What Is Test Data Management Implementation?

Test Data Management Concept, Process, And Strategy

The function that develops, manages, and delivers test data to application teams is known as test data management (TDM). Historically, application teams have created data in a segregated, unstructured manner for development and testing.

Table of content:

1. What is Test Data?
2. What is Test Data Management (TDM)?
3. Current State of Test Data Management
4. How Test Data Management works?
5. What are the benefits of Test Data Management?
6. Common Test Data Challenges
7. Common Types of Test Data

• Why Does Test Data Management matter?
• How to adapt Test Data Management (TDM)?
• What are the best practices of Test Data Management?
• What are the Best Tools for Test Data Management (TDM)?
• Conclusion    

What is Test Data?

The data used to test the software application is known as test data. For example, usernames and passwords are necessary to test the login feature. As a result, the username and password values are test data. The purpose of this article is to provide an overview of Test Data Management. There are two types of test data.

1. Static Data
2. Transactional Data 

Names, currencies, countries, and other non-sensitive data are examples of static data. However, when it comes to transactional data, such as credit/debit card numbers, bank account information, or your medical history, there is always the possibility of the information being stolen.

What is Test Data Management (TDM)?

Test Data Management is a way for meeting the test data needs of testing teams by including high-quality data in the appropriate quantity and format. To enhance testing efforts and maximize return on investment for the highest levels of success and coverage, efficient management of data used for testing is required.

Current State of Test Data Management

Every organization must bring high-quality applications to market at an increasingly competitive rate in today’s digital era. While many firms have adopted agile and DevOps approaches to achieve this aim, many have underinvested in test data, which has become a bottleneck in the race to innovate. The TDM market has evolved to a new set of strategies, owing to a greater emphasis on application uptime, shorter time-to-market, and reduced costs. TDM, like other IT projects like DevOps and cloud, is fast developing.

 Test data management (TDM), formerly considered a back-office function, is now a vital business enabler for organizational agility, security, and cost efficiency. As the number of application projects grows, many large IT organizations are realizing the value of consolidating TDM functionalities into a single group or department, allowing them to use innovative tools to create test data and operate more efficiently than siloed, decentralized, and unstructured TDM teams. TDM’s scope has since evolved to include the use of sub setting and synthetic data generation, as well as the most recent application of masking to modify production data, as increasing centralization has begun to offer considerable efficiency improvements.

How Test Data Management works?

Software testing is aided by four key TDM techniques.

1. Exploring the test data.
2. Validating test data.
3. Building test data for Reusability.
4. Automating TDM tasks to accelerate the testing process.

Exploring the Test Data

Data might exist in a variety of forms and formats, and it can also be shared across several systems. The relevant team must look for the appropriate test data sets based on their requirements and test cases. It’s vital to find the right data in the right format within the time constraints. As a result, there is a greater need for a strong Test Management tool that can manage end-to-end business requirements for application testing. It is obvious that manually seeking and retrieving data is a time-consuming operation that could reduce the process’ efficiency. As a result, it’s critical to implement a Test Data Management solution that allows for relevant coverage analysis and data visualization.

Validating the Test Data

Data can be acquired from genuine users in the current context, where associations are embracing agile approaches. This data is largely obtained through the application, which continues to be used as a practice for creating and exploring test data, which is then used by QA teams to run test cases (test runs). As a result, the test data must be protected against any break in the development process that could expose sensitive personal data such as names, contact information, financial information, and addresses. This test data can also be encouraged to create a realistic setting, which can influence the results even more. For testing apps, real data is required, which is collected from production databases and then masked to protect the data. When the application goes live, it’s critical that the test data is validated and the test cases that result provide a true image of the production environment.

Building Test Data for Reusability

To ensure cost-effectiveness and maximize testing efforts, reusability is critical. The goal should be to make the most of it and maximize the value of the work that has already been done. It should be getting information from a central location. Finally, no time was lost fixing any undiscovered data concerns. Datasets are saved in the central repository as reusable assets and distributed to the appropriate testing teams for further use and validation.

Automation can Accelerate the Test Data Process

Scripting, test data generation, data masking, cloning, and provisioning are all aspects of test data management. All of these activities could be automated with success. It will not only speed up the process, but it will also make it far more efficient. During the Management process, the test data is associated with a specific test, which may then be fed into a test automation tool that verifies that the data is delivered in the appropriate format at any time.

 During the development and testing phase, automating the process ensures the quality of the test results. Even the generation of test data is automated (automated testing), just as Regression Testing or any other common test. It aids in the creation of a production scenario for testing by simulating massive activity and a large number of users for an application. It saves time in the long term, decreases effort, and aids in the detection of data errors on a continuous basis. The QA team would eventually be in a better position to streamline and evaluate their test data management activities.

What are the benefits of Test Data Management?

The following are some of the advantages of test data management:

• Improve the quality of your program so that it can be deployed with confidence.
• Prevents bug fixes and rollbacks.
• Makes the software development process more cost-effective.
• Reduces the organization’s risk of noncompliance and security.
• Customized test data for several types of testing, including functional testing, integration testing, performance testing, and security testing.
• As a result, no test data is overstepped by numerous testing teams.
• The ability to trace test data back to test cases and business requirements aids in understanding test coverage and defect patterns.
• By granting insights-driven permissions, it builds partnerships and efficiencies.
• Decision-making at all levels of the company.
• The data refresh cycle has been shortened.

Common Test Data Challenges

Application development teams require quick, dependable test data for their projects, but many are limited by the speed, quality, security, and costs of transporting data across SDLC environments. The most typical issues that businesses confront with managing test data are listed below.

Test environment provisioning is a slow, manual, and high-touch process

Most IT firms use a request-fulfillment model, which means that developers and testers have to wait for their requests to be fulfilled. Because creating a replica of test data requires so much time and work, provisioning new data for a test environment might take days, if not weeks. The amount of time it takes to turn around a new setting is frequently proportional to the number of people engaged. When it comes to setting up and supplying data for a non-production environment, most businesses have four or more administrators involved. This test data management strategy(TDM strategy) not only puts a strain on operations teams, but it also adds time drains to test cycles, impeding application delivery.

Development teams lack high-fidelity data

Testing data that is fit for purpose is often unavailable to development teams. A developer, for example, may want a data set as of a given moment in time, depending on the release version being tested. However, due to the difficulty of refreshing a testing environment, he or she is frequently compelled to operate with a stale copy of data. This can lead to lost productivity as time is spent fixing data-related difficulties, as well as an increased likelihood of data-related errors making their way into production.

Data masking adds friction to release cycles

Data masking is essential for many applications, such as those that process credit card numbers, patient records, or other sensitive information, to ensure regulatory compliance and safeguard against data breaches (data privacy). The average cost of a data breach is $3.92 million, which includes the costs of cleanup, customer attrition, and other losses. Masking sensitive data, on the other hand, typically adds operational overhead; an end-to-end masking process might take up to a week, which can cause testing cycles to be delayed.

Storage costs are continually on the rise

Multiple, redundant copies of test data are created (data creation) by IT firms, resulting in insufficient storage usage. Operations teams must manage test data availability across numerous teams, apps, and release versions to satisfy concurrent requests within the constraints of storage capacity. As a result, development teams frequently compete for restricted, shared environments, causing essential application projects to be serialized.

Common Types of Test Data

There is no single technology that can meet all TDM requirements. Instead, teams must provide an integrated solution that includes all of the data types needed to support a wide range of testing requirements. Following the identification of test data requirements, a successful TDM method should attempt to supply the right types of test data, evaluating the benefits and drawbacks of each. The most comprehensive test coverage is provided by production data, but it usually comes at the tradeoff of agility and storage costs. It can also entail exposing sensitive data in some cases. Full copies of production data are substantially less agile than subsets. They can help you save money on hardware, CPU, and license, but getting enough test coverage might be tough.

Masked production data (full sets or subsets) allows development teams to use genuine data without exposing themselves to unnecessary risk. Masking procedures, on the other hand, can cause environment provisioning to take longer. Masking also necessitates additional storage and personnel in staging environments to ensure referential integrity after data has been changed.

Synthetic data solves security problems, but it saves just a little amount of space. While synthetic data may be required to evaluate new features, it accounts for a small portion of test cases. Creating test data by hand is prone to human mistake and necessitates a thorough understanding of data linkages both inside the database schema or file system and those inherent in the data itself.

Why Does Test Data Management Matter?

It is critical to have high-quality test data. Many issues can develop once an application is placed into production if it is tested against generic data. To avoid difficulties as close as possible to the actual data that will be utilized, applications must be carefully tested against data.

Data and Continuous Delivery

The fundamentals of Continuous Delivery – Test Coverage, Automation, and Continuous Testing – all require accurate, relevant, and high-quality data. With quality data, flaws can be discovered earlier in the development life cycle, resulting in a lower cost fix and a lower risk of bugs in production. If the end-product fails owing to poor data quality, testing and QA will fail as well. Unhappy customers complain to potentially millions of people on social media and switch to another brand, bringing their friends, family, and followers with them. Incredible data cleanliness, on the other hand, improved security and streamlined data management, resulting in improved Customer Experience (CX), Digital Happiness, customer loyalty, a better brand identity, and increased revenue.

Data Regulations

Another clear benefit of mastering data, not just for the test but for the entire company. To drive effective Decision-Making, the benefits include minimizing the risk of heavy fines, increasing income by leveraging quality data, and reducing the danger of security breaches.

How to Adapt Test Data Management (TDM)?

The vital phases involved in a TDM process (test data management process) are:

1. Planning
2. Analysis
3. Design
4. Build
5. Maintenance

Phase	Steps Involved
Planning	1. Assign a Test Data Manager (TDM) and set data management requirements and templates. 2. Create documentation, including a set of tests and a reference to the data landscape. 3. Set up the test data management team and establish a service level agreement. 4. Appropriate plans and paperwork have been signed off on.
Analysis	1. Data profiling for each datastore assignment/recording of version numbers for existing data in all settings are part of the initial setup and sync exercises. 2. Data requirements collection/consolidation 3. Refresh your project lists. 4. Examine the data needs as well as the most recent distribution log. 5. Check for gaps and the impact of data changes. 6. Establish policies for data security, backup, storage, and access. 7. Write up reports.
Design	1. Decide on a data preparation strategy and identify places where data has to be loaded/refreshed. 2. Determine the best methodology, data sources, and providers. 3. Determine the tools you’ll need. 4. Plans for data distribution. 5. A plan for coordination and communication. 6. Create a test activity plan. 7. Create a data plan document.
Build	1.Compile plans and, if necessary, masking/de-identification. 2. Data backups and log updates
Maintenance	1. Assist with change requests, unanticipated data requirements, and problems/incidents. 2. Prioritize requests and analyze needs to see if they can be met with current/modified data, including data from other projects. 3. Requires data alteration and new data backup. 4. Assign version marks and add an explanation to the log. 5. Evaluate the current state of ongoing projects. 6. Exercising using data profiles 7. Identify and resolve any deficiencies. 8. Update data as needed. 9. Establish a maintenance schedule and convey it to all parties involved. 10. Redirect requests if necessary. 11. Documentation and reports.

What are the Best Practices of Test Data Management?

The following are some best practices for managing test data:

• Unless it’s your last alternative, never utilize Excel as a test data source for automation.
• Data from tests should be externalized.
• Discover and comprehend the test results.
• Wherever possible, automate the generation of unique precondition data for each automation run.
• Take into account all test environments.
• Localization+Environment is an approach that combines the two.
• Sensitive test data should be masked or de-identified.

What are the Best Tools for Test Data Management (TDM)?

Some of tools for Test Data Management include:

1. Informatica Test Data Management tool (TDM tool).
2. CA Test Data Manager (Datamaker).
3. Compuware’s Test Data Management.
4. Tarantula
5. InfoSphere Optim Test Data Management. 
6. HP Test Data Management.
7. LISA Solutions for Test Data Management.

Additional Information on Test Data Management 

The following types of data should be covered in general when creating test cases:

1. Positive Path data: This is the data that is generally in sync with conducting the positive path scenarios, using the development use case paper as a guide.
2. Negative Path data: It is data that is commonly regarded as “invalid” in terms of the code’s right functional operation.
3. Null Data: Providing no data when an application or code requires it.
4. Data in an Illegal Format: Determining the code’s performance when data is supplied in an illegal format.
5. Data for Boundary Conditions: Test data from an index or array to see how well the code operates.

• Infrastructure costs: TDM teams must develop a toolset that maximizes the optimal use of infrastructure resources in light of the fast growth of test data (archiving test data and lowering storage costs)
• Since they don’t rely on external dependencies, unit tests are often less expensive to write and run. Unit tests, on the other hand, are not representative of how a real user interacts with the application.