Navigate the Complexities of Data Migration and Decommissioning for Legacy Banking Systems
Successful data migration is a critical activity for Digital Transformation in Banks. Many times data migration can be challenging due to insufficient time and resources being allocated, resulting in a not-so-ideal Data Migration Plan (DMP), along with insufficient testing and support.
Top Data Migration Challenges in the Big Move
1. Lack of well-defined cloud migration strategy: Migrating business applications and data requires careful strategy and planning. The most methods for migrating are —
Lift and Shift: Moving applications and infrastructure from on-premises to the cloud with minimal changes.
Replatforming: Making some modifications to the existing applications for better compatibility with the cloud environment.
Refactoring: Restructuring applications by leveraging cloud-native services and architectures for optimal performance.
Repurchasing: Replacing existing applications with cloud-based software-as-a-service (SaaS) alternatives.
Retiring: Phasing out outdated applications and systems that are no longer necessary.
The challenge lies in ensuring a seamless transition to the cloud while addressing complexities such as data security, application compatibility, and operational disruption.
2. Data security and compliance risks: Companies are still hesitant about handing over their data to third parties. A recent SANS Report shows that 56% of companies are concerned about cloud security. If data is exposed cyber attacks can cause serious disruptions.
3. Financial cost of the cloud migration process: Cloud migration is a costly process, especially if the company fails to analyze its financial potential.
The cost of using cloud platforms plus moving to the clouds & momentary risks involved from slow adoption can be a hindrance in the process.
As challenging as it can be, it is unavoidable. For Banks and Financial Institutions, to shift to cloud migration, the most important is to build a strategy and use it to the right potential.
Decommissioning Legacy Systems in Financial Institutions
In data migration processes it may be necessary to Decommission Legacy Systems for multiple reasons. It is essential to implement transferrence stages to maintain functionality without any data loss.
Decommissioning processes may be necessary in scenarios where legacy systems are no longer compatible with emerging use cases, commonly, for inclusions of AI/ML-powered solutions. However, it is not always mandatory for every data migration.
Here are a few situations when decommissioning processes may be necessary:
1. Obsolete or Outdated Systems: When migrating data from legacy systems that are no longer supported or maintained, it is advisable to decommission those systems. Continuing to operate outdated systems may result in increased security risks, higher maintenance costs, and limited scalability.
2. Consolidation and Rationalization: Organizations undergoing a consolidation effort, such as merging multiple systems or streamlining operations, may need to decommission redundant processes. This helps to eliminate duplicated efforts, simplify workflows, and optimize resource allocation.
3. Transition to Cloud or New Platforms: When migrating data to the cloud or transitioning to new platforms, decommissioning processes can be beneficial. Legacy systems may not integrate seamlessly with cloud environments or lack compatibility with modern platforms, making decommissioning a necessary step for a smooth transition.
4. Compliance and Regulatory Requirements: If certain processes or systems no longer comply with regulatory guidelines or industry standards, decommissioning becomes necessary to mitigate legal risks and ensure adherence to compliance requirements.
5. Cost Reduction and Efficiency: Decommissioning processes can contribute to cost reduction and operational efficiency. By eliminating unnecessary or low-value processes, organizations can free up resources, reduce maintenance and operational costs, and focus on more critical business areas.
It is important to carefully evaluate the need for decommissioning processes during data migration projects based on factors such as system relevance, operational efficiency, cost-effectiveness, and compliance considerations.
Each migration scenario may have its own unique requirements, and the decision to decommission processes should be made based on a thorough analysis of the specific situation.
How Does Decommissioning Commonly Take Place?
A Data warehouse is set up and ETL (extract/transfer/load) processes move information from one “soon to be decommissioned” system to the data warehouse environment.
The old system is shut down systematically, and the migrated data is sourced from the modern database for future business purposes.
A Streamlined Approach to Data Migration
1. Data Analysis – Examine and define the data before migration. This helps to determine the level of source information that can be included. Analyze the source and target systems by referring to end-users so that the process is fully functional.
2. Proper Allocation of Resources – Formulate a well-defined project scope by involving relevant stakeholders at an early stage for easy budget and resource allocation, and successful process implementation within the stipulated timeline.
3. Data Integrity Validation – Design contingency plans that identify and rectify ‘uncleaned’ data before its migration to the target system. This is crucial and could compromise process efficiency if not addressed in time.
4. Creation of the Migration Solution – Define the transformation logic on the data chosen for migration, and code the data migration logic to move the transformed data.
5. Testing and Verification – After the migration is complete, create test data in a test database, and subject it to various test scenarios. This reduces the risk of running into issues later when it will be greatly difficult to rectify them.
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