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Every organization runs on data. But as organizations grow and evolve, they eventually need to move their data from one system or application to another. Data migration is a critical part of every digital transformation initiative. Migrating successfully can boost a company's productivity and increase its revenue opportunities. But an unsuccessful migration can lead to data loss, business disruptions and compliance issues.
In this guide, we'll talk about the vital role data migration plays in helping organizations upgrade systems, consolidate their data and embrace the cloud, as well as how they can ensure the process happens without a hiccup.
At its most basic, data migration is how organizations move data from one place to another. That data might reside on personal devices, network storage systems, relational databases, cloud servers, backup archives, software-as-a-service applications or other locations.
Migration is usually necessary when organizations refresh their data center infrastructure, upgrade databases, shift on-premises operations to the cloud, switch SaaS vendors, centralize their information in a data warehouse or data lake, or decide to merge with another company. Companies may also choose to migrate data when modernizing legacy storage solutions, expanding into new markets with different regulatory frameworks or preparing their data for use in training AI models.
Done well, data migration can help enterprises optimize operations and address multiple business needs at once. But regardless of why they need to migrate their data, an organization's ultimate goal is to make the process as seamless and error-free as possible.
There are six basic types of data migration, each with their own specific use cases and concerns.
Probably the most common form of data migration is when organizations move data between different physical media — for example, upgrading from spinning disks to solid state drives, or shifting from on-premises to cloud-based storage. Enterprises typically do this to modernize legacy systems, improve performance, cut costs or increase scalability. This offers a good opportunity to clean and validate data, removing inconsistent or redundant information. But the process can be costly, may expose sensitive data to potential risk and could result in unplanned downtime if the migration runs into snags.
Upgrading from legacy database management systems to more modern ones is another common reason for migration, especially when moving from on-prem databases to the cloud. A newer DBMS can offer more powerful analytics capabilities, faster processing and better AI integration — while also lowering total cost of ownership. Here, data compatibility is crucial; shifting from one format or schema to a new system significantly increases the complexity of the move. Data engineers will need to make sure that data types, structures and relationships translate correctly, as well as ensure they thoroughly map application dependencies.
Faster performance, more advanced features, better security capabilities and lower costs are all excellent reasons for businesses to migrate to new applications — but they need to make sure the relevant data comes along for the ride. Mapping data from the old platform to the new can present significant challenges, especially if the two apps have radically different data structures and business rules. Engineers will need to ensure the target system connects properly with legacy systems, databases and third-party services. Users may need to be trained in new processes and workflows.
Whether moving to a new physical location or simply refreshing the current facility's infrastructure, data migration is an inescapable part of the data center lifecycle. In the simplest form of migration, companies move applications, systems and workloads from the old environment to the new one with minimal changes to system architecture — a strategy known as “lift and shift.” This allows organizations to migrate quickly and optimize systems later. In other scenarios, enterprises may elect to selectively optimize certain infrastructure elements, adopt new architectures or decommission aging parts of their tech stack. Each approach involves different levels of effort, risk and cost.
Enterprises are increasingly adopting a hybrid operating approach, mixing on-premises systems with cloud-based systems, depending on the needs of each workload. Storing data and applications in the cloud helps businesses avoid large capital investments, as well as some of the costs associated with IT management. This approach also makes it easier to scale as data volumes expand. But enterprises have less control over system performance in the cloud, and storing sensitive information there can make data protection and regulatory compliance more challenging. Organizations need to keep a close eye on cloud expenditures, especially around data egress fees and operating costs.
Organizations can gain efficiencies by transferring key business processes like customer onboarding or accounts receivable to a new system, especially when automating previously manual activities. But they need to ensure that their existing data is compatible with the new systems and that they maintain proper access controls and dependencies on other systems. With these considerations in mind, organizations should also make sure they can roll back changes quickly if the data migration fails.
Regardless of your reasons for migrating or your target destination, every data migration process entails the same fundamental steps. They are:
Planning. First, figure out what you have and where it's going. Inventory all your data, decide what's worth keeping and plan logistics. Then choose whether to move everything at once (so-called "big bang" migration), a little at a time or a mix of the two approaches.
Preparation. Before you move any data you need to prep it — fixing errors, removing duplicates and standardizing formats so everything fits neatly into the new system.
Testing. Running pilot migrations with small amounts of data can help ensure everything works correctly before committing to a full migration.
Migration. This is where the real data transfer happens, ideally during off-hours to minimize business disruption. Copy the data, then transform it to match the new system's requirements, and finally, load it into the target location.
Validation. After the move, data engineers verify that everything arrived and works correctly, check that all the data is complete and accurate, and run tests to confirm business processes function properly with the newly migrated information.
Data migration can be a risky process. Data loss or corruption can occur during transfer due to technical errors or incompatible systems. Migration processes that encounter unexpected errors can lead to unplanned downtime, potentially impacting business operations. Whenever data is in transit, there is always a heightened risk of loss, security breaches or compliance failures.
Organizations need to address all of the following issues before formulating their migration strategy:
What types of data are you moving and where are you moving them to? You need to make sure the data types, formats and structures of the source system are compatible with the target’s, or that you can fully transform them to meet the new system's requirements.
How much do you need to move? The larger and more complex the data volumes, the harder this process becomes. Complex data sets employing custom formats and intricate dependencies require more sophisticated transformation logic, specialized tools and extensive testing.
Is this data regulated? Security and compliance requirements may dictate specific encryption methods, access controls and audit logging throughout the migration process. Data may be subject to geographic restrictions or require specialized handling procedures.
How long can your business operate with limited (or no) access to data? You'll need to keep your old system operating until you're sure the new one works — and have a contingency plan if things go wrong.
How much is all this going to cost? Budget constraints, infrastructure and staffing limitations may determine how ambitious your data migration plans are. Insufficient budgets or resources may result in rushed migrations with inadequate testing, backup plans or support, significantly increasing the risk of failure.
Careful planning is the key to a successful data migration. These are the seven essential steps.
Establish specific, measurable goals for the migration. This prevents scope creep and helps teams focus resources on the most critical data and systems while avoiding unnecessary complexity.
A thorough inventory of existing data sources, volumes, quality issues and system dependencies provides the foundation for accurate planning and reduces risk. This should identify any technical constraints, integration requirements or potential problems that could derail the project.
The types and volume of data, along with your business requirements, will help determine the tools and approaches you need to take. These must align with budget constraints, your team's technical capabilities and expected time frame.
Establishing realistic timelines, resource assignments and milestones ensures that every member of your team is on the same page. Detailed planning also enables better communication with stakeholders and provides a framework for tracking progress and managing changes.
Detailed backup procedures and rollback plans provide a safety net if the migration encounters critical issues. These procedures must be thoroughly tested and documented so you can quickly return to the original system if needed.
Involving key stakeholders from day one can build support for the migration project and identify potential resistance or concerns. Regular communication throughout the process maintains alignment, manages expectations and facilitates smoother user adoption.
Comprehensive testing using realistic data samples validates that migration processes work correctly and helps identify issues when they're easier and less expensive to fix. Multiple testing phases, from initial proof-of-concept to full-scale rehearsals, build confidence in the migration approach and reduce the risk of problems during actual deployment.
You understand how migration works, have considered the key factors and have come up with a plan. But before you begin, you'll need to address the following challenges:
Massive data sets can overwhelm network bandwidth, storage capacity and processing resources, potentially requiring infrastructure upgrades or specialized transfer methods. Large volumes can also increase the risk of partial failures and make it more difficult to validate data completeness and accuracy within acceptable timeframes.
Intricate relationships, nested hierarchies and custom data formats require sophisticated mapping and transformation logic. These complexities often lead to unexpected errors during migration and the need for specialized technical expertise.
Poor data quality may require extensive cleansing efforts. Dealing with inconsistent formats, duplicate records and missing information will increase the time and resources needed to complete the migration.
Sensitive data must remain protected throughout the migration process, requiring encryption, access controls and audit trails that comply with regulatory requirements. This may restrict migration methods, require specific data handling procedures or mandate detailed documentation that adds complexity and extends project timelines.
Migration teams will need to develop complex strategies that maintain system availability for business-critical systems while transferring data. Minimizing downtime may require specialized tools and running old and new systems in parallel, increasing both complexity and cost.
Migrated data must work seamlessly with remaining legacy systems, third-party applications and business processes. These integration points often reveal unexpected dependencies and may require custom development work.
Users resistant to new systems can undermine even technically successful migrations. Effective change management requires ongoing communication, training programs and support structures that extend well beyond the completion of the technical migration.
Following best practices is vital to avoid the potential risks of a failed migration. Here are the key steps every migration must include:
Comprehensive planning and risk analysis helps identify potential problems early when they're easier and less expensive to address.
Creating complete, verified backups of all source data protects against data loss and allows for quick recovery if the migration encounters critical issues.
Automation reduces human error, accelerates processes and provides consistent, repeatable results. This is especially important for large-scale or complex migrations.
Running pilot migrations with sample data sets allows teams to identify and resolve issues in a secure environment before risking production data.
Maintaining the ability to quickly return to original systems protects business continuity if the migration encounters unexpected problems after it goes live.
Systematic verification of data completeness, accuracy and functionality validates that the new system works correctly before you decommission legacy systems.
Detailed documentation of procedures, decisions and outcomes supports compliance requirements and provides valuable reference material for troubleshooting issues or planning future migrations.
Data migration is something every organization will need to do at some point, and probably more than once. Having an effective strategy and adopting best practices are the best ways to avoid risks such as data loss, downtime or security breaches. Leveraging proven methodologies and automated tools can help to streamline the migration process and foster success.
The answer to this question varies based on the type, volume and complexity of the data being migrated, as well as issues you encounter around data cleanliness and compatibility. Because migration timelines directly impact business operations, it's crucial to set realistic expectations among stakeholders before embarking on a migration project.
As with the duration of each project, costs can vary widely. Migration teams will need to develop a comprehensive cost estimate, factoring in tools, resources, migration approaches, the cost of potential downtime and contingency planning to properly budget for the project.
Teams will need to establish clear criteria for measuring migration success, including data accuracy validation methods, performance benchmarks and user acceptance testing to confidently approve the go-live decision.
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