Effective Test Data Management Strategies For Improving Your Test Efficiency

You would agree that as computer science students when we are first introduced to the concept of integer, float, and double data types it hardly makes any difference to us until we see its application in the real world. This is when we learn its importance and become religious while using the same to make accurate codes. You might wonder what it has got to do with an effective Test Data Strategy. It indeed has, and this blog will help you understand the importance of an effective Test Data Strategy that will help you test your product accurately and reduce your production defects in advance. Be it a functional, performance, or automation test an effective Test Data Strategy is mandatory to assure quality testing.?

So let’s begin!

Table of Contents:

• What is Test Data management?
• Why do you need a Test Data Strategy?
• Challenges in using Production data.
• What should your Test Data Management Strategy include?
• Benefits of Test Data Management.
• Some tools for Test Data Management.
• Conclusion.

What is Test Data Management?

Consider a Test Scenario in which you need to test the IFSC code in a banking application. In this situation just creating random alphanumeric data would not provide complete Test Coverage for the scenario. You need to understand the code a bit deeper and generate data that matches the IFSC pattern i.e. 11 alphanumeric codes where the first four characters represent the name of the bank, and the last six characters represent the branch of the bank. So we need to create Test Data similar to the production data to achieve accurate results while testing and thus prevent production defects.

The process of Test Data Management creates data sets that mimic production data so that application developers and testers can perform rigorous and valid system tests on the product thereby ensuring minimal defects in the production environment.

Why do you need a Test Data Management Strategy?

Quality Test Data is a prerequisite for software quality assurance. It helps to create better quality software and so we need an effective strategy to manage this test data. This will help in preventing bugs in the production environment and will ensure that the application performs reliably on deployment. It must also be noted that having a Test Data Management Strategy can help in saving time before the actual start of test execution. Also, many times, testers are in need of large volumes of data for testing, and having such a strategy in place can help increase the Test Coverage thereby ensuring better software quality.

So why do we need a Test Data Management Strategy? The answer lies in the type of defects that we encounter in the production environment, as a result of the lack of an effective Test Data Management Strategy.

1. Missing or truncated data: Situations, where your test data does not match accurately with your production data, can lead to missing or truncated data in the production environment, thus impacting your testing efforts.
2. Mismatch errors: This occurs when the data type of your test data is different from what is expected in production. For example, insert characters in the ‘Date’ field.
3. Sequence generator errors: Occurs when the sequence of identifiers is not maintained which is required in the end-to-end and integration testing.
4. Duplicate key error: This occurs when there is already an instance of data available in the production environment and we are trying to insert the same data in the same key field.?

Challenges in using Production Data.

Although it would be quite easier to avoid creating Test Data and use production data instead, it is not always possible to use the production data for testing purposes. Using production data for testing comes with its own set of challenges and so organizations have to create Test Data either manually or through automated scripts.?

Challenges in using production data for testing include:

• Maintaining data privacy.
• Maintaining data dependencies.
• Handling production data securely.

What should your Test Data Management Strategy include?

With the number of production issues leading to business losses, Test Data Management is gaining importance and so it becomes imperative to have a Test Data Management Strategy, not only for testing purposes but also for being compliant.?

The following Techniques can help you implement your Test Data Management Strategy

1. Use data generators for synthetic data production.
2. Ensure data integrity.
3. Generate referentially intact data.
4. Keep data backup.
5. Data obfuscation.

Use Data Generators for synthetic data production

As you need a large volume of data to be generated in a short time, you can take the help of open-source or paid data generators for synthetic data production. While doing this you need to check if the tool suffices your requirements of mimicking operational or production data for test data generation. Once data is generated you can store the data and use the same as per your testing needs.

Ensure Data Integrity

Consider a Test Scenario in which bank account numbers start from 1000. In such a situation, there is no point in generating data from number 1 to number 999. This is why you need to ensure data integrity in the generated data. Data integrity helps in achieving overall accuracy, completeness, and consistency of data which helps in the Test Execution process.?

Generate Referentially Intact data

While it can be considered one of the most difficult steps it is important to generate data with keeping the referential integrity intact. In the case of end-to-end and integration testing, it becomes essential to keep the relationships between the data sets synchronized.

Keep Data Backup

Keeping a data backup helps you re-generate the Test Execution scenario which can be used in the future during defect resolution, in case a defect is present in the environment. As we progress with our testing efforts the generated data becomes huge to handle easily and so we need to have systematic data management arrangements which will further help in replaying Test Scenarios containing defects. This will save our effort in creating the same data set from scratch.

Data Obfuscation

With the rising challenges of maintaining data privacy, it is always better to have a data obfuscation plan in your Test Data Management Plan. Assume a Test Scenario where you need to use the Social Security number or Aadhaar number for testing purposes. In such a situation, you should ensure your data is masked or encrypted to save it from threat actors.?

Benefits of Test Data Management:

• Increased Test Coverage.
• As data is ready, less time is spent on data gathering.
• No dependency on other teams for data collection.
• A large volume of data is available in a short span of time.
• Data provisioning as per testing type.
• Reusability of data.

Some tools used in Test Data Management

• K2View
• Datprof
• Informatica?
• Test data generator: Benerator, Mockaroo

Conclusion:

In the above blog, we went through the importance of setting up a Test Data Management Strategy and how it can matter in improving your product quality. In the process, we need to ensure optimum data coverage is achieved and also avoid data corruption through a standard and structured implementation process. Efficient Test Data Management can accelerate the test cycle and would lead to fewer data-related defects in the production environment.

At Testrig Technologies , Test Data Management is a part of our test planning process to assist the overall testing activity and achieve the most efficient results.

To know more about Test Data Management and the ways our QA experts can assist you in your testing process contact Testrig Technologies.