Leverage RPA based Automation Framework to accelerate data migration and improve accuracy
Data migration involves moving data between locations, formats, and applications. This need is on the rise due to ongoing trends such as mergers and acquisitions (M&As), migration of applications to the cloud, and modernization of legacy applications. However, the execution of data migration using traditional methods is not at par with the increasing frequency!
According to Gartner, 50% of the data migration initiatives will exceed their budget & timeline by 2022 because of flawed strategy & execution. Most of the service providers in the connectedness industry adopt the traditional approach for data migration that involves three broad steps: migration planning & preparation, establishing governance, and execution.
Service providers follow the fundamental extract, transform, load (ETL) data migration execution methodology, which is full of challenges. It entails high cost and time due to mock runs and testing for each module. Moreover, it involves manual efforts, which leads to a lot of re-work due to errors and causes fallouts due to data integrity issues. Also, ramping up and down the teams is difficult.
To overcome these challenges, an RPA based automation framework for data migration execution could be an effective approach. The framework encompasses components such as:
- Smart processor: Identifies data quality & integrity issues in the source data at a very early stage
- Automation bot: Performs migration/upgrade by extracting & updating data at various layers of the application
- Fallout management mechanism: Automates the fallout handling, i.e., Fix data quality & integrity issues in CRM, inventory systems, etc.
” According to Gartner, 50% of the data migration initiatives will exceed their budget & timeline by 2022 because of flawed strategy & execution.”