Why RPA for Data Migrations?
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.
In previous entries we have shared the ideal characteristics a process would need to have in order to be a good candidate for RPA but to sum it up, activities that are highly structured and rules-based, are what robots do best.
These two main characteristics align perfectly with the fundamental data migration methodology ETL (Extract, Transform, Load), making a great fit to use RPA for data migrations.
Uipath deepens into this by describing 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.