Test Data Management Overview

Test Data Management: A Complete Overview

With the large number of businesses shifting from the Waterfall methodology to Agile, the need to deliver a high-quality software application has become quite a challenge for software testing. One of the anticipations for a sound test practice is appropriate to test data. So here, we will see one of the testing elements that revolve around test data: Test data management (TDM). For testing teams and development operations (DevOps) leaders, delivering high-quality data environments at a rapid pace is vital. This post outlines the basics of test data management. 

Table of components:

1. Test data management definition
2. Significance of test data management
3. Benefits of test data management
4. Test data challenges
5. Best practices for test data management (TDM): How to effectively prepare your software test data
6. What is a test data manager
7. The best test data management tools
8. Types of test data
9. Test data management process

Test Data Management 

Test data is any information deployed as an input to perform a test. It can be transactional or static. Static data containing currencies, countries, names, etc., are not sensitive, while data pertaining to Social Security Number (SSN), medical history, and credit card information may be sensitive. In addition to the static data, software testing teams need the right combination of data sets or conditions to test business features and scenarios.

Generally, TDM is the process of fulfilling the test needs of testing teams by making sure test data of the right quality is provisioned in a suitable quantity, proper testing environment, and correct format at the appropriate time. The provisioned data should not be too small to fulfill the testing needs or too large in quantity like production data. Typically this data can be provisioned by either synthetic data creation or production extraction and data masking or sourcing from lookup tables. 

TDM can be adopted efficiently with well-defined processes, proprietary utilities, and manual methods. It can also be put into practice by deploying well-evolved TDM tools like IBM InfoSphere Optim Test Data Management, Informatica Test (and cloud) Data management, Dataprof, CA Test Data Manager, Oracle Test Data Management, and others available in the market. A TDM strategy can be developed based on the type of data requirements in the project.

Why test data management?

• Test data determines the quality of testing: Regardless of how good the testing process is, if the test data utilized is not right or is of inadequate quality, then the whole product’s quality will be impacted.
• Test data needs to be as production: Not only that test data should be of quality, it should be close to production data- because we do not want to build a product or application for three to five months just to fail in the production because there was inadequate real-time data to test.
• Increases efficiency of the process by lowering data-related defects: Due to the accuracy of the test data, data-related defects will reduce enormously, thereby increasing the efficiency of the process.

Significance of test data management 

Test data management is fast gaining importance in the testing industry. Behind increasing interest in Test management are significant financial losses resulting from production defects, which could have been detected by testing the correct test data. 

In the past, test data was restricted to a few rows of data in the database or a few sample input files. Because then, the testing landscape has come a long way. Now companies depend on powerful test data sets with unique combinations giving them high coverage to drive the testing, including negative testing.

Test data management tdm introduces the structured engineering approach to test data requirements of all possible business use cases—large financial and regulatory compliance. Penalties for regulatory non-compliance can run into a lot of money. Obfuscating (data masking) of sensitive information and synthetic data creation are some of the primary TDM services that can assure compliance. Any production data deployed in testing environments must be adequately masked with data privacy. Flaws in this area may result in heavy fines and brand reputation damage in being non-compliant with regulations such as GDPR and CCPA.

Benefits of Test Data Management

The benefits of test data management implementation can be the following:

• It prevents bug fixes and rollbacks.
• Lowers a business’s legal compliance and security risks
• Enhances the testing quality efforts in functional testing/ functional tests, Data Virtualization, Performance Testing/ Performance tests and regression testing.
• Adopting the TDM process saves considerable effort because of data requests, overall data testing process improvement, and data reuse.
• The availability of production-like data to test teams can eliminate testing defects.
• TDM brings enhanced compliance with the data protection policy and regulatory framework
• Data preparation effort and condensed test design help achieve cost savings.
• Testers can concentrate on system testing instead of test data creation.
• Maintaining the test data repository can prevent rework.

Testing Data Common Challenges

Application development teams need reliable test data for their projects; however, many are constrained by the security, quality, speed, and costs of moving data across software development lifecycle (SDLC) environments. Here are the most common tests data challenges that companies face when managing test data.

Testing environments provisioning is slow, manual, and high-touch process

Most IT companies depend on a request-fulfill model, in which testers and applications developers find their requests queuing behind others. Since creating a copy of test data takes significant time and effort, it can take days or even weeks to provide updated data for a test environment.

Usually, the time to turn around a new environment is directly correlated to the number of people involved in the process. Typically, businesses have four or more administrators engaged in setting up and test data provisioning  for a non-production environment. The testing strategy places a strain on operations teams, but it also creates time sinks during test cycles, slowing the pace of software delivery.

Data masking increases friction to release cycles

For many software applications, like those processing a patient record, credit card number/ debit card number, or other sensitive information, data masking is vital to ensuring regulatory compliance and protecting against data breaches. Masking sensitive data often adds operational overhead; an end-to-end masking process might take a whole week, prolonging the testing cycle.

Development teams lack high-fidelity data

Development teams often lack access to test data that is fit for purpose. For instance, based on the release version being tested, a developer might need a data set at a particular time. But all too often, s/he is forced to work with a stale copy of data because of the complexity of refreshing an environment. This can lead to lost productivity because of time spent resolving data-related issues and increasing the risk of defects escaping into production.

Storage costs are constantly on the rise

IT businesses create multiple, redundant copies of test data, resulting in inefficient use of storage. Operations teams should coordinate test data availability across multiple teams, apps, and release versions to meet concurrent demands within the limits of storage capacity. TDM teams are usually content with limited, shared environments, leading to the serialization of vital application projects. 

Best Practices for Test Data Management (TDM): How to Effectively Prepare Your Software Test Data

A comprehensive approach should seek to improve test data management in each of the following areas:

Data security	Lowering security risks without compromising speed
Data quality	Meeting requirements for high-fidelity test data
Data delivery	Lowering the time to deliver test data to a tester or developer
Infrastructure costs	Reducing the costs of storing and archiving test data.

The Best Test Data Management Tools

This section summarizes the best test data management tools and frameworks.

1. TCS MasterCraft DataPlus
2. DataProf
3. IBM Infosphere Optim Test Data Management
4. Informatica Test Data Management 
5. Oracle Test Data Management
6. CA Test Data Management

If you are looking for data testing, we recommend the following test data management solutions:

1. HCL GateKeeper
2. QueySurge

Data Delivery

Developing a copy of production data for testing is often a time-consuming and labor-intensive process that often lags demand. Companies must build a solution that streamlines this process and creates a path toward fast, repeatable data delivery. Specifically, team leaders should look for solutions that feature:

Toolset integration

 An efficient Test Data Management approach should unit a heterogeneous set of technologies, including subsetting, masking, and synthetic data creation. This will require compatibility across test data tools and exposed APIs( or other clear integration mechanisms to DevOps tools) to allow a factory-like approach to TDM. 

Automation test feature

 Modern software application toolsets already include technologies for automated testing, and infrastructure delivery, among other DevOps capabilities. However, companies often lack equivalent tools for delivering copies of test data with the same level of test automation. A streamlined TDM approach eliminates manual processes- like validation checks, configuration steps, and target database initialization- providing a common touch approach to standing up new data environments. 

Self-service

 Instead of depending on IT ticketing systems, an advanced TDM approach puts sufficient levels of automation in place that allow end-users to provision test data through self-service. Self-service capabilities should extend to data delivery and also control over test data versioning. For instance, testers or developers should be able to bookmark and rest, archive, or share copies of test data without involving operations teams. 

What is a Test Data Manager?

The test data manager can quickly create, design, secure, locate, and provision fit-for-purpose test data for the efficient, cost-effective test cycles required to deliver applications faster. Additionally, test data managers can also enhance the quality of production data by filling gaps in test data coverages and creating all the necessary data to cover continuous testing requirements fully. 

Deploying innovative functionality to find and match data to the specific tests it can run and provision it automatically on demand. In parallel, some companies have reported a 90 to 95-percent reduction in the time taken to provide high-quality test data. Test data manager assists in ensuring that teams receive the right data, in the right place, at the right time to speed up the delivery of quality in the software development lifecycle (SDLC) and increase compliance via synthetic data creation.

Types of Test Data

There is no single technology that fulfills all TDM requirements. Instead, teams must build an integrated solution that provides all the data types needed to meet diverse testing needs. After test data requirements have been identified, a successful TDM approach should aim to offer the suitable types of test data, gauging the pros and cons of each. 

• Subsets of production data are more agile than full copies. This type of test data offers savings on hardware, licensing costs, and CPU costs, but it can be hard to achieve sufficient test coverage. 
• Synthetic data circumvents security problems, but the space savings are limited. Whereas synthetic data might be needed to test new features, this is only a comparatively small percentage of test cases. If manual testing is done, creating test data is also prone to human error. It requires an in-depth understanding of data relationships within database schema or file system and those implicit in the data itself. 
• Most companies have some data that is sensitive and needs to be masked for testing purposes. Masked production data (either subsets or full sets) makes it possible for development teams to deploy real data without inaugurating unsafe levels of risk. However, data masking processes can elongate environment provisioning. Additionally, masking requires staging environments with additional storage and employees to ensure referential integrity after data is transformed.
• Production data provides complete test coverage; however, it often comes at the expense of agility and storage costs. For some solutions, it can also mean exposing sensitive data. 

Test Data Management Process

The test data management process can be broadened into the following phases:

• Data masking/test data generation: Generally, after subsetting the test data from production, it must be masked to prevent the client’s personally identifiable information or sensitive data exposure. This part of the software development process ensures legal compliance with the industry and government and data regulations.
• Data identification: Typically intended for a particular test scenario.
• Data analysis: Since data is located at many sources and in various formats, it’s the first to analyze what types of data can be captured for end-test scenarios. Thus different rules are to be applied by a test tool to find appropriate test data.
• Comparison of resultset from Gold copy or baseline data: There would be certain discrepancies with data subsetted or masked during software testing. Thus, the TDM tool would be comparing the actual data baseline test or golden copy in successive runs to identify the accuracy.
• Refresh test data: This is the most significant step in TD adoption. The latest test data is refreshed or loaded to the test database from the production database or other data sources. A medial repository is to be maintained that can save test data creation effort.
• Synthesize: When test teams can not extract a sufficient volume of test data from production, they need a data synthesizing solution to generate the required dataset. A test data management strategy should incorporate the means to generate synthetic data based on real production data quickly. 
• Data requirements gathering process: Test data requirements at the test-case level should be documented and marked as non-reusable or reusable during the test case scripting. This ensures that the test data needed for testing is clear and well documented. A simple summation of the same test data provides the necessary test data to be provisioned.

Final thoughts

Production data does not stand still- and neither should test data. Companies need test data management solutions created to accommodate changing test requirements. Thus managing test data nets real business value. The appropriate test data management solution accelerates time to value for business-critical applications and builds relationships and efficiency across the company.