Data Migration
Are you taking a leap of faith and anticipating the best by defining your data migration scheme?
Imprecise evaluation has led to the overproduction of several data migration projects. Mountain climbers would know that no two mountains are alike with reference to the course of path and movement. Similarly, no two migrations are the same. Hence, initializing with a data migration strategy is crucial for any enterprise to ensure seamless movement throughout their project.
Significance of an effective data migration strategy
Well, it has been established conventionally that the consistent successful movement of the data from a legacy environment to the target system assures true achievement of the data migration.
However, migration failure can occur even in the presence of adequate and operable data resulting in inaccurate data creating redundancies.
In order to ensure a sound data migration scheme and avoid any kind of low-quality occurrence, the concerned teams need to render keen attention to the process of migration.
Some of the fundamental circumstances that are vital for the success of a strategic data migration plan are listed below:
Data knowledge: Source data needs to be scrutinized prior to migration, violation of which may lead to unforeseen consequences.
Data clean-up: Swift resolution is required in the presence of any issues in the source data which may call for third-party resources or additional software tools depending on the standard of work.
Data security and sustenance: Timely inspections need to be held to ensure data quality, as degradation of data occurs over a time frame resulting in unreliable data.
Data governance: To acquire know-how regarding data integrity data quality must be maintained. In addition, the tools used in the production of the data must be potentially functional and automated.
Furthermore, a procedure guaranteeing the selection of the right software and tools must be included in the data migration plan.
Types of data migration strategies
There are several ways involved in the establishment of a productive data migration scheme. The unparalleled business requirements of a business counts to the building up of the most adaptable one. Following are some of the data migration strategies:
- Big Bang Migration
The entire transfer of data is accomplished within a short span of time in the big bang data migration. While live systems face a downtime, data is extracted, transformed and loaded through the ETL process into the new database. However, the model has a disadvantage that all of the processes occur in a one-time boxed event. which necessitates the business to have one of its resources offline while functioning, leading to a compromised implementation.
2. Trickle Migration
The trickle migration ensures the completion of data migration in phases. The parallel functioning of the old and new systems throughout implementation decimates any operational interruptions or downtime. However, this migration scheme has a highly complex design compared to the big bang migration scheme. Risks can be reduced if the process is carried out in the right manner.
Steps involved in a data migration strategy
- Exploration and assessment of the source
Bypassing the source review may eventually cause loss of time and resources on migration. Catastrophically, organizations may even face a critically faulty data mapping that would abruptly freeze the ongoing process.
2. Definition and design of the migration
The type of migration is discovered through the design phase by any organization. Based on the target system, pulled over data and design, the timelines and other concerns can be outlined. Towards the closing of the step, documentation of the entire project needs to be done.
3. Build the migration solution
Customarily, the data is divided into subsets, after which categories are built individually each succeeded by a test. However, in the case of very large migrations, building and testing of data is done parallelly.
4. Conduct a live test
The testing process does not end with the code testing on the building phase, and needs to be tested with real time data to guarantee the precision of the integrity and implementation of the application.
5. Flipping the switch
As per the style outlined in the plan, implementation can be carried out after the last testing.
6. Audit
Once the implementation is live, set up a system to audit the data to ensure the accuracy of the migration.
Best practices for data migration
Some of the key practices to be followed irrespective of the kind of data migration methods are listed below:
- Back up : Ensure the availability and creditability of backup resources before proceeding.
2. Strategy : Due to the complexity of the migration process, define a migration strategy and strictly follow the plan.
3. Test : Keep testing the data migration often throughout the planning, design, implementation and maintenance phase to ensure accomplishment of the proposed outcome.
Kickoff with data migration
These steps will help ease the way for anyone overlooking data migration in their enterprise.
1. Concentrate on the critical data (business processes/files) that are to be migrated.
2. Keep track of all outcomes and findings which can be later used for advanced planning, design, and phase building.
3. Keep the information stored in a shared repository so that the data is freely accessible.
4. Ensure active communication and verification. In case of a supposition, challenge, validate, share and document the results.
All the above steps will help any organization looking out for the implementation of data migration.
Summary
Thus, it can be summed up that data migration is vital for safeguarding the security and privacy of data belonging to an organization. Moreover, defining a data migration strategy and having the right set of employees will assure a seamless and successful process.
