by CData Software | January 02, 2024

What is Data Migration? Definition, Examples & Strategies

Data migration is not merely a task of moving data from one place to another—it’s a strategic maneuver that can reshape a business's approach to data management. Successfully relocating data to a new system or a more advanced and suitable environment enables businesses to refine their operations, sharpen decision-making, and reduce risk.

In this article, we'll describe what data migration is, examining the various forms it can take. We'll also navigate through the critical best practices for a successful migration, touch on potential risks, and provide a practical step-by-step guide, all designed to equip you with the knowledge to execute an effective data migration strategy.

What is data migration?

Data migration, as it’s defined, is pretty straightforward: It’s the process of transferring data from one location—a storage system, file format, database, or environment—to another.

While the concept is simple, the process encompasses much more than just moving data to a different system. It's a strategic process that can be part of a broader initiative like digital transformation, where companies shift from legacy systems to more agile, cloud-based platforms to better align with modern business practices. It's also a major element of data consolidation in M&A (mergers and acquisitions), ensuring that all critical data is harmonized and accessible in a unified system.

From a strategic perspective, data migration is a fundamental step in keeping an organization’s data infrastructure aligned with its evolving needs, market conditions, and technological opportunities.

4 types of data migration

There are several different types of data migration, and some migration strategies can involve more than one type. Choosing the type that’s best for your organization will depend on your specific tech stack and requirements.

1. Storage migration

Storage migration is the process of transferring data from one storage system to another, often undertaken by organizations to upgrade to better storage solutions, increase capacity, or shift to more efficient platforms, like cloud-based storage. The main objective is to move data securely and efficiently, ensuring its integrity and accessibility in the new location.

2. Database migration

Database migration involves moving data from one database to another and is driven by reasons such as upgrading to a superior system, finding a system that better suits organizational needs, or consolidating databases for operational efficiency. The process focuses on transferring all data, including its structure, schema, and indexes, accurately and efficiently.

3. Application migration

Application migration is the process of moving software applications from one computing environment to another, such as upgrading to newer software versions, transitioning from on-premises hosting to the cloud, or switching between cloud services. The goal is to transfer the application, along with its data and configurations, without disrupting business operations that depend on the software.

4. Cloud migration

Cloud migration is the process of transferring digital assets, including data, applications, and IT processes, to take advantage of the flexibility of a cloud computing environment. This can involve moving from on-premises infrastructure to the cloud or transitioning between hybrid or multicloud environments.

Data migration process steps

There’s a lot that goes into the overall process of data migration. In addition to the ones outlined below, there may be smaller sub-steps, and in some cases, the order of the steps themselves might be changed to suit a particular strategy or method.

1. Planning

Successful data migration hinges on thorough planning. Clearly defining migration objectives, like system upgrades or cloud adoption, helps to align with broader business goals. A comprehensive migration plan should detail the scope, timeline, required resources, and assigned roles. Key elements of the plan include selecting appropriate migration tools, deciding between a phased, trickle, or big-bang approach, and ensuring data quality and integrity. This is where choosing the right ETL (extract, transform, load) tools comes in and can make a difference in ensuring a smooth data migration.

2. Risk assessment

This crucial stage informs the mitigation plan, which encompasses preventive actions like data backups, encryption, and access controls, as well as strategies to handle migration issues. Key factors analyzed include the complexity of the data environment, data sensitivity and volume, system compatibility, and security vulnerabilities. The assessment focuses on the risks of data loss, corruption, breaches, downtime, and operational disruptions.

3. Backup and archival

Prior to migration, all data is backed up to restore the original data if migration issues occur. This step involves duplicating and securely storing data on external drives, separate servers, or cloud storage tailored to the data's size and sensitivity. Archiving transfers historical or infrequently accessed data to long-term storage, which reduces the volume of data to be migrated and aids in optimizing system performance and costs.

Pre-backup cleaning (optional)

Scrutinizing and cleansing the data prior to backup can help streamline the backup process by preventing outdated or corrupted data from being included and simplifying the mapping process. It can also reduce conflicts or other snags that might come up during backup.

4. Data extraction

This marks the beginning of the actual migration (!), where data is collected from source systems for transfer to the new destination. Data extraction requires meticulous planning and execution, often utilizing specialized tools or scripts to accommodate different data formats and structures. Extracted data may be temporarily stored in a staging area for further assessment and preparation for subsequent migration stages.

5. Data mapping

Here, source data elements are aligned with their corresponding elements in the target system. This crucial step ensures that the data fits accurately and maintains its meaning and functionality in the new environment. It involves establishing relationships between data fields in the old and new systems, often requiring translation of data formats, units, and values to meet the new system's requirements.

6. Data cleansing and transformation

Data cleansing involves identifying and fixing errors, inconsistencies, and redundancies in the data. Following cleansing, the data is transformed—converted or reformatted—to align with the structure and format requirements of the new system. This may include changing data types, standardizing units of measure, or modifying data values to ensure compatibility with the new system's architecture.

7. Data loading

The cleansed and transformed data is then moved into its new environment. During this process, careful attention is required to ensure the data is properly loaded, a task that becomes particularly challenging with large datasets or complex systems. Batch processing or incremental loading is often employed to handle data volumes efficiently and to reduce the impact on operational systems.

8. Testing and evaluation

Testing the data includes various checks and validations, employing both automated and manual testing methods, to ensure that the data is correctly loaded and maintains integrity, structure, and content consistency with the original source. This stage also includes assessing the new system's performance with the migrated data to ensure it meets operational standards and business requirements.

9. Data verification

Verification involves a thorough comparison of the newly migrated data with the original source, verifying that all data has been accurately and completely transferred, free from any loss or alteration. The process typically includes validating data counts, inspecting key attributes, and confirming the preservation of relationships and hierarchies within the data. It’s also important to confirm that the data functions correctly within the new environment, including its interactions with other systems and processes.

10. Deployment

This is the final stage of the data migration process, where the migrated data and new system are officially activated for use. This transition requires careful coordination to phase out the old system and integrate the new one. The deployment might be gradual or executed all at once, depending on the project's scale and complexity. Post-deployment, final checks are conducted to ensure the system's optimal operation and user accessibility to the data. This phase also often includes end-user training and updates to documentation to reflect changes in data management and system operations.

11. Decommissioning

Decommissioning involves safely retiring the old system after the new system is fully operational, which is crucial for preventing data redundancy and managing costs. Once the data migration is confirmed successful and all necessary functionalities are verified in the new system, the old system is methodically shut down. This process includes securely archiving or deleting any residual data, dismantling the infrastructure, and revoking access to block future use.

Data migration strategy

Selecting the right data migration strategy is core to a successful operation and depends on the organization’s particular circumstances. There are three primary strategies: Big bang, trickle, and phased. Each approach offers a unique method for handling the complexities and challenges of data migration, catering to different organizational needs and scenarios.

Big bang

Big bang data migration is a strategy where the entire migration process is executed at once. This approach consists of a total and simultaneous shift from the old to the new system—transferring all data, bringing the new system online, and decommissioning the old system—within a short period. This method demands extensive planning and preparation due to significant downtime during the transition, planning for a period when both systems are down. It’s a simple and straightforward data migration approach and avoids running two systems simultaneously.

The big bang approach is more suitable for smaller or less complex data environments or where system downtime is manageable. However, it carries higher risks, as any issues can affect the entire operation. Thorough testing, robust backup strategies, and contingency plans are critical for mitigating these risks.

Trickle

The trickle approach is where the data is incrementally transferred, often in real-time or near real-time, without disrupting the existing system's operations. This strategy is ideal where business continuity is critical, minimizing downtime.

Phased

This strategy is sometimes confused with the trickle approach, but the phased approach breaks migration into stages, migrating and validating data segments sequentially, allowing for risk reduction and issue resolution without affecting the entire system at once. The trickle approach is often paired with a phased migration, ensuring a constant data flow and keeping the new system current while maintaining both systems' operability.

Combining trickle and phased methods can provide a balanced solution, merging the advantages of ongoing incremental transfers with the structured, risk-managed progression of phased migration.

Data migration best practices

Data migration, a critical and often complex process, requires meticulous planning and execution. Here are some best practices to ensure a smooth and successful data migration process:

Consider specialized tools and expertise

Utilize appropriate data migration tools and technologies. Consider engaging with data migration experts or consultants for their specialized knowledge and experience, especially for complex migrations.

• Comprehensive planning: Develop a detailed migration plan that outlines the scope, objectives, timeline, resources, and responsibilities. Understand the data landscape, including volume, format, and dependencies.
• Data assessment and cleaning: Conduct a thorough analysis of the current data. Identify and clean up any redundant, obsolete, or corrupt data before migration. This ensures only relevant and high-quality data is migrated.
• Choose the right migration strategy: Select a migration strategy that aligns with your organization’s needs, considering factors such as the size of the data, system downtime tolerance, and resource availability.
• Robust data backup: Ensure you have a complete backup of all data before starting the migration process to prevent any data loss.
• Testing and validation: Implement rigorous testing at every stage of the migration process. Validate the integrity, security, and quality of the migrated data to ensure it meets the required standards.
• Risk management: Identify potential risks and challenges associated with the migration and develop contingency plans to address these issues.
• Stakeholder communication: Maintain clear and regular communication with all stakeholders throughout the migration process. This includes setting expectations, providing updates, and being responsive to concerns.
• Training and support: Provide necessary training and support to the users on the new system. Ensure that they are comfortable and proficient in using the new setup.
• Documentation and compliance: Keep detailed documentation of the migration process for future reference and compliance needs. This includes data mapping, transformation logic, and any changes made during the migration.
• Post-migration review: After the migration, conduct a thorough review to ensure all data has been accurately transferred and is functioning as intended in the new environment. Monitor the system’s performance and address any post-migration issues promptly.
• Security measures: Ensure that data migration is conducted securely to protect sensitive information from potential breaches or leaks.

Risks of data migration

Data migration is essential for organizational growth and efficiency, but it does not come without risks. Understanding potential problems and how to avoid them should be included in your migration strategy. Here's a list of common data migration risks that businesses often encounter, which underscores the importance of meticulous planning and execution in the migration process.

• Data loss: During the transfer process, there's a risk of losing some data, especially if the migration isn't carefully planned and executed.
• Data corruption: The data can become corrupted during migration, either due to issues in the transfer process or compatibility problems between the old and new systems.
• Downtime: Data migration often requires taking systems offline, which can lead to operational downtime and impact business activities.
• Cost overruns: Without proper planning, data migration can take longer and cost more than anticipated, straining the organization's resources.
• Security risks: The process of transferring data can expose it to security vulnerabilities, especially if the data is sensitive.
• Incompatibility issues: There's a risk that the migrated data may not be fully compatible with the new system, leading to functionality problems.
• Inaccurate or incomplete data transfer: There’s a chance that not all data is transferred accurately or may not transfer at all, which can lead to errors and inconsistencies in the system.
• Lack of user training: Post-migration, users may not be adequately trained to use the new system, leading to reduced productivity and errors.
• Regulatory compliance issues: If the data contains sensitive information, there's a risk of non-compliance with data protection regulations during the migration.

Create a smooth data migration with CData

CData Sync offers a flexible solution for data migration challenges, enabling easy on-premises and cloud data integration. Installation and operation are quick and simple, enabling you to start your tasks right away.