Test data management (TDM) is critical for any data-driven organization to consider. Companies are increasingly adopting rapid application development techniques to deploy new applications or make significant enhancements while minimizing time and cost.
However, the lack of data management procedures in a rapid application development environment can result in data quality issues. With proper knowledge and planning, companies can avoid such problems.
We cover the basics of test data management to help you develop test plans that support your organization’s efforts.
What Is Test Data?
Test data is any set of test values designed for a test scenario. It provides how a tester can determine if the underlying application works as designed and coded.
Test data is different than production data, which is any set of real-world data that an application uses.
What Is Test Data Management?
Test data management is creating, planning, designing, and storing testing methodologies, tools, and processes. It includes the extraction, transformation, and loading of data from source systems into staging environments and the maintenance, management, cleansing, auditing, and archiving of test data.
The specific activities for test data management will vary by organization, but each organization must have a strategy to manage its test data.
3 Approaches to Test Data Preparation
You can manage test data effectively in a variety of ways. The following three approaches are practical in different situations:
Cloning Production Databases
Cloning is a simple approach to test data management. It involves duplicating production databases in staging environments and swapping them with test data programs. This approach works for organizations where You can easily clone the production databases or those with low data volume.
Generating Synthetic Test Data
Synthetic test data involves database simulation tools or scripts that generate random data and test values to create a database. This approach requires a highly skilled database administrator with a profound understanding of the database schema.
Because it is usually resource-intensive and more time-consuming, this approach works for organizations that need a high degree of control and scripting capabilities.
Subsetting Production Databases
Subsetting involves selecting a portion of a production database to copy, modify, and maintain. This approach is suitable for organizations that have moderate data volumes. It, however, still requires highly skilled resources or an automated solution.
Why Is Test Data Management Important?
Test data management is a critical but often overlooked development process. The lack of effective test data management can negatively affect an organization’s bottom line.
These six reasons demonstrate why test data management is essential:
Improved Test Data Coverage
TDM begins with proper test design and planning. The better the test data management, the higher the coverage you can achieve for a given test. Lack of test designs, poor test approach matching, and inconsistent or poor test coverage degrade a company’s ability to measure its testing efficiency and effectiveness.
Finding the Bugs Early
If you are testing with real-world data, you risk including bugs not discovered in development. Creating and storing test data separately from production environments will enable companies to know how well the application works before deployment. It also allows organizations to set up a strategy for handling future changes to those database structures.
Data Compliance and Security
With the number of data breaches rising regularly, organizations must include test data management as a critical component of their security strategy. Properly managed test data will help to ensure that test data is adequately protected from unauthorized access and minimize risk.
Reduced Copies of the Data
Test data management can be an effective way to reduce costs by reducing the redundancy of test data. You won’t need to store multiple copies of production data in staging environments or scripts.
Depending on the size of your company, this can result in significant cost savings.
Enhances Customer Trust
Organizations must enhance the user experience as they transition from traditional to agile development approaches. Customers want to know that their data is protected and you will resolve any issues with the quality promptly.
Using test data management in your processes helps prove that you’re committed to building quality in the application for your customers.
Test Data Management Challenges to Consider
It is crucial to consider the following when developing a test data management plan:
Data Validity and Consistency
The quality of the test data is affected by its validity and consistency. Data validity means that all data should be relevant for the tests. This consistency means that all data should be structured and formatted to facilitate testing.
Tests with incorrect test data may not illuminate real-world issues.
Compromised Data Privacy
In today’s highly regulated environment, data privacy is of paramount importance — conduct test data management in a secure environment to prevent unauthorized access. Create an environment where data cannot be accessed or changed by people outside of the organization.
Data Selection and Sub-setting
Data selection involves determining which records to include when preparing the test data. Subsetting is selecting test data records used for testing. The wrong combination of data may result in a distorted view of the underlying database.
Complex Data Tools and Expertise
Test data management requires sophisticated tools and expertise to ensure accurate results. You can achieve better results with the right combination of tools, technology, and workforce. However, acquiring them may be a challenge for small organizations.
Data Security Compliance
Consider the regulatory environment and changing requirements from various governing bodies. If a company does not have a well-established data security program might not meet the regulatory requirements for data privacy or protection.
5 Test Data Management Strategies
The following strategies can help you achieve the desired test data management outcomes:
Discover and Understand Test Data
The process of understanding test data requires a thorough analysis of all incoming test data. It should include defining the test objectives, identifying the causes of the defects, and determining which should consist of data in the test suite.
Subset Production Data from Multiple Sources
Select and test data from multiple sources. This process involves creating a master table with various sources, such as production databases. Compare and trigger the test data from each source to validate the integrity and accuracy of the entire data set.
Mask Sensitive Test Data
You might divulge test data in the event of unauthorized access. Masking sensitive test data can mitigate this risk and is especially helpful for compliance and regulatory requirements.
The process of masking sensitive data involves hiding the identity or other sensitive attributes of the data. For example, you can mask sensitive data with fictional data to ensure its appropriateness for testing.
Refresh Test Data
Refreshing test data involves creating a new copy of the original test data to keep it updated and protected. This process helps streamline testing and allows you to maintain a consistent, manageable test environment.
Perform Automated Tests
Automate test scripts, such as SQL queries, to execute manual tests seamlessly. These automated tests require minimal involvement from team members and can expedite execution and analysis.
Automated tests also allow you to skip unnecessary steps in the testing process and help identify problems.
The Bottom Line
Test data management determines the quality of testing and hence, the quality of the finished product. Test data must be accurate and well-organized to ensure that a product delivers the required results.
The key is to develop a successful test data management strategy consistent with your organization’s goals and objectives.
