This article describes what a Test Data Management (TDM) is. The challenges associated with TDM and why the right approach and test data management tools are useful in ensuring the quality of the test process.
In a disruptive market, there is constant pressure to put out high-quality software in record time. This encourages the software testing industry to optimize the costs and efforts required for testing. One of the main areas for optimization is the management of test data. Let’s take a look at what test data management is before that let us see:
What’s the test data?
Test data is data specifically designed for use in testing, usually a computer program. There are two types of test data required by the application testing team:
- Static data: This is data that does not change once recorded and usually contains insensitive information such as city names, PIN codes, etc.
- Dynamic data (transactional data): This data can change after being recorded and usually contains sensitive data such as customer medical history, number of employees, etc.
A combination of static and dynamic data is usually required for testing purposes. Data can be in different formats, different databases, and different types. Testing may require data from multiple sources depending on the specific requirements of the application under test (AUT). Most of the data used for testing is production data because it includes all the different types of data that an application may encounter in the live environment.
Now imagine a scenario where transaction data, which includes credit card numbers, cell phone numbers and bank details, is available to the testing team for testing purposes. In the event of misuse of important and risky data, customer legal action is categorical. This violation not only resulted in financial loss, but also loss of customer confidence, which in turn would have catastrophic damage to the bank’s business.
In such a case, how do you test business-critical banking applications without production data, where faulty data will help prevent production errors? The answer is data masking.
We will use production data after covering or hiding sensitive information. This masking is done under TDM (Test Data Management), where we intend to separate sensitive production data from test data. Let’s learn more about Test Data Management (TDM).
What is test data management?
Let’s start with the definition of test data management (TDM). Test data management is the data management process required to meet automated testing requirements without the need for human intervention.
This means that the TDM solution is responsible for generating the necessary test data as needed for testing. It should also ensure that the data is of the highest possible quality. Poor quality test data is worse than no data because it produces untrustworthy results. Another important requirement for test data is accuracy. This means that the data should be as similar as possible to the actual data on the production server.
Finally, the TDM process must also ensure the availability of test data. It makes no sense to have high-quality data that is as realistic as possible but does not reach the test cases when needed.
So it can be said that the test data management process has three main objectives: To provide high-quality, realistic and affordable test data.
Then!
What is test data management in testing software?
Any data that is used as input to run a testing is called test data. Data can be static data such as currency, country, name, etc. or transaction data. Testing teams need the right combination of static and transactional records to fully test the characteristics and business scenarios. Test data management is the process of ensuring that the test team has the right quality test data, in the right volume, in the right format, and in the right environment, at the right test time. The test coverage depends primarily on the quality of the test data. The test data must match the actual production data for performance tests.
What is a test data management tool?
The test data management tool manages the test data used by the test team. This TDM tool helps implement the test data management process. The main functions of the test data management tool are:
- Data discovery and classification
- Self-service data warehouse
- Provision of data subsets
- Reporting
Five ways of test data management best practices
Application testing easier – in the 1980s. They have mainframes, unlimited data sets, all internal data, and a bit of security and privacy awareness. There are many other factors to consider when managing test data today.
If you ignore or do poorly in an area, your test results will be suspicious and expose you to procedural risks, fines, and litigation for regulatory compliance. In short, the accuracy of your test data is very important for your business. Even so, this was the testing team’s first task.
It’s helpful to have a clear process so you can be sure that you’ve ticked every box before you mark the test as complete. Because careful preparation will lead to better results, there are five steps you should consider when managing your test data:
- Data Extraction/Generation
- Protecting Sensitive Data
- Planning
- Maintenance
- Auditing
Stages of the software testing life cycle
Validation of any software or application modules is mandatory to ensure product accuracy and precision. Since software testing itself is a complex process, testers do it step by step. Complexity can arise if testing is not regulated. Complexity can include unresolved errors, undetected regression errors, or in the worst case scenario, modules skipping exams when a deadline approaches.
Each Software testing life cycle stage has specific goals and outcomes. This includes starting, running, and ending the testing process.Let’s take a closer look at the various stages of the software testing life cycle.
- Requirement Analysis
- Test Planning
- Test Case Designing and Development
- Test Environment Setup
- Test Execution
- Test Closure
Hopefully you have an idea of how test data works over its life cycle. Hope you found this informative.
The benefits of using an effective TDM approach
- The optimal data coverage level is achieved using the TDM approach.
- Test data requirements are effectively captured by the test team to maintain versioned data requirements. It also aids in traceability and data replication.
- A detailed analysis of data requirements will help identify potential problems that may arise during testing.
- Synthetic data can easily be generated to test new functions.
- Guidelines for data security and security can be applied effectively.
- The best tool for managing test data
Below is a list of the best tools for managing test data.
- Informatica
- DATPROF
- HP
- TechArcis
- Compuware’s
- SAP Test Data Migration Server
- CA Test Data Manager (Datamaker)
- Delphix
- InfoSphere Optim
- Solix EDMS
- Original software
- LISA Solutions for
- vTestcenter
Let us also see data discovery!
What is data discovery?
Data discovery is a term used to describe the process of collecting data from multiple sources by finding models and possibilities using advanced parsing analysis and visual data navigation, which enables the consolidation of all business information.
Suppose you are a business owner, analyst, CIO, or program manager. All employees in the organization must be able to read, understand and derive value from all information in the form of data.
Extracting value from data in today’s business environment is critical to a company’s success. The ability to identify and analyze patterns and trends in data sets allows companies to gain a competitive advantage, meet business goals, ensure success, and stay relevant in the digital age.
As noted earlier, the concept itself is not a tool or platform for data discovery. Rather, it is a term that can be used to generate business value. However, there are data analysis tools that you can use to improve your efforts. These levels of recognition can be described and categorized by:
- Data preparation
- Visual analysis
- Leading advanced analysis
Now Let us see about one of the data analytic
What Is Prescriptive Analytics?
Prescriptive analytics is a type of data analysis that uses technology that enables organizations to make better decisions through analysis of raw data. In particular, prescribe factor analysis information about possible situations or scenarios, available resources, past and current results and suggest an action or strategy. It can be used to make decisions about any time horizon from the short term to the long term.
How prescriptive analytics works?
This analysis is based on artificial intelligence techniques such as machine learning – the ability of a computer program to understand and develop received data without additional human input and to continually adapt. Machine learning makes it possible to process the amount of data currently available. As new or additional data appears, the computer program automatically adapts to use it. This is a much faster and more extensive process than any human skill can manage.
A number of types of data care companies and government agencies can benefit from the use of mandatory analytics, including in the financial services and healthcare sectors, where the costs of human error are high. Prescriptive analysis works with another type of data analysis, predictive analysis, which uses statistics and models to find future results based on current and historical data. But it goes a step further: Using predictive analytical judgment of likely events, he recommends which path to take in the future.
Conclusion:
In short, with well-designed test data management, you can identify and correct serious functional defects. The selection of the test data chosen should be reassessed at each phase of the multi-phase product development cycle. So, always check.
To simplify the testing process, it is important to use time and resources efficiently. Adhering to a systematic STLC not only leads to fast debugging but also improves product quality. By increasing customer satisfaction, you benefit from a higher return on investment and a better brand presence.