Successful Data Migration During Banking Mergers & Acquisitions
Table of Contents

Successful Data Migration During Banking Mergers & Acquisitions

Banks that are looking to expand through mergers & acquisitions will need to go through a data migration process, whether it's by migrating their systems to the cloud or by integrating on-premise / hybrid data after purchasing small to medium local banks. Post-merger, banks will inevitably face the challenge of integrating their data into their organizational systems.

While the data migration process may appear straightforward with all the cloud solutions out there, it can be fraught with challenges without the right team and plan. First, however, it's important to understand what data migration is, what cloud migration solutions can be used and how the migration process can be effectively executed to integrate data from your acquisitions into your main organization.

Data migration, in the context of banking, is more than just a technical task; it's a critical strategic operation that involves moving valuable and sensitive information from one system to another. This process is fundamental when incorporating new entities into an existing banking framework, as it ensures that all data is unified, accessible, and most importantly, secure. Given the highly regulated nature of the banking industry, data migration is not just about transferring bytes; it’s about maintaining data integrity, ensuring compliance, and minimizing disruption to services.

In this article, we'll delve into the nuances of data migration specific to the banking sector. From understanding the types of data that need migration, to the selection of appropriate cloud services, tools and methodologies, we will guide you through the best practices to ensure a smooth, secure, and efficient data migration process. 

Key Considerations for Data Migration in Banking

As in every development, there are some requirements that should be addressed before delving into executing any data migration plans. You must keep in mind that data is the bread and butter of the banking industry, and there are heavy regulations in place to ensure data protection for the average banking customer. Below are some key areas that you might want to address first.

Regulatory Compliance and Security

Understanding Regulatory Requirements: It’s crucial to understand and comply with various financial regulations, such as GDPR in Europe, the Sarbanes-Oxley Act in the U.S., and other local regulatory requirements. These regulations dictate how data should be handled, stored, and transferred, making compliance a top priority during migration.

Implementing Data Security and Privacy Measures: Data migration must ensure the confidentiality, integrity, and availability of customer data. Implementing robust encryption techniques, access controls, and data masking are essential to protect sensitive information during and after the migration process.

Data Quality and Integrity

Ensuring Data Accuracy: The accuracy of data is paramount in banking. Incorrect or outdated information can lead to erroneous decision-making and regulatory non-compliance. It’s important to establish processes for cleaning, deduplicating, and validating data both before and after the migration.

Addressing Data Redundancy and Inconsistencies: Redundant and inconsistent data can create issues in data analysis and reporting. Identifying and resolving these issues is crucial for maintaining a streamlined and efficient database.

System Downtime and Customer Impact

Minimizing Downtime During Migration: Banks need to ensure that their services remain uninterrupted during migration. Planning for migrations during off-peak hours and having contingency plans in place can help minimize downtime.

Maintaining Customer Service and Access: Customer access to banking services should be a top priority. Clear communication about any potential service interruptions and swift resolution of any migration-related issues will help maintain customer trust and satisfaction.

In summary, data migration in banking is a delicate balance between technical execution and strategic foresight.

Data Migration Approaches for Banks

Whether opting for a phased approach or a big bang method, the right choice of tools and the incorporation of cloud computing can significantly enhance the efficiency and security of the data migration process.

Phased Migration vs. Big Bang Approach – Pros & Cons of Each Method

The Phased Migration involves transferring data in stages. It allows for testing and adjustments after each phase, minimizing risk and reducing the impact of potential issues. However, this method can be more time-consuming and may require more resources. For this method, dependencies must be accurately mapped out to ensure compatibility issues don’t arise.

The Big Bang Approach entails migrating all data at once, usually over a short period, often during off-peak hours. This method is faster and can be less costly, but it carries a higher risk, as any issues can affect the entire dataset at once.

Role of Cloud Computing in Data Migration

Cloud computing offers scalability, flexibility, and cost-efficiency. It can also enhance security and compliance measures, given that many cloud service providers are equipped to handle the stringent requirements of the banking industry.

Azure for Cloud Computing

Azure is a pivotal cloud computing platform for data migration in banking, offering services like Azure Blob Storage, Azure Data Factory, Azure Files, and Azure SQL Database. These tools are designed to meet the banking sector's specific needs for data storage, processing, and archiving.

Here’s how it works:

A Data Lake ingest model

Banks using Azure can expect streamlined data migration, robust security, and better regulatory compliance tailored to each bank’s unique requirements. The platform's scalability allows for cost-effective data management, essential for handling large volumes of sensitive financial data. Azure also facilitates efficient data transformation and integration, key to decision-making in banking operations.

Moreover, Azure's disaster recovery and backup options ensure data is always secure and accessible. It enables banks to manage their data lifecycle effectively, complying with regulations while enhancing operational efficiency. Azure's comprehensive services make it a valuable asset for banks in their digital transformation journey.

AWS Database Migration

While Azure provides a comprehensive suite of services tailored for data management and migration in the banking sector, AWS Database Migration Service (AWS DMS) offers a flexible and robust solution for migrating various types of data stores, including relational databases and NoSQL databases, into the AWS Cloud.

Key features of AWS DMS include the discovery of source data stores and schema conversion, facilitating easy data migration. The DMS Fleet Advisor assists in identifying and cataloging on-premises database and analytic servers for migration to AWS Cloud. For migrating to a different database engine, AWS DMS offers Schema Conversion, which automatically adapts source schemas to a new target engine. Users can also utilize the AWS Schema Conversion Tool (AWS SCT) for local schema conversion.
Source: What is AWS Database Migration Service? - AWS Database Migration Service (

AWS DMS supports both one-time migrations and continuous replication to synchronize source and target databases. It integrates seamlessly with AWS Cloud, offering cost efficiency, rapid deployment, robust security, and flexibility. The service involves creating source and target connections for data extraction and loading, followed by scheduling tasks for data movement.

AWS DMS can automatically create the necessary tables and primary keys in the target database, or users can manually set up these tables or use AWS SCT for this purpose. This service streamlines the data migration process, making it an efficient tool for modernizing database infrastructure.

Google Cloud’s Database Migration Service

Google Cloud's Database Migration Service (DMS) is ideal for transitioning from monolithic architectures to microservices on Google Cloud, significantly reducing downtime during migration.

DMS supports a variety of data stores and is designed for minimal disruption, allowing migrations that traditionally take days to be completed in hours, and reducing expected downtimes from hours to minutes. Its seamless integration with Google Kubernetes Engine (GKE) facilitates a smooth transition of both databases and applications to the cloud, underscoring Google Cloud's commitment to a holistic cloud ecosystem. Google Cloud's DMS is a robust choice for businesses seeking reliable, rapid database migration with minimal operational impact.

Best Practices for Successful Data Migration

The success of a data migration project in the banking sector hinges on meticulous planning, effective stakeholder engagement, and thorough testing and validation.

Comprehensive Planning and Analysis

Establishing Clear Objectives and Scope: Define what you want to achieve with the migration, including the specific data sets to be moved, the timeline, and the budget. Clear objectives help in focusing efforts and measuring success.

Performing Data Audits and Assessments: Before initiating migration, conduct thorough audits of the existing data. This helps in understanding the quality, structure, and intricacies of the data, which is essential for planning the migration process effectively.

Stakeholder Engagement and Communication

Involving Relevant Departments and Teams: Data migration impacts various parts of the organization. It's important to involve IT, compliance, operations, and other relevant departments from the start to ensure their requirements and concerns are addressed.

Keeping Stakeholders Informed Throughout the Process: Regular communication with stakeholders, including updates on progress and any challenges encountered, is vital for maintaining trust and alignment throughout the migration process.

Testing and Validation

Importance of Rigorous Testing: Before going live with the new system, conduct extensive testing to ensure the data has been accurately migrated and that all systems function as expected. Whether this is by working on duplicates of the production system in an isolated environment or in staging environments pre-launch, make sure to test for data integrity, performance, and security.

Approaches to Validation and Quality Assurance: Implement a validation plan that includes checks at different stages of the migration. Use tools and methodologies that ensure data accuracy and completeness post-migration.

Post-Migration Activities

After the completion of a data migration project in the banking sector, the focus shifts to ensuring the stability and optimization of the new system. Post-migration activities are critical to confirm that the migration objectives have been met and to maintain the integrity and efficiency of the data in its new environment. 

Data Monitoring and Optimization

Continuous Monitoring for Anomalies and Errors: After migration, it is important to continuously monitor the data to quickly identify and rectify any anomalies or errors that may arise. This ensures data integrity and helps in maintaining trust in the system.

Performance Tuning and Optimization: Analyze the performance of the new system and make necessary adjustments. This may include optimizing database queries, revising data storage methods, or enhancing data retrieval processes to improve efficiency and performance.

Training and Support for Staff

Comprehensive Training Programs: Ensure that staff are adequately trained on the new system. This includes training on new features, data access protocols, and any changes in data processing procedures.

Providing Ongoing Support: Establish a support system to assist staff with any queries or issues they may encounter. This could involve setting up a helpdesk, providing online resources, or scheduling regular check-in meetings to address concerns.

Review and Feedback for Continuous Improvement

Retrospectives - Gathering Feedback from Stakeholders: Collect feedback from various stakeholders, including employees, management, and end-users, to gauge the effectiveness of the migration and identify areas for improvement.

Implementing Lessons Learned: Analyze the feedback and the overall migration process to understand what worked well and what could be improved. Document these lessons learned for future reference, as they can be invaluable for guiding future data migration projects or other large-scale IT initiatives.

The post-migration phase is crucial for ensuring the long-term success of the data migration project. Through diligent monitoring, optimization, staff training, and a continuous improvement approach, banks can ensure that their data migration efforts yield the desired results and support their ongoing operational and strategic goals.

Case Studies

BNP Paribas – Data Migration and Integration Layer Implementation

Bank Overview: BNP Paribas, a prominent player in the banking sector, offers a range of financial products and services including savings, investment solutions, loans, and financing for businesses, particularly in the agriculture and food sector.

The Challenge: BNP Paribas faced a significant challenge in managing real-time data processing with large volumes and handling & migrating data from multiple internal and external systems. The primary issues involved migrating data from multiple systems and handling diverse systems, necessitating efficient message processing and dispatching between various sources and systems.

The Solution: ITMAGINATION helped BNP Paribas address this challenge by designing and implementing an Integration Layer. This solution enables the real-time exchange and search of data through a mobile application for retail customers. It features full-text search capabilities and integrates the core banking system with mobile and internet banking platforms.

The system, utilizing API translation is comprised of two main modules: Transaction History and Notification Hub. Three months post-production, the data managed by the system reached 3 TB, serving 2.6 million customers and handling 2.5 million transactions daily.

The Results 

  • Efficient real-time transaction processing.
  • High-availability and fault tolerance, making the system resilient to failures.
  • Scalability, as the system was designed to be easily extended with additional modules and messages.
  • Establishment of a common data repository and a unified message processing point.

Credit Agricole – Comprehensive Data Migration

Bank Overview: Credit Agricole, offers a broad spectrum of financial products. Its focus areas include consumer finance products and services for the agriculture sector.

The Challenge: The bank faced the complex task of migrating data related to loans, credit cards, clients, and debt collection to its central banking system.

The Solution: ITMAGINATION spearheaded the data transformation process, utilizing its proprietary CLIO solution. CLIO controlled the migration engine (ETL), ensuring data validation and reconciliation. It also provided detailed, real-time migration reports via a dedicated dashboard accessible through a web browser.

The Results: 

The production migration, completed in just 13 months, achieved remarkable outcomes:

  • Successfully migrated over 4 billion records, maintaining high data quality (only 14 records rejected due to incorrect source data).
  • Narrowed the difference in the balance sheets of source and target systems to an acceptable PLN 216, in line with client agreements.
  • Designed and implemented data migration across 18 migration flows (from 9 source systems to 4 target systems), including over 3.2 million credit accounts and 1.3 million credit cards.
  • Developed over 650 reconciliation reports, available in real-time on the CLIO dashboard, to assess migration quality.

DNB Bank – Data Warehouse

Bank Overview: DNB Bank, part of the Norwegian DNB group, is a key player in the corporate banking sector. DNB ASA, the parent company, boasts significant financial assets and a strong market presence with its headquarters in Oslo, Norway.

The Challenge: ITMAGINATION was tasked with developing and deploying a new data warehouse to serve as the principal data repository for mandatory and operational reporting, as well as Management Information Systems (MIS) for the bank.

The Solution: The Data Warehouse created by ITMAGINATION integrates over 20 systems, including two core banking systems. The team also managed the creation and maintenance of metadata (Banking Data Model), crucial elements of this solution. 

ITMAGINATION's proprietary CLIO solution was utilized to oversee the ETL (Extract, Transform, Load) engine, ensuring data validation and reconciliation. Furthermore, for the EBA2 project, ITMAGINATION executed the ETL process with thematic Data Marts and reporting in compliance with BASEL II requirements.

The Result: ITMAGINATION's experts are integral to the team managing the bank's business needs in the domains of DWH (Data Warehouse), BI (Business Intelligence), and MIS. The partnership persists, with the team actively gathering business requirements and adding new functionalities to the system.

PayU's Data Warehouse Migration to Azure Cloud

Bank Overview: PayU is a leading fintech company that specializes in payment technologies for online merchants. With its headquarters in the Netherlands, it operates within the Naspers group and provides services across 17 countries.

The Challenge: PayU faced the complex task of transferring its data warehouse from the Allegro group's Oracle Exadata infrastructure to the Azure cloud using the MS SQL Enterprise database engine, aiming to become IT-independent from Allegro.

The Solution: A dedicated team from ITMAGINATION, consisting of 7 data engineers and architects, completed the necessary migration work within approximately 3 months, allowing PayU to achieve complete independence from Allegro's IT resources.

The Results:

  • Redesigned data model and business logic to fit the new environment.
  • Implemented Azure Cloud for the first time in PayU's architecture.
  • Successfully migrated over 10 TB of structured data, with thorough quality and performance testing.
  • Achieved a remarkably fast migration timeline of 3 months from the initial need to the go-live date.

ITMAGINATION continues to collaborate with PayU, developing new features and optimizing additional data sources.


Successful data migration isn’t only about moving data from one system to another; it encompasses a wide range of activities such as: ensuring regulatory compliance, maintaining data integrity, minimizing downtime, and involving all stakeholders in the process.

What does this mean? Data migration in banking is more than a mere technical challenge; it's a strategic initiative that impacts the entire organization. Banks that approach data migration with a detailed plan, clear objectives, and adherence to best practices are more likely to experience a smoother transition with minimal impact on their operations and customer service.

Looking ahead, the banking sector will continue to evolve with advancements in technology and changes in regulatory landscapes. Data migration strategies will need to adapt to these changes, ensuring that banks can leverage their data assets effectively while maintaining the highest standards of security and compliance. 

If you believe that data migration should be viewed as an ongoing journey of improvement, adaptation, and strategic importance in the dynamic world of banking, then you are one click away from scheduling a call with our team of experts. Let’s discuss how ITMAGINATION can kickstart your data migration and integration project; we have a team of adaptable and experienced architects and engineers ready to help you complete a successful data migration in a very short time frame.

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