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Every organization at some point will encounter the need to migrate data for any number of business and operational reasons: required system upgrades, new technology adoption, or a consolidation of data sources, to name a few. While the process of moving data from one system to another may seem deceptively straightforward, the unique dependencies, requirements, and challenges of each data migration project make a well-defined strategy instrumental to ensuring a smooth data transitionone that involves minimal data loss, data corruption, and business downtime.

In this article, well explore the crucial strategies and best practices for carrying out a successful data migration, from planning and preparation to post-migration validation, as well as essential considerations for ensuring replicable results.

Since data can reside in various different places and forms, and data transfer can occur between databases, storage systems, applications, and/or a variety of other formats and systems, data migration strategies will vary depending on the migration data source and destination.

Some of the more common data migration types include the following.

An application migration involves moving applications and their data from one environment to another, as well as moving datasets between different applications. These migration types often occur in parallel with cloud or data center migrations.

A cloud migration occurs when an organization moves its data assets/infrastructure (e.g., applications, databases, data services) from a legacy, on-premises environment to the cloud, or when it transfers its data assets from one cloud provider to another. Due to the complexity of cloud migrations, organizations commonly employ third-party vendors or service providers to assist with the data migration process.

A data center migration involves moving an entire on-premises data center to a new physical location or virtual/cloud environment. The sheer scale of most data center migration projects requires extensive data mapping and preparation to carry out successfully.

A database or schema migration happens when a database schema is adjusted to a prior or new database version to make migrations more seamless. Because many organizations work with legacy database and file system formats, data transformation steps are often critical to this data migration type.

A data storage migration involves moving datasets from one storage system or format to another. A typical use case for data storage migration involves moving data from tape-based media storage or hard disk drive to a higher-capacity hard disk drive or cloud storage.

Learn more: Data Migration vs. ETL: Whats the Difference?

Depending on the data complexity, IT systems involved, and specific business and/or industry requirements, organizations may adopt either a Big Bang or a Trickle Data migration strategy.

A Big Bang data migration strategy involves transferring all data from the source to the target in a single large-scale operation. Typically, an organization would carry out a Big Bang data migration over an extended holiday or weekend. During this period, data-dependent systems are down and unavailable until the migration is complete. Depending on the amount of data involved, the duration of downtime could be significant.

Though the Big Bang migration approach is typically less complex, costly, and time-consuming than the Trickle Data migration approach, it becomes a less viable option as an organizations data complexity and volume increases.

Big Bang data migrations typically take less time and are less complex and costly than Trickle Data migrations. However, they require data downtime and pose a higher risk of failure. For this reason, the approach is best suited for smaller organizations or data migration projects that use limited data volumes and datasets, as well as straightforward migration projectsbut should be avoided for complex migrations and mission-critical data projects.

A Trickle Data migration strategy involves taking an Agile approach to data migrations, adopting an iterative or phased implementation over an extended period. Like an Agile project, a Trickle Data migration project is separated into smaller sub-migrations chunks, each with its own timeline, goals, scope, and quality checks. Migration teams may also use the same vernacular and tools as Agile teams in breaking the migration up into Epics, Stories, and Sprints. By taking Trickle Datas Agile approach to data migration, organizations can test and validate each phase before proceeding to the next, reducing the risk of catastrophic failures.

A key attribute of the Trickle Data migration approach is source/target system parallelismthat is, the source and target systems are running in parallel as data is migrated incrementally. The legacy system continues to function normally during the migration process until the migration completes successfully and users are switched to the new target system. Once the data is fully validated in the new system, the legacy system can be safely decommissioned.

Because of its incremental approach and source/target system parallelism, Trickle Data migration allows for zero downtime and is less prone to unanticipated failures. However, keeping the source and target systems running at the same time incurs a cost, so organizations evaluating this migration strategy should expect a more expensive and time-consuming migration journey. Developers and data engineers must also keep both systems synchronized continuously until the migration completes, which again requires significant technical expertise and overhead to successfully carry out.

Regardless of which data migration strategy is in play, a successful data migration project starts with an initial comprehensive analysis and assessment of the datas journey. This includes the following planning tasks and preparation activities:

After completing planning and assessment activities, the data migration project should commence with data migration process testing. The following activities should be carried out to ensure the accuracy and reliability of the data in the new system.

Perform a trial migration by creating a test environment that mirrors the production environment. This will allow you to identify and resolve issues without impacting live data.

To assess the accuracy of the migration and identify any potential data quality issues, test the migration process using a representative data sample.

In software engineering, UAT is the crucial final phase in the software development life cycle (SDLC) before a software product is deployed to production. This phase plays a pivotal role in ensuring the successful delivery of a software application, as it verifies that the achieved success criteria matches the end-users expectations. For this reason, its also referred to as End-User Testing or Beta Testing, since the actual users or stakeholders test the software.

During this phase, real-world scenarios are simulated to ensure that the software meets the intended user/business requirements and is ready for release.

Taking cues from the software world, modern organizations will often incorporate UAT testing into their data migration processes in order to validate that they meet data end-users specific requirements and business needs. Adopting UAT in the migration process will bring end-users into the fold, incorporate their feedback, allow for necessary adjustments as needed, and validate that the migrated data is working as expected.

Although every data migration is unique, the following principles and best practices apply universally to every data migration project. Be sure to keep these procedures top-of-mind during the course of your data migration project.

Your data migration project may involve downtime or service disruptions, which will impact business operations. Schedule the data migration during off-peak hours or weekends to minimize its impact on regular business activities.

Incremental data migrations are usually the safest route to followif feasible, migrate your data incrementally and allow the system to remain operational during the migration. This may require the implementation of load balancing to distribute the migration workload efficiently and avoid overloading the target system.

Ongoing stakeholder communications is crucial throughout the data migration process. This should include keeping everyone informed about the migration schedule, potential disruptions, and expected outcomes, as well as providing end-user training/instructions to smooth the transition and prevent any post-migration usability issues.

Once the migration is complete, perform post-migration validation to verify that all data is accurately transferred and that the new system functions as expected. Conduct regular audits to ensure data integrity and compliance with data regulations.

Ongoing monitoring of the new systems performance is vital for surfacing any post-migration data loss and/or data corruption issues. Regularly assess the target systems performance and investigate any potential data-related performance bottlenecks/issues.

Last but certainly not least, ensure that data security and compliance requirements are met during and after the migration process. This may include implementing data encryption at rest and in transit, access controls, and data protection measures to safeguard sensitive information.

Data migrations may be unavoidable, but data migration failures can certainly be avoided by following a well-defined data migration strategyone that incorporates comprehensive planning, ongoing data quality analysis, proper testing, and continuous monitoring. By planning ahead, choosing the right approach, and following best practices, organizations can minimize the risk of data loss, ensure data integrity, and achieve a successful and seamless transition to new systems or environments.

Read next: Top 5 Data Migration Tools of 2023

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Data Migration: Strategy and Best Practices - Datamation