Plan the Data Centre Migration: Top 10 Steps to a Successful Migration

Plan the Data Centre Migration: Top 10 Steps to a Successful Migration

data center migration steps

Data migration is the process of moving software or hardware from one data centre to another. A data center migration plan may take place for various reasons like company relocation, organization acquisition or merger to accommodate remote working.

Popular Data center migration tools

There are various tools to assist with data center migration steps. The tools can be broadly categorized into the following categories:

Data migration tools

The tools facilitate the actual transfer of data from the source to the target environment. These tools include extract, transform, load( ETL) tools, backup and recovery tools.

Testing and validation tools

The tools help in verifying the migrated data and ensuring it functions correctly in a proper environment. Testing and validation include data profiling and quality tools, performance testing tools, and migration simulation tools.

The top ten steps to a successful migration

As part of the data center migration strategy, from your existing environment to a new one, it is suggested that you comply with the following guidelines:

1. Obtain a viable business case

A data center migration business case outlines a clear case for making the transition. It includes a risk assessment and discovery phase, an implementation plan, and cost analysis and identifies who will run the project.

2. Get buy-in from all stakeholders

Migration should benefit all staff, but without buy-in, it can lead to frustration and impact productivity. Clear and open communication should be established with stakeholders and regular updates should be provided as part of the data center migration strategy. ensure that the benefits of migration are clearly communicated.

3. Establishing responsibilities along with roles

Find and assemble a team to help you with the migration. You must include key players who keep tabs on the project’s progress and oversee the budget. Technical specialists and business stakeholders that recognize the risks or challenges related to migration are available to help you with your data center migration strategy.

4. Reviewing the contractual obligations

Migrations could necessitate moving to a new physical location. Verify that all contracts and leases have been ended in advance and that all leases have been revoked.. According to the lease agreement obligations, it is necessary to avoid unwanted costs.

5. An inventory of assets is to be created

Before data migration, you need to ensure what assets are to be moved, and identification of all application developments with technical requirements is necessary. Ensure that the hardware is accounted for and figure out which infrastructure will be moved or replaced during migration.

6. Determining the architecture and design specifications

Establish how data removal will occur, where data will be stored, and how it will be tested.

7. Migrating in a concise way batch-wise

Infrastructure is a mechanical system that could malfunction. Moreover, divide data into categories or batches. Categorized or small datasets are easy to migrate and test for integrity rather than large data volumes. It is necessary to map the network dependencies and have full visibility as part of your data center migration plan.

8. Developing efficient staff schedules

Data center migration impacts the entire organization. Ensure that the work planning has been accounted for, or complexly remove any impact on business operations during the migration.

9. Examine audit logs and trails

In order to prevent dependencies or apps from creating productivity bottlenecks, it is vital to configure that the data has been appropriately transferred after a migration is complete.

10. The new environment is to be optimized

Post-migration, you need to examine the new environment for improvement. Optimization ensures that you need to identify opportunities to trim costs, even by identifying which services are not required. It may leverage features like automation, machine learning, and artificial intelligence to free up human capital and focus on high-level tasks.

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