Data migration is the process and strategy for transferring data from one system, database, or storage device to another environment or setup. It can be used in various situations, such as when new hardware needs to be set up and old hardware needs to be decommissioned, or when a company is working to move all its workloads to the cloud.
When upgrading to new software versions, companies often migrate their existing data to continue using it in their new environment without rebuilding everything from scratch.
SEE: What Is Data Quality? (TechRepublic)
How does data migration work?
The first step in data migration is to analyze the existing data and determine the migration project’s scope, requirements, and goals. A detailed plan is created to outline the steps involved, timelines, resources needed, and potential risks.
Pre-migration planning is an important step in the process. It primarily involves developing an integration strategy and assessing its risks. During this step, your team should identify key stakeholders, evaluate data sources, and consider the destination of the migration.
After completing the steps above, practitioners can extract the data from the source system. This may involve using tools or scripts to extract the data in a format that can be easily transferred to the new location. Once it has been extracted, it may need to be transformed or cleaned up before transferring it to the new location. This can involve data cleansing, mapping, normalization, and other transformation processes to ensure the data is accurate and consistent.
Test and validate the migration process to ensure the data has been transferred correctly. Once the migration has been successfully tested and validated, the new system can be put into production.
SEE: Data Governance Checklist (TechRepublic Premium)
Data migration benefits and challenges
This chart highlights the key pros and cons.
| Aspect | Benefits | Challenges |
|---|---|---|
| Data quality | Enhances overall data accuracy and relevance | Data mapping and transformation complexities |
| Resource utilization | Optimizes resource allocation | Potential disruptions to ongoing processes |
| Data governance | Strengthens data governance practices and helps in meeting regulatory requirements | Ensuring regulatory compliance throughout |
| Data accessibility | Improves data retrieval efficiency and enables real-time data access | Security risks during transfer |
| Business continuity | Minimizes disruption to operations and customer service | Data validation and reconciliation post-migration |
SEE: How to Measure Data Quality (TechRepublic)
Benefits of data migration
Enhances overall data accuracy and relevance
It often involves cleansing, normalization, and validation processes, which help improve the overall accuracy and quality of data. By migrating clean and relevant data, organizations can make better-informed decisions, reduce errors, and enhance business processes.
Strengthens data governance practices and helps in meeting regulatory requirements
Organizations typically implement or improve data governance practices during migration, including access controls, lineage tracking, and security measures. This strengthens overall data governance frameworks and ensures compliance with regulatory requirements such as GDPR and HIPAA.
Improves data retrieval efficiency and enables real-time data access
Centralizing data through migration enables faster and more efficient retrieval processes. With structured and organized data repositories, users can access relevant information more quickly, enabling real-time decision-making and enhancing overall business agility.
SEE: Best Practices to Improve Data Quality (TechRepublic)
Data migration challenges
Data mapping and transformation complexities
Different sources often have varying formats, structures, and semantics, leading to complexities in mapping and transforming data during migration. It can be challenging to ensure that the elements from the source system map accurately to the target system, especially when dealing with large volumes of data or complex relationships.
Potential disruptions to ongoing processes
Migration activities can disrupt ongoing business processes if not carefully planned and executed. Downtime, inconsistencies, or performance issues during migration can impact operations, leading to delays, reduced productivity, and potential financial losses.
SEE: What Is Data Science? (TechRepublic)
Data migration best practices
- Develop a strategy: Create a well-defined migration strategy that outlines the goals, timeline, resources, and potential risks.
- Assess and clean data: Before migration, analyze and clean the data to ensure its accuracy, completeness, and integrity. This process helps in preventing data loss or corruption during migration.
- Plan for downtime: Depending on the data’s size and complexity, downtime may be needed during the process. Plan for this in advance and communicate with stakeholders to minimize disruptions.
- Test the process: It’s recommended that the migration process be thoroughly tested using sample data. This helps identify potential issues or errors that may arise.
- Implement migration tools: Utilize tools and software to automate the migration process and ensure a smooth data transition from one system to another.
- Backup: It’s important to create data backups before migrating to safeguard against data loss or corruption.
Throughout the migration process, monitor and track the progress to ensure the data is migrated successfully and promptly. Keep stakeholders informed of the progress to maintain transparency and manage expectations.
After the migration is complete, conduct thorough validation checks to ensure the data has been migrated accurately and successfully. Be sure to document all aspects of the process, including the strategy, tools used, test results, issues encountered, and solutions implemented. This documentation serves as a reference for future migrations and helps improve the process.
SEE: How to Balance Data Storage, Features, and Cost in Security Applications (TechRepublic Premium)
Data migration vs data integration vs data conversion
The following concepts are used interchangeably but serve entirely different purposes:
| Parameters | Data migration | Data integration | Data conversion |
|---|---|---|---|
| Definition | Moving data from one system to another | Combining data from different sources | Converting data from one format to another |
| Purpose | Transfer data for system replacement | Provide a unified view of data across systems | Convert data for compatibility or upgrade |
| Scope | Entire datasets or databases | Real-time or batch processing | Typically focused on specific datasets or formats |
| Transformation | May involve data mapping and cleansing | Involves data harmonization and transformation | Data format or structure conversion |
| Timing | Occurs when migrating to new systems | Ongoing, as data updates or as needed | Typically, this happens during system upgrades |
| Impact on operations | May require downtime or disruptions | Seamless integration with ongoing operations | May require downtime during the conversion |
| Tools/technologies | ETL (Extract, Transform, Load) tools | APIs, middleware, data integration platforms | Conversion tools specific to data formats |
| Example use cases | Migrating data to a new ERP system | Integrating customer data across CRM and ERP systems | Converting legacy database to modern format |
SEE: IT Leader’s Guide to Data Loss Prevention (TechRepublic Premium)
Top data migration tools
Having a reliable tool is vital for organizations looking to simplify their migration process and ensure a smooth data transition from one system to another. These tools offer profiling, mapping, transformation, validation, and monitoring features, making the migration process more efficient.
- AWS Database Migration Service: With this tool, your source database stays active throughout the migration. This reduces downtime for apps that depend on the database and allows you to migrate the database quickly and securely.
- Fivetran: It simplifies data migration by automating all integration tasks.
- Microsoft Data Migration Assistant: This tool allows you to move schema, data, and uncontained objects from your source server to your target server.
- Hevo Pipeline: Via this tool, you can preload transformation and override auto schema mapping. Users can leave the app to do its tweaking to get your data ready for migrations.
- Integrate.io: Integrations are available via REST API or direct FTP uploads, making it possible for even non-technical personnel to work with their technology stack.
This article was originally published in October 2022. An update was made by the current author in April 2024. The latest update was by Antony Peyton in June 2025.
