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Data Migration: Types, Best Practices & Strategies in '24

Gulbahar Karatas
Updated on Jan 2
3 min read

Some business projects require moving data from one source to a new data center, applications, or systems. However, moving data to a new platform can lead to data loss or data quality problems as it is a complex and time-consuming process and requires multiple technologies. Implementing a data migration strategy can make this process more seamless and less costly for organizations.

What is data migration?

Data migration is the process of transferring data from one location to another storage system, database, or other location.

What are the differences between data migration and data integration?

Data migration may sound like data integration. Both data migration and data integration help businesses to move their data. However, they are completely different things, and each requires different strategies. 

Data integration is more than moving data as it involves combining data from different sources and sharing data between applications or systems. In order to provide a unified view of all data, data integration combines different data repositories into one large repository. Data migration involves transferring data between different types of data storage.

Why do you need a data migration strategy?

Every data migration project has its own challenges, but there are common challenges that demonstrate the importance of a data migration strategy. A poorly executed data migration process can:

  • Lead to data losses or damage the accuracy of well-structured data
  • Cause increased storage costs due to data redundancies and other data problems
  • Ruin data integrity and interrupt business activities
  • Reduce the overall productivity and growth of businesses
Source: Gartner

What are the main types of data migration?

  • Storage migration: The focus is on transferring data from one hardware store to another. It typically involves upgrading to more modern hardware.
  • Database migration: Moving data between different platforms such as migrating data from one database to a new database. Generally, database migration means updating a database or switching to a new one.
  • Application migration: It is the process of moving software applications from one environment into another.
  • Data center migration: A data center is where an organization’s critical data and applications are stored. Data center migration means moving an entire data center environment to another location. It is a large-scale data migration project.
  • Cloud migration: More than 45% of spending on traditional IT solutions will shift from traditional solutions to cloud by 2024. This is the process of transferring data from a local data center to the cloud. It can be done in two ways:
    • Online migration: Data is transferred over a private WAN connection or the internet.
    • Offline migration: Data is physically moved from the data source to the cloud with the help of a storage appliance.

What are the best practices of data migration?

  • Automate: Using automation tools when moving data from one storage location to another can reduce potential errors and save time.
  • Create migration plan: Before migrating data, create a plan that defines which data needs to be moved, who needs the data, etc. Knowing why you are moving and storing data will improve the migration process. 
  • Implement policies: You need to create organization-wide migration policies to protect your data after migration.
  • Validate migrated data: After data migration, you need to schedule testing and validation procedures to ensure that all data has been migrated to the target location.

What are some data migration strategies?

Choosing the right approach is critical to the data migration process. The main approaches are:

  • Big bang data migration: Move all data from the source in one operation. Systems are not available to users while the data is moved. Migration is usually done when customers don’t use the application. It takes less time and is less complex to use.
  • Trickle data migration: This approach reports progress incrementally and it requires more complex planning. You can check the quality and success of each migration phase. However, it is more complex and time-consuming compared to big bang data migration.

We recommend data and technology leaders explore data migration solutions. If you have questions about which solutions to choose, we are happy to help:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collections and applications of web data.

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