The ever-increasing amount of data is becoming a major challenge for our companies. The variety of data ranges from employee and customer information to complex scientific data – data that impacts decisions and strategic business movements. The systems that process this data are often very complex and the technical requirements for these systems are evolving at an unprecedented rate. Therefore, these systems need to be adapted in good time to the new requirements. As a result, the data must be moved or integrated into the new system. The process of moving data from one system to another – data migration – is an important process in any organization. For example, companies move their data to start a new application, update databases, or merge data from different sources.
Why is data migration testing so important?
While moving data from a source system to a target system without a clever testing strategy seems straightforward, organizations can easily encounter serious problems. If no data is available in the target system after data migration – this is called data loss – or if the data is not qualitative and integer, the company incurs high costs or important deadlines may be missed.
It is therefore important to have a strategy that guarantees the success of data migration projects. Such projects require systematic planning, the right tools, knowledgeable team members, and intensive testing to be successful. In this article, we focus on the requirements of a successful test strategy.
The following points should be considered to perform a successful data migration and avoid data loss.
1. Planning the Test Activities
Planning and strategizing the testing activities need to get the same attention as planning the appropriate migration approach, since testing allows you to actually validate the success of your migration in respect to data completeness, content, or integrity. Therefore, the following considerations should be included in a data migration testing Plan.
2. Define the Test Objectives
In a first step, the business requirements related to data migration need to be identified. It is important that the testing scope covers these requirements.
3. Define the Testing Scope
Validation of the complete data in the source and the target system is the best approach to identifying data corruption. If you can only test a subset of your data, ensure that you choose a large enough sample size so that you have a high enough confidence and you can trust in your testing result.
4. Prepare your Source Data
Before migrations, the source data needs to a thorough audit. It is crucial to understand the source data in order to perform data mapping between your source system and your target application. In your audit, it is important to include data types, tables, field names, transformations and business rules. Further, semantic risks need to be identified. For example, columns in the source and the target system have the same meaning but differ in their unit of measurement.
5. Data Clean-up
Once you identified any issues with your source data, unnecessary data must be removed and wrong data must be corrected. Any issues in your source data can be amplified during your migration.
6. Risk Analysis
In a next step, identify possible errors within the migration process and try to mitigate these risks. Testing needs to uncover these scenarios.
7. Control Mechanisms
During migration, data gets transformed what can potentially result in mistakes. Consequently, there must be controls based on your planning in place to maintain data integrity. Check for data completeness, data quality, or misplaced data. So, include in your testing if these data transformations are in accordance with your business rules and are correct according to your mapping.
Monitoring and reporting the data quality during the migration process is crucial and it enables a better understanding of the subsequent steps you need to undertake in order to guarantee a successful migration.
Regardless of the testing strategy you follow, there are some best practices to remember: Stick to your testing strategy even if the migration run seems to go very smoothly, and test as much as possible. The more information you can collect about your migrated data the more reliable they are.