What is data migration: Strategies & Best Practices

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Data migration is the process of transferring data from one system, format, or location to another. It involves extracting data from its current source, transforming it to fit the target system's requirements, and then loading it into the new destination. This may be necessitated by system upgrades, data center migrations, or consolidating information from various sources. 

Data migration is critical for businesses to ensure data integrity and continuity during transitions, system changes, or technology updates. However, it is a complex procedure that demands careful planning, execution, and verification to maintain data accuracy and consistency throughout the process. 

An effective data migration strategy encompasses thorough planning, mapping, and testing. Likewise best data migration practices include data validation, documentation, and continuous monitoring. This blog will delve into what is data migration, strategy and best practices to help businesses with a smooth transition while maintaining data integrity. 

According to a report by Markets and Markets, the data migration market size is expected to grow from 4.50 billion 11.49 billion in 2023.

What is Data Migration - An Example

Let's consider a multinational retail corporation with thousands of stores worldwide, each maintaining its sales and inventory data on local servers. 

To streamline operations, the organization decides to implement a centralized Enterprise Resource Planning (ERP) system. To carry this out, there is a need to migrate data that remains spread across disparate systems, making global analytics, inventory management, and reporting incredibly challenging.

In such a scenario, data migration involves extracting sales and inventory data from individual stores, standardizing it to fit the new ERP system's structure and loading it into the centralized database. 

What are the types of data migration?

Data migration comes in various forms, each serving specific needs. Storage migration involves relocating data between storage systems for performance or cost reasons. Database migration moves data between databases, often for upgrades or consolidation.
To know more about this read our blog: A Comprehensive Guide to Data Migration. 

Data Migration Strategy for Businesses

Here is a detailed overview of strategies for data migration: 

Big Bang Migration

Big Bang migration is a data migration strategy characterized by its rapid and comprehensive nature. This strategy transfers all data from the source system to the target system in a single, coordinated effort. It is typically chosen when there's an urgent need for a swift transition, requiring the entire dataset to migrate simultaneously.


  • Speed:  Minimizes prolonged transition periods.
  • Simplicity: Simplifies planning and coordination because it's a one-time event.
  • Immediate Adoption: Transit to a new system swiftly as there is no coexistence with the old one.
  • Minimal Disruption: Rapid transition ensures there is minimal disruption to ongoing operations.

1. Phased Migration

Phased migration is a strategic approach to data migration that involves breaking down the migration process into distinct phases or stages. Each phase focuses on migrating specific subsets of data, functionality, or departments. This method is chosen by organizations seeking a structured and gradual transition from an old system to a new one.

Phased migration allows a more systematic and controlled transition. Coordinated planning and execution are vital for the success of this type of data migration strategy. 


  • Risk Mitigation: Reduces risk by allowing issues to be identified and addressed in smaller, manageable portions of data.
  • User Adaptation: Users can gradually adapt to the new system as each phase is implemented, reducing potential resistance and improving user acceptance.
  • Testing Opportunities: Provides multiple opportunities for testing and validation throughout the migration process
  • Continuous Operations: Ensures that the organization can continue its operations with minimal disruptions, especially during the transition period.
Data Migration Strategy for Businesses

2. Re-Platforming

Re-platforming is another data migration strategy where data is transferred from an existing system to a new one, often with minimal changes to the data structure or format. This approach prioritizes simplicity and cost-effectiveness. Unlike other strategies that involve extensive data transformation, re-platforming aims to minimize disruptions to existing processes.


  • Data Continuity: Allows organizations to maintain data continuity by transferring data to a new system with minimal structural changes.
  • Cost-Effective: Often requires fewer data transformations and less complex migration efforts.
  • Familiarity: Users find it easier to adapt to a new platform that retains some familiarity from the previous system.
  • Minimal Disruption: Involves less disruption to ongoing operations compared to more complex migration strategies

3. Data Replication 

Data replication is a significant data migration strategy that involves creating and maintaining duplicate data copies in real-time or near-real-time. It is often carried out in a separate location or system. This approach offers continuous access to data during the migration process and serves as a valuable disaster recovery solution.


  • Real-time Data Availability: Provides real-time or near-real-time access to duplicated data, ensuring its availability for immediate use.
  • Business Continuity: Enhances business continuity by offering a redundant copy of critical data, reducing the risk of data loss due to system failures.
  • Improved Performance: Users can access data from local replicas, which can lead to improved system performance and response times.
  • Disaster Recovery: Serves as an integral part of disaster recovery plans, enabling quick recovery from data loss or system failures.
Data Migration

4. ETL (Extract, Transform, Load)

ETL, or Extract, transform, load, is a widely-used data migration strategy that involves three key phases. First, data is extracted from the source system. Then, it undergoes transformation to meet the specific requirements of the target system. Finally, the transformed data is loaded into the new environment. Organizations often choose ETL when data quality and transformation are top priorities. 


  • Data Transformation: Allows for data transformation, including cleansing, enrichment, and normalization, to align with the target system's requirements.
  • Data Quality Improvement: Provides an opportunity to improve data quality and accuracy during the transformation process.
  • Flexibility: Processes can be tailored to meet specific data migration requirements, making them adaptable to different scenarios.
  • Comprehensive Mapping: Allows for comprehensive mapping of source-to-target data attributes

5. Parallel Migration

In a parallel migration strategy, both the old and new systems operate concurrently for a specified period. Data is migrated incrementally, and users transition gradually to the new system. Organizations opt for Parallel migration when they prioritize high confidence in the migration's success.


  • Reduced Risk: Allows thorough testing and validation of the new system before full migration, minimizing the risk of data issues.
  • Continuous Data Access: Ensures that both the old and new systems run concurrently, providing continuous access to critical data.
  • Data Synchronization: Facilitates synchronized data updates between the old and new systems, maintaining data consistency throughout the transition.

Data Migration Best Practices

Here are some essential practices of data migration best practices to sail through an intricate journey:

Data Assessment and Profiling

  • Initiate data migration journey with a comprehensive assessment of the data to be migrated.
  • Gain an in-depth understanding of the source data's structure, format, and overall quality.
  • Employ data profiling tools to meticulously identify inconsistencies, anomalies, and errors within organizational data. 

1. Data Mapping and Transformation:

  • Craft a detailed data mapping document that intricately outlines how data from the source system will be harmonized with the targeted system.
  • Embrace data transformation as a fundamental aspect of migration, encompassing data cleansing, enrichment, and normalization 

2. Thorough Testing and Validation:

  • Prior to the full-scale migration, conduct rigorous testing within a controlled environment.
  • The test data you employ should accurately represent the characteristics of your actual data, ensuring a reliable assessment of the migration process.
  • Include meticulous data accuracy checks to ensure that the migrated data aligns perfectly with the source data, leaving no room for discrepancies.

3. Data Backup and Rollback Plan:

  • Safeguard against potential data loss by executing a comprehensive backup of your source data before commencing the migration process.
  • Prudently establish a well-defined rollback plan designed to seamlessly revert to your old system should any unforeseen issues arise during migration.

4. Data Governance and Security:

  • Uphold robust data governance practices consistently throughout the migration process.
  • Implement stringent access controls and robust encryption mechanisms to fortify data security during transit and storage.
  • Ensure strict compliance with pertinent data protection regulations, such as GDPR or HIPAA, by deploying the requisite controls.

5. Documentation and Knowledge Transfer:

  • Methodically document every facet of your migration processes, detailing decisions, configurations, and pivotal milestones.
  • Promote knowledge transfer from the migration team to the operational team tasked with managing the data post-migration.

6. Post-Migration Validation:

  • Following the migration, embark on a comprehensive validation process to confirm that all data has been transferred successfully.
  • Engage stakeholders in a meticulous review process to validate that the migrated data fully aligns with their specific requirements.

7. Monitoring and Continuous Improvement:

  • Implement robust monitoring tools and processes to track the migration's progress in real-time, swiftly addressing any emerging issues.
  • Beyond migration, maintain vigilant monitoring of data quality and system performance in the new environment.


Data migration is a critical process for businesses but often comes with several challenges. Firstly, there's the risk of data loss or corruption during the transfer, which can have severe consequences for a company's operations and reputation. Ensuring data integrity is a top concern. Secondly, data migration can be time-consuming, causing potential disruptions in daily business activities. It requires meticulous planning and execution to minimize downtime. Compatibility issues between legacy and new systems also pose hurdles, demanding thorough testing and validation.

This is where third-party partners come into play. As experts, they can help businesses overcome the challenges and ensure a smooth and successful transition. They bring specialized knowledge and experience to the table, significantly reducing the risk of data loss or corruption during migration. They meticulously plan and execute the process, optimizing data integrity and minimizing downtime.

At Phygital, we have wide experience in providing access to expertise, technology, and resources to streamline data migration and mitigate risks to businesses. We provide end-to-end solutions that encompass planning, execution, and post-migration support. We also facilitate comprehensive testing and validation to identify and rectify potential issues, ensuring a smooth transition. Contact us to achieve a secure and efficient data transition while saving time and resources with our data engineering services.

Article by
Alex Mitchell

Alex Mitchell is a seasoned authority in the dynamic world of Data Science. With a rich tapestry of experience, Alex expertly traverses the intricacies of data analytics and machine learning. His impressive portfolio showcases his prowess in optimizing data-driven strategies across diverse industries, consistently driving impactful results. Alex excels in crafting innovative data solutions, nurturing collaborative data teams, and harnessing the power of data for business success. A prominent figure in the Data Science arena, Alex imparts concise and actionable insights, empowering organizations to thrive in the data-driven age.



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