Data migration is the process of moving data from one system to another. While it may seem like a fairly simple task, migration involves a change in storage and the database or application, meaning it can’t be done haphazardly.
In the context of the extract/transform/load process, any data migration will involve at least the transformation and loading steps. This means that the extracted data has to go through a series of preparations before being loaded into a destination location.
In this post, we will understand the importance of data migration and how the process should be carried out. Check out!
What drives a company to perform data migration?
Organizations perform data migrations for a variety of reasons. They may need to overhaul an entire system, update databases, establish a new data warehouse, or merge data from a new acquisition or another source. Data migration is also required when deploying a new system that will integrate with existing applications.
Why is a data migration strategy important?
Regardless of the purpose of data migration, the objective is usually to improve the company’s performance and competitiveness. But for this to be possible, the process must be carried out correctly.
When the migration is not successful, it can result in inaccurate data that contains redundancies and incompatibility. This can happen even when the source data is completely correct. Also, any issues that existed in the source data can be amplified when it is brought into a new, more sophisticated system.
A complete data migration strategy avoids a bad experience when changing systems, which can end up causing more problems than solutions. In addition to missing deadlines and exceeding budgets, incomplete plans can cause migration projects to fail. In planning and strategizing work, teams need to give full attention to migrations rather than subordinating them to another large-scope project.
What can’t be missing in a migration plan?
See four essential points for any data migration plan:
- Data Recognition: Prior to migration, source data must undergo a thorough audit. Unexpected problems may arise if this step is skipped.
- Cleanup: After identifying any issues with your source data, they should be resolved.
- Maintenance and protection: It is normal for data to degrade after a period of time, which can make it unreliable. That’s why it’s important to create controls to maintain data quality.
- Governance: Tracking and reporting on data quality is important because it allows for a better understanding of data integrity.
In addition to a structured step-by-step procedure, a data migration plan should include a process for the team to have access to the right software and tools for the project.
What are the key steps in a data migration strategy?
Each migration strategy has its specifics based on the organization’s needs and goals, but generally, a migration plan must follow a common and recognizable pattern. See below for the main steps.
Evaluate the data to be migrated
Before migrating data, you must know (and understand) what you are migrating and how that information fits into the target system. Understand how much data is being migrated and what the state of that data is. Define what needs to be migrated, what can be left behind, and what might be missing.
In addition to meeting the requirements of the data fields to be transferred, audit the data they contain. If there are poorly populated fields, incomplete data, inaccuracies, or other issues, you may want to reconsider whether you really need to go through the process of migrating that data.
If an organization skips this source review step and assumes an understanding of the data, the result could be wasted time and money on migration. Worse still, the organization may be faced with a critical data mapping failure that halts any progress.
Define and design the migration
The design phase is where organizations define the type of migration to adopt – big bang or trickle. This process also involves designing the technical architecture of the solution and detailing the migration processes.
Considering the design, the data to be extracted, and the target system, you can start defining timelines and other project details. At the end of this stage, the entire project must be documented.
During planning, it is important to consider security plans for the data. Data that needs a greater margin of safety should have protection threaded across the entire plane.
Create the migration solution
It can be tempting to approach migration with a development approach. However, since you’ll only go through the implementation once, it’s crucial to get it right the first time. A common tactic is to subset the data and assemble one category at a time, followed by a test. If the migration is very large, the best solution may be to build and test in parallel.
Run tests in real-time
The testing process does not end after testing the code during the build phase. It is important to test the data migration project with real data to ensure implementation accuracy and application integrity. After final testing, implementation can proceed using the style defined in the plan.
do an audit
Once the implementation is live, set up a system to audit the data to ensure migration accuracy.
We hope that, after reading this post, you have understood the importance of good data migration and the mistakes you should not make. We are talking about an important process for the company to maintain productivity, that is, a vital process to keep your company competitive in its market.
Therefore, when choosing the new software, choose a supplier that contributes to ensuring that the data migration process is carried out with safety and quality. In the case of an insurance agent, which is migrating to specialized software in the sector, the choice must be made based on the reliability of the specialized provider. TEx is a supplier prepared to deliver the best technological solutions for your company and to contribute to good data migration.