Test data management (TDM) involves planning, designing, storing, and maintaining big data for automated testing purposes. TDM manages the required data for automated tests — primarily source codes of software and applications — so organizations can facilitate quality testing processes with little or no human intervention. Another name for TDM is software test data management.
Why is Test Data Management Necessary?
Test data management is necessary for five primary reasons:
1) TDM Improves Quality Testing
Software testing and quality assurance is a long and laborious process, usually absorbing a large chunk of IT budgets. But quality testing is critical for software deployment, identifying performance and security issues before software or an application enters production.
TDM speeds up quality testing by minimizing testing data and centralizing testing resources. Organizations that invest in TDM can optimize software testing processes, resulting in better-quality products. They can also reduce the high costs and long timelines often associated with deployment.
2) TDM Improves Test Data Quality
Data quality can make or break the software deployment life cycle. Poor quality data increases the chances of a poor quality application, which jeopardizes the integrity of developers.
When organizations execute TDM, they plan, design, store, and manage the data required for automated testing. This process identifies bugs and errors, so organizations can change source codes of software and applications and enhance the quality of data used for testing.
3) TDM Improves Data Fidelity
Test data should resemble data found in production servers. Otherwise, quality testing won't provide genuine insights into the product undergoing testing.
TDM lets organizations manage test data that accurately represents production data, enhancing the effectiveness of automated testing.
4) TDM Improves Data Availability
Automated testing relies on data being readily available at specific times. If required data is not available, quality testing breaks down, which reduces the effectiveness of deployment.
TDM improves data availability, ensuring required data is ready for automated testing.
5) TDM Improves Data Compliance
Organizations need to be wary of data governance, even during quality testing. Automated tests require enormous sets of consumer data, and this data often includes personally identifiable information such as names, addresses, email addresses, and credit card details.
TDM lets organizations plan and manage data used for automated tests, ensuring data complies with GDPR, HIPAA, CCPA, and other data governance frameworks worldwide. This process increases internal and external compliance and reduces the possibility of government-imposed penalties.
How to Perform Test Data Management
TDM separates test data from production data so organizations can perform accurate and reliable automated tests. (Production data is ineffective for automated tests because it usually contains personally identifiable information.)
When an organization selects the required data for testing, TDM uses a process called data masking that conceals sensitive information without compromising the data format.
Data masking takes many forms in TDM:
- Anagramming: The shuffling of letters and numbers in data entries (like an anagram).
- Encryption: The scrambling of sensitive data. Only authorized persons with a key can decrypt this data.
- Nulling: When placeholder characters replace data values.
- Substitution: When random data values from a separate database replace original data values.
After data masking is complete, the data is ready for automated testing. This data should now comply with the relevant data governance frameworks and not contain personally identifiable information.
Test Data Management Challenges
Test data management brings many challenges. Automated testing requires and produces large amounts of big data, and organizations need to develop practical, compliant, and productive processes when preparing this data for testing.
Challenges often arise not because of test data itself but the technologies used to manage this data. Ineffective tools and technologies delay and disrupt TDM, which in turn impacts the entire software deployment process.
One of the most effective methods for managing test data is Extract, Transform, and Load (ETL), which collects and processes data from multiple sources into an operational data store for quality testing purposes. Organizations often have hundreds of data sources, and collecting data from these locations proves difficult. ETL streamlines the process in these ways:
- ETL extracts data from data sources such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems.
- ETL transforms this data into readable, usable formats for quality testing.
- ETL loads the data into a data warehouse or data lake so organizations can prepare it for automated testing.
An ETL platform such as Xplenty facilitates this process end-to-end.