Automation in Data Migration

Let’s take a closer look at where automation has been brought into the data migration space and why it has become so important in this process.

Firstly, automation provides a means of making the entire migration efficient, more accurate, and faster. For this reason it delivers a superior result at reduced cost. It does this the following ways:

1. Speed – automation provides the ability to perform specific tasks and functions at a speed that cannot be achieved manually, hence reducing the overall time required for specific migration tasks.

2. Consistency – automation allows tasks to be executed in a consistent manner throughout the migration process and across different migration runs which would normally spans across weeks or months depending on the number of runs planned.

3. Effectiveness – automation streamlines tasks within the migration process in a way that reduces manual work in turn leading to fewer errors. Automation also leads to a reduction in the risk of losing data and increases overall efficiency.

4. Precision – automation will increase the precision and accuracy of data migration tasks (data transformations, business rule execution, and data mappings) resulting in a more accurate migration of data, run after run.

Automation is also making a big impact at various phases of the data migration process, for example:

1. Data validation and data profiling – Validating and profiling the data from the source system requires understanding the quality of the data needing to be migrated to the target system (in terms of the target system data requirement). Data validation rules are created in line with the data requirements which are stipulated by the target system. Automation assists with data validation by programmatically executing the validation rules on the source data and reporting on results. This provides a view into the quality of the source data.

2. Data analysis – Analysing the source data provides a better understanding of the data that needs to be migrated. Automation allows for the identification of relationships within the data and dependencies between the data. Automation allows for this to happen at scale and at a fraction of the time it would take to identify these manually.

3. Transformations and data mapping – Transformation means to map the source system data to the target system data structure using transformation, translation and mapping rules. Automation allows the implementation of rules to ensure the rules are executed correctly, accurately, consistently, and timely, across all migration runs.

4. Testing – The migration process has various steps to test and verify that the migrated data is correct, accurate, and complete. Automation ensures the correct testing and verification checks are conducted at the right time during the migration process ensuring accuracy.

5. Data Reconciliation – Every data migration run requires migration assurance. Data migration assurance provides the required proof, reporting and sign-off from all stakeholders that the migration has been completed accurately and correctly. Automation is used to execute the reconciliation of control totals, field level data, migration rules, as well as generating the reconciliation reporting used for sign-off.

6. Post-migration activities – Post migration activities can be automated to ensure that all activities are executed in the correct sequence. During post migration, the correct hyper-care areas are identified in time and early warning notifications are put in place. Automation provides the flexibility to create required safety nets and actions for post migration activities.

Automation is a critical part of any data migration process. It allows a certain level of predictability within the process when running a specific dataset. Automating the migration process means reusable functionality and workflow tasks that are created will provide a running start to your migration process.