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7 Data Migration Techniques You Need to Know

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Data happens to be the paramount and salient element that is known to drive the affluence and success of the business organization.

It is known to have an impact on the vital aspects of the strategizing and planning phases. It also plays a crucial role in the optimization of different processes.

The technical experts is witnessing a tremendous change in the data in terms of quantity, quality, and nature.

As the legacy systems are becoming inefficient to cope with different datasets, the business organizations are compelled to migrate to the latest data processing systems. Data migration contributes to being the procedure of moving the data from one location to the other.

It involves preparing, choosing, and transforming data before it is transferred from one specific system storage to the other.

The objective of business enterprises is to enhance different technological and optimization advancements. As you go through this write-up, you will find information about various data migration techniques:

Recognizing the data format, sensitivity, and location

Before starting the data migration process, it is recommended to recognize the type of data you are willing to migrate.

Besides this, you need to find the latest form of data, the place where it is living, and the format in which it will remain after the migration. After the recognition of the information, you will be equipped with the prerequisite knowledge.

During the pre-planning process, you will find different potential risks that are required for the move.

Besides this, it helps realize the security measures taken during the migration of the data. Such a pre-planning step helps make any sort of crucial errors during the execution of the migration procedure.

Read more about: Data Analytics Best Practices

Planning for the project scope and size

After understanding the moved data, it is recommended to define the data migration plan’s scope. In this regard, it is recommended to plan out different resources, which is required for the data migration.

Now, you should make sure to perform the advanced analysis of the target and source system, after which you should be writing the flexible timeline for the specific project.

After this, you should consider if the migration of the data will interrupt the business’s normal operations. You will be successful in planning the data migration during weekends or after several hours. It helps in avoiding the continuity of the business.

Taking a backup of the data

Before migrating the data, you should ensure that the backup of the data is taken. Primarily, it is recommended to take a backup of the files, which are needed to migrate.

In case there are any issues during the migration of data, such as missing, incomplete, or corrupt files, you will be capable of rectifying the errors with the restoration of the data within the original state.

You should remember that cloud backup is recognized to be the most secure and the safest technique for data backup.

Assessing the migration tools and staffs

Data migration happens to be a big job, primarily if you are trying to move files in bulk. It is recommended to refer to the project’s scope and size.

You should make sure to use the information to determine whether the QA team is equipped with the prerequisite skills and knowledge, which are necessary to complete the project. It helps in assuring whether the team has the prerequisite resources and time, which are necessary to handle the project within the designated frame of time.

Performing data migration plan

You need to ensure that the right system permission is applicable. It helps in the successful migration of data.

After this, the migrated data should be extracted from the source system to the target. You should ensure that the data is cleaned to protect the target system after it is transformed into the prerequisite format.

After this, you should take the prerequisite steps to load the deduplicated and cleaned data within the target systems.

Next to this, you should track the data migration closely during the process. It helps in recognizing the resolving the issues, which might arise during the migration of data.

Testing of the final system

After the completion of the migration, you should make sure that no connectivity issues are present in the target and source systems.

The main objective is to ensure that the migrated data is secure, correct, and present in the proper location. If you want to verify it, you should perform the system, unit, web-based applications, volume, and batch application tests.

Maintenance and follow-up of the data migration plan

Even after the execution of software testing, there are risks that errors are made during the data migration.

So, it is recommended to perform the full audit of the system. After this, you should make sure to check the quality of the data to ensure that all things are correct as the data migration is accomplished. In case you find corrupt, incomplete, missing data, you need to restore such files from the backup.


Data migration decreases the storage and media costs along with enhanced improvements in the Return On Investment.

Data migration reduces the interruptions in the daily operations of the business, with the least manual efforts. It is useful in scaling different resources, which help to meet the growing requirements of different business datasets. Data migration is responsible for upgrading different underlying services and applications.

It helps in boosting the effectiveness and efficiency. If the business enterprise is upgrading the systems, consolidating the data, or moving into the cloud, the data migration is located on the horizon. Data migration provides an opportunity to the business enterprise to expand the data management and storage capabilities, after which they should ensure the right use of the data to enhance the business decisions. Data migration tools are useful for the successful movement of data from one repository to the other. Data Migration Services refer to the technique to move the data between the systems, formats, and locations. It is inclusive of data cleansing, data profiling, and data validation.

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