What is Data Migration? Types, Best Practices & Strategies in ’23

Data migration is the complex process of transferring business-critical data from one system or location to another. Unlike data integration, which combines data from multiple sources, data migration focuses on moving data between storage systems, databases, or environments.

With many organizations undertaking digital transformation and cloud migrations, having a sound data migration strategy is more important than ever before. This comprehensive guide examines data migration in-depth, providing key insights to help technology leaders migrate their data safely and efficiently.

What is Data Migration? A Detailed Overview

Data migration refers to the process of transferring data from one storage system, database, application, or data center to another. It involves moving an organization‘s most critical data assets from legacy systems and databases to newer, more agile ones that allow for improved analytics, reduced costs, and greater competitiveness.

At its core, data migration is about moving data from point A to point B. But in practice, it encompasses everything from extracting the data from source systems, transforming it for the target environment, mapping data elements, and then loading it into the destination databases or applications.

Proper data migration ensures continuity of operations during the transition window and maintains data integrity and quality throughout. It is a complex undertaking that requires careful planning and execution.

Key Differences Between Data Migration and Data Integration

Data migration is sometimes confused with data integration. However, while the two serve related purposes, they have distinct differences:

  • Data integration focuses on combining data from different sources and applications into one unified view and data store. The goal is to break down data silos and consolidate data from across the organization to provide a single version of truth.

  • Data migration centers on moving data between systems or locations. The data stays in its existing formats and does not necessarily need integration.

  • Data integration offers real-time visibility by linking systems together through APIs and connectors. Data migration is a one-time bulk transfer of data.

  • The scope of data integration is broader across the organization, while data migration tackles point-to-point data transfers.

In short, data integration provides a consolidated, holistic data environment by uniting disparate data sources. Data migration simply transfers data as-is from point A to point B.

Why a Data Migration Strategy Matters

Data migration projects carry substantial risks if not managed properly. Without a sound strategy, organizations can face a multitude of problems:

  • Data loss or corruption – A faulty data migration can lead to partial or total data loss. Even if all data arrives, the quality and integrity may be severely compromised.

  • Disrupted operations – Downtime and business disruption occur if the migration is not meticulously planned and executed.

  • Cost overruns – Unexpected complexities in the migration process can result in budget overages. According to McKinsey, data migration projects often exceed cost estimates by 25-50% on average.

  • Delayed benefits – Organizations postpone realizing the benefits of new data systems if migration takes longer than anticipated due to missteps.

Data migration challenges

A data migration roadmap helps organizations avoid these pitfalls. It aligns stakeholders, details objectives, assigns responsibilities, sequences tasks, and outlines contingency plans. This upfront planning is the key to successful data migration.

Major Types of Data Migration

There are several distinct types of data migration initiatives that organizations may undertake:

  • Storage migration – Moving structured data from one storage technology to another, like transferring data from an on-prem SAN to cloud object storage. Often done to upgrade legacy storage systems.

  • Database migration – Moving relational data from one database system, such as Oracle, to another, like MongoDB. This allows companies to modernize infrastructure and gain expanded database capabilities.

  • Application migration – Moving an entire software application from one runtime environment to another. For example, migrating a legacy app from an on-prem data center to the cloud.

  • Data center migration – Moving an organization‘s full data center footprint including all data, apps, services, networks, and infrastructure to a new facility. A massive operational undertaking.

  • Cloud migration – Transferring data from on-premises systems to the cloud. May involve offline transfer appliances or online replication through direct network connections. Critical for unlocking scalability and agility.

Data Migration Best Practices

Migrating data successfully requires meticulous planning and flawless execution. Here are some proven best practices to follow based on my experience with dozens of complex migration initiatives:

  • Create a detailed data migration plan – Clearly define scope, timelines, resource allocation, risks, mitigation strategies, and rollback contingencies. Engage stakeholders early in planning.

  • Leverage specialized data migration tools – Automation accelerates migrations and reduces errors compared to manual work. Tools provide validation checks and failure reporting.

  • Start with non-critical systems first – Migrate lower-priority data sets initially to validate processes before tackling mission-critical systems and data.

  • Break up migrations into batches – Divide migration into smaller batches based on data types, modules, applications, priority levels, or other factors. Simplifies monitoring and troubleshooting.

  • Continuously validate migrated data – Confirm that all data has arrived complete, intact, and uncorrupted in the target system throughout the migration process.

  • Manage cutover meticulously – Have rollback contingency plans in place in case issues arise during the final transition to production systems.

Data Migration Strategies and Methodologies

Organizations have two primary options for carrying out a migration initiative:

Big Bang Data Migration

With big bang migration, all data is migrated in one major initiative across all systems. This approach condenses the migration timeline but can involve substantial downtime as the migration occurs.

Big bang migration requires impeccable planning and coordination to succeed at scale. It can make sense for smaller data volumes or less critical systems where downtime is tolerable.

Phased Migration

With phased migration, data is transferred in a series of smaller stages over an extended period. This involves methodically migrating data sets, applications, or business units one-by-one.

Phased migration reduces downtime risks and is easier to manage. The tradeoff is increased complexity in planning and managing the incremental workstreams. But for mission-critical systems, a phased approach is often preferable.

Finding the right migration methodology involves balancing business factors like uptime requirements, data criticality, migration costs, and resource constraints. Leveraging database migration tools and cloud migration services can enable smooth execution under either model.


As digital transformation accelerates, data migration has become an essential capability for organizations seeking to harness new data platforms and infrastructure. Migrating systems improperly can lead to business disruption, data losses, and substantial costs.

By understanding the types of data migration, following best practices, and utilizing proven methodologies, technology leaders can migrate their data safely, efficiently, and cost-effectively. With careful planning tailored to your organizational needs and objectives, you can unlock innovation from new data environments and avoid migration missteps.