The Top 10 Data Migration Challenges in 2025

Data migrations are the backbone of ERP upgrades, cloud transformations, and AI-readiness initiatives. But despite significant investment, too many projects still stumble, often going over budget, running behind schedule, or failing to meet business expectations. In fact, inefficiencies in migration execution are expected to waste approximately $100 billion over the next three years alone.

Achieving a successful data migration isn’t just about avoiding risks like data loss or downtime—it’s about recognizing and overcoming the deeper challenges and structural issues that cause those data migration risks in the first place. These challenges are inherent to the migration process and they can derail your project if left unaddressed.

Learn how you can overcome the most pressing data migration problems enterprises face and the practical steps you can take to solve them before they become costly, post-launch setbacks.

Table of contents

• Data migration challenges vs risks: What’s the difference?
• The top data migration challenges in 2025 (with solutions)
• What these data migration challenges have in common
• “Load early, load often”: A better way to approach migration
• Challenge-proof your data migrations with Syniti

Data migration challenges vs risks: What’s the difference?

A challenge is a known difficulty you must overcome, while a risk is a potential issue that could occur if challenges aren’t addressed. Knowing whether you’re facing a challenge or a risk can help you respond effectively and improve the chances of your migration’s success.

Risks are unwanted situations that might happen, but they’re preventable with thorough planning, testing, and safeguards. Common data migration risks include data loss, extended downtime, and security breaches.

Challenges, on the other hand, are recurring problems in the process, structure, or visibility that could lead to those risks if left unresolved. These are obstacles you will inevitably encounter. Addressing challenges often requires resource allocation, process adjustments, and proactive problem-solving.

In data migration, a risk might be inaccurate reporting post-go-live. One challenge that could lead to this is incomplete test data or bad assumptions about system behavior.

The top data migration challenges in 2025 (with solutions)

Data migrations involve a variety of complex challenges that can hinder even the best-planned initiatives. Understanding these common obstacles—and how to overcome them—is key to achieving a smooth, successful migration in 2025.

1. Dirty, disconnected legacy data

Challenge: Migrating without first addressing dirty, disconnected legacy data means carrying over existing problems such as inaccuracies, inconsistencies, and inefficiencies into your new system. These issues can lead to various problems, including faulty reporting, compliance risks, and reduced system performance. They can also drive up post-migration costs due to cleanup and troubleshooting.

Solutions: Conduct a comprehensive data quality assessment, standardize formats, and use data profiling tools to clean and unify records before migration.

2. Unclear ownership and decision making

Challenge: Without clearly defined responsibilities, migration decisions can stall, leading to misaligned priorities and inconsistent execution. This often causes project delays, budget overruns, and conflicting data standards. It also results in critical data migration issues being overlooked until after go-live.

Solutions: Establish clear data governance structures, assign accountable data owners, and define escalation paths for disputes early in the project.

Learn the key questions to consider when evaluating a data partner. Download the latest resource from Syniti, here.

3. Underestimating integration and complexity

Challenge: Migrating from legacy systems or across diverse platforms is often complex because it may involve outdated architectures, proprietary formats, or siloed databases that don’t align with the current technology. This can make it difficult to transfer data seamlessly, leading to workflow disruptions, data inconsistencies, reduced functionality in the new system, and extended downtime.

Solutions: Conduct a full system dependency mapping and develop system integration strategies. Employ a data management platform to centralize and standardize data, making it more accurate, compatible, and easy to manage before migration.

4. Inflexible timelines

Challenge: Overly tight schedules leave little to no margin for addressing unforeseen issues, which may force teams to rush or skip validation steps. This increases the likelihood of data errors, incomplete migrations, and post-launch instability. Compressed timelines can also create unnecessary stress for teams, leading to burnout and reduced productivity.

Solutions: Build buffer time into the project plan for testing, error resolution, and unexpected challenges. Consider a phased approach instead of one-and-done migration to resolve issues early before they snowball and limit the impact of any problems.

5. Testing on incomplete or fake data

Challenge: Relying on partial datasets or artificial test data creates a false sense of readiness, as it fails to replicate real-world conditions. This can lead to critical defects only becoming visible after launch, when fixes are more expensive and disruptive. Inaccurate testing also undermines performance benchmarking.

Solutions: Test with representative, anonymized production data to mimic real-world scenarios and validate performance under actual conditions.

6. Large data volumes

Challenge: Migrating massive datasets—especially those spanning years of records—can slow down transfer speeds, strain infrastructure, and increase downtime risk. Large volumes also make validation and testing more time-consuming, raising the chances of errors slipping through.

Solutions: Break the migration into manageable phases or batches and prioritize high-value or frequently used data. Use compression or parallel processing techniques to improve transfer efficiency. Consider building data migration strategies that incorporate incremental loading, real-time replication, or automation tools that speed up quality checks without compromising accuracy.

7. Security and compliance complexities

Challenge: Large-scale migrations often involve transferring sensitive or regulated data across multiple systems, environments, or even jurisdictions. Coordinating security measures while ensuring compliance can be difficult, especially when working with hybrid or cloud infrastructures. Misaligned policies, inconsistent encryption standards, and unclear regulatory requirements can slow progress, create friction between teams, and increase the workload.

Solutions: Encrypt data in transit and at rest, apply role-based access controls, and ensure compliance requirements are embedded into migration workflows.

8. Poor communication

Challenge: Data migration often involves multiple departments, each with different priorities, processes, and terminology. Miscommunication between teams can lead to duplicated work, missed dependencies, or conflicting changes.

Solutions: Set up regular cross-team updates, shared documentation, and a single source of truth for migration progress and requirements.

9. Tool or vendor limitations

Challenge: Relying on migration tools or third-party vendors with limited capabilities can slow progress or force workarounds. These constraints can lead to loss of data fidelity or the need for costly custom solutions.

Solutions: Evaluate vendors and tools early for scalability, compatibility, and compliance. Determine if they can handle your data volume, complexity, and regulatory requirements before committing.

Learn the key questions to consider when evaluating a data partner. Download the latest resource from Syniti, here.

10. Change management issues

Challenge: Even if the migration is technically successful, unprepared users can cause disruptions through errors or resistance to change. This can slow adoption, reduce productivity, and result in increased support requests post-launch. Inconsistent usage also undermines the value of the new system.

Solutions: Provide hands-on training, clear documentation, and post-migration support to help users adapt quickly and effectively.

What these data migration challenges have in common

While each of these data center migration challenges may look different on the surface, they share a common root cause: breakdowns in processes and communication. Missed dependencies, unclear ownership, inaccurate testing, and even security lapses are often the result of silos, reactive decision-making, and poor planning. They’re human and procedural issues that surface as technical problems when the pressure is on.

A proven strategy to address these pitfalls is implementing agile, iterative practices like the “load early, load often” approach. This approach encourages ongoing validation, collaboration across teams, and continuous alignment between business and technical requirements. When combined with a comprehensive data migration framework, it reduces last-minute surprises, keeps stakeholders aligned, and ensures quality checks happen early and often, preventing errors from rippling across systems.

“Load early, load often”: A better way to approach migration

Instead of waiting until the end of the project for a single, large-scale transfer, the “load early, load often” strategy involves loading smaller, more manageable sets of data into the target environment early and repeating the process at regular intervals. By doing so, teams can uncover and address issues in stages, reducing risk and building confidence ahead of go-live.

The key benefits of “load, early, load often” approach are:

• Earlier issue detection: Identify mapping errors, missing fields, or data quality problems months before final migration, when fixes are faster and cheaper.
• Improved data quality: Gain multiple opportunities to clean and enrich data, avoiding the chaos of last-minute cleanup during a high-pressure cutover.
• Improved collaboration: Frequent loads create more touchpoints between business and technical teams, breaking down silos and ensuring alignment.
• Minimized downtime: Since most data transfers occur in the background while the source system remains operational, downtime at go-live is greatly reduced.
• Better testing: With a realistic dataset available early, you can conduct more thorough and accurate testing of the new application, workflows, and reporting—a process explored in detail in our migration data testing guide.

Challenge-proof your data migrations with Syniti

No matter how complex your data migration, Syniti helps you navigate past the challenges that put projects at risk. Our Data First strategy ensures your data is clean, validated, and migration-ready, eliminating costly rework and post-launch surprises. Backed by a proven methodology and intelligent technology with built-in automation and AI-assisted data quality, we keep data projects aligned, on schedule, and within budget.

From managing massive data volumes to ensuring compliance in highly regulated industries, Syniti’s approach has delivered thousands of successful, low-risk migrations for clients worldwide. We give you the visibility, control, and confidence you need to make go-live a non-event—in the best way possible.

Ready to eliminate the most common migration headaches? Talk to our data experts today to start building your plan to a smooth, stress-free migration.

Learn the key questions to consider when evaluating a data partner. Download the latest resource from Syniti, here.