Businesses are constantly changing and adapting according to emerging trends, new technology, or growth plans. Central to this process is moving data, and data migration has become an indispensable process that organisations of all sizes and industries must navigate.
Although there are different ways to approach data migration, the goal is moving data while maintaining accuracy and integrity.
In this article, we will discuss what data migration is, what strategies you can use, and the best practices for moving data. Keep reading to learn more.
What is data migration?
Data migration refers to the process of transferring data from one location, format, system, or storage environment to another. This can involve moving data between different software applications, databases, file systems, servers, or even cloud platforms.
This process is commonly undertaken when an organisation upgrades its technology infrastructure, adopts new software, changes its storage systems, or consolidates data from multiple sources.
The main goals are to ensure data continuity, maintain data accuracy and integrity, and minimise disruptions to business operations.
Strategies for data migration
There are several data migration strategies that organisations can employ based on their specific needs, resources, and goals. Each strategy comes with its own advantages and challenges.
The choice of strategy depends on factors such as the complexity of the migration, the volume of data, acceptable downtime, the risk tolerance of the organisation, and available resources. It's important to thoroughly plan and test the chosen strategy to ensure a smooth and successful data migration process.
Big bang migration
In this strategy, the entire dataset is migrated from the source to the target system all at once. This approach can be quick and simple, but it carries a higher risk because if something goes wrong during migration, it can lead to significant downtime and data loss.
Phased migration
With this strategy, the migration is divided into phases, where smaller subsets of data are migrated one at a time. This reduces the overall risk and allows for easier monitoring and validation at each step. However, it can extend the migration timeline.
Trickle migration
Trickle migration involves continuously migrating small portions of data in the background while the systems are operational. This approach minimises downtime and disruption but can extend the migration process significantly.
Parallel migration
Parallel migration involves running both the old and new systems in parallel and keeping them synchronised during the migration. This strategy ensures that the data in the old and new systems remains consistent until the migration is complete, but it requires careful synchronisation mechanisms.
Data syncing
Data syncing involves initially copying the data to the new system and then synchronising changes that occur in the source system to the target system until the migration is complete. This strategy reduces downtime and allows for a smoother transition.
Shell migration
In shell migration, the new system is set up to look like the old system, and only the structure and interfaces are migrated initially. The actual data migration is done in subsequent steps. This approach helps in minimising disruptions but might require more effort in data transformation later.
Storage migration
Storage migration involves moving data between different storage devices or mediums, such as from on-premises servers to cloud storage. This can be part of a broader data migration effort or a standalone task.
Database migration
When switching databases, organisations can use database migration tools and techniques to move data from one database system to another. This involves transforming data and adapting it to the target database's schema.
Application migration
Application migration involves moving not only data but also the entire application stack to a new environment. This strategy is often used when organisations are transitioning to new platforms or cloud services.
Cloud migration
Moving data to cloud platforms involves various strategies, including "lift and shift" (migrating applications as-is), rearchitecting for cloud-native services, or replacing on-premises systems with cloud equivalents.
Hybrid migration
Hybrid migration combines aspects of different migration strategies, allowing organisations to gradually move data and functionality while maintaining some elements in the old system.
The importance of a data migration strategy
Having a well-defined data migration strategy helps organisations navigate the complex process of moving data from one system to another while minimising risks and disruptions.
A structured strategy ensures that the migration process is well-planned and executed.
Without a strategy, data migration could lead to unexpected downtime, affecting productivity and customer satisfaction. Following a proper strategy usually includes data validation, cleansing, and transformation processes, ensuring that data remains accurate, consistent, and meaningful after migration.
Data migration involves risks such as data loss, corruption, security breaches, and compatibility issues. A strategy helps identify potential risks in advance and provides mitigation plans to handle them effectively.
Furthermore, planning ahead allows organisations to allocate the right resources, tools, and personnel for the migration process. A strategy helps avoid unnecessary delays and rework that can occur due to a lack of planning.
What to consider when developing a data migration strategy
Establishing a data migration strategy requires careful consideration of various factors to ensure a successful and smooth transition.
Here are key aspects that businesses should take into account:
Goals and objectives
Clearly define the goals of the data migration. Are you migrating to a new system for better performance, cost savings, or improved features? Understanding your objectives will guide the entire migration process.
Scope and data inventory
Identify the scope of the migration, including the types of data to be migrated and the data sources. Create an inventory of all data sets, their formats, relationships, and dependencies.
Data mapping and transformation
Determine how data will be mapped from the source to the target system. Plan for any necessary data transformations, conversions, or enrichments to ensure compatibility and accuracy in the new environment.
Risk assessment and mitigation
Identify potential risks such as data loss, corruption, downtime, or compatibility issues. Develop strategies to mitigate these risks and have contingency plans in place to address unexpected challenges.
Data quality and cleansing
Assess the quality of the data in the source system and clean up any inaccuracies, duplicates, or inconsistencies before migration. Poor data quality can lead to issues in the new system.
Testing and validation
Establish a comprehensive testing plan to validate the migration process. Test the migration in a controlled environment to ensure that data is accurately transferred and that the target system functions as expected.
Downtime and business impact
Determine the acceptable downtime for your business operations during migration. Minimise disruptions by choosing a migration strategy that aligns with your downtime constraints.
Data security and compliance
Ensure that data security and compliance requirements are met throughout the migration process. Implement encryption, access controls, and other security measures to protect sensitive information.
Resource allocation
Allocate the necessary resources, including personnel, tools, and budget, for a successful migration. Consider involving IT experts, data specialists, and business stakeholders.
Communication and stakeholder management
Keep stakeholders, including employees, customers, and partners, informed about the migration timeline, progress, and potential impacts. Clear communication helps manage expectations.
Training and support
Provide training for users who will be interacting with the new system. Ensure that adequate support is available during and after migration to address any issues that may arise.
Backup and rollback plan
Establish a backup plan that allows you to roll back to the previous system if necessary. This provides an additional layer of safety in case the migration encounters major issues.
Scalability and future growth
Consider the scalability of the new system and how it will accommodate future data growth. Ensure that the chosen system can handle increased data volume and usage over time.
Documentation and knowledge transfer
Document the entire migration process, including decisions, configurations, and procedures. This documentation is valuable for troubleshooting, future reference, and knowledge transfer.
Post-migration monitoring and optimisation
Plan for monitoring the new system post-migration to identify and address any performance bottlenecks or issues. Continuously optimise the system for better efficiency.
Key steps for a smooth data migration
Conducting a successful data migration involves a series of well-defined steps to ensure that data is transferred accurately, securely, and with minimal disruption to business operations.
Each of these steps is essential for a successful data migration. By following a systematic approach and ensuring thorough planning, testing, and validation, organisations can minimise risks and disruptions while achieving a seamless transition to the new system.
1. Planning
The planning phase is crucial for defining the scope, goals, and objectives of the migration. Identify the source and target systems, the types of data to be migrated, and the timeline for the process. Consider the resources, tools, and expertise required.
2. Data profiling and analysis
Analyse the source data to understand its structure, quality, relationships, and any potential issues. This analysis informs decisions about data transformation and cleansing that may be needed during migration.
3. Data mapping
Create a detailed mapping of how data in the source system corresponds to the target system. This mapping includes field-to-field relationships, data types, and any necessary transformations or conversions.
4. Data cleansing and transformation
Cleanse and transform the data as needed to ensure accuracy, consistency, and compatibility in the target system. This might involve removing duplicates, standardising formats, and resolving inconsistencies.
5. Testing
Test the migration process in a controlled environment before moving to the production system. Validate data integrity and verify that the migration steps work as intended.
6. Piloting
Conduct a pilot migration with a smaller subset of data to identify any issues that might arise during the full migration. This allows you to fine-tune the process before migrating all data.
7. Data migration
Perform the actual data migration based on the mapping and transformation rules established earlier. This step involves moving the data from the source to the target system using the chosen migration strategy.
8. Validation and verification
After migration, thoroughly validate the data in the target system. Verify that the data has been accurately transferred, transformed, and loaded. This includes comparing data counts, values, and relationships.
9. Cutover
Plan and execute the cutover from the old system to the new system. This involves finalising the migration, ensuring that the new system is operational, and transitioning users to the new environment.
10. Post-migration testing
Perform additional testing in the new environment after the cutover. Test various functionalities, workflows, and scenarios to ensure that the new system meets expectations.
11. Data integrity checks
Continuously monitor the data in the new system to identify any anomalies, discrepancies, or data quality issues that might have been introduced during the migration process.
12. User training and support
Provide training to users on the new system and its functionalities. Offer support and assistance to address any questions or issues that users may encounter.
13. Documentation
Document the entire migration process, including decisions made, challenges faced, and solutions implemented. This documentation serves as a valuable resource for future reference.
14. Monitoring and optimisation
Monitor the performance of the new system post-migration. Optimise and fine-tune the system as needed to ensure its efficiency, scalability, and effectiveness.
15. Feedback and continuous improvement
Gather feedback from users and stakeholders about the new system's performance. Use this feedback to make improvements and adjustments as necessary.
Conclusion
The process of transferring data from one system to another is more than just a technical task; it is a pivotal moment that can shape the trajectory of an organisation's success.
Planning, choosing the right strategy for your business, and contacting experts are steps that can ensure a smooth data migration, mitigating risks.
If you need to learn more about this process and would like to count on Stratiis expertise in data migration, contact us.
