What is data migration?

Many people have experienced data migration in some form. Think about when you upgrade to a new phone and move your contacts, photos, and apps from the old one: that’s data migration in action, just on a smaller scale.

Data migration definition 

Data migration is the process of transferring digital data from one system, format, location, or application to another. It involves selecting, preparing, extracting, and sending data into the new system while making sure that it is complete and secure. 

The most common use cases are system upgrades, cloud adoption, application changes, business changes such as mergers & acquisitions, etc. 

Data migration types 

Here are the main categories of data migration: 

• Storage migration: The most common type of data migration. Example: moving data centers, using different storage types, etc. For example: moving the data from an HDD to an SSD. 
• Database migration: From one database to another. Usually involves a new version upgrade or switching to another system. 
• App migration: Moving data from the old computing environment to a new one. For example: rehosting from an on-prem server to a cloud virtual machine. You can either rehost, refactor, replatform, or even retire the application. 
• Cloud migration: Moving the data either from an on-prem datacenter to the cloud or from one cloud system to another.  
• Business process migration: Digitalizing business processes or moving data and applications for optimization, reorganizing, mergers/acquisitions, etc. 

Data migration challenges and how to prevent them 

• Data Loss:  No backups? Say goodbye to important files. Always back up before you move anything.
• Data Semantics Errors: Fields don’t line up, and values get scrambled. Run multiple test transfers to catch mismatches early.
• Downtime Risks: Migrate during peak hours and watch productivity tank. Do it off-hours, and split it into chunks.
• Data Corruption: Mixed data types and messy sources = broken records. Audit your data before you hit “go.”
• App Performance Issues: Sluggish apps after migration? That’s usually old, unreviewed code or unspotted bugs. Review and test performance beforehand.

Data migration process

Here is how the process works in stages: 

• Planning: No plan, no chance. Assess the scope, choose the migration type, form a team, and plan the retirement of the legacy system. 
• Preparation and auditing: Ensure there’s data auditing, a data backup, and established permissions. 
• Design and mapping: Design the mappings for fields, data types, and file types from the legacy systems and how they’ll be organized in the new system. 
• Extract, transform, load (ETL): Old data is extracted, transformed as required to a proper format, and then loaded into the new system. 
• Testing and validation: Involves data testing and final validation (a final test). “It should work” isn’t good enough. Test, validate, and test again before you go live.
• Maintenance post-migration: Don’t just walk away. Audit the results, retire the old system, and set up support for the new one.

Data migration best practices

• Plan and design: The data migration should be planned and designed carefully, as it is not something to be rushed or done on a whim. Map it out before you move a single byte.
• Do thorough research on providers and services: This ensures that you find exactly what you need. Compare services until you find the right fit.
• Add security measures: No explanation needed. 
• Optimize the data/apps for the cloud migration: Some things need to be refreshed before being moved to the cloud to avoid data corruption or app crashes. 
• Test, test, test: Start small before you move everything. 
• Always monitor parts such as the performance, analytics, and security of the new system. Migration isn’t “set it and forget it.”

The bottom line is: data migration is a heavily used practice in the industry and beyond. However, some precautions should be taken to ensure a smooth data ride.