Test Data is the lifeblood of testing – it’s what enables us to evaluate the quality of software applications across various industries such as healthcare, insurance, finance, government, and corporate organizations.
However, accessing production databases for testing purposes can be challenging due to the size and sensitive data, i.e., personal information contained within. This is where creating a separate set of simulated test data becomes beneficial.
In this post, we’ll explore the fundamentals of test data management, including its definition, creation, preparation, and management.
By providing you with the essential skills required to become an expert in this important field, we’ll help you ensure that your test data is accurate, reliable, and secure.
Definition
A Definition of Test Data
In short, test data validates the correctness, completeness, and quality of a software program or system. Typically, teams use it to test functionality before release to production.
Teams can also use it to compare versions and confirm that changes haven’t caused unexpected behavior.
Despite the importance of data in the Software Development Lifecycle and across Software Testing (such as security testing, performance testing, or regression testing), there is surprisingly little discussion on how to handle the data needed for software testing.
This is concerning, as software development and testing rely heavily on well–prepared data cases.
Random or arbitrary data won’t test software effectively. Instead, you need a representative, realistic data set that finds errors with as little data as possible.
Ultimately, a small but realistic, valid, and versatile (test) data set is essential.
How do we Create Test Data?
Creating test data is essential, because it lets developers find and fix errors before release.
To ensure that the data set is representative of real–world scenarios, manual creation, data fabrication tools, or retrieval from an existing production environment are all viable options.
Manual creation of test data is the most straightforward method and involves creating sample data that adheres to the structure of an application’s database.
This works well for relatively small databases, but is not a viable option when dealing with larger data sets. However, manual creation requires testers to understand the application, its database design, and its business rules.
Data fabrication tools are another popular way to create test data and can be used to simulate real-world scenarios.
These tools allow users to define field types and constraints as parameters in order to create realistic datasets with various distributions and sizes based on their requirements.
Finally, retrieving existing production data is an efficient way of generating test data sets. As a result, the test data is accurate and current, because the original schema has already validated it.
However, production data needs care. Most importantly, mask or encrypt sensitive information before you use it in test environments.
The Challenges of Preparing Test Data
Using or preparing test data can be a challenging task due to several factors. Some of the main challenges include:
1. Data access
For example, testers may lack access to the data sources they need, or developers may take too long to grant it.
2. Large data volumes
Large data volumes can make data preparation and provisioning a time-consuming and challenging task. Tip: One potential solution for resolving the volume and provisioning challenge is the use of Data Virtualization or Data Cloning.
3. Data dependencies
Applications often have data dependencies, meaning that a change in one piece of data can impact other related data. For example, accounting for every data dependency when preparing test data can be hard.
4. Data combinations
Similarly, with so many data combinations, testing all of them is difficult.
5. Data quality
Data quality issues can impact the validity of test results. It is important to ensure that test data is representative of the data that is present in the production environment and that it accurately reflects the real-world usage of the application.
6. Data privacy
Moreover, test data often contains sensitive data, so teams must handle it carefully to stay compliant.
7. Resistance to Change
Another challenge of using or preparing test data is resistance to change. Implementing a new Test Data Management system or process requires a change in the organization’s culture and workflow.
Employees may resist change, especially if they have been using the same old manual methods for years. This can lead to a lack of adoption and adherence to new processes, resulting in poor quality testing and increased costs.
Enov8 TDM, Test Data Profiling: Screenshot
Why Use TDM Tools for Test Data?
Overall, preparing test data can be a complex and time-consuming task. However, it is crucial to ensure that test data is representative, accurate, and comprehensive to facilitate effective software testing and ultimately improve software quality.
Test Data Management (TDM) solutions like Enov8 TDM can help organizations overcome some of these challenges by providing a structured approach to test data analysis, preparation, management, and ultimately delivering:
1. Efficiency
TDM tools automate the process of generating, masking, and managing test data, which saves time and effort compared to manual methods.
2. Reusability
In addition, TDM tools create reusable test data sets for multiple projects, which cuts redundant preparation.
3. Scalability
As the volume of data required for testing grows, TDM tools can help scale the process to meet the demand.
4. Consistency
As a result, TDM tools keep test data consistent across environments, which improves the accuracy of results.
5. Compliance
TDM tools can help ensure that test data is compliant with regulatory requirements and industry standards, which is critical for industries such as healthcare and finance.
6. Security
Test data often contains sensitive or confidential information such as personally identifiable information (PII), financial data, or intellectual property.
Therefore, TDM tools help organizations protect and mask this data outside production to avoid breaches.
TDM tools provide security features such as data masking and anonymization, which help to maintain data privacy and security while still allowing testers to have access to realistic and representative test data.
By implementing proper security measures through TDM tools, organizations can prevent unauthorized access or disclosure of sensitive information and reduce the risk of data breaches.
Overall, TDM tools help streamline the test data preparation process, improve test data quality, and reduce risk, which ultimately leads to higher software quality and better business outcomes.
Test Data FAQs
What is test data?
Test data is the information fed into an application during development and testing to check that it behaves correctly. It can include valid and invalid inputs, edge cases and realistic records that reflect how the system is used.
How is test data created?
Teams typically create test data by generating synthetic data, masking a copy of production data, subsetting production into smaller data sets, or combining these approaches.
Why is test data management important?
Without good test data management, teams wait for data, test with unrealistic or stale data, and risk exposing sensitive information. Managing test data well improves test quality, speed and compliance.
Conclusion
In conclusion, Test Data Management (TDM) tools provide a structured approach to test data preparation and management that helps organizations overcome some of the challenges associated with traditional manual methods.
TDM tools automate time-consuming processes such as generating, masking, and managing test data sets, which improves efficiency, scalability, and accuracy.
Additionally, TDM tools can help ensure compliance with regulatory requirements and industry standards while also protecting sensitive information from unauthorized access or disclosure.
Ultimately, using TDM tools can improve software quality and lead to better business outcomes.
Other TDM Reading
Enjoy what you read? Here are a few more TDM articles that you might find interesting.
Enov8 Blog: A DevOps Approach to Test Data Management
Also on the Enov8 Blog: Types of Test Data you should use for your Software Tests?
Enov8 Blog: Why TDM is so Important!
Author Andrew Walker
Andrew Walker is a software architect with 10+ years of experience. Andrew is passionate about his craft, and he loves using his skills to design enterprise solutions for Enov8 in the areas of IT Environments, Release & Data Management.