Data migration is the process of moving data from one system to another, usually an older legacy system to its new replacement system. While this might seem straightforward, it may involve a change in storage, databases or applications.
Many Enterprises today embark on the data migration journey for various reasons. Some examples include the need to overhaul entire systems, upgrade databases, establish a new data warehouse, or merge new data from acquisitions or from other sources.
Data migration is also necessary while deploying another system that works alongside existing applications. No matter what the end the goal is, such deployment is generally carried out to enhance performance and increase competitiveness. Companies approach data migration depending on their priorities and often pick either of the 2 strategies mentioned below –
- Big Bang Approach: Where the full transfer is completed within a limited time window
- Trickle Migration: Where migration process is completed in various phases
Data migration involves three steps:
- Extract: The source data is processed and extracted based on one’s need
- Transform: Its format is then mapped to the target system
- Load: The data is introduced into the new system using custom interfaces. Finally, it is ensured that migration has been successful and complete
The above process clearly suggests most of these activities if not all are manual in nature and need very less cognitive faculty because they are all rule based. Though these are routine tasks, they are structurally complex and require a lot of time and a high cost is associated with them. This is where the challenge lies. Not to forget that once the data is migrated, the source system is switched off and any remaining data on it is discarded. If something were to go wrong during data migration, it would be a herculean task to recover from the losses.
Automation helps in end to end data migration making it simple and fast. The speed at which the automation enabled end to end activity gets over saves cost. Another advantage over manual automation is that unlike the traditional data migration where the data inconsistencies may be detected after the event, the entire data set can be scanned in very less time compared to manual scanning by a Digital worker/BOT and any error can be rectified before the data is transferred to the new system.
Here are some case studies that have been dealt successfully by Zensar:
- A Business was outsourced from Company X to Company Y. The company Y had a dual task of maintaining all its case management data in two systems. It had employed 17 temporary data entry operators to manually load the data from one system into another. This was not only a slow process that was creating a huge backlog of cases to be migrated and updated but also was highly error prone. Robotic Process Automation (RPA) helped overcome the challenge by adding speed and efficiency to the process. Only two BOTs compared to 17 associates performed the activity releasing the bandwidth of human talent for more creative purposes.
• A leading insurance company acquired a “Book of Business” or block of policies from a company Y. Policy documents were scanned images instead of being in electronic format that could have been digitized. Hence manual intervention and manual data entry process were required which were not only time consuming but also vulnerable to errors. This end to end process was ‘Botified’ using intelligent digital workers that utilized computer vision to read the scanned documents, converted the images into text files, read the data from the CSV files and loaded them into destination systems. This improved the productivity by more than 90% and reduced the Average Handling Time (AHT) from hours to minutes.
Read through the below mentioned few other use cases with respect to data migration where the BOTs have helped to improve efficiency, save time and cost and reduce errors are
- Mergers or Acquisitions with other companies
- Expanding into new lines of business
- Initiating Green IT projects to reduce hardware
- Synchronizing data and processes across organizational boundaries
- Conducting a Global Rollouts of ERP or CRM Systems and many more.
On a concluding note, RPA can help organizations tackle many challenges related to data migration. The above use cases and case studies show how customers have effectively deployed RPA in multi-sized data migration projects, and accomplished the following –
- Speedy content migration
- Extraction of metadata and transformation of content using business rules
- Synchronization of content between various systems
- Reduction in IT costs for document and content migrations
- Providing ongoing system integrations with additional systems to minimize manual work
- Capturing of the day-forward data into the proper repository and workflows
If you are interested in a data migration POC using RPA and are keen to add value to your customer, please get in touch with us. Leave a comment below.