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.

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 data migration process. It primarily involves developing a data integration strategy and assessing its risks. During this step, your team should identify key stakeholders, evaluate data sources and consider the destination of the data migration.

After completing the steps above, data 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 the data 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 data 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 data migration has been successfully tested and validated, the new system can be put into production.

Data migration benefits and challenges

This chart highlights the key benefits and challenges associated with data migration.

Data qualityEnhances overall data accuracy and relevanceData mapping and transformation complexities
Resource utilizationOptimizes resource allocationPotential disruptions to ongoing processes
Data governanceStrengthens data governance practices and helps in meeting regulatory requirementsEnsuring regulatory compliance throughout
Data accessibilityImproves data retrieval efficiency and enables real-time data accessSecurity risks during transfer
Business continuityMinimizes disruption to operations and customer serviceData validation and reconciliation post-migration

Benefits of data migration

Enhances overall data accuracy and relevance

Data migration often involves data 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 often implement or improve data governance practices during data migration, including data access controls, data lineage tracking and data security measures. This strengthens overall data governance frameworks and ensures compliance with regulatory requirements such as GDPR, HIPAA or industry-specific standards.

Improves data retrieval efficiency and enables real-time data access

Centralizing data through migration enables faster and more efficient data 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.

Data migration challenges

Data mapping and transformation complexities

Different data 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 data elements from the source system map accurately to the target system, especially when dealing with large volumes of data or complex data relationships.

Potential disruptions to ongoing processes

Data migration activities can disrupt ongoing business processes if not carefully planned and executed. Downtime, data inconsistencies or performance issues during migration can impact operations, leading to delays, reduced productivity and potential financial losses.

Data migration best practices

  • Develop a migration strategy: Create a well-defined migration strategy that outlines the goals, timeline, resources and potential risks associated with the migration.
  • 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 migration process. Plan for this in advance and communicate with stakeholders to minimize disruptions.
  • Test the migration process: It’s recommended that the migration process be thoroughly tested using sample data. This helps identify potential issues or errors that may arise during the migration.
  • Implement data migration tools: Utilize data migration tools and software to automate the migration process and ensure a smooth data transition from one system to another.
  • Backup data: It’s important to create data backups before migrating. Backups help safeguard against data loss or corruption during migration.

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 that the data has been migrated accurately and successfully. Be sure to document all aspects of the migration 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 for subsequent migrations.

SEE: Explore these best practices to follow for data migration.

Data migration vs. data integration vs. data conversion

The following data concepts are used interchangeably but serve entirely different purposes:

ParametersData migrationData integrationData conversion
DefinitionMoving data from one system to anotherCombining data from different sourcesConverting data from one format to another
PurposeTransfer data for system replacementProvide a unified view of data across systemsConvert data for compatibility or upgrade
ScopeEntire datasets or databasesReal-time or batch processingTypically focused on specific datasets or formats
TransformationMay involve data mapping and cleansingInvolves data harmonization and transformationData format or structure conversion
TimingOccurs when migrating to new systemsOngoing, as data updates or as neededTypically, this happens during system upgrades
Impact on operationsMay require downtime or disruptionsSeamless integration with ongoing operationsMay require downtime during the conversion
Tools/technologiesETL (Extract, Transform, Load) toolsAPIs, middleware, data integration platformsConversion tools specific to data formats
Example use casesMigrating data to a new ERP systemIntegrating customer data across CRM and ERP systemsConverting legacy database to modern format

Top data migration tools

Having a reliable data migration tool is vital for organizations looking to simplify their data migration process and ensure a smooth data transition from one system to another. These tools offer data profiling, mapping, transformation, validation and monitoring features, making the migration process more potent.

Here are some of the best data migration tools.

  • 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 data integration tasks.
  • Microsoft Data Migration Assistant: With this migration tool, you can move schema, data and uncontained objects from your source server to your target server.
  • Hevo Pipeline: Hevo allows you to preload transformation and override auto schema mapping. With this feature, you can leave the app to do its tweaking to get your data ready for migrations.
  •’s ease of use enables users to focus on their business without worrying about the complexities of data migration. Integrations are available via REST API or direct FTP uploads, making it possible for even non-technical personnel to work seamlessly with their technology stack while using this tool.

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