Using RPA for Data Migration



Data migrations are a common activity in large and medium enterprises that are going through a merger, an acquisition, an implementation, a modernization effort, a retirement of a legacy system, an ERP upgrade, or a move to the Cloud. Whatever your business reasons to go through it, using Robotic Process Automation (RPA) for data migrations is a logical approach. This can be easily understood if you consider the nature of the data migration process itself.


RPA is ideally suited to processed that are repetitive, high-volume, rule based and prone to human error. These characteristics align perfectly with the fundamental data migration methodology ETL (Extract, Transform, Load), making a great fit to use RPA for data migrations.

Lets explore a little more as to why each step of the methodology fits the profile:

  • Extract Design: well-defined requirements for the manner in which data will be extracted, held, and verified.

  • Transform: solution design rules guide data transformation for the targeted to-be data structure.

  • Load: clearly defined steps dictate how extracted and transformed data is mapped into the target structure.

  • Test and Recovery: specific unit and integration test plans, along with exit criteria, reporting, roll-back, and recovery procedures for every migration stage.

Additionally, since data migration often involves legacy systems that lack API access, thanks to its user interface (UI)-level integration using RPA for Data migrations, is a low-risk choice, by avoiding impacting underlying systems and databases.


Benefits of Using RPA for Data Migrations

By implementing RPA for data migration processes, you can expect the following results:

  • Increase data migration speed. Multiple robots could be deployed to be migrating and validating continuously.

  • Lower the cost of migration. Due to its simplicity using RPA for data migrations can lower the cost of the migration itself, as it doesn’t require coding or a specific UI.

  • Reduce error. By automating many of the intensive validation steps, RPA reduces human intervention and consequently human error.

  • Integrate data from multiple systems. RPA can help to bring legacy data in line with RPA and data migration.

  • Adding flexibility and scalability. Robots can handle multiple data formats, and create log files as required in a particular situation.

  • Delivering Trackability. RPA has the capacity to track the migration process, identify data inconsistencies or low-quality data and faulty datasets, and rectify the errors in real-time.

Regardless of the triggers to migrate, leveraging RPA for data migrations can be a great ally to help the process be more accurate, fast, and simple.


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