Data migration is the process of moving data from one location or system to another. It is most commonly used to migrate or consolidate data when legacy systems are replaced by new applications that share the same data. This includes migrating from on-premises infrastructure to a cloud environment.
SEE: Research: Cloud vs. data center adoption rates, usage and migration plans (TechRepublic Premium)
While there are many reasons for an organization to perform data migration, one of the primary objectives is to improve the performance and efficiency of the system. Read on as we explain how data migration works and some of the pros and cons that come with moving data to new environments and platforms.
- Developing a data migration strategy
- Types of data migration
- The steps in the data migration process
- Risks of data migration
- Leading Business Intelligence Solutions
Developing a data migration strategy
Developing a solid data migration strategy will minimize unplanned downtime, which ultimately saves businesses from budget strains and other inconveniences. Less successful data migrations can lead to redundancies in the system, which will require time and effort to fix.
A successful data migration strategy includes establishing goals, setting a timeline and anticipating any potential challenges that will arise throughout the process. Moreover, there are a few critical factors to consider when developing a data migration strategy.
Determining the volume of data
The importance and volume of data to be migrated will significantly impact the process. Transferring a few terabytes of data to a new storage device differs greatly from migrating multiple petabytes of data across various devices.
Optimizing the speed of migration
Whether data is being migrated via cloud computing or offline migrations, the migration rate must be factored into your data migration strategy. It helps if you have adequate bandwidth available to transfer data while still minimizing data downtime. If there are networking constraints, it is best to perform offline data migration.
Selecting the right type of data migration
A data migration strategy must factor in the specific type or types of data that are being migrated. This helps you to use the right tools and create an appropriate plan for all data variables involved.
SEE: How to handle a multicloud migration: Step-by-step guide (TechRepublic)
Knowing data type, volume and other specifics ensures you select a data migration strategy that minimally disrupts normal operations. For example, a large-scale transfer outside of regular production hours might be needed if your team cannot afford data downtime during business hours.
Types of data migration
When data is migrated from one storage system to another, it is a type of storage migration. The most common reason for storage migration is to upgrade storage devices to newer technologies or larger format storage systems. A storage migration might move data from paper to digital or from legacy hardware to the cloud.
Cloud migration involves moving data from on-premises storage to a cloud computing environment. This type of storage migration is becoming increasingly popular as organizations look to leverage the scalability and security of cloud computing.
An application migration may be needed when your company is ready to switch application vendors or software to something new. This type of data migration requires moving data from one application environment to another and often involves using application programming interfaces to protect data integrity.
Database migration involves moving data from one database management system (DBMS) to another. This type of migration usually occurs when a company wants to upgrade to a more current version of its DBMS or move to an entirely new DBMS.
Business process migration
Business process migration refers to transferring applications and databases related to business processes to a new environment. This type of data migration is common in mergers and acquisitions, business optimizations and organization restructuring.
The steps in the data migration process
Premigration planning is an important step in the data migration process. It primarily involves developing a data ingratiation strategy and assessing its risks. During this step, your team should identify key stakeholders, evaluate data sources and consider the destination of the data migration.
Throughout this process, your team should also be backing up all data and performing a data audit. This will highlight any problems that need to be addressed before the data migration is initiated.
After a robust data migration strategy is in place, data migration processes can be put into motion. Many organizations choose to use data migration tools such as Google DMS, IBM Informix or Azure Cosmos DB to facilitate data migration.
Execution of data migration
All data is extracted from the source and migrated to the target destination. During data migration, the process should be carefully monitored to ensure any issues can be quickly identified and resolved. Any applicable rules and permissions need to be set up to complete the data migration process.
The testing phase of data migration includes coding migration logic and testing it in a production environment. This step confirms data is viable for business use.
Monitoring and maintenance
Systems should be monitored after data migration to ensure the migration is successful. A data audit can provide additional insight into any problems at this stage.
Risks of data migration
There are several benefits of data migration, especially over the long term. However, it is also essential to account for the risks associated with data migration, as moving sensitive data or upgrading a legacy system can put stakeholders on edge. These are some of the biggest risks you’ll want to mitigate with the right tools, processes and people:
- Security: Most data migration tasks are performed after data is encrypted. Any lapse in security during data migration can result in the loss or leak of sensitive data, such as customer information.
- Long transfer times: It is not easy to predict the time required to complete a data migration fully. Risk factors for long transfer times include network bottlenecks and system hardware limitations.
- Exceeding expected costs: Any delays, security breaches or need for additional resources can exceed expected data migration costs quickly and significantly if you’re not careful.
Read next: Top cloud and application migration tools (TechRepublic)
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