HR Data Migration Strategy

Posted by
Natalia Tomchyshyn

Updated December 21, 2023

Data migration is an unpredictable process that ought to be drawn with a well-considered and solid game plan. As human asset management and analysis keep transforming into a new and improved data-driven state, the need emerges to strategize the development of sources and datasets, without investigating the normal business investigation.

What is Data Migration?

Depending on the relationship of the lodging frameworks, just as the type of data being moved, the data should be handled in various paths to make it compatible with the new platform. Whenever done mistakenly, migration can bring about huge data damage or loss.

By reading this article, the reader will go through comprehensive HR data migration techniques. It concludes with a simple-to-follow checklist that assists you with tracking your process.

Your enterprise might need to undertake this process for many reasons. For instance, you might be decommissioning, consolidating data centers, or replacing storage devices or servers. Data migration is also an important step in a complete process of migrating IT infrastructure to an environment based on the cloud computing environment.

No matter if you are moving to a multi-cloud environment, hybrid cloud, private cloud, or a public cloud, you will need an efficient, cost-effective, and secure method for migration of your data into the new location.

Advantages of Data Migration

Here are some of the advantages of data migration that you should know:

1. Organizing Exploratory Business Workshops

Every data migration has an expectation to absorb information where the project team starts to become familiar with how the legacy (and target) organizations work. By beginning your workshop with a discovery activity to make a reasonable data model, you can quickly concentrate on data plus to get all parties effectively talking about the inheritance and target conditions at a level that doesn't dive into minute technical details.

2. Resolving "turf-wars" and Policy Centered Issues Right off the Bat in the Project

By making a more business-centered model, we can distinguish where ownership conflicts might be occurring. In any migration, there is some political sensitivity, as significant business change is frequently a generator for the migration process. By making more elevated level maps, we can rapidly discover where data ownership is overlapping that may be leading to issues when we wish to decommission the inheritance environment.

3. A Helpful Device for Checking the Legacy Scene and Assigning Appropriate Assets

By making significant level business objects, you can gain a more convenient perspective on what data is engaged in migration. In the event that we are just given a physical model, the sheer unpredictability and absence of information can delay the checking process significantly.

4. Great for Chunking the Migration and Partitioning Workload

Another side-effect of the scoping exercise is that we are increasingly ready to assign appropriate assets. With our Project separated into more significant level branches of areas, we can distinguish the fundamental expertise for every domain and make increasingly precise designs for every area.

5. Helps Discover "Hidden" Data and Stores "Data Gaps" Early in the Project

By making a reasonable underlying model, then lower-level sensible models, we can distinguish quickly where there are gaps in the inheritance datastores that may require further investigation. For instance, our business examiners may demonstrate that the business deals with a connection between a specific service and a customer section that doesn't seem, by all accounts, to be found in the physical systems in scope. All the time, this information is held in a private store as neighborhood paper records or spreadsheets and so forth.

Data Migration Strategy

1. Assess the Data

Before going any further, it is essential to understand what type of data you are moving to. Also, you need to know how and where the data fits in the receiving system. Before you consider moving the data, you need to classify, map, and understand the structure of the data. If you ignore this result, you will be wasting precious resources and time, and your progress may stop due to critical flaws in the mapping of the data.

2. Design the Migration

Organizations in this stage should determine how they will carry out data migration. This includes choosing a suitable method, defining acceptance criteria, mapping the elements of the data to target from source, assigning responsibilities and roles to the staff. You have a choice to select from two avenues: big bang or trickle. In trickle migration, the process runs in phases. The process takes time, but organizations can limit the time spent by any program offline. As the new and the old system are capable of running in parallel, you may continue the normal business.

3. Build the Migration Solution

You should take an adequate amount of time to understand the implementation process perfectly, till the last detail. If you want to avoid losses, it is essential to perform the task right the first time. The only suggested tactic that you should follow is to break the datasets into different subcategories and to test one after another after they are built.

However, it is always easier to hire a professional team for your data migration process, just like our HR migration service. This way, you can save a lot of your organization's time on figuring out the methods of translating the data between the systems.

4. Conduct a Live Test

It is important for you to test the code after completing the build phase, but the organization can't leave the phase at that. Firstly, you will need to test the design with real data to make sure that the plan is working, and if you find any flaws or bugs in the design, you can solve it instantly. Also, run the process before the actual event. This will enable a confirmation from your team about the efficiency of the plan, and you will achieve the desired results. However, you should test multiple phases if you choose the trickle method.

We can also facilitate you by paying close attention to your testing. This is so you can see how the migration works before you decide to begin live migration.

5. Make the Switch

After running successful testing, you can run the migration of the data then. When you initiate the migration, your physical structure gets frozen, and interfaces shut down where required. After that, you can identify the run and errors by quality reports, and then these errors are fixed in the staging location. After the landing process, reconciliation reports are run, and data is then migrated. If everything works according to the parameter of acceptance, interfaces become activated on the target platform and start running live.

• Audit

Once your implementation is live, you should necessarily set up a system for auditing the data and ensuring a successful transfer. Thus, if you find any physical errors, you can repair it with syntax correction in the migrations scripts. You may find logical errors because of a problem in data mapping.

Data Migration Checklist

1. Assessment Phase

• Analyzing and classifying the data.
• Considering the requirement of target platform data mapping.
• Setting requirements for clear mapping.
• To ensure data comprehension, you need to audit the data mapping.
• Determining the best procedure or technique for data migration.

2. Design Phase

• Assigning responsibilities and roles for qualified members.
• Mapping data elements to target from the source platform.
• Define the criteria of acceptance.
• Choose a methodology that is appropriate, for example, big bang or trickle.

3. Build Phase

• Setting tactics in accordance with the type of data and volume.
• Sourcing tools and software.
• Determining the time frame.
• Testing the tactics to determine suitability and efficiency.

4. Test Phase

• Testing code while ending the build phase.
• By using actual data, test the migration strategy.
• Addressing errors, bugs, and flaws.

5. Migration Phase

• Initiating migration.
• Running reconciliation reports.
• Addressing errors (if any).

6. Audit Phase

• Creating a system to audit the data.
• Repairing physical errors with syntax correction.
• Repairing logical errors with corrections of the data mapping.
• Running live new system.

Data migration is a method required by an ever-developing dependence on HR solutions with data-driven functionality. But you can easily achieve painless and successful data migration with comprehensive knowledge of the datasets and thorough strategic planning.

Conclusion

Finally, we've come to the end of our HR data migration checklist. And if you're planning the migration any time soon, choose your pair and try our solution! The tool that Relokia provides automatically associates your systems and data to power all that you have to do over the entire talent lifecycle. Whether you are automating, integrating, analyzing, refreshing the data, machine learning, migrating or any other priority, we assist you in performing any other strategic tasks, book a demo with our service to know more about our tool and migration services.