Top Challenges and Best Practices in the Data Migration Process

New age digital technologies and innovations have a profound impact on several industries while redefining customer expectations. This wave of change has even swept through the banking industry, which is now gradually focusing on evolving and identifying new revenue streams.

However, digital adoption among banks and financial institutions has been slow to pick up pace and several banks are still using legacy systems. These systems lack flexibility in architecture and cannot provide anything close to the revolutionary new digital experiences.

To this effect, with the aim of evolving and becoming more futuristic in operations, banks are now focused on core banking transformation.

Transforming core banking systems however, presents some challenges in the process of migrating data from existing systems to the new platform.

The very essence of Data Migration lies in the key process of data extraction, data cleansing, transformation and mapping, and data archiving.

These activities demand significant investment of time and money. Accurate migration strategies are crucial to ensuring effective transformation of their core banking systems.

Let’s explore some key challenges banks face in the Data Migration process.    

  • Limited Data Knowledge

A key factor is the failure of many Data Migration projects is a lack of understanding of the data in legacy systems. This could be due to

  • Incomplete documentation of legacy systems
  • Data correlations not defined accurately
  • Lack of resources that understand the data in the legacy system
  • Assumptions about data structure’

  • Quality of Source Data

Data quality in migration projects are critical to success. Data quality issues that are found in legacy systems pose one of the biggest challenges and reasons for delays in the project. Many banks are unaware of these data quality issues prior to beginning a core banking transformation. These data quality issues must be identified early to avoid target system failures.

  • Large Volumes of Data

The large volumes of data in legacy systems brings increased complexity to the migration process. This affects the data quality and effectiveness of data governance.

  • Alignment of Business Standards and Accounting

Evolving business rules have an impact on the bank’s accounting activities. It is important that data is mapped in line with prevalent business standards.

  • Mapping of Data

Mapping data on the basis of assumptions causes several errors, thereby hindering the process of Data Migration.

  • Duplicate Data

Banks must ensure that data is handled in such a way that data duplication is avoided.   

  • Reconciliation of Data

All the data from the legacy system – both financial and non-financial should be properly migrated to preserve data sanctity. Complex business rules and large volumes of data makes reconciliation a challenging task. 

Best Practices in the Data Migration Process

Let’s look at some best practices that should be implemented for a seamless and successful Data Migration process.   

  • Ensure the migration strategy finalization involves all stakeholders including business, marketing, operations and customer relations
  • Ensure mapping and reverse mapping of data fields are given equal importance
  • Use the staging area for cleansing and de-duping
  • Identify reconciliation requirements and automate reconciliation reports generation ahead
  • Plan sufficient mocks to test migration of all systems incrementally
  • Perform at least 2 drills to mimic the migration, before going for the real-life migration
  • Prepare and test roll back plans for each stage of migration as part of the drill runs
  • Co-ordinate between business, IT and customer support to ensure customer has a seamless experience during transition

The Last Word

An effective Data Migration process enables banks to upgrade to the latest systems with minimal disruption to business operations.

Evolvus’ Data Integration solution gives banks intelligent tool kits that address the complete Data Migration lifecycle and allow seamless consolidation of disparate systems.