Data Integration vs Data Migration: What’s the Difference and Why It Matters

Many organizations struggle at the first technical fork in the road: choosing between data integration and data migration. While both options involve moving and shaping data, the implications for your business couldn’t be more different.

• Data integration is an ongoing, strategic process that ties together data across multiple systems so you can gain real-time insights and power workflows.
• Data migration, on the other hand, is a project-based effort to move data from one system to another, often happening during modernization, consolidation, or platform upgrades.

Understanding this distinction matters more than ever in modern enterprises. Data quality and integration are central to turning fragmented data into a reusable business asset. Many businesses cite poor data consistency across systems as the most challenging hurdle to overcome, and accurate alignment across sources is essential for supporting AI-driven analytics.

Meanwhile, projections underscore how rapidly data volume is growing: global storage already exceeded 120 zettabytes in 2023, with an estimated rise to 180 zettabytes by 2025. This rapid growth heightens the complexity—and urgency—of migration and integration efforts alike.

If you treat data migration purely as a technical shipping task, you could introduce unnecessary risk. Instead, approach ERP implementations as a strategic initiative—an opportunity to clean and standardize data, align stakeholders, and lay a stronger foundation for automation, analytics, and governance.

Together, these trends highlight why businesses today can’t afford to ignore the differences between integration and migration. One delivers accessibility and real-time value; the other preserves continuity and gives you a clean slate for what comes next.

In the sections ahead, we’ll explore:

• What each process really involves.
• The scenarios where each one shines.
• When and how you might need both.

And most importantly, we’ll help you choose the right strategy to align with your organization’s goals and technology priorities. 

What is data integration?

Data integration is about combining data from multiple systems into a single, connected view so your team can access consistent information across the business.

In most companies, different teams use different tools: sales may live in a CRM, finance works out of spreadsheets or ERP software, and operations might rely on industry-specific systems. When these systems don’t talk to each other, it’s difficult to get a full picture of performance or make data-informed decisions.

Data integration solves this challenge by creating connections between systems and syncing data in real time or near real time. It enables dashboards, automated workflows, and self-service reporting by centralizing the data your business already has.

Why data integration matters for your team

• Gives leaders a single version of truth. You can track KPIs across departments without manually piecing together spreadsheets.
• Supports real-time decision-making. Instead of waiting for end-of-month reports, teams can act on current data.
• Reduces errors and rework. With connected systems, you don’t have to update the same data in multiple places.
• Enables automation. Integration unlocks things like triggered alerts, workflow handoffs, and AI-powered insights.

Common integration methods

Most modern data integration strategies rely on technologies like:

• Connectors and APIs: These allow systems to communicate and share data automatically.
• ETL tools (Extract, Transform, Load): These clean and standardize data as it moves between systems.
• Data warehouses and lakes: These act as centralized hubs for storing integrated data.
• Federated queries or virtual integration: In some cases, data is connected “just in time” without needing to be physically moved.

What makes integration challenging?

Integration isn’t just about moving data—it’s about making sure that data makes sense once it arrives. Teams often face:

• Inconsistent naming conventions
• Different data formats (e.g., dates, currencies)
• Data silos between departments
• Lack of real-time access or delays from batch syncing

Platforms like Domo address these challenges with built-in connectors, low-code ETL tools, and automated governance controls that ensure consistency across every source. 

What is data migration?

Data migration is the process of moving data from one system to another. It’s typically a one-time project that happens when your organization changes platforms, upgrades systems, or consolidates tools.

For example, if your company is switching to a new CRM, all customer data needs to be transferred from the old system to the new one. That’s data migration.

But it’s not as simple as dragging and dropping files. Migration involves extracting data from the old system, cleaning and transforming it to match the new system’s requirements, and loading it into the new destination without losing, duplicating, or corrupting the data.

When businesses use data migration

Data migration is typically part of larger initiatives, such as:

• System upgrades: Moving from legacy on-prem tools to cloud-based platforms.
• Digital transformation: Standardizing systems across departments to modernize operations.
• Mergers or acquisitions: Combining data from multiple companies into a single platform.
• Cloud adoption: Migrating on-premise databases or workloads to cloud infrastructure.

Why data migration is more than a technical task

At first glance, migration can seem like a purely IT responsibility—but the consequences ripple across the business. If the wrong data is moved—or if it’s moved incorrectly—it can lead to broken reports, missing customer records, compliance risks, or poor user adoption.

Handled well, however, data migration becomes a strategic opportunity to improve data quality, eliminate outdated records, and set the foundation for better analytics. A recent Forbes Council article even called ERP data migration a “chance to turn risk into long-term return” by cleaning and aligning critical business data during the move.

Common challenges in migration projects

Even experienced teams face roadblocks during migration. Common issues include:

• Data inconsistency: Formats and fields often vary between the old and new systems.
• Incomplete records: Some legacy systems contain missing, duplicated, or outdated data.
• Downtime risks: Migrations must be carefully planned to avoid disrupting operations.
• Post-migration validation: Teams need processes in place to confirm that data landed correctly and completely.

That’s why successful migrations include pre-migration audits, data cleansing, transformation logic, testing, and stakeholder alignment—not just technical execution.

Modern platforms can streamline this process. For example, with Domo’s Magic ETL and cloud connectors, teams can stage and validate migration flows visually, reducing reliance on custom code or risky manual transfers.

Data integration vs data migration: Key differences

If you’re evaluating how to get more from your data or are modernizing tools and infrastructure, it’s essential to know whether you need data integration, data migration, or both.

These two processes can sound similar, especially if you’re new to data strategy. But choosing the wrong one can lead to wasted effort, mismatched systems, or poor business outcomes. For example:

• If you’re trying to sync CRM and marketing data in real time to improve lead conversion, migration won’t help—you need integration.
• If you’re retiring an old database and moving everything into a cloud-based data warehouse, integration alone won’t get the job done—you need migration.

The table below highlights the core differences—purpose, timing, outcomes, and more—to help you determine which approach aligns with your specific project or business goal.

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While both involve moving and transforming data, their objectives are quite different. Integration keeps systems working together. Migration helps you move forward.

When should you use data integration?

Data integration is the right choice when your goal is to connect systems and make data usable in real time. It supports ongoing operations, analytics, and automation and empowers teams across the business to act on up-to-date information.

Use integration when:

• You want real-time reporting or dashboards that reflect multiple systems (e.g., marketing + CRM + finance).
• Your teams are duplicating data across tools manually, often in spreadsheets or one-off exports.
• You’re trying to automate workflows that span multiple platforms (e.g., when a deal closes in your CRM, notify finance and kick off fulfillment).
• You want to democratize access to data without giving everyone access to raw systems.

Business signal: If you’re hearing questions like “Why don’t these numbers match?” or “Can we automate this report?”, that’s a good sign integration is needed.

Follow-up question to ask internally:
“What are our most common cross-system workflows, and where is manual data entry slowing us down?”

When should you use data migration?

Data migration is the right choice when you’re transitioning from one system to another and need to preserve existing data. It’s usually a finite, one-time project with long-term implications.

Use migration when:

• You’re replacing or upgrading a system and need to move historical data to maintain continuity.
• You’re consolidating platforms across departments, regions, or acquired businesses.
• You’re moving from on-premise to cloud and need to preserve legacy data for compliance, analysis, or future access.
• You want to archive and clean outdated systems without losing key business information.

Business signal: If you’re hearing “We’re switching platforms next quarter” or “We need to keep our old records accessible,” that points to a migration project.

Follow-up question to ask internally:
“What data do we need to bring forward, and what can we clean, archive, or leave behind?”

When should you use both?

In many cases, the smartest data strategy involves both migration and integration, especially if your organization is in the middle of a digital transformation or rolling out new systems department by department.

Here’s how they often work together:

• Start with migration to move historical data from a legacy platform into a new cloud-based system or warehouse.
• Then layer on integration to connect the new system with other tools for ongoing analytics, workflows, and automation.

Use both when:

• You’re upgrading systems but need to keep operations running during the transition. Integration ensures data keeps flowing while migration happens in stages.
• You’re modernizing architecture across the business, and some teams are ready to switch systems while others are not.
• You want to clean and consolidate historical data before syncing it across other platforms.
• You need to preserve compliance and reporting continuity, even as new tools come online.

Hypothetical example:
A company migrates five years of financial data from a legacy ERP to a cloud platform. Then, it integrates that new ERP with CRM, HR, and business intelligence tools so the finance team and others can build live reports without logging into multiple systems.

Business signal: If you’re hearing “We’re moving to [new system], but need reports to stay up during the change,” you likely need both.

Follow-up question to ask internally:
“How will people access and use this data after the migration is complete, and what other systems need to talk to each other to support that?”

Common tools and technologies

Whether you’re integrating or migrating data, choosing the right tools can make or break your success. These technologies handle everything from extracting raw data to transforming it into clean, usable formats—and getting it into the right hands.

Core categories to know:

• ETL and ELT tools: These platforms extract data, clean and restructure it, and move it to its destination.
  → Best for structured data workflows or consolidating data into a warehouse.
• APIs and connectors: Enable real-time or near-real-time syncing between platforms without manual exports.
  → Best for syncing operational data across tools like CRM, marketing, and finance.
• Data warehouses and lakes: Central repositories that store large volumes of structured (or unstructured) data.
  → Best for analytics, reporting, and long-term storage.
• Data visualization and BI platforms: Turn your integrated data into dashboards, alerts, and reports.
  → Best for making data accessible to business users—not just analysts.
• Migration utilities: Specialized tools that help move and map data from legacy systems to modern platforms.
  → Often bundled with ERP, CRM, or cloud providers.

Tip for your team: Focus on tools that are low-code or no-code when your resources are limited. Visual tools like Domo Magic ETL help bridge the gap between business users and technical teams, reducing handoffs and bottlenecks.

Avoiding common pitfalls

Even well-planned data projects can run into issues. Here’s how to sidestep the most frequent mistakes that cost time, money, and stakeholder trust.

For both integration and migration:

• Assuming all your data is clean
  → Reality: Most systems have outdated, inconsistent, or duplicate records.
  → What to do: Build in time for data profiling and cleansing early in the project.
• Skipping a clear business objective
  → Reality: Projects lose momentum fast without a defined “why.”
  → What to do: Tie your work to a measurable goal—like faster reporting, automated handoffs, or a single source of truth.
• Underestimating how systems differ
  → Reality: Field formats, naming conventions, and structures vary wildly.
  → What to do: Map fields carefully and plan for edge cases—especially during migration.
• Failing to validate the outcome
  → Reality: Just because data moved doesn’t mean it’s correct, complete, or usable.
  → What to do: Build in automated checks, record counts, and sample reviews post-load.

Integration-specific pitfalls:

• Trying to connect everything at once
  → Start with one high-value workflow (e.g., syncing sales and finance data for revenue reporting) before scaling up.
• Ignoring real-time vs batch tradeoffs
  → Know when you need up-to-the-minute data and when daily syncs are enough. Over-engineering real-time connections can increase cost and complexity.

Migration-specific pitfalls:

• Treating it as an IT-only project
  → Include data owners early. Business context is critical for deciding what to keep, update, or retire.
• No rollback or contingency plan
  → If the migration goes sideways, can you restore previous data or pause the cutover? Always test in a sandbox first.

Pro tip: Even with modern tools, success still depends on process. Clear ownership, aligned expectations, and testable milestones are just as important as your tech stack.

Choosing the right data strategy to drive business forward

Data migration and data integration aren’t just technical processes—they’re foundational building blocks of a modern, scalable data strategy. Understanding the difference isn’t academic. It directly impacts your ability to deliver what you’ve learned, reduce risk, and respond faster to business needs.

Here’s what we’ve covered:

• Data migration is your go-to when replacing or consolidating systems. It ensures continuity, preserves critical history, and sets the stage for future innovation.
• Data integration powers day-to-day operations and analytics. It connects your systems so teams can act on real-time information, automate workflows, and uncover information that was previously siloed.
• In many cases, you’ll want both—migration to modernize your stack, and integration to unlock its full value.

As organizations face increasing pressure to make smarter, faster decisions, the quality, timeliness, and accessibility of your data matter more than ever. Whether you’re centralizing your systems, scaling AI initiatives, or just trying to deliver more reliable reporting, the right approach to data movement makes all the difference.

Ready to take the next step?

Domo helps you handle both sides of the equation—seamlessly. With visual ETL tools, hundreds of built-in connectors, and real-time data pipelines, Domo gives you the power to integrate and activate your data—before, during, and after a migration.

See how Domo can simplify your data migration—and make integration effortless. Start your free trial or request a demo today.