Data Migration Types, Strategies, and More
What is Data Migration?
Data migration occurs when an organization moves data from one storage location to a new target system. It can also mean that the format of data changes. Migration projects require preparation, strategies, and quality control. A data backup system and a method to test results will make sure the data migration process is successful. Data transfer is complete when the original data warehouse or software system is shut down. Data migration is typically part of a larger business project, such as
- Replacing an old software system with a new system
- Expanding storage system capacities
- Introducing a new system to function in conjunction with a current system
- Shifting company information into one centralized data storage solution
- Transferring data into cloud storage
- Complying with new requirements due to a merger, in which all information must be stored in one location.
To prevent an improper migration process, more business owners utilize a specific migration strategy. They become more knowledgeable about data integration and migration tools. While it is complicated to transfer information without damage or loss, a set of best practices can help. First, each organization needs to understand what data migrations are and how they work.
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Data Migration vs. Integration
Integration requires an organization to combine information from external and internal sources into one centralized location. Integration is part of a larger data management plan to connect two or more systems and improve access to information.
It is critical to consolidate information to enable reporting, analytics, and the creation of business intelligence. Unlike data migration, integration is continuous. One system collects real-time information and then transfers that data into a centralized database. Users can then easily access this information to perform their jobs.
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Data Migration vs. Replication
When an organization migrates a data source to a centralized location, it disbands the old system at some point. Replication does not require anyone to delete the old system. While it still involves the transportation of information to a target database, nobody deletes the original source. Just like integration, this process is continuous and has no endpoint.
Data integration and replication can work together as part of a larger company project. If either process turns into migration, IT must disband the original database.
6 Types of Data Migration
While there is not a strict divide between different types of migration processes, organizations may use six types of data migration. Some data transfer situations are a part of cloud migration or business process migration. Other types of data migration don't involve other processes.
A data migration strategy depends on the needs of the organization and the type of migration project. When businesses understand these different types, they can optimize their migration processes and utilize the correct data migration tools. These six types include
1. Storage Migration
When an organization invests in a new technology system, it discards its outdated software. This process is called storage migration. IT is moving data from one system to the next or digitizing physical paperwork. For example, an organization may transfer data from a hard disk drive to an SSD, or upload information into the cloud.
Businesses use this process because of a technology change, not for a lack of storage capacity. Larger enterprises take longer periods to complete storage migration. For example, one large global distribution company took 10 years to move data into a new system as part of its storage migration process.
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2. Database Migration
Databases store and organize data under the direction of a database management system. Therefore, database migration refers to the upgrade of a current database or a switch from an old database to a new vendor. It is more difficult to switch out an old database for a new provider than it is to improve upon a current system.
It is even harder when an organization uses data migration software to switch from a hierarchy, flat file, or network database. While these source target systems are outdated, most businesses continue to use them because they are expensive to redesign and move information out of.
3. Application Migration
When an organization invests in a new software solution, IT needs to move all of the information into that system. This type of data migration occurs more frequently because businesses need to regularly upgrade their software to maintain a competitive edge.
Difficulties arise when the legacy data system and the new system operate in varying formats and models. An experienced specialist needs to handle an application migration process if this is the case.
4. Data Center Migration
Businesses maintain their important applications and information in a data center. A
data center is a physical place, not a digital one. It refers to the room with equipment and other IT technology.
An organization may engage in this type of migration when it moves all digital property or relocates current systems to other parts of the operating facility. An organization needs to be careful when it moves equipment because it is delicate and expensive to replace.
5. Business Process Migration
When there is a merger between two companies, one or both companies need to transfer information into a new system. Other forms of business process migration include the transfer of information due to a competitive risk or evolving customer demands.
6. Cloud Migration
Cloud migration is a popular business term that encompasses any movement of information from one location to the cloud. Because the cloud offers so much storage space at such a low cost, most companies move information to the cloud.
The time it takes to migrate information into the cloud depends on how much potential cloud data there is and where it comes from. Small amounts of data can take less than an hour, while larger projects take as long as a year.
Importance of Data Migration Strategy
A data migration plan improves the performance of the migration project and gives an organization a competitive edge. If IT does not migrate data properly into target systems, there may be duplicate efforts or errors. With the wrong strategies, any problems in the original source data strategies can enter the new system.
Incomplete strategies can result in missed deadlines, too much money spent, and a failure to transfer some data at all. A migration team needs to prioritize this process to ensure a successful outcome. Each strategy should include
- Knowledge of Transfer Data - Teams should perform an audit on all source-target data to check for data quality and prevent potential problems during migration.
- Clean Data - Teams should resolve any problems in the source data before migrating data. This probably requires the use of software solutions and other tools from a third-party.
- Maintain and Protect - Data deteriorates after a period and becomes unreliable. Teams need to enact data management strategies to optimize data quality.
- Data Governance - It's critical to track and report the quality of all company information to gain an understanding of data integrity. All applications that generate information need to be intuitive and automated to ensure a successful outcome.
Data Migration Strategies
Since the digital transformation, businesses have more available software solutions to choose from. A normal responsibility for business owners is to invest in new tools to optimize business processes.
Data migration is a critical component of an organization's brand because it relates to the upgrade and consolidation of databases and other applications. An effective migration strategy ensures data integrity, lower costs, and less manual effort. But how should an organization go about migrating data? Here are the top strategies
1. Big Bang Migration Strategy
The big bang strategy refers to the transfer of all information from one source system to a target database all at once. Most businesses execute this data migration plan when employees and customers do not need to use the platform.
One advantage of this strategy is that a company can complete the process within a very short time frame. It is also more convenient because users don't need to work across both systems at the same time.
Unfortunately, big bang migration is costly and comes with a high risk for failure.
It also requires a period of downtime in which neither employees nor customers can access the database. This is a profit loss and inconvenience to organizations that operate outside the normal 8-5 schedule. The big bang is a better approach for smaller companies or those who only handle small quantities of data.
2. Trickle Data Migration Strategy
Trickle data migration divides the entire migration process into a series of small migration steps. Think of it as a one-year project with a set of required tasks per month. Each step contains its objectives, milestones, and performance checks.
In trickle data migration, an old database and a new database run at the same time. A specialist transfers small units of data from the old system into the new system. This process takes a much longer time than big bang migration, but there is no need for employee/customer downtime. The application remains available as long as the amount of data transferred over is small.
One disadvantage of trickle data migration is that the process is much more complicated. A team needs to monitor and track all of the information they transfer at each step. They also need to ensure that users can go back and forth between both systems to access the data they need.
Some businesses ensure the original database is operating until all of the data transfers into the new system. This allows customers to access the old application and then use the new one once it is ready. While this makes it easier for the user, it is more difficult for engineers. They need to ensure all information is synchronized across both applications. In other words, all changes in the original system need to cause an update in the new database.
This strategy is more effective for mid-large-sized companies who need to ensure customers and employees can access the application. However, an enterprise should hire the correct engineers with the expertise to handle a process as meticulous and complex as this.
Steps in the Data Migration Process
Data migration ensures data integrity, data quality, and easy access to information. It is critical in a time where technological upgrades are the norm. Cloud storage reduces overall media and storage expenses, which provides a better return on investment.
If engineers perform the process properly, there is less disruption to end users. Data transfers also allow businesses to use better and faster applications without the fear of losing sensitive information. All data is still available, but it's located in a more optimized and automated software solution.
Here are the steps for transferring data properly in an organization
1. Planning out a Data Migration Strategy
The planning phase of data migration is composed of four separate steps. These include
- Refine - Engineers eliminate unnecessary data and identify the smallest amount of data needed to operate a system. This requires an analysis of both the old system and the new system, along with feedback from users.
- Assess - Teams need to assess the existing system's requirements and how those can be applied to a new database.
- Set Standards - Teams can identify bottlenecks in the migration process and prevent problems that occur after the migration is finished.
- Set Budgets and Timelines - Engineers pick their approach (big bang or trickle) and determine how much money they need to complete the task. They also create deadlines and schedules and present them to management.
2. Data Migration Auditing and Digital Tools
During this stage, engineers examine and clean all of the information they need to transfer. They identify any potential problems, data quality concerns, and delete replicated data. Because this process is time-consuming, engineers use digital automation tools. This decreases the workload and resources needed to complete this stage of the process.
3. Data Backup and Protection
While it's not required to backup data, engineers should do so. Backing data up provides more protection against data loss if there is a migration failure. Unfortunately, this process can also be time-consuming so a company should discern whether a full backup is worth it.
The Importance of Backup:
4. Data Migration Design and an ETL Technician
Engineers identify migration rules and assign responsibilities to other members of the team. Extract transform and load (ETL) is the most common way to execute migration design. This is particularly true if there are large quantities of data to transfer. Engineers will generate scripts or utilize third-party applications to transition data. An ETL developer and system analyst have the expertise and ETL tools to ensure this process runs smoothly.
5. Execution of Migration Plan
This is when extract, transform, and load occurs. Execution can take anywhere from a few days to a few months, depending on the type of strategy an organization uses. The execution mustn't interfere with normal operations in the trickle approach. Teams need to let all departments know how the execution phase will impact them and their access to data.
6. Data Quality Checks
Engineers perform data quality checks throughout the entire migration process. A trickle approach requires a quality check at each step and timeline. If teams test frequently, it ensures that all data will transfer safely and at its highest level of quality. If they wait too long to test, there is a chance that data integrity and reliability could be compromised.
7. Data Migration Audit
Before the new system is fully functional, engineers need to share the full scope of results with employees and other users. The purpose of the audit is to ensure that engineers have transferred all information without compromising quality or integrity. It also makes certain that any changes, deletions, or updates are documented.
Best Practices of Data Migration
There are a set of best practices to follow regardless of whether a business decides to use the trickle or big bang approach. These include
- Backup Data - Several things may go wrong during the execution process. A data backup ensures that no information is lost along the way. All backup resources must be available and ready for usage before implementation.
- Stick with a Migration Plan - Unfortunately, many specialists choose a specific strategy and then stray from it after implementation. Because the migration process is complex and challenging, it's critical to stay with the strategy the entire time.
- Continuous Testing - Engineers need to test data migration during the planning, design, and maintenance stages of the migration process. This will ensure that the strategy delivers successful results.
Other Suggestions and Best Practices:
Key Takeaways of Data Migration
In conclusion, here is what to know about data migration-
- Data migration refers to the transfer of information from one system to other storage systems. This occurs when a business upgrades an application or increases its storage capacity.
- The six types of data migration include storage migration, data migration, application migration, data center migration, and business process migration.
- The two types of strategies to migrate data include big bang migration and trickle migration. The big bang is better for smaller companies that can afford downtime, while trickle is better for larger enterprises that can't.
- Steps of a successful data migration process include planning, auditing, backup, design, execution, quality checks, and audits. Migration involves a set of best practices. It's best to backup data, stick within the parameters of a migration plan, and continue to test throughout the entire process.