Test data management: developing a strategy

Test data is a group of input values applied when testing an application (like software, web, mobile apps, or API). These inputs mirror what users would insert into the system in an actual situation. Typically, testers can create a test script for automatically and flexibly deciding suitable kinds of values to be put into the system. Then, they can observe how it reacts with such data.

Without good test data, even well-thought-out tests can give wrong outcomes. Managing data is very important to get dependable and precise testing results. With this, we come to test data management (TDM). It’s a methodical procedure that includes planning, making, keeping, and managing test data all through the software development cycle.

After the test data is made, it gets stored in a safe and simple-to-access way. Teams can get this data when they need it for testing. Maintenance that happens all the time is extremely important, so test data stays useful and correct as time goes on. Regular checks for validation help confirm if the data is still intact while controlling versions lets teams monitor any modifications.

Including TDM in the general testing plan helps organizations increase test coverage, lower expenses, and enhance overall software product quality.

Importance of test data management

Ensuring data integrity and relevance

Test data needs to be precise and applicable to the situations that are being tested. TDM makes sure that the data used is similar to real-life circumstances. For instance, when an application is made to manage customer dealings, test data must resemble real customer details and transaction habits.

When it comes to data integrity, teams can have confidence that their tests will produce valid results. This lessens the chances of defects making it into the production phase.

Following time- and cost-efficient TDM practices can greatly cut down the hours and expenses linked with testing.

Reducing testing costs and time through efficient TDM

Following time- and cost-efficient TDM practices can greatly cut down the hours and expenses linked with testing. For example, if we automate the process of generating and managing data, it’ll make our work more smooth. This allows teams to concentrate on important tasks related to testing without having to spend much time handling these types of jobs manually.

When handling data is decreased, errors are lessened as well, which causes quicker test cycles and a faster time to market for organizations.

Enhancing test coverage and accuracy

Organizations can use TDM to create datasets that are diverse and include many types of test scenarios. This makes the tests more accurate, as it covers a wide range of possibilities for testing. For example, if testers use different variations of data like edge cases or null values, they can confirm whether an application behaves correctly under different conditions.

Increased test coverage assists in spotting possible difficulties at the beginning of the development phase, resulting in better-quality software.

Key components and concepts

Test data generation

The generation of test data means making synthetic data that imitates real-world data. It’s very important for testing situations when we don’t have actual data, or the available information might be too sensitive and private regulations restrict its use.

With the help of tools such as GenRocket, teams can produce required data in a customized manner for their testing needs, which ensures its pertinence and precision.

Data masking and anonymization

The masking and anonymization of data is a method to safeguard sensitive information by making it less identifiable. For instance, true customer names can be replaced with made-up ones but the format remains unchanged. This ensures adherence to privacy rules like GDPR or HIPAA. This makes it feasible for organizations to utilize genuine datasets without endangering data exposure or encountering legal problems.

Data subsetting and cloning

Data subsetting is making a smaller, manageable set of data, while cloning is duplicating a dataset for testing. These methods assist in saving storage and enhancing the efficiency of testing. For example, you can make a subset of a big production database that only has the necessary records for testing. This lessens the time needed to get and handle data.

Test data validation and maintenance refers to the ongoing process of checking if the test data is appropriate, correct, and up to date.

Test data validation and maintenance

Test data validation and maintenance refers to the ongoing process of checking if the test data is appropriate, correct, and up to date. This involves confirming that the data meets specific criteria before it can be used for testing purposes.

Additionally, continuous maintenance helps in updating or removing old information from tests to make sure they align with any changes made within your system’s production environment. Regularly verifying and maintaining test data is crucial for guaranteeing its relevance so that it continues to accurately represent real-life scenarios.

Strategies for handling/managing test data

Understanding test data requirements

Knowing the particular data requirements for each test case is very important. This knowledge helps in the next steps of the TDM process. For example, various types of tests like functional, performance, or security might need different datasets, and acknowledging these demands beforehand can make readying data easier.

Data generation and selection

The selection of generation methods and datasets are crucial elements in good testing. Organizations should think about aspects like the range of data, the amount of it, and how fast it comes to make sure the created information matches with real-life situations. For instance, this might involve utilizing automated tools to produce different datasets that encompass a variety of testing conditions.

Data masking and anonymization

With data masking, sensitive information can be protected while still maintaining realistic datasets for testers to work with. Organizations can use industry-standard techniques like format-preserving encryption or tokenization to manage the masking process and keep important data safe during testing.

Data versioning and cloning

Keeping various versions of test data allows for the monitoring of changes that occur over time, and cloning assists in creating parallel testing environments. Versions make certain that teams can go back to old data states when required, while cloning permits testing to happen at the same time in many environments, which enhances overall efficiency.

Test data automation

By using automation tools, the TDM process can become more efficient and less prone to human errors while maintaining uniformity in test data usage. These tools could help with generating data or masking it so people working on teams have more time for testing instead of preparing information, which might speed up test cycles as well as increase productivity.

Getting started with test data management strategies

Assessing current TDM practices

To measure the present TDM practices, one must examine the methods already in use, tools employed, and where data is sourced from. Recognizing areas of congestion and gathering inputs from team members are ways to comprehend what a company does well and not so well. This examination assists in determining places for enhancement and establishing a more effective TDM plan.

Defining TDM goals and objectives

Setting clear TDM goals and objectives is crucial for matching testing results with the work put in. Establishing particular, measurable aims like raising the quality of data or lessening preparation duration allows teams to concentrate their attempts, monitor advancement well, and make required changes for betterment in their TDM methods.

Choosing the Right Tools and Technologies

1. Delphix: A top TDM solution, it has data virtualization. This helps in fast data provisioning and masking. Delphix is good at managing big datasets, as it provides an easy and quick way to get test data while keeping up with rules related to the privacy of information.
2. GenRocket: This method is focused on creating synthetic data, which means it allows teams to make data as per their testing needs. The capacity of GenRocket to generate various datasets helps enhance test coverage and precision.
3. Informatica: Recognized for its strong data integration features, Informatica also offers TDM solutions that have characteristics like data masking and profiling. This full package helps businesses control information in different settings to maintain uniformity and meet rules.

Regarding the selection of a TDM tool, you should think about elements like simplicity in usage, its capacity to grow with your needs, the capability for integration, and assistance in following rules related to data privacy. Additionally, organizations need to assess if the tool can handle different types and formats of data along with its capacity for automation as well as reporting features.

For the successful application of TDM methods, it’s important to create a defined structure. This should include the roles and duties of everyone on the team who manages data.

Implementing the Strategy

For the successful application of TDM methods, it’s important to create a defined structure. This should include the roles and duties of everyone on the team who manages data.

Using tools with automation can make tasks such as making data, covering it up, and checking for mistakes easier to do. This ensures that everything happens consistently and effectively.

By including TDM practices in the CI/CD pipeline, we can make sure there are no delays when providing data during automated tests. Regular training sessions will help in updating teams about new tools and compliance rules.

When collaboration is encouraged between the development, testing, and data management teams, a TDM strategy can be created that responds well to changing testing requirements.

Conclusion

After reading this post, you now know that test data management is very important in software testing. By knowing its parts, tactics, and good ways to execute it, organizations can make their test processes effective, lower expenses, and guarantee they follow data security rules.

Putting strong TDM practices into action not only makes test coverage and precision better. It also helps improve the success of software creation. By focusing on TDM, you can attain more excellent software and streamlined testing procedures.

This post was written by Gourav Bais. Gourav is an applied machine learning engineer skilled in computer vision/deep learning pipeline development, creating machine learning models, retraining systems, and transforming data science prototypes into production-grade solutions.