Introduction

The process of data migration practically refers to moving data from one place to the other or converting it from one format to another format. There are several reasons why organizations decide to conduct data migration to protect important and sensitive information. The most common reasons include upgrading a particular database or application, establishing a new data warehouse, or mixing new data with the older version to improve the quality of it.

  1. Why is data migration seen as difficult and risky?
  2. Types of data migration
  3. How to plan a data migration
  4. Top 10 data migration challenges
  5. Data Migration vs Data conversion Vs. Data integration
  6. NetApp and data migration

1. Why is data migration seen as difficult and risky?

Although the entire procedure may seem easy in an apparent manner, it takes a lot of effort and precautions to conduct a successful data conversion with no error whatsoever. Data gravity makes it difficult at times for organizations to conduct successful data migrations. There are data migration challenges that prevent its way, which include but are not limited to data corruption risk, extended downtime risk, application stability risk, semantics risk and data loss risk.

If the target system does not work properly for some defective settings, the data migration process would not be successful, and it may eventually lead to data corruption.

A properly planned Data migration strategy is extremely important as far as conducting successful data conversion is concerned. While conducting data migration, its order must be correct, and a chronological order should be maintained. Otherwise, the risk of orchestration emerges. Orchestration may further lead to interference risks. 

2. Types of data migration

There are several ways of conducting data conversion. Some of the major types of data migration are as follows:

  1. Storage Migration: Data is transferred from one storage to another and is conducted in a relatively smooth and easy way.
  2. Cloud Migration: Data is transferred from a data centre to a cloud or from a cloud to another cloud. 
  3. Application Migration: At times, companies decide to switch vendors to platforms. Most of the time, they use middleware to bridge certain gaps; scripts may be used for a smooth data migration procedure; an API can be used to protect data integrity.
  4. Business Process Migration: Data is often transferred to another storage that contains information about certain products, customers or technological operations. 

3. How to plan a data migration

In order to conduct a proper data conversion, certain data migration steps should be taken into account by the organizations. The entire data migration process consists of three simple steps: Extracting data, transforming it and finally restring it in the target system.

Some of the most effective data migration techniques include proper planning the conduction of the process with data migration tools beforehand; evaluating the nature of the data properly. Landscape analysis is another major step. Data migration testing is an essential step that should not be avoided to conduct a successful conversion.

Properly executing the transferring data is important. After the whole process, the removal of the old version is of utmost importance.

4. Top 10 data migration challenges

Some of the major data migration challenges that often interrupt the data conversion process are listed below:

  1. Not allowing the stakeholders to know information: This is a major mistake that is often made by many big organizations. Someone will always be there who would care about the data that is being moved. It is extremely important to explain the entire procedure to them to avoid interruption.
  2. Lack of communication with the business becomes a huge challenge, and it eventually harms the overall image of the organization in the long run.
  3. Lack of awareness of data privacy: When the organizations lack the knowledge of exactly how many people have access to the data that is being migrated, it may lead to several errors in the data migration services.
  4. Unproven migration plans: Conducting a successful data migration requires a solid plan that is preferably well known for being a proven one.
  5. Lack of expertise: It leads to unsuccessful data migrations. Therefore, having an experienced professional is needed. One should know data migration rules and responsibilities thoroughly to be allowed to conduct such functions. Having professionals, therefore, acts as one of the major data migration solutions.
  6. Cross object dependencies: If an extra dataset is involved in a data migration process that was not included in the original plan, it may cause disruptions.
  7. Lack of proper planning: IT teams need to plan and prepare to conduct the process to avoid any disruptions that may emerge in the middle of the procedure. Nothing can be worse than having clueless individuals with insufficient software skills to conduct the process.
  8. Project and supplier management: Projects and vendors should be managed in a parallel manner. A proper data migration framework solves most of this problem.
  9. Defective target system: The target system needs to be in a perfectly working condition with no glitches; otherwise, the entire procedure collapses in the middle of data migration.
  10. Complex data conversion approach: Unnecessarily making the data migration approach complex and adding extra steps to it only makes the process difficult to end. Only performing the necessary steps does the job pretty well.

These are some of the major data migration risks that are often seen to take place.

5. Data Migration vs Data conversion Vs. Data integration

These terms are interchangeably used frequently. Although the nature of all these procedures seems to be similar, there is a huge difference in nature that makes each one of these different from each other. Data migration refers to the process of data cleaning, filing, transferring, and it can refer to location changes and format changes as well. Whereas data conversion mainly refers to transferring data from one format to another, i.e. a partial process that falls under the data migration process.

However, data integration refers to the process of merging one data-information to another. Therefore, it refers to the process of combining data from different sources to provide unified data to users.

6. NetApp and data migration

The most convenient options of moving to infrastructure as a service (IaaS) would be re-hosting, modifying and extending application code and finally replacing the older version. To move to a platform as a PaaS service, running the application over the cloud and then discarding code should be enough.

Conclusion

Data migration is a complex procedure that should be done with utmost guidance, protection and supervision. One tiny step may lead to big negative repercussions, including unsuccessful conversions. Therefore, pre-planning and evaluating the data migration process before conducting it of utmost importance. 

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