Data migration strategy and best practices

Data is one of the most valuable sources a business might have. It enhances companies' abilities to make data-driven decisions and drive their business forward. Data gathering is a continuous process, and as a result, data in various formats is pouring from different systems into the company's data storage every day. At some point, the current system might need an upgrade to accommodate data, or there might be a need for migrating data from one server to another. Moving data helps companies expand their data storage and increases their management capabilities of efficient data usage in the process of decision-making. 
 

Simply put data migration is the process of moving data from one storage device to other, from one location or system to another. However, migration is not just a transfer. It is a complex, often complicated process that includes collecting, cleansing, mapping and transformations between source and target data. 

Most often, organizations need data migration to transfer data from one server to another, to increase the ability to accommodate large volumes of data. However, it is not the only reason for migration. Other reasons include:  

- Transferring data on the cloud, which recently gained a lot of popularity, as it helps companies eliminate the cost of IT infrastructure. 

- Staying competitive on the business field by adopting leading technologies, creating the path for data migration. 

- Cutting down operational costs by migrating data from storage to a system that consumes less space and power. 

- Replacing outdated legacy systems that are unable to keep pace with the organization's evolving performance requirements. 
 

Usually, we categorize data migration into four main types: Storage migration, database migration, application migration, and cloud migration. But there is the fifth type that we need to include as well - business process migration.  

Storage migration is moving data from one storage system to another for technology refreshes. For example, transferring data from hard disk to the cloud is a storage migration. Organizations decide on storage migration during technology upgrades. Transferring data to the new technology might be an attractive idea because the new system offers improved experience of accessing data, lower cost or higher efficiency. After all, the goal of technology refreshes is dynamic scaling, faster performance and improved features for data management. 
 

Database migration is transferring data between two database engines, for example, from an existing database to a new one, or from on-premise into the cloud. As the databases are the central points of modern technologies, it is not surprising that they might require an update time after time. There are few circumstances when organizations need to use database migration: 

- The existing database software requires an update.  

- The organization wants to change the database vendor.  

- The organization wants to migrate database on cloud.  

While it might not sound like it, database migration is a complex process and organizations need to consider little details upfront. Including checking the target database storage capability, testing applications, ensure data confidentiality within the target database and last but not least, test the migration process for compatibility.  

 Cloud migration is moving data from one cloud to another or from premises to a cloud. One should not mistake it with backing up data to a cloud. Cloud migration is a distinct project within which data transfers from the source environment to a new one. It is one of the latest and already very popular trends in the industry of data management. Organizations choose cloud migration because of its scalability, cost-efficiency and need of fewer storage resources. Companies deploy not only data but services and applications to the cloud. 
 

As the term itself suggests, application migration is moving an application from its environment to another. It includes migrating the application from a public to a private cloud or from one data centre to another. Similarly to database migration, organizations migrate application when they change platform or vendors. One example of it can be switching from one HR system to another. In such cases, companies need to ensure that data can be communicated between the two applications as each of them might have a different data model.  

Business process migration is a more complex process. It occurs during business reorganizations, optimizations, mergers and acquisitions and includes the transfer of applications and databases that contain information about business customers, operations and products.  

Data migration has the reputation of being difficult, complicated and risky and not without reason. While done right, it will enhance performance and competitiveness and increase efficiency. But without proper strategy and execution, one might face a lot of difficulties. As data migration is a long process and includes a lot of planning and several implementation steps, there is a lot of room for mistakes. 

Creating a complete data migration plan will help you mitigate possible challenges from the very beginning, like data loss, compatibility issues and poor execution. It will also help you stay within your timeline and budget. 
 

There is no perfect migration strategy that suits all the organizations out there. Businesses need to create one based on their specific needs, goals and requirements. However, in most cases, every data migration strategy falls in one of two categories: "Big Bang" and "Trickle" migration. 
 

A Big Bang migration is the process where the full transfer happens in a limited window of time. During the transfer, while data goes through ETL processing and transitions to the new database live systems experience downtime. 

This strategy has advantages and disadvantages as well. The main benefit is that the transfer requires relatively little time to complete. However, it also means that the process is more intense. 

If you are planning to use the "Big Bang" strategy for data migration, make sure to go through the process upfront. 
 

Trickle migration is opposite to Big Bang migration strategy. The process of data migration is in phases. During the migration, the old and new systems are running simultaneously in real-time and thus, can keep data continuously migrating.  

Since the Trickle migration happens in phases, it can be more complex in design. However, if done right, it reduces risks significantly.  

As the data migration process is quite complex and difficult, many companies choose to hire data experts to handle it. Usually, it is preferable to hire a company, if you do not have data migration expert in your team already. While it might seem expensive, hiring experts will save you money and time. However, if it is outside of your budget, you can use the best practices for data migration.    

 The first step is choosing or creating a strategy that works best considering your requirements and desired result. Design requirements that include migration priorities, schedule, backup and replication settings, prioritize by data value and plan capacity. Choose which strategy you will be using the Bing Bang or Trickle migration and stick to your plan. 

The next step is to audit the data you are going to migrate. Compared to storage migration, data migration is more difficult. As while migrating storage, you do not have to update the old one and map the new.  

When migrating data, you need to audit it first and fix issues if there are any. Audit the source database for obsolete records, unused field and database logic and make necessary changes before migrating data to the new system.  

One of the worst things that might happen during data migration is losing the data. Before the process starts, you need to make sure that you can restore any lost data. The best practice is to create a backup that allows you to restore data immediately. 
 

The best practice for data migration is to invest in software that allows you to schedule migrations of data subsets. It should also validate data integrity in the target platform, and issue reports for troubleshooting and verification.   

Once you complete the migration process, test it by using a mirror of the production environment. When you see that everything is in order, you can go live and conduct the final test.  

Once you complete data migration successfully, get ready for the next one, as you will have to do it in future as well. Use the information and experience you get during the migration to make the next one smoother.