The industry is always looking for ways to improve testing, and one such area is test data management (TDM). It is significant since test data quality primarily determines testing completeness and coverage.
However, it is well-known that testing assurance is impossible to achieve without high-quality data.
However, excluding TDM steps from the testing life cycle frequently leads to TDM ignorance on the side of the software development team.
We can discover he best data can be discovered in production; they are the actual entries used by the application. When working with production data, it is always good to construct a subset of the data. This decreases the effort required for test preparation and execution and aids in optimization.
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What Is Test Data Management?
The process of planning, designing, storing, and maintaining software quality-testing methods and methodologies is known as test data management.
It gives the software quality and testing team authority over the data, files, rules, and policies generated throughout the software testing life cycle.
Software test data management is another name for test data management.
In a nutshell, Test Data is the information that we offer in our application in order to test it. Every project or firm stores or saves test data in excel sheets so that QA teams can use it for manual execution or execute automated tests, which will be valuable for future references.
Benefits Of Test Data Management You Must Know
#01 Customer Satisfaction
The TDM method has several advantages, the most important of which are high data quality and extensive data coverage. When these characteristics are present during the testing phase, bugs we can discover early.
As a result, the application is stable and of excellent quality, with few manufacturing flaws.
When a customer sees such attractive benefits from using a TDM procedure, his or her belief in the firm grows and thus provide much better customer satisfaction to the customer.
#02 Saves Cost
The most valuable characteristic of the TDM is that we can reuse it, which leads to cost reduction. The reusable data is identified and saved in a centralized repository for future use. When the need for reusable data arises, the testers can refer to the archived data.
Because there is more test data coverage and traceability, the picture becomes clearer at an early stage, which aids in the early detection of faults and reduces the cost of production corrections.
#03 Data Regulation
Another clear advantage of mastering data, not just for the test but for the entire organization. The advantages include lowering the danger of heavy fines, increasing income by exploiting quality data and lowering the chance of security breaches in order to drive effective decision-making.
With data privacy rules such as the GDPR test, data management becomes more crucial since it assists firms in complying with regulations through compliance analysis and data masking strategies.
#04 Data is managed efficiently
A TDM process is distinguished because data is controlled in a single location. Data for several types of testing, such as functional, integration, and performance testing, can be given from the same repository.
Effectively managing test data assists firms in avoiding the storage of too many copies of test data. As a result, the complexity of data management is reduced.
#05 Better Data Coverage
Test Data Management aids in the traceability of test data to test cases and, ultimately, to requirements. This gives you a birds-eye perspective of the test data coverage and defect trends.
Read also: Test Case Management: Complete Guide
Test Data Management Challenges
#01 Lack of Integration
The vast majority of tools were designed to support waterfall approaches and do not interact well with continuous integration and deployment technologies, which are a must for Agile projects.
Due to a lack of integration support via APIs or Plugins, testing organizations are currently having difficulty integrating test data management with automation, service virtualization, and performance testing frameworks.
#02 Complexity and lack of expertise
The majority of testing technologies on the market necessitate specific training and experienced resources. The lack of test data management knowledge within the software testing community exacerbates the problem.
#03 Centralized Test Data Management Approach
In many businesses, the centralized team owns the test data management role separate from the Agile Sprint and DevOps teams.
Due to the enormous volume of test data requests, the centralized team must cater to the needs of numerous sprint teams, which frequently results in longer data provisioning processes.
As a result, Agile and DevOps teams are unable to reap the full benefits of continuous integration and testing processes.
#04 Heterogeneous Data Sources
The requirement to provide data masking or synthetic data synthesis of structured and unstructured test data has resulted from advancements in technological architectures.
Teams must also have a process to ensure the referential integrity of data sent for testing environments for activities such as end-to-end testing.
In today’s world, corporations want to minimize time to market and provide developers with faster feedback on application quality.
To maximize the benefits of Agile/DevOps, meet regulatory standards, and improve the overall user experience, a straightforward test data management strategy and the correct set of technologies are necessary.
#05 Time Wasted
The testing team spends a reasonable amount of time discussing back and forth with solution architects, database administrators, and business analysts rather than testing.
Read also: Automation Testing: The Beginner’s Guide To What, Why, & How?
Test Data Management Techniques
#01 Validating your test data
In today’s world, where firms are using agile processes, data can even be sourced from actual users.
This data is mainly obtained through the application, which is used to produce and explore test data, which is then used by QA teams to run test cases.
As a result, We must safeguard the test data against any breach in the development process, to stop the exposure of any sensitive personal data such as names, addresses, financial information, and contact information
This test data can then be recreated to create a real-world setting, which can have an impact on the final outcomes.
Accurate data is necessary for testing apps, which is obtained from production databases and then masked to protect the data. When the application goes live, it is vital that the test data is validated and the generated test cases provide an actual image of the production environment.
#02 Exploring the Test Data
Data is found in many different forms and formats, and it can also be dispersed across multiple systems.
Individual teams must look for acceptable data sets depending on their requirements and test scenarios.
Therefore, it is critical to locate the appropriate data in the acceptable format and within the time constraints.
This emphasizes the need of having a good test management solution that can handle end-to-end business requirements for application testing.
Obviously, manually seeking and retrieving data is a time-consuming activity that may reduce the process’s efficiency.
As a result, it is challenging to execute a test data management solution that ensures practical coverage analysis and data visualization.
Exploring and analyzing the data sets further is crucial for establishing a successful Test Data Management approach.
#03 Building reusable Test Data
Reusability is critical for assuring cost-effectiveness and optimizing testing efforts. To make test data more reusable, we need to build and segment it.
It should be available from a central repository, and the goal should be to use it as much as possible to maximize the value of previous work.
To make the data reusable it is necessary to eliminate bottlenecks and issues within the data. Finally, no effort is lost in fixing any unseen data concerns.
Data sets are saved in the central repository as reusable assets and distributed to the appropriate teams for further use and validation.
As a result, the test data is readily available for the creation of test cases in a timely and convenient manner.
#04 Automation will enhance the process
Scripting, data masking, data generation, cloning etc., are all aspects of test data management. Automation of all of these activities has the potential to be highly effective. It will not only speed up the procedure, but it will also be a lot more efficient.
During the Data Management process, test data is associated with a specific test, which may then be fed into an automation tool, which ensures that the data is given in the expected format whenever it is necessary.
During the development and testing processes, automating the process ensures the quality of the test data.
Production of test data, like regression testing or any other type of periodic test, can be automated.
It assists in simulating tremendous traffic and a large number of users for an application in order to generate a production situation for testing.
It saves time in the long term, decreases effort, and helps disclose any data errors on an ongoing basis.
Encrypts data with special characters and deletes formatting, rendering the database unreadable. Decryption keys restore data to its readable state.
Read also: Codeless Automation Testing: Getting Started
Test Data Management Strategies
#01 Identify sensitive data and protect it
Many times, a substantial volume of highly sensitive data is required in order to adequately test apps. A cloud-based test environment, for example, is a popular alternative because it allows for on-demand testing of various goods.
However, even something as simple as ensuring user privacy in the cloud is a reason for concern.
So, especially in circumstances when we will need to reproduce the user environment, we must identify the strategy to hide sensitive data.
The mechanism is heavily influenced by the amount of test data used.
#02 Analysis of data
In general, test data is created depending on the test cases conducted.
In a system testing team, for example, the end-to-end test scenario must be identified, based on which the test data is designed. This could necessitate the use of one or more programmes.
For example, in a solution that manages workloads, the management controller application, the middleware apps, and the database applications must all work in tandem.
We need to disperse the necessary test data for the same. To achieve effective management, we need to perform a complete examination of all types of data is necessary
#03 Determination of the Test Data clean-up
The test data may need to be adjusted or developed as described in the preceding point based on the testing requirements in the current release cycle (where a release cycle can span a lengthy time).
Although this test data is not immediately relevant, it may be necessary at a later date. As a result, a clear process for determining when test data can be cleaned up should be developed.
It is viable to automate test data creation in the same way we use automation to run repetitive tests or the same tests with different data types.
This would aid in exposing any data problems that may occur during testing. We can accomplish this by comparing the results of data collection from the subsequent test runs. Then, automate the comparison process.
#05 Data setup to mirror the production environment
This is an extension of the preceding stage that allows you to grasp what the end-user or production situation will be and what data is necessary for it. Use the data and compare it to the data that is currently available in the current test environment. This new data may need the creation or modification of further data.
#06 Determination of the Test Data clean-up
We may need to adjust or develop the test data accordingly in the preceding point based on the testing requirements in the current release cycle (where a release cycle can span a lengthy time).
Although this test data is not immediately relevant, it may be necessary at a later date. As a result, a clear process for determining when test data can be cleaned up should be developed.
Read also: Regression Testing: Complete Guide
Test Data Management Framework
#01 Effective sharing and testing environment
As previously stated, one of the significant issues of test environment preparation is that numerous teams or individuals must access the same set of resources for testing reasons.
As a result, it is mandatory to design a suitable sharing mechanism that meets the demands of all groups and personnel without delaying schedules.
This can be accomplished by keeping a repository or information link where all data about:
Who is utilizing the environment, when the environment is available for use
A considerable portion of chaos is automatically eliminated by proactively detecting where the demand for resources is high vs the restricted availability of those resources.
The second component of this is to go over the teams’ resource requirements for each testing cycle and see which resources are underutilized.
#02 Virtualize wherever possible
This is especially important when testing must be done in a shared environment; there is a pressing need for resource minimization. In such cases, the answer is to use a virtualized environment, such as the cloud, for testing reasons.
When adopting such an environment, all testers need to do is give an instance, which, once provided, will constitute an autonomous Test Bed or Test Environment with all the different resources required for testing, such as a dedicated OS, database, middleware, automation frameworks, and so on.
Once we complete the testing we can destroy the instances which significantly lower the organization’s costs. Cloud environments are very beneficial for functional verification testing and automated testing.
#03 Keeping track of any outages
Like any team that owns a test environment, an organization has all conceivable test environments maintained by global support staff.
Furthermore, just as teams responsible for their test environment have their own local downtime in the event of firmware/software upgrades.
Global teams must verify that all environments adhere to the most recent standards, which may include power or network disruptions.
As a result, individuals responsible for maintaining the test environment must keep an eye out for any such disruptions and; notify the test team ahead of time so that they may manage their work accordingly.
#04 Regression/Automation Testing
We should do regression testing for these functions for each release cycle when the development of new functions and features happens.
As a result, while the test environments for regression testing appear to be operating on the same test setup with the same data, they are constantly evolving with each release it integrates the new feature.
We must perform one or more rounds of regression testing during each product release cycle.
Thus, creating regression test environments for each product release cycle and reusing them throughout the process would demonstrate the test environment’s reliability.
Creating automation frameworks and using automation for regressive testing also aids in improving the efficiency of a test environment because automation assumes that the environment is stable.
Drivers of Enterprise Test Data Management Software Testing Needs
IT firms spend 30% of their time and effort handling difficulties related to testing data management, in addition to expensive test environment CAPEX and support expenses due to data size as large as production.
While there is a lack of a defined, consistent and repeatable method that provide test data that fit for purpose and provides enhanced test coverage, organizations who use live data for testing subject themselves to compliance, regulatory, and consumer confidence risks:
73 per cent of DBAs have access to all data, raising the likelihood of a breach.
Data has been hacked or stolen by a malevolent insider, such as a privileged user, according to 50% of respondents.
As a result, the following are the primary industry drivers of TDM:
- Managing requests for test data
- Standardization and synchronization of data
- Regulations and adherence
- Threats to data privacy, as well as data breaches
- The price of data storage
Best Practices for Test Data Management
#01 Data delivery
Duplicating production data for development or testing is a time-consuming, labour-intensive procedure that frequently lags behind demand. Organizations must develop a solution that streamlines this process and lays the groundwork for fast, repeatable data delivery.
Application team leaders should seek solutions that include the following features:
Automation: Among other DevOps features, modern software toolkits currently contain technology to automate build processes, infrastructure delivery, and testing. On the other hand, organizations frequently lack equivalent methods for sending copies of test data with the same level of automation.
Instead, a streamlined TDM strategy reduces manual operations, including target database initialization, configuration stages, and validation checks, resulting in a low-touch method to set up new data environments.
Integration of toolsets: An effective TDM strategy should bring together a diverse range of technologies, such as masking, subsetting, and synthetic data generation. To enable a factory-like approach to TDM, we need both test data tool compatibility and open APIs.
Self-service: Rather than depending on IT ticketing systems, an advanced TDM method implements appropriate degrees of automation to allow end-users to furnish test data through self-service. Self-service capabilities should include control over test data versioning as well as data delivery. Developers or testers, for example; should be able to bookmark and reset, archive, or exchange test data without involving operational teams.
#02 Data Quality
Operations teams go to considerable lengths to ensure that the appropriate test data forms, such as masked production data or generated datasets, are available to software development teams.
TDM teams must balance requirements for various forms of test data while also ensuring data quality across three major dimensions:
Data ageing: Because it takes time and effort to prepare test data, operations teams are frequently unable to handle a high volume of ticket requests. As a result, data typically grows stale in non-production, affecting testing quality and resulting in costly, late-stage failures. A TDM approach should decrease the time it takes to refresh an environment; allowing access to the most recent test data.
Data accuracy: When we require various datasets at a precise time for systems integration testing, a TDM procedure might become difficult. For example, evaluating a procure-to-pay process may necessitate federating data from customer relationship management, inventory management, and finance systems. Multiple datasets should be provided at the same point in time and simultaneously reset between test cycles using a TDM technique.
Data size: Due to storage limits, developers frequently need to work with subsets of data that are unlikely to meet all functional testing requirements. Subsets can result in missed test case outliers, raising rather than decreasing project costs due to data-related errors. By sharing standard data blocks across copies; provision of full-size test data copies in a fraction of the space of subsets in an optimal technique.
#03 Data security
Masking tools have emerged as an efficient and dependable means of safeguarding test data. Masking ensures regulatory compliance and eliminates the danger of data breaches in test environments by irrevocably replacing sensitive data with fictional but realistic values. However, in order for masking to be practical and effective, businesses need to consider the following requirements:
Complete solution: Many businesses fail to mask test data appropriately because they lack a complete solution with out-of-the-box functionality for discovering sensitive data and subsequently auditing the trail of masked data. Furthermore, an effective technique should consistently hide data while retaining referential integrity across many heterogeneous sources.
There is no requirement for development expertise: Lightweight masking tools that can be set up without scripting or specialist development experience can be considered by organizations.
Tools with rapid masking algorithms; for example, can drastically minimize the complexity and resource requirements that prevent masking from being in use consistently.
Integrated masking and distribution: Because of the difficulties in providing data downstream, only around one out of four enterprises uses masking technologies. Therefore, masking processes should be firmly in line with a data-delivery mechanism to address this difficulty.
Many enterprises will also benefit from a method that enables them to mask data in a safe zone. And, transmit that protected data to targets in non-production environments, such as offshore data centres or private or public clouds.
#04 Infrastructure costs
With the continuous explosion of test data, TDM teams must develop a toolset that best uses infrastructure resources. A TDM toolbox should, in particular, meet the following requirements:
Data consolidation: It is fairly uncommon for enterprises to keep non-production settings with 90 per cent duplicate data. A TDM strategy should attempt to consolidate storage and save costs by sharing common data across environments; not only those used for testing but also those used for development, reporting, production support, and other use cases.
Data archiving: By reducing storage utilization and enabling quick retrieval; a TDM method should make it possible to retain libraries of test data. Data libraries should be automatically version-controllable in the same manner that code versioning tools such as Git exist.
Environment utilization: Due to competition for environments, most IT businesses serialize projects. At the same time, we use environments due to the time required to populate an environment with adequate test data.
Therefore, a TDM system should intelligently employ “bookmarking” to separate data from blocks of CPU resources. The datasets can exist at any time and we bookmark them to import into environments on-demand. As a result, an efficient TDM strategy can remove conflict while increasing environment usage by up to 50%.
Read also: Smoke Testing: Everything You Need To Know
Testgrid Test Data Management Services
Top features of TestGrid Test Data Management Services
- Test Data Request Management.
- Synthetic Data Generation.
- Robust Data Search & Data Reservation.
- Data Subset & Masking.
- Self Service Portal.
- Jenkins Integration to support CI/CD, DevOps methodologies.
Benefits Of TestGrid Test Data Management Services
- An on-demand request of data.
- Time-based publishing & refreshing.
- Real-time Email alerts & Dashboard displaying module wise test data request progress.
- Reservation of data to avoid data reuse.
- Acceleration of the TDM process by offering user-friendly, comprehensive and integrated workbench.
Increasing data coverage is important in offering value-add in functional testing. However, the enormous volume of test data that we use regularly in regression suites; makes it a key focus area in terms of ROI.
The correct TDM tools can assist in providing a wide range of data while ensuring ongoing ROI in each cycle.
TDM use in performance testing projects can provide immediate benefits and highlight major improvements because; we can generate vast volumes of identical data quickly and efficiently.
TDM, along with automation testing tools like TestGrid.io, will surely provide a lot more benefits and improvement in your project. Without automation, it will cost you a lot and will take more time.