be specified before the test data can be designed. It may be necessary to utilize one or more programs to accomplish this. The management controller application, middleware apps, and database applications, for example, must all operate together in a solution that controls workloads. For this, we'll need to spread the relevant test data. To accomplish successful management, we must conduct a thorough review of all forms of data.

#03 Determination of the Test Data clean-up

Based on the testing needs in the current release cycle, the test data may need to be updated or produced as indicated in the preceding point (where a release cycle can span a lengthy time). Although this test data is not immediately useful, it may be required in the future. As a result, a method for deciding when test data can be cleaned up should be devised.

#04 Automation

It is probable to automate the creation of test data in the same way that we automate the execution of repeated tests or the execution of the same tests with different data types. This would help to expose any data issues that might arise during testing. This can be done by comparing the data gathering results from future test runs. Then, make the comparing process automatic.

#05 Data setup to mirror production environment

This is a continuation of the previous stage that helps you to see what the end-user or production situation will be like and what data will be required. Utilize the information and compare it to the information currently accessible in the test environment. This new information may necessitate the development or change of further information.

#06 Determination of the Test Data clean-up

Based on the testing needs in the current release cycle, we may need to alter or develop the test data in the preceding point (where a release cycle can span a lengthy time). Although this test data is not immediately useful, it may be required in the future. As a result, a method for deciding when test data can be cleaned up should be devised.

Test Data Management Framework

#01 Effective sharing and testing environment

One of the major challenges with test environment preparation, as previously stated, is that many teams or people must access the same set of resources for testing purposes. As a result, a suitable sharing mechanism must be designed that fits the needs of all organizations and personnel while not delaying deadlines. This can be performed by maintaining a repository or information link that has information on who is using the environment and when it is available for usage. By proactively detecting where there is a strong demand for resources vs. the limited supply of those resources, a significant amount of turmoil is automatically eliminated. The second step is to review the teams' resource needs for each testing cycle and determine which resources are underutilized.

#02 Virtualize wherever possible

This is especially significant when testing is required in a shared environment, where resource efficiency is critical. In such circumstances, the solution is to test in a virtualized environment, such as the cloud. All testers need to do is offer an instance, which will create an autonomous Test Bed or Test Environment with all the resources needed for testing, such as a specialized OS, database, middleware, automation frameworks, and so on. We can eliminate the instances once the testing is over, lowering the organization's costs dramatically. Functional verification testing and automated testing benefit greatly from cloud environments.

#03 Keeping track of any outages

An organization, like every other team that owns a test environment, has all possible test environments maintained by global support workers. Furthermore, in the event of firmware/software upgrades, teams responsible for their test environment have their own local downtime. Global teams must ensure that all environments meet the most recent requirements, which could include power outages or network outages. As a result, those in charge of maintaining the test environment must keep an eye out for any potential disruptions and alert the test team in advance so that they may plan properly.

#04 Regression/Automation Testing

When new functions and features are developed, we should perform regression testing for these functions at the end of each release cycle. As a result, while regression testing environments appear to be using the same test configuration with the same data, they are constantly evolving as new features are added to each release. During each product release cycle, we must do one or more rounds of regression testing. As a result, establishing regression test environments for each product release cycle and reusing them throughout the process would demonstrate the test environment's dependability.

Because automation implies that the environment is stable, using automation frameworks and employing automation for regressive testing can help improve the productivity of a test environment. Enterprise Test Data Management Software Testing Requirements Due to data sizes as large as production, IT organizations spend 30% of their time and effort dealing with issues connected to testing data management, in addition to expensive test environment CAPEX and support expenses. Organizations that use live data for testing face compliance, regulatory, and customer confidence issues because there is no clear, consistent, and repeatable approach for providing test data that is fit for purpose and delivers better test coverage. 73% of DBAs have full access to all data, increasing the risk of a data breach. According to 50% of respondents, data has been hacked or stolen by a malicious insider, such as a privileged user.

As a result, TDM's key industry drivers are as follows:

  • Managing test data requests
  • Data synchronization and standardization
  • Regulations and follow-through
  • Data breaches, as well as threats to data privacy
  • Data storage is expensive.

Best Practices for Test-Data Management

#01 Data delivery

Duplicating production data for development or testing is a labor-intensive and time-consuming process that regularly falls behind demand. Organizations must devise a solution that streamlines this process and establishes the foundation for quick, repeatable data delivery.Leaders of application development teams should look for solutions that include the following features:

Automation: Modern software toolkits now include technology to automate build processes, infrastructure delivery, and testing, among other DevOps capabilities. Organizations, on the other hand, usually lack analogous mechanisms for distributing test data copies with the same level of automation.

A streamlined TDM strategy, on the other hand, lowers manual processes such as target database initialization, setup phases, and validation checks, resulting in a low-touch method of setting up new data environments.

Integration of toolsets: Masking, subsetting, and synthetic data generation are just a few of the technologies that should be included in a successful TDM strategy. We need both test-data tool compatibility and open APIs to enable a factory-like approach to TDM.

Self-service: Rather than relying on IT ticketing systems, an advanced TDM technique employs suitable levels of automation to allow end-users to self-serve test data. Control over test data versioning and data distribution should be included in self-service capabilities. Without contacting operational teams, developers or testers should be able to bookmark and reset, archive, or share test data.

#02 Data Quality

Operations teams go to great pains to guarantee that software development teams have access to the proper test data types, such as masked production data or produced datasets. TDM teams must strike a balance between the needs for various types of test data while also ensuring data quality on three fronts:

Data aging: Operations teams are frequently unable to manage a high volume of ticket requests due to the time and effort required to prepare test data. As a result, data in non-production environments generally becomes stale, compromising testing quality and resulting in costly late-stage failures. The time it takes to refresh an environment should be reduced with a TDM method, allowing access to the most recent test data.

Data accuracy: A TDM technique may become complicated when we need several datasets at a specific moment for systems integration testing. To evaluate a procure-to-pay process, for example, data from customer relationship management, inventory management, and financial systems may need to be federated. Using a TDM approach, several datasets should be delivered at the same time and simultaneously reset between test cycles.

Data size: Developers frequently have to work with subsets of data that are unlikely to meet all functional testing criteria due to storage constraints. Due to data-related inaccuracies, subsets can result in missed test case outliers, increasing rather than decreasing project expenses. By sharing standard data blocks between copies, an ideal technique allows for the supply of full-size test data copies at a fraction of the space required for subsets.

#03 Data security

Masking tools have shown to be an effective and dependable method of protecting test data. By permanently replacing sensitive data with fictional but accurate values, masking assures regulatory compliance, eliminates the risk of data breaches in test environments, and aids in insider risk management. Businesses must, however, consider the following characteristics in order for masking to be feasible and effective:

Complete solution: Because they lack a complete solution with out-of-the-box capabilities for detecting sensitive data and auditing the trail of masked data, many firms fail to mask test data effectively. Furthermore, a good approach should consistently hide data while maintaining referential integrity across a variety of sources.

There is no requirement for development expertise:Organizations should explore lightweight masking tools that may be set up without scripting or expert development experience.

Integrated masking and distribution: Only around one out of every four businesses uses masking solutions due to the difficulty in supplying data downstream. To address this issue, masking operations should be tightly coupled with a data-delivery system.

Many businesses will also benefit from a technology for masking data in a secure zone. And then send that secured data to non-production targets like overseas data centers or private or public clouds.

#04 Infrastructure costs

TDM teams must design a toolset that makes the greatest use of infrastructure resources as test data continues to grow.The following requirements should be met by a TDM toolbox in particular:

Data consolidation: It's unusual for businesses to have non-production settings that include 90% redundant data. A TDM strategy should aim to reduce storage costs by exchanging common data across environments, including those used for reporting, development,production support, and other purposes.

Data archiving: A TDM approach should make it possible to save libraries of test data by decreasing storage usage and enabling speedy retrieval. In the same way that code versioning tools like Git exist, data libraries should be automatically version-controllable.

Environment utilization: Most IT organizations serialize projects due to competition for environments. At the same time, we employ environments because populating an environment with sufficient test data takes time.

As a result, a TDM system should use "bookmarking" to intelligently isolate data from CPU resource blocks. The datasets can exist at any moment, and we save them as bookmarks so that we can import them into environments as needed. As a result, a good TDM approach can reduce conflict while also increasing environmental consumption by up to 50%.

Test-grid Test Data Management Services

Top features of Test-Grid Test Data Management Services

  • Test Data Request Management.
  • Synthetic Data Generation.
  • Robust Data Search and Data Reservation.
  • Data Subset and Masking.
  • Self Service Portal.
  • Jenkins Integration to support CI or CD, DevOps methodologies.

Benefits Of TestGrid Test Data Management Services

  • A data request that is made on demand.
  • Refreshing and publishing on a time basis.
  • Real-time email alerts and a dashboard that shows the status of test data requests by module.
  • Data must be reserved in order to avoid re-use.
  • The TDM process is sped up by providing a user-friendly, comprehensive, and integrated workbench.

Conclusion

In order to provide value-add in functional testing, increasing data coverage is critical. However, because of the large amount of test data that we utilize in regression suites on a regular basis, it is a critical emphasis area in terms of ROI. The right TDM solutions can help you provide a wide range of data while maintaining a consistent ROI across each cycle. Because we can generate large numbers of identical data fast and efficiently, TDM can bring immediate benefits and reveal big improvements in performance testing programs. TDM, in conjunction with automation testing solutions like TestGrid.io, will undoubtedly benefit and improve your project significantly. It will cost you a lot of money and take a long time if you don't automate it.