As you probably know, data is absolutely essential for businesses of all sizes. Whether you’re a Fortune 500 company or a small mom-and-pop shop, accurate and relevant data is needed to make informed decisions.
But in the world of software testing, data is usually messy. In order to make sure that test data is usable for making informed business decisions, it's important to have a system in place for managing it. This is where test data management comes into play.
In this article, we'll discuss what test data management is and why it's so important. We'll also provide some tips on how to get started with test data management. Let's jump in.
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What is test data management?
Test data management (TDM) is the practice of managing and organizing the test data used in software testing. It's an essential part of quality assurance, as it helps ensure that the data used in testing is accurate and reliable. This, in turn, helps reduce the number of software defects and helps ensure that the software meets end-user requirements.
There are three main types of test data management:
1. Control Data: This is the main data set against which all other data is compared. It's used to verify the accuracy of the test results.
2. Reference Data: This is data that is used to test specific functions or features of the software.
3. Test Data: This is the actual data that is used in testing. It can be derived from the control or reference data, or it can be created specifically for the testing process.
Benefits of test data management
When it comes to test data, you can never be too careful. Test data management is essential to ensuring the quality, accuracy and reliability of your test data. By taking the time to properly manage your test data, you'll enjoy more optimal test coverage, reduced costs as a result of early bug-detection, and peace of mind knowing that your data is compliant and secure.
But what does that actually mean? Let's break it down.
Data provisioned per testing type: Good test data management is all about provisioning the right data for the right tests. For instance, you don't want to use production data for your unit tests because that could lead to inaccurate results. And you don't want to use sample data for your performance tests, because that won't give you an accurate picture of how the system will perform under real-world conditions.
Better compliance and security: Test data management ensures compliance and security. All of your sensitive data should be properly encrypted and hidden away from prying eyes.
Reusability of data: TDM promotes reusability. Once you've created a set of accurate and reliable test data, you should make sure to store it in a safe place so that you can use it again and again.
Avoiding redundancy in data: Multiple teams within a project may create copies of the same production data for their use. Due to this, redundant copies of the same data are made and storage space is wasted. Because all teams use the same repository when a TDM tool is used, the storage capacity can be optimized.
Better performing applications: The main benefit of TDM is high-quality data. High quality, trusted data enables the early discovery of bugs during the testing phase. The end result is a high-quality, stable application with few production flaws.
In an Agile world, requirements and features are constantly changing, which means your test data has to change with it.
But if you don't have a TDM strategy in place, it can be hard to keep track of all the different versions of test data you need, and even harder to make sure that the right data is being used for the right tests. This can lead to a lot of wasted time and effort, not to mention frustration on the part of your team.
A good TDM strategy can help you avoid all of this by giving you a way to manage your test data more efficiently. And that's why TDM is so important to not just creating test data, but managing it effectively so that you can get the most out of your testing efforts.
Read the report: The State of ERP Testing
Steps for test data management
Now that you know the basics of test data management, here are four steps to get you started on your test data management journey.
- Figure out what data you need: The first step is to determine which data is needed for testing. This will vary depending on the project, so it's important to take some time to figure out exactly what data is required.
- Create or obtain the test data: Once you know what data is needed, the next step is to create or obtain the actual test data. This can be done in a number of ways, such as generating synthetic data, using real data from production systems (with proper security and privacy controls in place), or using a combination of both.
- Prepare the test data: After the test data has been created or obtained, it needs to be prepared for use. This includes things like ensuring that the data is complete, accurate, and consistent, and making sure it is formatted correctly for use in the testing environment.
- Use and manage the test data: The final step is to actually use the test data in testing. This includes things like setting up the test environment, running tests, and storing and managing the test data after testing is complete.
Main challenges in test data management
- Testing teams have inadequate or incomplete test data
- Testing teams don’t have access to data sources
- Testing teams get slow response from development teams, due to other priorities
- Large volumes of data may be needed, but QA teams don’t have the appropriate data management tools or skill sets to manage so much data
- Testers often spend significant time and effort in communicating with architects, database admins, and business analysts to gather data, rather than focusing on actual testing
- The provided data is often sensitive, and thus unsuitable for testing purposes
- Huge volumes of data are needed to be analyzed in a short time-frame
The current state of test data management for automation testing
One of the biggest bottlenecks in test automation is the creation of test data. Typically, test data is created through hours-long sessions with business users where they map out their process flows in large, complex Excel files. The process is monotonous and time-consuming, and as such, is typically rife with human error. As we know, data like this is not optimal for test automation.
Opkey’s TDM solution for test automation
Opkey leverages test mining technology to autonomously mine test data from the client’s environment and ensure it’s in the correct format. Opkey also mines master data details such as Chart of Account, Employee, Customers, Item, Supplier, Procure to Pay, Order to Cash, and more, which can reduce QA teams’ data collection efforts by up to 40%.
Opkey’s test data management solution is highly effective in scenarios when QA teams need to execute multiple testing cycles during an EBS to Cloud migration, or in executing regression testing for Oracle’s quarterly updates. In short, Opkey’s TDM solution saves companies time and money by ensuring their test data is always ready for Oracle testing.
Test data management is important because it helps ensure that the data used for testing is accurate and up-to-date. It also helps to keep track of changes to the data so that it can be easily rolled back if necessary. Additionally, test data management can help to automate the process of creating and maintaining test data, which can save time and resources.
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