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Data Migration Strategy for a Legacy App: Step-by-Step Guide

Last updated on
March 1, 2024


Data migration strategy in a nutshell

Take a look at the short version of data migration strategy process:

  1. Comprehensive data assessment: Thoroughly evaluate the data in the legacy system for quality, volume, dependencies, and formats.
  2. Clear Migration Objectives: Define specific, measurable goals for the migration that align with broader business objectives.
  3. Detailed Project Planning: Develop a robust plan detailing the scope, timeline, resources, budget, and key milestones.
  4. Choosing the Right Tools and Technologies: Select appropriate migration tools and technologies that are compatible with both legacy and new systems.
  5. Data Mapping and Integrity Checks: Map how data from the old system will transfer to the new one and plan for maintaining data integrity.
  6. Risk Management Strategy: Identify potential risks and challenges and develop strategies to mitigate them.
  7. Testing and Validation: Implement comprehensive testing before, during, and after migration to ensure data accuracy and system functionality.
  8. Post-Migration Review and Support: Conduct a thorough post-migration review to ensure data quality and provide ongoing support and training to users.

To deepen your understanding of these steps, discover more, and grab some useful data migration tips, proceed to the whole article:


Data Migration Strategy for a Legacy App: Step-by-Step Guide


As businesses increasingly recognize the limitations of their legacy systems, the need for effective data migration strategies becomes undeniable. 

This article is tailored for decision-makers who face the task of transitioning valuable data to new platforms. 

Get familiar with each phase of the migration process - from planning to seamless execution and diligent post-migration practices - and build your strategy with ease.

Why you shouldn’t underestimate data migration strategy 

Data migration strategy plays a crucial role in the success of a legacy system modernization project for several reasons:

1. Data integrity and quality

Migrating from a legacy system often means dealing with outdated or inconsistent data formats. A well-planned strategy ensures that data is not only transferred to the new system but also cleaned, standardized, and transformed as needed to maintain or improve its quality and integrity.

2. Minimizing downtime and disruption

Legacy system modernization can be disruptive to ongoing operations. An effective data migration strategy includes plans for minimizing downtime and ensuring business continuity during the transition.

3. Compatibility and integration

Modern systems often use different data structures and storage methods compared to legacy systems. A solid migration strategy addresses these differences to ensure seamless integration and compatibility with new technologies.

4. Risk management

Data migration comes with risks such as data loss, corruption, or breaches. A comprehensive strategy includes risk assessment and mitigation plans to protect sensitive information during the migration process.

5. Cost and resource management

Improperly managed data migration can lead to significant cost overruns and resource drain. A strategic approach helps in estimating and controlling costs, and efficiently allocating resources.

6. Regulatory compliance and data governance

Many industries are subject to strict data governance and regulatory compliance standards. A migration strategy must consider these requirements to ensure that the new system adheres to legal and regulatory obligations.

7. Future scalability and flexibility

A well-thought-out data migration strategy considers not just immediate needs but also the future scalability and flexibility of the data architecture, making it easier to adapt to evolving business requirements.

8. User acceptance and training

Effective data migration includes considering the impact on end-users. This involves planning for user training and acceptance testing to ensure that the new system is well-received and efficiently utilized.

Key considerations of a data migration strategy during the legacy system modernization project

When developing a data migration strategy, addressing a few common considerations helps in creating a comprehensive and effective strategy, reducing risks, and ensuring a successful legacy system modernization project.

Understanding data sources and structures

It's crucial to have a comprehensive understanding of the data in the legacy system, including its format, structure, and interdependencies. This involves analyzing the existing data schema, types of data stored, and any customizations or unique configurations.

Data quality and cleansing

Assess the quality of the existing data. Identify any inaccuracies, duplications, or outdated information. Plan for data cleansing processes to correct these issues before migration to ensure that only accurate and relevant data is transferred to the new system.

Mapping and transformation

Develop a detailed mapping plan that defines how data from the legacy system will be transformed and fit into the new system's structure. This often involves data conversion or transformation to match the data models of the modernized system.

Regulatory compliance and data security

Ensure compliance with data protection regulations (such as GDPR, HIPAA, etc.) during the migration process. Data migration strategies must include methods to protect sensitive data and maintain privacy standards.

Choosing the right migration tools and technologies

Select appropriate tools and technologies that support efficient data extraction, transformation, and loading (ETL) processes. The choice of tools can significantly impact the speed and effectiveness of the migration.

Check also:

Testing and validation

Plan for rigorous testing of the migrated data to ensure integrity and accuracy. This includes validating that the data works as expected in the new environment and that all necessary data has been successfully migrated.

Managing disruptions during legacy system modernization
Report: State of Software Modernization 2024

Planning for downtime and business continuity

Develop a strategy to minimize downtime during the migration process. This might involve phased migrations or operating parallel systems temporarily to ensure business continuity.

Training and support for users

Prepare for user training and support. Users of the system need to be familiar with any changes in data structure or access methods in the new system.

Backup and rollback plans

Have robust backup and rollback plans in place. In case something goes wrong during the migration, it's essential to be able to restore the original data and minimize disruptions to business operations.

Phased approach and scalability

Consider adopting a phased approach to migration, where data is moved in stages. This can reduce risk and make the process more manageable. Also, plan for future scalability to ensure that the new system can handle growing or changing data needs.

Three approaches to data migration 

There are three major approaches to data migration:

Big Bang approach

This approach means transferring the data in just one operation, in a relatively short time. It is less complex and costly, and the transition is faster. However, during the migration, the systems are not available to users, so it’s typically performed during public holidays to reduce the costs of downtime. This strategy might be a good fit for companies operating with a quite small amount of data that don’t use applications that must be available 24/7. 

  • Advantages: Quick implementation, less complex technically, and lower overall costs.
  • Challenges: High risk of disruption to business operations, significant pressure on resources during the migration window, and limited scope for error correction.
  • Best suited for: Smaller or less complex migrations where downtime can be managed, and the risk of extensive data problems is low.

Trickle approach

The trickle approach means iterative migration is completed in phases. The process is broken down into several migrations, each with a separate scope and timeline. The main advantage is that the process is less susceptible to failures and doesn’t require downtime. However, it is more expensive, as keeping both systems running and synchronizing data in real-time require more resources and effort. It is recommended for larger companies that can’t afford a long downtime and must keep their critical processes operating undisturbedly. 

  • Advantages: Reduced risk of business disruption, more time for troubleshooting and quality checks, and the ability to learn and adapt from earlier phases.
  • Challenges: Longer project timeline, potentially higher costs, and the need for complex coexistence strategies for old and new systems during the migration.
  • Best suited for: Larger organizations with complex systems, where minimizing risk and business disruption is a priority.

<span class="colorbox1" fs-test-element="box1"><p>Read also: Big Bang Migration vs Trickle Migration Approach in Legacy Modernization [Key Differences & Considerations]</p></span>

Parallel migration

In a parallel migration,  old and new systems run simultaneously for a period of time. Data is mirrored in both systems, ensuring functionality remains unaffected.

  • Advantages: Lowest risk of business disruption, provides a high level of operational redundancy, and allows extensive testing and user training.
  • Challenges: Resource-intensive, can be costly, and requires significant effort to ensure data consistency between the two systems.
  • Best suited for: Critical systems where continuity is paramount, and for organizations willing to invest in a lower-risk transition.

How to choose the right approach?

The decision on which migration approach to adopt should consider several factors:

  • Risk tolerance: Assess the organization's ability to handle disruptions. A Big Bang approach carries the highest risk, while Parallel is the safest.
  • Resource availability: Consider both the financial and human resources available. Parallel migration can be resource-heavy.
  • Complexity of migration: Evaluate the complexity of the systems and data. More complex migrations may benefit from a Phased or Parallel approach.
  • System criticality: Determine how critical the system is to daily operations. Highly critical systems might necessitate a Parallel approach to ensure continuity.
  • Project timeline and budget: Balance the need for a swift migration against budget constraints. Big Bang is faster and often cheaper, but riskier.
  • Data volume and quality: Large volumes of data or data with quality issues might be better managed in a Phased migration.
Big bang approach vs other migration strategies
Report: State of Software Modernization 2024

What should be included in a data migration strategy?

Time to create your data migration strategy.

It needs to include several critical components that cover planning, execution, and post-migration activities.

But let’s go through it step-by-step.

Planning phase

Step 1: Assessing the legacy system


  • Evaluate the current legacy system to understand data structure, volume, and dependencies.
  • Identify critical data and any data quality issues.

Why: Understanding the data architecture and relationships in the legacy system is vital for planning the migration without losing data integrity or encountering unforeseen complexities.

Practical Tip: Use tools to automatically analyze data structures, schemas, and dependencies. This will save time and reduce errors compared to manual assessment.

Step 2: Defining migration goals


  • Establish clear objectives for the migration, including performance targets and business requirements.

Why: Clear goals will guide the entire migration process, ensuring that it meets the needs of the business and delivers the expected benefits.

Practical tip: Align migration goals with business objectives and involve key stakeholders in this process to ensure that the migration supports overall business strategy.

Step 3: Selecting the right migration tools


  • Choose appropriate data migration tools based on the complexity and size of the data.

Why: The right tools can significantly streamline the migration process, reduce manual effort, and minimize the risk of data corruption or loss.

Practical tip: Opt for tools that offer scalability, support for your data types, and compatibility with both the legacy and new systems.

Step 4: Developing a data migration plan


  • Create a detailed project plan including timeline, resources, and roles.
  • Plan for data backup and recovery mechanisms.

Why: A detailed plan helps in managing resources effectively, tracking progress, and ensuring that the migration stays on schedule.

Practical tip: Create a comprehensive timeline that includes milestones, dependencies, and buffer time for unexpected challenges.

Step 5: Data mapping


  • Map data fields from the legacy system to the new system to ensure compatibility.

Why: Accurate data mapping is crucial for maintaining data functionality and integrity in the new system. It helps in understanding how data will transfer and transform during migration.

Practical tip: Utilize automated data mapping tools where possible, but also conduct manual reviews to ensure accuracy, especially for complex data relationships.

Step 6: Risk assessment and mitigation plan


  • Identify potential risks and develop strategies to mitigate them.

Why: Being proactive in risk management helps in minimizing disruptions during migration. It’s important to have strategies ready for potential data loss, system downtime, or budget overruns.

Practical tip: Identify risks at both macro (organizational level) and micro (data level) perspectives. Develop contingency plans for critical risks.

Step 7: Compliance and security check


  • Ensure the migration plan adheres to legal and regulatory compliance requirements.
  • Plan for data security during and after migration.

Why: Ensuring compliance and security is not just about legal adherence but also about protecting sensitive data during the migration process. This step helps in maintaining trust and integrity.

Practical tip: Conduct a thorough audit of data privacy laws (like GDPR) and industry-specific regulations that apply to your data. Implement necessary security protocols.

Additional tips for the planning phase:

  • Involving key stakeholders early: Ensure that business leaders, IT staff, and end-users are involved from the start. Their insights can provide valuable perspectives on business needs and system requirements.
  • Prioritizing data: Not all data needs to be migrated. Prioritize based on what is critical to the business operations. Archive or discard outdated or irrelevant data.
  • Setting realistic deadlines: Avoid overly ambitious timelines. Provide enough time for each step, considering potential setbacks.
  • Creating a detailed inventory: Document every type of data in your legacy system, including its format, location, and usage. This will help in understanding the scope and complexity of the migration.

<span class="colorbox1" fs-test-element="box1"><p>Need some help? Check out this ranking of top legacy system modernization companies. It gathers 16 top companies that specialize in legacy modernization and was made entirely by humans.</p></span>

Execution phase

Step 8: Data extraction


  • Extract data from the legacy system using chosen tools and methods.

Why: Automated extraction minimizes human error and improves efficiency. Backups are essential to prevent data loss in case of issues during extraction.

Practical tip: Automate the extraction process where possible and ensure backups are in place before extracting data.

Step 9: Data cleansing and transformation


  • Cleanse data for inconsistencies or errors.
  • Transform data into the required format for the new system.

Why: Cleansing improves data quality, while transformation ensures compatibility with the new system. This step is critical for the usability of data post-migration.

Practical tip: Use data quality tools to identify and correct issues such as duplicates, inconsistencies, and incomplete data.

Step 10: Data loading


Load the cleansed and transformed data into the new system.

Why: Trial loads help identify potential issues before the final migration. Monitoring during loading ensures any issues are quickly identified and addressed.

Practical tip: Perform trial loads if possible, and use data loading tools that allow for monitoring and reporting.

Step 11: Validation and testing


  • Conduct thorough testing to ensure data integrity and system functionality.
  • Validate data accuracy and completeness.

Why: Validation confirms data integrity, and testing ensures the new system meets the specified requirements and functions correctly with the migrated data.

Practical tip: Use automated scripts to validate data and conduct user acceptance testing to ensure the data works as expected in the new system.

Step 12: Iterative process


  • Repeat extraction, transformation, loading, and validation steps as necessary.

Why: Iteration allows for refining the migration process, ensuring all data is accurately and completely migrated.

Practical tip: Be prepared to repeat steps as needed, based on testing and validation results.

Additional tips for the execution phase:

  • Conducting pilot migrations: Run a pilot migration with a small, representative set of data. This can reveal potential issues before the full-scale migration.
  • Real-time monitoring: Implement real-time monitoring tools to track the migration process. This can help in quickly identifying and addressing issues as they arise.
  • Regular status updates: Keep stakeholders informed about the migration's progress and any challenges encountered. This fosters transparency and builds trust in the process.

<span class="colorbox1" fs-test-element="box1"><p>Thinking about legacy system modernization? Explore 8 common challenges during the app modernization process and learn how to prepare.</p></span>

Post-migration phase

Step 13: Data Quality Assurance


  • Perform additional checks to ensure data quality and consistency.

Why: Post-migration data quality checks are crucial to ensure ongoing data integrity and usability.

Practical tip: Continuously monitor data quality post-migration and have a team in place to address any issues.

Step 14: System integration testing


  • Ensure the new system integrates well with other business systems and processes.

Why: This ensures that the migrated data works seamlessly across the entire IT ecosystem, without disrupting other business processes.

Practical tip: Conduct comprehensive testing to ensure the new system integrates well with other existing systems.

Step 15: User training and support


  • Provide training to users on the new system.
  • Set up support for addressing any post-migration issues.

Why: Effective training and support facilitate smoother transition to the new system, reducing resistance and increasing user efficiency.

Practical tip: Provide detailed training to users on the new system and ensure support is readily available for any queries or issues.

Step 16: Performance monitoring


  • Continuously monitor system performance and data integrity.

Why: Ongoing monitoring helps maintain system health and ensures that performance standards are met.

Practical tip: Implement tools for continuous monitoring of system performance to quickly identify and address any issues.

Step 17: Documentation and reporting


  • Document the entire migration process for future reference.
  • Create reports on migration outcomes and any lessons learned.

Why: This documentation is valuable for future migrations and for understanding the impact of the migration on various business processes.

Practical tip: Keep detailed records of the migration process, including any challenges and how they were resolved.

Step 18: Final sign-off


  • Obtain final approval from stakeholders confirming successful migration.

Why: This step formalizes the acceptance of the new system and marks the official completion of the migration project.

Practical tip: Have a formal sign-off process with all key stakeholders to confirm the successful completion of the migration.

Additional post-migration phase tips:

  • Conducting a post-migration audit: After migration, conduct a thorough review to ensure all data has been accurately migrated and is functioning as expected.
  • Updating documentation: Make sure all system documentation is updated to reflect changes made during the migration. This includes data dictionaries, user manuals, and technical specifications.
  • Establishing a feedback loop: Gather feedback from end-users on the new system’s performance and usability. This can help in identifying areas for improvement.
  • Planning for ongoing maintenance: Develop a plan for regular maintenance and updates to the new system to ensure its long-term efficiency and security.

Data migration best practices

What are the other data migration best practices that we recommend to follow?

  • Always remember to make a backup before initiating the migration. This will protect you against data loss.
  • Start with a good grasp and understanding of the data that will be migrated - its format, source location, and target location.
  • Make sure that you have skilled specialists on board who can steer and manage the process. 
  • Take the opportunity to clean and update the data as well as reduce its amount before migration. This will prevent transferring the issues and drawbacks to the new environment.
  • Test the migration at every stage, from the planning stage to execution and maintenance. This will help you to detect and fix failures as soon as possible.

Next steps

When starting your migration process,remember to:

  • Maintain clear and consistent communication throughout the migration process. 
  • Set up regular meetings and provide channels for feedback and questions.
  • Invest in training for your team, especially in new tools and technologies that will be used during the migration.
  • Don’t hesitate to consult with external experts or vendors, especially for complex migrations. Their experience and knowledge can be invaluable.

Frequently Asked Questions

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Olga Gierszal
IT Outsourcing Market Analyst & Software Engineering Editor

Software development enthusiast with 6 years of professional experience in the tech industry. Experienced in outsourcing and nearshoring market analysis. Our expert in presenting tech, business, and digital topics in an accessible way.

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