Digital Service Providers (DSPs) face challenges in data migration projects and service upgrade operations. Ongoing trends such as M&As, migration of applications to the cloud, and modernization of legacy applications have increased the frequency and scope of data migration in the DSP ecosystem. However, the execution of data migration using the 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 DSPs adopt the traditional approach for data migration that involves three broad steps: migration planning & preparation, establishing governance, and execution. DSPs follow the fundamental extract, transform, load (ETL) data migration execution methodology.
Major challenges in the data migration execution are:
- Lack of API access in legacy systems
- Higher cost & time – Mock run & sanity test need to be performed for each module
- Lot of re-work – Any minor human error while performing migration activities can result in loss of functionality and more re-work
- High manual efforts – Fallouts due to data integrity issues increases manual efforts
- Difficulty in rapidly ramping-up and ramping-down teams
These challenges combined with the highly structured, rule-based nature of activities make a clear case for (RPA) to automate the execution of data migration. RPA helps in performing data migration easily from legacy systems lacking API access, using UI-level integration. Ramp-up & ramp-down of bots can be done quickly, and it also avoids the impact on underlying systems and databases that makes it a low-risk approach.
This insight details further on RPA based automation framework for DSPs’ data migration execution. The framework encompasses components such as smart processor, automation bot & fallout management mechanism.
- Tamilmani R
- Sathish P
- Abhay Goyal
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