How to Plan a Data Migration Project?

There can be many reasons why you might want to do this, ranging from changing or upgrading servers, moving from an on-premise framework to the cloud, or migrations related to application versions. No matter your needs, data migration is a high-risk project that needs to be planned meticulously.

Let us take an in-depth look at some of the most important facets of the process and how you can structure your plan for the best results. Here are a few critical considerations. 

The Importance of a Solid Data Migration Plan

Any data migration must have the right data migration strategy. You want to prevent any corruption of the source data or any incremental amplification of any issues in the source data. You want to prevent missed deadlines, budgets spilling over, or massive downtime that affects important areas of the business. Therefore, data migration needs to be a complete project that merits attention to detail.

Ideally, a solid data migration plan needs to include the following components.

• A full-scale audit of the data to start the proceedings to understand what you are migrating. This way, you can make informed decisions about the destination platform and the migration process to go smoothly.
• If your source data has any issues, it is a good idea to clean it up properly before migration. Cleaning prevents problems during and after the migration process, especially if it is complex and needs to be done on a large scale.
• Have a workflow in place to evaluate and maintain the quality and integrity of the data before, during, and after the migration to avoid data degradation over time.
• Design appropriate data governance protocols in place, preferably with automated tracking and reporting systems that allow you to stay on top of data quality.

In addition, you should put a lot of thought into your destination platform and figure out a data migration plan that suits your use case. Figure out the right tools for the project, set a reasonable timeline, and then you can proceed with the actual migration.

Interesting Read: “Data Pseudonymization”

Migration Techniques

Depending on your use cases, one of the two popular approaches might be a good idea. In many cases, businesses use the 'big bang' approach for data migration. This approach requires the company to carry out the complete data migration process to the new platform within a set time window. Then, the data undergoes ETL processing and gets transitioned to its new home, during which time all live systems may face downtime.

The advantages of this method include the fact that the migration can happen quickly, and you can straightaway start to enjoy any benefit you might have from your new data platform. For time-sensitive migrations, this might be the best way forward. On the flip side, the downtime involved can hurt the business. Due to the critical nature of the downtime, the migration can feel rushed, and there can be great pressure on the team. The quality of the migration can also get compromised.

The other approach is the 'trickle' approach. The trickle approach involved dealing with the migration process in small batches. The previous and newly migrated systems run parallel during the migration process. The migration takes place in small increments over an extensive period. There are many benefits to this approach. Since the migration is done in phases, there is no downtime involved. Regular business operations do not need to get interrupted. A gradual migration also lets you test the characteristics and limitations of the new platform for your data and create workflows and workarounds that allow you to make the best use of this new platform. On the flip side, trickle migration can be slow and extremely complex to pull off. You would also not be able to leverage the total benefits of your new data platform until the entire process is complete.

No matter which technique you prefer, it is always good to create a complete backup of the data you are migrating. Then, create a comprehensive strategy, drive it throughout the process, and conduct thorough testing to ensure the migration will go as planned.

Suggested Read: “Data Sprawl”

Step-by-Step Planning of Data Migration

While the steps might vary based on the technique of data migration and the idiosyncrasies of the platforms involved, these general steps tend to be common across all forms of data migration.

• First, understand your data. See how it fits on the destination system. Do you need to migrate all your data? Are there specific data sets or fields that do not require to be a part of the migration process? 
• Are you performing a data audit to weed out poor-quality data before the migration? These steps help you understand your data better and give you much-needed information and insight for the migration process to go smoothly.
• Design the migration process. Decide on the type of migration technique that is right for your organization. Then, go deep into the nitty-gritty of the process and create a roadmap for the migration, along with outlining the technical architecture of the process. 
• As the roadmap becomes more defined and complete, you can start defining timelines and deadlines for the component parts of the process. 
• Evaluate data security risks and create an end-to-end security plan for sensitive data.
• Start building your migration solution. It is important to remember that do-overs with migration are extremely expensive, so you have to get it right the first time. Build accordingly, bank on small, incremental changes, and create testing algorithms that can help validate the processes in the early parts of the migration. For large migrations, you can make the most efficient use of your time by building and testing in parallel.
• Run tests. Use real data for this test and check if the implementation remains accurate over different sample volumes and types of data. 
• After conducting one or more live tests, you can start the actual migration process. Make sure that the implementation plan is followed to a T.
• After the migration, run a thorough audit to verify the reliability.

Interesting Read: “Cloud Data Security Challenges”

In Conclusion

The most important thing with data migration is to understand the high stakes. Data migration is a critical process, and there is little room for error. Any misstep can result in disastrous consequences for your organization. Therefore, it is crucial to have a concrete data migration plan, double-check frequently, and follow the plan down to the last detail while carrying out the migration.