When it comes to test data management, there are various components and complex processes that come across in the test environment. It is a most integral part of the software development process where poor test data management can lead to software failure.
So, it’s essential to maintain a proper test environment to get accurate results. However, test data management is a challenging process, and there are chances of making mistakes.
Here, we will explore the eight common mistakes that you should avoid during test data management.
8 Common Mistakes You Should Avoid During Test Data Management
Test Environment Preparation Without Understanding the Requirements
The very first mistake you should avoid during test data management is preparing a test environment without understanding the requirements.
Because if you’re preparing a test environment without understanding the requirements, then it may lead to severe data breaches between the expected and required results.
So, first, you need to understand the requirements carefully on what exactly needs to be developed as an end result. Also, designing a test environment depends more on the requirements, whether to test software, hardware, etc.
It’s the most common mistake that leads to software development failure. So try to avoid the mistake to save cost and time of test data management.
Not Maintaining a Centralised Test Data Management Approach
Many test data managers don’t follow a centralised data management approach, and that leads to gaps, expensive processes and inefficiencies during the test results.
It is essential that you follow a centralised data management approach to maintain alignment between ownership and the team.
By following a centralised test data management approach, only a single source of requirements and changes will be established.
So, both the test manager and team are on the same page to make changes and updates. It also helps in reducing the risk and offers real-time visibility to see the changes in the test environment.
Not Automating the Test Environment Deployment
Building a test environment from scratch can be challenging, time-consuming and expensive. So, it’s essential for test managers to automate the test environment and focus on a sustainable way to handle the test environment whenever it is required.
When you automate the test environment, including designing and building, then it becomes cost-efficient and faster.
Automation in test data management can bring consistency and accuracy, which eliminates manual efforts and other delays.
Not Following Data Masking Standards
When you’re using real-time data as test data without following the data masking standards, then it may have a huge risk. It needs to replicate the test environment and data closely if possible.
However, it’s not recommended to use the production data for testing performance, but it gives quick results as compared to synthetic test data, and it leads to more vulnerable data thefts.
By not following the data masking standards, the software still has bugs and vulnerabilities that can be easily accessible for hackers to unmask data.
So, it’s important to keep following the data masking standards to ensure the software application is protected and encrypted from any malware attacks.
Unplanned Data Collection from Different Sources
During the test data management, the testing team collected data from different sources across internal and external databases.
That’s why it’s a most time-consuming process because data is so big, and the testing team is limited and unable to handle such a large number of databases.
So, it’s better to plan your data collection sources to reduce the time of finding the test data from limited sources during the testing process.
Practising Poor Data Profiling
When you’re developing a high-performance data integration application, it requires accurate data profiling.
Having accurate data can help ETL developers clean and process data sets, but poor data profiling can disrupt the TDM practice in the long term.
So, if there are similar data entries, then various complexities arise when you ignore data profiling. That’s why the TDM team should understand the unpredictable nature of data sets and perform data profiling initially with minimal effort.
Having No Plan
When there’s no generation strategy, then it’s one of the mistakes that test managers make during test data management.
It’s essential to understand that a test has nothing to do with test automation code. It doesn’t work in most of the environment but serves as a foundation for many patterns.
Testing Through User Interface
When you’re doing testing through the user interface, it can work at the initial stage, but later, it takes more time to test. When you search “test automation” on a browser, various search results show testing through the user interface, but they end up with multiple flows over time.
Frequently Asked Questions
Q.1 What is Test Data Management (TDM)?
Test data management is a process that helps organisations create high-quality software that performs reliability on deployment. It helps to reduce the bugs and risks and fix the flaws during the software development process.
Q.2 What are the types of testing data?
There are mainly four types of testing data:
- Normal Data
- Extreme Data
- Abnormal Data
- Live Data
Q.3 What’s the key role of the test data manager?
Test data managers ensure that their team receives accurate data in the right place at the right time. So that the process runs faster and delivers quality during the software development cycle.
Q.4 What is the test data also called?
Test data is a data set, which is also known as a holdout data set.
Q.5 What is Test data automation?
Test data automation is a process of delivering the test data into lower environments automatically to test and used during the software development process.
These are the eight most common mistakes that are necessary to avoid in test data management. Every organisation must be aware of these challenges in the test environment, work on best practices and ensure to avoid mistakes and drive better and more accurate results.
We hope this guide helps you find out the common mistakes test data managers can make during the test data management to avoid.
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