5 Data Migration Challenges Explained (and How to Avoid Them)

Organizations are increasingly migrating to the cloud to scale infrastructure on demand, unify their data, and accelerate access to insights for decision-making. A 2024 study found that 63% of organizations surveyed had accelerated their cloud migrations over a 12-month period, highlighting the urgency behind this shift. Yet, migration is far from easy. 

Modern data comes in many forms, including structured, transactional, log, text, machine, and semi-structured data formats. This introduces a level of complexity that simply moving data won’t solve—a well-planned data migration strategy is necessary. To create one, though, you first need to understand the most common migration obstacles. 

Key takeaways

• Cloud migration requires expertise in architecture and security. It benefits from early preparation to reduce errors.

• Identifying critical or sensitive datasets and mapping their downstream use helps prioritize migration and support compliance.

• Automated impact analysis, schema monitoring, and rollback plans minimize risk and streamline data migration.

• A data catalog acts as a control plane, guiding priorities, enforcing policies, and building trust in cloud data.

What are the top challenges of cloud migration?

The cloud data migration process can raise various challenges. Some result from shifts in workflows and responsibilities, while others stem from current trends. For instance, many organizations have had to raise the bar for how they approach ongoing and comprehensive data management to meet the growing demand for self-service analytics. 

Below is a closer look at five of the top challenges teams encounter during cloud migration and how to address them. 

Building an effective cloud migration plan

Many cloud migrations fail not because the technology is complex, but because the planning is weak. Organizations often overlook potential pitfalls and likely challenges. This is particularly true for leaders who favor a “lift-and-shift” approach—in other words, moving all assets directly to the cloud without adjustment. This method can quickly become costly and disruptive for several reasons. To name a few, it replicates existing inefficiencies, inflates storage and compute expenses, and often requires extensive rework once systems are live. 

Migrating to the cloud presents an opportunity to transform and right-size your data estate. In other words, moving low-quality or never-utilized data is a waste of time and resources.

Wise data leaders instead prioritize migrating the most useful data first, following this process: 

1. Shift applications that require no changes for compatibility.

2. Establish and align clear interactions between virtual and on-premise infrastructure.

3. Reconfigure applications while disconnecting outdated ones.

Ultimately, a thoughtful, phased plan like the above makes transitions smoother and prevents roadblocks that can stall analysis and innovation.

Creating the pipeline and infrastructure for new environments

It’s not uncommon for organizations to find that their current employees don’t have the skills or expertise needed to lead a data migration project. For example, those responsible for planning and execution would need to be able to:

• Configure cloud platforms: This encompasses setting up services, allocating resources, and optimizing performance for distributed systems.

• Apply security best practices: They would need to be able to implement identity and access controls, encryption, and compliance safeguards.

• Manage data across environments: It’s imperative that they can ensure consistency, monitor pipelines, and handle integrations between on-premises and cloud systems.

To bridge the gap and equip your team to be successful, you can create repeatable processes, such as setup guides and recovery plans. You can also use monitoring tools to track system performance and implement frameworks that keep database consistency during changes. Investing in training and documentation early will ultimately reduce mistakes and keep migrations on track.

Classifying data properly

Data classification determines the value of any data set based on a common starting point for data governance. However, when data isn’t properly classified, there’s a higher risk of exposing sensitive data or violating regulations during the migration. 

To avoid this, your team must identify which datasets are sensitive or high priority. These would be the top datasets to focus on when making decisions about storage, encryption, and access, so you can be sure that sensitive information is always protected from theft or loss.

Beyond classification, data mapping is also helpful for visibility into data sources and their relationships across systems. Without it, even low-risk data can disrupt BI dashboards, ML feature stores, or other downstream systems.

Ensuring data governance and trust 

Moving data to the cloud can expose weak governance practices. If ownership is unclear or monitoring is inconsistent, data quality suffers. Lineage breaks, audit trails disappear, and compliance gaps open up. As a result, business teams stop trusting the data. 

Strong data governance, with clear policies and synced catalogs, helps avoid these problems and keeps data credible. Before migrating, leaders should ensure the chosen data is trustworthy, valid, and clean. They should also establish who can access data, how to use it, and a common vocabulary for analysis. This will support compliance and data democratization, while also ensuring migrated data remains useful throughout its lifecycle in the cloud.

Managing legacy systems

Legacy systems often rely on outdated code and undocumented processes. Migrating them may require rewriting applications or adjusting integrations, which often aren’t the best use of resources. Hidden dependencies can add further risk. 

To manage this complexity, teams should conduct careful assessments of existing applications, their dependencies, and integration points with other systems. This helps surface hidden risks like unsupported interfaces that could break during migration. 

Tracking lineage throughout your migration will then provide visibility into how data flows across your systems, while post-migration validation ensures that the data remains accurate and continues to support decision-making.

Cloud migration is complex, but the right approach can overcome common challenges. If you address skills, data, governance, planning, and legacy systems head-on, your organization can move to the cloud with confidence and unlock its full potential.

5 best practices to overcome data migration challenges 

In addition to the recommendations above, there are several best practices worth keeping in mind to help your team avoid the most common pitfalls of data migration. 

1. Leverage automated impact analysis tools   

Before moving data, it’s essential to understand how each change may affect the broader system. Without this visibility, migrations risk breaking dependencies and disrupting downstream processes.

Automated impact analysis tools solve this problem by mapping dependencies between datasets, applications, and workflows. Platforms such as Alation, Collibra, and Talend Data Fabric make these connections visible through clear lineage views, highlighting which systems or processes could be affected by changes.

The result is less guesswork, shorter planning cycles across teams, and quicker responses to issues. Automated analysis ultimately makes migrations smoother, more predictable, and less error-prone.

Alation, for example, offers impact analysis pages that show how data objects connect to downstream systems. Its lineage views—shown below—give teams a clear map of data flows. This enables them to anticipate and mitigate the effects of changes before they cause disruptions.

2. Implement continuous schema monitoring  

Data schemas change frequently, and even small updates can introduce errors or inconsistencies into pipelines or analytics. To prevent this, implement continuous schema monitoring during migration so any mismatches or noncompliant updates are caught in real time before they become larger problems. 

With continuous monitoring in place, your data engineering teams will be able to preserve data integrity, minimize downtime, and maintain compliance. These benefits are especially desirable in heavily regulated industries such as finance or healthcare, where it’s essential that reporting datasets remain consistent and trustworthy. However, regardless of industry, this proactive approach will contribute to a successful data migration and help your team maintain confidence in the reliability of your cloud data.

3. Establish validation and testing pipelines  

Ensuring data integrity is a critical part of any migration. Validation pipelines confirm whether data has moved correctly and whether any transformations applied during migration produce accurate results. These pipelines should ideally be built into the initial design phase to allow for early detection of any potential issues before they affect production systems.

To support validation, teams use reconciliation checks. These include tasks like: 

• Comparing record counts between the source and target

• Calculating hash totals to ensure data has not been altered

• Performing source-to-target comparisons to confirm that values match expectations

These checks are relevant in industries such as finance, retail, and any domain where accurate, trustworthy data is essential for operations. However, organizations need repeatable pipelines to ensure these checks are applied consistently and reliably. 

For example, a solution like Alation can enable automated data validation workflows, both through the core platform and integrations with various data quality partners. By embedding checks directly into the migration processes, batch jobs automatically generate reconciliation reports before downstream systems use the data. 

4. Define and test rollback plans  

Even with careful planning, migrations can encounter unexpected issues such as schema mismatches and data validation failures. These may disrupt operations or affect data integrity, so rollback plans are necessary to resolve such issues quickly. 

Rollback plans are pre-defined strategies for reversing changes safely to reduce downtime and prevent data loss. Typically, multiple teams are involved in their execution. For example, data engineers focus on testing the technical steps, while operations teams monitor execution. Business stakeholders, on the other hand, are responsible for clarifying priorities and recovery targets.

In any case, to prepare an effective response plan, teams should define triggers for each rollback scenario. From there, the next step is to document the corresponding rollback procedures and assign clear roles, so all stakeholders know their role when an issue arises. Additionally, automate responses to migration incidents wherever possible to speed up time to resolution. 

Once you have plans in place, test them ahead of time. This ensures that when problems arise, your migration can continue with minimal disruption.

5. Enforce governance throughout the process 

At its core, data governance provides the guardrails for how information is handled across an organization. It defines the standards, policies, and procedures that shape how data is managed, accessed, and protected. During cloud migration, these guardrails are critical for preserving data quality and meeting regulatory or industry compliance requirements. Why?

• For younger organizations, migration presents a modernization opportunity—the chance to refresh the data estate with governance practices that may not have been formalized on-premises.

• For more mature organizations, governance ensures continuity by extending existing policies into the cloud environment.

In both cases, though, leaders must enforce clear rules around data ownership and quality at every stage of the migration. This includes monitoring lineage, setting appropriate permissions, and documenting processes as data moves to the cloud.

Ultimately, strong and ongoing governance pays dividends even after migration. It fosters adoption and trust among end users, while also providing the foundation for AI and analytics initiatives.

How does a data catalog support on-premises to cloud migration?

A data catalog plays an important role in the success of cloud migration. Not all data is equally useful for generating insights, so a catalog spotlights the most popular, valuable data. This helps data leaders determine what to migrate first. 

Additionally, data catalogs further support cloud migration by:

• Ensuring data governance throughout migration empowers data stewards with the tools they need to protect data.

• Centralizing data access and management allows a business to scale governance effectively.

• Providing easy access to data helps deliver faster cloud migration results.

• Offering valuable information on data, such as its popularity, ensures that a business migrates only what is needed.

In addition to applying general migration best practices, using a data catalog will set the precedent for smooth transitions now and in the future.

Migrate your organization’s cloud data successfully

Moving data to the cloud can feel daunting, but it can be done successfully with careful planning and the right migration tools. 

Alation’s unified data catalog, with powerful metadata management functionality, will give your team complete visibility into critical data assets. You’ll be able to see where data resides, how it flows across systems, and which applications and reports depend on it. This level of visibility will help you plan your migration with confidence, reduce risk, and maintain governance at every step.

In addition, Alation combines visibility with intelligent automation to streamline the migration process. It can handle everything from automatically detecting anomalies to optimizing workflows to enforcing governance policies. These automation capabilities reduce time-consuming manual work and keep cloud operations running smoothly.

To learn more about how you can overcome common data migration challenges with the help of Alation, watch our on-demand cloud migration webinar.