Financial institutions, including banks, face unprecedented challenges from rising consumer expectations, mergers and acquisitions, continued digital disruption, and intense competition from FinTechs. With a growing focus on implementing newer systems and processes, banks are continuously introduced to fresh data sources and diverse data formats. Data migration in banking and other financial institutions most commonly involves transferring data from legacy systems to newer ones, which is often easier said than done.

Data migration is one of the key activities for any system implementation or upgrade that entails significant amounts of risk. Migration is rarely simple, and there are very few situations in which everything can be fully validated. Validating migrated data is an assessment and balancing act between enterprise risks and business priorities. So, let’s understand more about data migration in banking in the guide below.


What is Data Migration?

Data migration in banking is an essential process as financial institutions frequently update their systems to take advantage of new technologies and capabilities, often with the help of data management service providers. This process involves moving data from legacy systems to new core banking platforms, often with different data structures, formats, and architectures. Effective data migration ensures that all relevant information is transferred accurately, consistently, and securely, minimizing the risk of data loss or corruption.

Importance of Data Migration in Banking Industry

In the banking and finance industry, trust is paramount. An Accenture study revealed that a drop in trustworthiness cost companies $180 billion in revenue over two years.

Technology is at the core of every financial institution today, enabling analytics for personalization and trust-building with digital customers who demand anytime/anywhere banking. This necessitates core banking migrations to seamlessly move customer data and contracts across systems and improve efficiency, experience, and costs, though data’s deep embedment in siloes makes migration highly challenging.

Gartner reports that over 80% of data migration projects exceed deadlines or budgets, with some resulting in complete failure.

With accelerated digitization and technology becoming pervasive, especially in the context of “Risk management in banking,” data migration experts have invested in building migration accelerators, tools, and specific methodologies to improve the success rate of migration projects.

Here are some of the key reasons why data migration in banking sector is important:

  • Consolidating data from multiple sources during mergers and acquisitions.
  • Transferring data during legacy system replacement without loss.
  • Maintaining accurate, consistent data for regulatory compliance and reporting.
  • Consolidating and cleansing scattered data to improve overall quality.
  • Providing seamless, consistent customer experience across all channels.
  • Enabling quick data recovery for business continuity in disasters.

Core Banking Data Migration Strategy


To ensure a seamless data migration process in the banking industry, it is essential to develop a well-defined core banking data migration strategy. This strategy should consider the following best practices:

Thorough Planning and Analysis

Conduct a comprehensive analysis of the existing data landscape, including data sources, formats, volumes, and dependencies. Identify critical data elements, emphasizing big data analytics in banking, and prioritize their migration based on business requirements and regulatory compliance.

Data Quality Assessment

Assess the quality of the source data and address any data cleansing or data transformation requirements before migration. Ensure that the data is accurate, consistent, and complete to maintain data integrity during the migration process.

Data Mapping and Transformation

Develop a detailed data mapping plan that aligns the source data structures, emphasizing “Data Management service in Banking,” with the target system’s data models. Identify any required data transformations, such as data format conversions, calculations, or data enrichment, to ensure compatibility with the new system.

Robust Testing and Validation

Implement rigorous testing and validation procedures to ensure the accuracy and completeness of the migrated data. This may involve creating test cases, performing data sampling, and conducting reconciliation checks to verify that the migrated data is consistent with the source data.

Secure Data Transfer

Implement secure data transfer protocols to protect sensitive customer and financial data during the migration process. Ensure that data is encrypted in transit and at rest and that access controls are in place to limit exposure to authorized personnel only.

Comprehensive Monitoring and Auditing

Establish monitoring and auditing mechanisms to track the progress of the data migration, identify any issues or errors, and maintain a detailed audit trail for regulatory compliance and future reference.

Fallback and Contingency Planning

Develop a comprehensive fallback and contingency plan to minimize the impact of any unexpected issues or failures during the migration process. This may involve creating backup copies of the source data, establishing rollback procedures, and implementing disaster recovery strategies.

Why Choose Intone?

Thus, by following the best practices for data migration in banking, banks can execute data migration projects with minimal disruption to their operations, ensuring the accuracy, security, and continuity of critical data assets during the transition to new core banking platforms. Hence, we at Intone take a people-first approach to data optimization. We are committed to providing you with the best data integration and management service possible, tailored to your needs and preferences. We offer you the following:

  • Knowledge graph for all data integrations done
  • 600+ Data, and Application and device connectors
  • A graphical no-code low-code platform.
  • Distributed In-memory operations that give 10X speed in data operations.
  • Attribute level lineage capturing at every data integration map
  • Data encryption at every stage
  • Centralized password and connection management
  • Real-time, streaming & batch processing of data
  • Supports unlimited heterogeneous data source combinations
  • Eye-catching monitoring module that gives real-time updates

Contact us to learn more about how we can help you!