What are the differences between manual and automated data migration testing?

1 Manual testing

Manual testing is the process of verifying the data migration results by manually checking the data in the source and target systems, using queries, reports, or tools. Manual testing is often used for small-scale or simple data migration projects, where the data volume, structure, and transformation rules are manageable and straightforward. Manual testing can also be useful for exploratory testing, where the testers can identify and report any unexpected issues or anomalies in the data. However, manual testing has some drawbacks, such as being time-consuming, error-prone, inconsistent, and dependent on the skills and availability of the testers.

2 Automated testing

Automated testing is the process of verifying the data migration results by using scripts, tools, or frameworks that can perform the data validation tasks automatically, without human intervention. Automated testing is often used for large-scale or complex data migration projects, where the data volume, structure, and transformation rules are too cumbersome or complicated to test manually. Automated testing can also provide advantages such as being faster, more reliable, more consistent, and more scalable than manual testing. However, automated testing also has some challenges, such as requiring more upfront effort, resources, and expertise to design, develop, and maintain the automation scripts or tools.

3 Hybrid testing

Hybrid testing is the process of combining manual and automated testing methods to achieve the optimal balance of quality, efficiency, and coverage for the data migration testing and validation. Hybrid testing is often used for medium-scale or moderate data migration projects, where the data characteristics and requirements vary across different domains, sources, or targets. Hybrid testing can also leverage the strengths and mitigate the weaknesses of both manual and automated testing methods, by using automation for repetitive, high-volume, or low-risk data validation tasks, and using manual testing for exploratory, low-volume, or high-risk data validation tasks.

4 Test strategy

The test strategy is the document that defines the overall approach, scope, objectives, and criteria for the data migration testing and validation. The test strategy should be aligned with the data migration plan and the business requirements, and should consider factors such as the data quality, complexity, volume, transformation rules, source and target systems, test environment, test data, test cases, test tools, test schedule, test roles, and test reporting. The test strategy should also specify the choice of manual, automated, or hybrid testing methods for different phases or scenarios of the data migration testing and validation.

5 Test execution

The test execution is the phase where the data migration testing and validation tasks are performed according to the test strategy and the test cases. The test execution should follow a systematic and structured process that includes steps such as preparing the test environment, setting up the test data, running the test cases, comparing the test results, logging the test defects, and reporting the test status. The test execution should also ensure the quality, accuracy, and integrity of the migrated data by applying various types of tests, such as data completeness, data correctness, data consistency, data transformation, data reconciliation, and data performance tests.

6 Test evaluation

The test evaluation is the phase where the data migration testing and validation results are analyzed and assessed to determine the success or failure of the data migration project. The test evaluation should involve reviewing the test metrics, test reports, test defects, and test feedback, and comparing them with the test objectives, criteria, and expectations. The test evaluation should also provide recommendations, lessons learned, best practices, and improvement opportunities for the data migration project or future data migration projects. The test evaluation should also ensure the satisfaction, confidence, and acceptance of the stakeholders and users of the migrated data.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?