“Going digital” is a popular slogan in most of the industries. Banking industry is not an exception in the digital economy. According to a Forrester report, "about 60% of banks around the world are planning or performing a transformation project”. But data migration is not that easy as it looks. It involves records from different sources and in different formats. The prime challenge is in managing the legacy systems. An ineffective migration leads to high maintenance costs, lack of efficiency & scalability, technical obsolescence and lack of flexibility. Below are some of the data migration challenges in core banking and best practices to overcome them.
Most of the infrastructure experts are not expressing interest in documenting the process than completing the process. But documentation is a crucial part of data knowledge. Poor documentation of legacy systems hinders the effective migration of data. Without a clear understanding of legacy data, it would be tedious to plan the process itself. But majority of the banks’ legacy systems are poorly documented.
Data Quality is one of the major reasons for project delays and cost overruns. It is closely linked to the legacy data. If legacy data is in low quality, the migrated data will be the same. This is due to the lack of expertise in taking prior decisive action on dirty data in the legacy systems. Usually, low data quality is not identified until the target system fails.
The volume of data to be migrated has to be determined in the pre-migration stage itself. With large volume comes increased complexity. This in turn increases the burden of data governance and affects the data quality.
Business Standards & Accounting
Changing business rules affects the data migration process. Some critical scenarios like bank mergers greatly impact the project. There will be uncertainties in source data mapping in accordance with the new business standards. On the other hand, different accounting standards in various geographies have to be taken in to account.
It is common for banks to have multiple data entries for the same customers. Since redundant data is process-oriented, it is inevitable in the legacy system. Depends on the bank’s requirement and the target system specification, the data should be handled. Data duplication or redundancy is closely related to data quality.
Limited conversion period
Based on the volume of data and the nature of targeted system, the conversion period is determined. The short conversion period is a significant challenge for implementation. It increases errors and results in conversion delays.
Data Migration Best Practice
- Proper pre-migration planning
- Constant alignment with the project delivery team
- Cleanse the data before migration to avoid poor data quality
- Timely meeting with high-level decision-making body
- Ensure enough knowledge about the legacy data
- Create reusable documentation of migration methodology
- Quick fix any glitches to minimize turn-around time
- Plan migration rollback in-case of migration failure or data reconciliation
With the proven track record of providing data migration solutions to the banking industry, ISL stands ready with powerful capabilities and expertise to overcome the challenges. From handling legacy data to defining rollover strategies, we help banks to have an error-free transformation and advanced core banking system.