What is Data Migration? Strategy & Examples

Sure, data migration might not be the flashiest part of your tech stack. But it is often the most essential. Whether you’re moving to the cloud, modernizing your infrastructure, or consolidating tools after a merger, data migration is the foundation that makes transformation possible.

But here’s the thing: data migration isn’t just about copying files from point A to point B. It’s about preserving the integrity, usability, and security of your data while keeping your operations running smoothly. That means choosing the right approach, anticipating risks, aligning stakeholders, and validating success at every step.

In this guide, we’ll break down what data migration really involves. We’ll look at the different types of migration projects and how to build a strategy that minimizes disruption and maximizes value. Whether you’re leading an enterprise-wide migration or scoping a targeted move, you’ll learn how to chart the smartest path forward.

What is data migration?

At its core, data migration might sound like a straightforward process of moving data from one system to another. But in practice, it’s a lot more than just a drag-and-drop. You’re often shifting data across formats, platforms, or environments while also keeping it clean, consistent, and accessible throughout the process.

Data migration is necessary for a number of reasons. You might want to modernize outdated systems, move to the cloud, consolidate tools after a merger, or make data more accessible across departments. No matter the trigger, the goal is the same. Moving the right data to the right place, in the right condition, with minimal disruption.

Types of data assets involved

Migration projects often deal with a mix of:

• Structured data: Relational databases like MySQL, PostgreSQL, and spreadsheets.
• Semi-structured data: Formats such as XML, JSON, and log files.
• Unstructured data: Documents, images, videos, and other file types stored across different devices or systems.
• Applications and systems: Operating systems, software applications, and entire data centers or cloud environments.

Each type of asset comes with its own complexity. The key is making sure your data stays intact so context, accuracy, and usability are preserved in the move.

Typical data migration steps

A successful migration usually follows this sequence:

• Assessment: Understanding what data you have and where it lives.
• Planning: Defining scope, timelines, and success metrics.
• Extraction: Pulling data from the source system(s).
• Transformation: Cleaning, reformatting, and organizing it for the target system.
• Loading: Transferring the data to its new home.
• Validation: Testing and verifying to ensure accuracy, completeness, and performance.

Whether you’re migrating a handful of reports or an enterprise-wide warehouse, these steps help ensure your data arrives intact—and ready to drive value.

Types of data migration

Not all data migrations are created equal. Depending on your starting point and destination, the scope, tools, and risks can vary widely. Here are the most common types of data migration projects and what they typically involve.

Storage migration

This type of migration involves moving data from one storage medium to another, like from on-premises servers to network-attached storage (NAS) or from traditional hard drives to solid-state systems. It also involves database migrations, where teams move structured data from one relational database system, like MySQL, to another, like PostgreSQL. Or it can involve migrating an entire data center, where the infrastructure itself is being relocated or modernized.

Cloud migration

Cloud migration means transferring data, applications, or systems from local infrastructure to a cloud environment or between cloud providers. Whether you're adopting a public cloud (like AWS or Azure), shifting to a private cloud, or transitioning between SaaS platforms, the goal is often greater scalability, flexibility, and performance. These projects often include data reformatting and updating how applications communicate with each other—such as modifying API connections—to ensure everything works correctly in the cloud environment.

Application migration

When your organization switches tools—say, from one CRM or ERP system to another—data has to be ported between applications. This type of data migration often requires re-mapping fields, cleaning legacy inputs, and preserving relationships between records. It’s a high-stakes move that demands precision, especially when customer data or financial systems are involved.

Each of these migration types comes with its own technical and operational challenges. Knowing which category your project fits into is the first step to choosing the right strategy and ensuring a smooth transition from old to new.

Data migration vs data integration vs data conversion

These three terms—migration, integration, and conversion—are often used interchangeably, but they refer to distinct processes with different goals. Here’s a quick comparison to help clarify how they differ:

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Understanding these distinctions helps teams scope their projects more accurately, choose the right tools, and align stakeholders around clear objectives. Whether you're moving data, connecting systems, or reformatting for compatibility, each process plays a unique role in your broader data strategy.

The benefits of data migration

Data migration is more than a technical update—it’s a way to reduce complexity, improve data usability, and make systems easier to manage. When teams move to more modern platforms, they often gain cleaner data, clearer reporting, and more reliable access across the business.

Here’s what thoughtful migration can help accomplish:

• System modernization: Moving to cloud-native platforms helps you eliminate legacy bloat and support modern analytics tools.
• Improve data quality: Migration offers a chance to organize, clean, and standardize data, making it easier to analyze and share.
• Enhanced cross-team access: Centralizing data sources makes it easier for sales, ops, finance, and marketing to work from the same truth.
• Reduced overhead: Decommissioning outdated systems lowers licensing costs, maintenance workloads, and IT complexity.
• Support compliance goals: Migrating to updated infrastructure can help meet evolving privacy and governance standards.

A well-executed migration doesn’t just relocate data; it improves how teams work with it, share it, and make decisions from it.

Critical considerations for a smooth data migration

A successful data migration strategy anticipates variables that can impact timelines, performance, and outcomes. Every environment is different and rushing in without a clear understanding of what’s at stake can lead to delays, cost overruns, or lost trust in your data. Before you dive in, here are three key factors your team will want to consider:

Volume of data

The size of your data set can significantly influence how long the migration takes, how much it costs, and how complex it becomes. Large volumes require more bandwidth, more storage, and stricter coordination, especially if you're working with sensitive data or legacy systems that aren’t optimized for speed.

Workload types

Understanding your data’s behavior is just as important as understanding its structure. Are your systems handling real-time streaming or batch processing? Are you migrating transactional data (OLTP) or analytical workloads (OLAP)? These distinctions impact how you stage your migration, what tools you’ll want, and how you’ll validate success.

Migration speed and cutover window

Some teams choose a “big bang” migration, moving everything at once during scheduled downtime. Others take a phased approach, migrating data in stages while both systems run in parallel. Your choice depends on business tolerance for disruption, internal resourcing, and how tightly systems are integrated.

Factoring in these elements early helps set realistic expectations, align your stakeholders, and avoid costly surprises. The more you understand the moving parts, the more prepared your team will be to execute a migration that delivers long-term value.

How to create a data migration strategy

Data migrations can be high-stakes. Without a clear strategy, even small projects can lead to unexpected downtime, broken workflows, or unusable data. A well-planned approach doesn’t just reduce risk—it gives your team the structure, tools, and timeline to stay in control from start to finish. 

Here’s how to build a data migration strategy that works in practice.

1. Define the goal
   Clarify the purpose of the migration. Are you modernizing infrastructure? Consolidating systems? Cleaning up historical data? Clear goals guide every decision that follows.
2. Audit your data
   Take inventory of what data exists, where it’s stored, and how it’s used. Identify dependencies between systems, teams, and reports to avoid unexpected breakpoints during the transition.
3. Choose the right tools
   Select migration tools or platforms that support your data types, allow for automation, and provide monitoring, logging, and rollback options. Tools should accommodate structured and unstructured formats and integrate cleanly with your target environment.
4. Build a realistic timeline
   Determine if the migration will happen all at once (big bang) or in phases (trickle). Consider business hours, system availability, and the impact of downtime on different teams.
5. Test in stages
   Run test migrations in a staging environment. Validate completeness, structure, and usability, especially for high-impact data sets.
6. Monitor post-migration
   Once the migration is complete, track system performance and data accuracy closely. Capture what worked and what didn’t to inform future efforts.

Examples and use cases of data migration

When done well, data migration enables teams across industries to modernize systems, unify information, and support informed decision-making. Below are just a few examples of how data migration solves real-world problems and improves operations.

Retail

A national retailer migrated product, sales, and inventory data from a patchwork of legacy systems into a centralized cloud platform. The result: real-time visibility across store locations and more consistent inventory management, especially during peak seasons.

Healthcare

A hospital network moved from on-premises EHRs (electronic health records) to a secure, cloud-based system. During migration, strict governance protocols were followed to maintain HIPAA compliance. Clinicians now access patient data in less time and without waiting on IT.

Finance

A regional bank consolidated customer and transaction data after a merger. By cleaning and mapping fields during migration, the team reduced reporting inconsistencies and improved onboarding for new accounts without disrupting existing operations.

IT and operations

An enterprise IT team migrated operational metrics from siloed databases into a unified analytics environment. Data migration allowed teams across departments to monitor uptime, ticket volume, and SLA performance from a single dashboard.

The risks and challenges of data migration

Even well-planned migrations come with risks, especially when data is tied to core systems. The key is recognizing those risks early and planning around them.

Data loss or corruption

Without thorough validation before and after the move, records can be missed, reformatted incorrectly, or stripped of context, making them unreliable for decision-making.

Missing stakeholder input

Overlooking input from teams who use the data daily can result in a solution that looks good on paper but doesn’t meet real-world needs. That misalignment often shows up after launch when critical reports or systems stop functioning as expected.

Disruption to operations

Migrating live systems without proper staging or fallback plans can interrupt daily work. Even a short outage can cause delays, missed deadlines, or downstream data issues.

Poor communication

Migration touches multiple teams. If responsibilities aren’t clearly assigned across technical and business teams, delays, duplication of effort, and confusion are almost guaranteed. Without clear communication, expectations get misaligned and accountability becomes unclear.

Compatibility issues

Source and target systems may handle data differently. Skipping the transformation strategy leads to integration issues and logic mismatches that are difficult to troubleshoot post-migration.

Security and compliance risks

Data in transit is exposed and more vulnerable. Without strict controls, sensitive or regulated information could be leaked, misrouted, or stored improperly.

These challenges don’t mean you should avoid migration—but they do mean it’s worth taking seriously. With the right preparation, testing, and monitoring, your team can manage risk and migrate with confidence.

Data migration best practices

Planning sets the stage, but the execution is where most data migrations succeed or fall apart. The best outcomes come from disciplined follow-through, clear communication, and a shared understanding of what “done” really means.

1. Communicate early and often

Migration isn’t just an IT project. Keep business stakeholders in the loop with regular updates, especially if timelines shift or new risks emerge. Transparency helps maintain trust and alignment.

2. Clean and confirm as you go

Data quality isn’t a one-time task. Check for inconsistencies throughout the process, not just before or after the move. Build in time for review and rework; it always takes more than one pass.

3. Validate across roles

Don’t just verify that the data moved; make sure it still makes sense to the people using it. A table that looks fine to IT might break a report in finance or marketing.

4. Document for continuity

Keep a record of field mappings, transformation logic, and exceptions. This practice saves time during audits, handoffs, or future migrations.

5. Keep support ready post-migration

Once the migration is complete, questions and edge cases will still surface. Make sure teams know where to go and who to ask.

A migration doesn’t end when the data is transferred. With the right support and coordination, it becomes the foundation for clearer reporting, simpler workflows, and more consistent access across teams.

Your data migration strategy starts here

Data migration is an opportunity to simplify systems, improve data quality, and help teams make decisions with more clarity and confidence. But the process requires more than tools; it demands alignment, structure, and support at every stage.

Whether you're moving to the cloud, consolidating platforms, or modernizing legacy systems, a thoughtful migration plan can help you do it right the first time. Domo helps teams handle complex data challenges with confidence—from migration to integration to real-time insights. 

Want to see what your data could do with a smarter foundation? Start a free trial or talk with our team today.