Data Migration Challenges: Strategies for a Smooth Transition

Smooth and effective data migration helps organizations move data across systems efficiently to maintain their competitive advantage.

Still, Gartner reports that only 17% of initiatives involving data migration are completed within their budgets or set timelines.

Understanding these data migration challenges is the first step toward overcoming them. In this blog, we’ll explore data migration and its different types, challenges, and strategies for dealing with them.

What Is Data Migration?

Data migration is the process of selecting, extracting, preparing, and transforming data, followed by a permanent transfer to a new destination. The new destination can be a new file format, location, storage system, computing environment, database, or data center.

Due to increased digital transformation and cloud migration initiatives, data migration has become more prominent in the current business landscape. In fact, Zippia reports that 67% of enterprise infrastructure in the US is now cloud-based.

Moreover, organizations are now conducting cloud-to-cloud migrations to optimize their data stack and consolidate their data assets, with the cloud computing market expected to cross the $1 trillion mark by 2028.

Data Migration Use Cases

Data migration is central to many IT activities, especially in digital transformation and cloud migration. Here are some specific use cases that typically require data migration:

• Upgrading or replacing legacy systems, whether software, hardware, or both, so the organization can retain or improve its competitive advantage or meet its performance goals.
• Reducing environmental impact and increasing cost savings by migrating to a system that uses less energy and has a smaller footprint.
• Relocating to a new, more secure data center.
• Centralizing data to enable and facilitate system interoperability.
• Reducing or eliminating the costs and inflexibility of hosting data on-premises by migrating to the cloud.
• Data backups to ensure preparedness for disaster management and recovery.

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Why Do Data Migration Projects Fail?

McKinsey reports that inefficiencies in data migration cost enterprises 14% more than their planned spending. Furthermore, 38% of companies experience delayed migration efforts by over a quarter.  Let’s look at some reasons data migration projects fail:

Risk of Data Integrity Loss

Data quality maintenance is crucial to a smooth data migration process, especially when dealing with large volumes of data. Loss of data integrity can happen if data validation and cleansing aren’t performed prior to the migration.

When an organization is dealing with petabytes of data, loss of data integrity during migration can set them back by months, if not years.

Variety of Data Assets

Data migration projects become more technically challenging and complex if data assets exist in different file formats and locations.

Some examples of data assets that are typically involved in a data migration process include:

• Unorganized data of different file formats stored across multiple locations and devices,
• Relational databases such as MySQL, SQL Server, and PostgreSQL,
• Unstructured data such as multimedia, PDFs, and text files,
• Data lakes, data warehouses, or even entire data centers.

The presence of diverse data assets requires organizations to plan, implement, and validate the source data during migration. Improper planning can lead to data corruption or loss. Similarly, unstructured data lacks a predefined model, which requires organizations to index and organize it systematically in the target system.

Data Migration Misconceptions

Data migration is typically part of larger initiatives like cloud migration and legacy system modernization. As a result, organizations underestimate the effort required for successful execution due to misconceptions, such as:

• assuming that existing data will be compatible with the new system,
• migration is just about moving data from point A to point B,
• data migration is a one-time activity conducted by IT that doesn’t require the attention of other domains,
• limited insight into the quality of data (or lack thereof) and the efforts required to make data usable,
• focusing mainly on modeling business processes without considering the data required to implement them successfully.

Lack of Planning

Lack of planning around data migration can cost organizations time, resources, and, most importantly, competitive advantage.

Data migration projects often require more planning and buy-in from the different business functions. They are often seen as an IT activity when the reality is that the challenges of data migration are far-reaching for any data-driven organization.

10 Data Migration Challenges to Consider

For organizations, the benefits of modernizing their IT infrastructure or migrating to the cloud outweigh the risks, especially in the long run. Still, it is important to understand some common data migration risks.

Following are some data migration challenges that organizations can and should account for:

1.   Security Issues

Organizations should ensure all data is securely encrypted at rest and in transit during all stages of data migration.

For offline migrations that may involve shipping data storage devices, teams must confirm the credibility and experience of shipping services.

2.   Longer Transfer Times

Estimating online transfer times with complete accuracy can be challenging. Organizations should plan for setbacks, keeping in mind that time overruns occur in 41% of projects involving data migrations. Some issues that can cause delays include network bottlenecks throttling the transfer speeds or hardware constraints limiting the amount of data that can be read or written.

3.   Unanticipated Costs

Cost overruns average 30% in data migration projects. Unanticipated costs are typically a sign of insufficient planning but can arise from various avenues. For instance, additional costs can occur due to the migration of unnecessary data or inadequate storage provisioning. Similarly, unplanned downtime and business disruption can hurt an organization’s revenue.

4.   Data Loss or Corruption

Data loss or corruption during migration can occur due to many reasons. Some common reasons are format compatibility issues, automated truncation, unknown or incomplete validation settings, and network issues.

Similarly, data corruption can also happen for many reasons. For instance, transferring unwanted data types to a new location can lead to a system crash.

5.   Schema Management and Database Differences

Even in the cloud, databases store data differently. These minor differences can turn into colossal data migration challenges.

For instance, consider two databases that store dates differently. The source database stores the date in the DD/MM/YYYY format, while the target database stores them in Unix timestamps. In this example, transferring the data with the dates in the existing format without the necessary conversion can lead to a mismatch.

6.   Extended Business Disruption

Downtime and business disruption are expected during a data migration project. However, it gets tricky when the project takes longer than anticipated, extending downtime and affecting business activities.

Even if the downtime is only extended by a few hours or days, the ensuing business disruption can lead to the loss of revenue and clients.

7.   Data Dependency Issues

Data in organizations is typically highly dependent. Multiple data sets feed each other through complex layers of queries and transformations.

For instance, an organization discards certain data sets during the migration, deeming them useless. Even if this data wasn’t valuable, it may have fed into other crucial dataflows. This can lead to the organization losing a lot of valuable data after migration.

8.   Poor Data Orchestration

Data orchestration is the process of taking siloed data from different sources, combining and organizing it, and presenting it in a unified data view. Although it is a common step in cloud migration projects, it can be challenging.

Without a proper data migration plan in place, organizations can lose track of the data points they’re using. This can lead to the creation of new silos and leave the migration process incomplete. Organizations with multiple disconnected teams in different domains are more susceptible to this challenge.

9.   Poor Data Governance, Access, and Security

Transferring data is one thing, but what about the access permissions and governance policies surrounding that data? If organizations don’t refactor their data access and governance during the migration, users can find it difficult to access data, which can lead to a loss of productivity.

Data security can be another challenge when migrating unstructured data. Organizations may end up putting sensitive information at risk without even realizing it.

10.   Integration Issues

Modern data stacks have numerous integrations. These integrations may need reconfiguring during data migration to ensure critical workflows are unaffected. The data migration plan should ensure the target system will support all the required integrated tools.

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Data Migration Best Practices and Strategies

While there’s no denying the potential risks and challenges involved in data migration, not all hope is lost. Organizations can ensure successful data migration by following these best practices and strategies to overcome potential challenges:

Preparation and Planning

Organizations should create a comprehensive data migration plan after thoroughly analyzing their data, business processes, and project goals. Organizations should ask six key questions to get started:

1. Assessing the present state of data: What does the data look like right now?
2. Determining the target state of data: What should the data look like after migration?
3. Locating ROT (Redundant, Obsolete, Trivial) data in the source: Is there any redundant, obsolete, or trivial data that shouldn’t be transferred?
4. Rebuilding requirements: What dependencies and workflows need to be recreated in the new ecosystem?
5. Clear project objectives: What does the organization want to achieve with this migration, i.e., what should the data be able to do?
6. Communication plan: How will the organization communicate with its end users about the project?

Furthermore, this plan should comprise relevant techniques, realistic schedules, implementation methods, and preemptive solutions to common data migration challenges.

While the IT function will oversee the technical aspects, data users from different domains should assess the data needs and the new system. Since end users are familiar with their domain’s data needs, they can better identify potential issues and inconsistencies.

End-to-End Testing

Organizations often assume that their hardware and networking requirements are met without test runs, leading to several data migration challenges. Running end-to-end tests before the migration ensures that all components are functioning properly.

Data migrations should follow the planning, implementation, and validation route to be successful. Validation helps organizations ensure that the migrated data is accurate, consistent, and in the correct format.

Migrate During Off-Hours

When it comes to data migration, organizations can never be too cautious. Performing the migration during off-peak hours ensures that business isn’t interrupted if the organization needs to shut down temporarily to restore and recover.

Plan for Rollback and Recovery

By covering all bases, organizations get ever closer to a smooth migration. Still, if things go south, it’s important to be ready to roll back. With a recovery plan in place, organizations can ensure that the migration can be rolled back and attempted again later.

Using a Specialized Data Migration Solution

As discussed before, organizations often underestimate the challenges of data migration. This can also lead to not investing in the right solution. A data solution with specifically designed features for smooth data migration can be the difference between success and failure.

A solution designed for a specific use case will always be a cut above the rest. Because it’s designed keeping in mind the challenges of data migration and the pain points of end users.

Read more: The 7 Best Data Migration Tools In 2024

Final Thoughts

To sum it up, data migration is a crucial process for every data-driven organization. It has many potential risks and challenges that can be overcome by following certain best practices, implementing the right strategies, and using a specialized solution like Astera.

Astera offers an automated, no-code data solution that can make data migration cost-effective, simpler, and more accessible for organizations.

By automating processes like creating data models from the source system, database consolidation, data synchronization, data migration, etc., Astera does all the heavy lifting involved in a data migration.

Book a personalized demo today to learn how Astera can help your organization overcome data migration challenges.