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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