5 Critical Big Data Migration Mistakes to Avoid

The task of transferring or relocating Big Data between computer systems or storage formats is a critical one for most organizations. The complexity of the task increases as it involves both structured and unstructured data.

The failure of a Big Data migration job results in delayed go-lives and expensive budget overruns. As such, it is important for organizations to avoid the common mistakes that can hamper the cause of data migration.

Photographer Jeff Sheldon

Listed below are 5 critical data migration mistakes that must be avoided for a successful Big Data migration:

Neglecting the business data users

In any business, data is handled by people who work on it. As such, when a company or an organization relocates or integrates data from a single or multiple systems to a new system, it needs to identify the business data users at the very beginning.

This means the organization must have a very clear idea about the people who understand the business data and have expertise in handling the data. The organization must involve such people in the data migration project. This step is essential for smooth functioning of the new system once the migration is completed and it goes live.

Ignoring data accessibility and management issues

Often companies and organizations plan to execute data migration projects without giving much thought to the data itself. Generally, the main concern of organizations during a data migration project is the infrastructure involved in the process.

However, an organization must have complete information about the governance structure. The organization must be aware of the facts like who possesses the rights to access, create, approve, edit or delete data from the system.

An organization may also consider cleansing their data before the start of their migration project. It must be remembered that cleansing of data must not be carried out while the data migration project is on.

Overlooking data quality issues

An essential step for organizations, before they embark on a data migration project, is to assess the existing data. This step involves gaining clear knowledge about the quality of the existing data. A complete assessment of data quality provides organizations with crucial information. They can become aware of certain issues like whether the existing data will support new users, how much the migration will help in data analysis, etc.

Data assessment helps organizations to make an educated guess regarding the amount of work involved in the migration process. This also helps organizations to adopt a more organized approach towards the data migration project.

Forgetting to validate and redefine business rules

Sometimes organizations and companies forget to update their business validation rules. This often results in organizations not ensuring that the data complies with their business rule.

In a data migration project, data validation must be carried out before migration of data actually takes place. The validation issue assumes much importance when the data migration project involves information about data such as financial, inventory and payroll data.

Failing to evaluate the data migration process continuously

The general tendency for organizations and companies is to validate and test after the data migration process is completed. This is not a good approach to follow. Organizations must have a clear idea about testing and must make sure that testing takes place throughout the migration process. Infact, testing must be done after every step of the migration process is completed.

This will ensure constant evaluation and identification of any problem or issue that may crop up. If any issue is found, it will be easier to look for a solution or rectify any mistake that occurs. It will also be less time consuming than if the problem is discovered after the migration process is completed. As such, it is essential to align data evaluation in the migration project lifecycle to avoid any problems later on.

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