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HR Data Migration Strategy and Checklist

SwoopTalent
June 1, 2019

Data migration is a complex process which should be approached with a solid and well-considered plan of action. As human resource management and analysis continues to metamorphose into a new and improved data-driven state, the need arises to strategize movement of datasets and sources, without interrupting regular business analytics.

What is Data Migration?

Data migration is the process of moving data from one system to another. Depending on the relationship of the housing systems, as well as the type of data being transferred, the data will need to be processed in different way in order to make it compatible with the new platform. If done incorrectly, migration can result in significant information loss or damage.

Organizations who follow a focused HR data migration checklist stand a favorable chance of successful data transference with minimal to no loss of assets, time, and data operations functionality.

This article aims to guide the reader through a comprehensive HR data migration strategy, and concludes with an easy-to-follow checklist to help you keep track of your process.

Data Migration Strategy

  1. Assess the data. Before you take any further steps, it is important to comprehend what kind of data it is that you’re moving, as well as where and how it fits into the receiving system. Data must be classified and mapped, and the data structures should be well understood before movement is even considered. Ignoring this step could result in wasted time and resources, or even critical flaws in the data mapping which halt any progress.
  2. Design the migration.In this stage, organizations should determine how they are going to carry out the migration. This involves assigning roles and responsibilities to staff, mapping data elements from source to target, defining acceptance criteria, and choosing an appropriate method. There are two avenues one may choose to take: trickle, or big bang. - Trickle migration runs the process in phases. It takes time, but it allows an organization to limit time spent with any program offline. Normal business operations may continue during the migration as the old system and the new are able to run in parallel.- Big Bang migration runs the process en masse within a limited timeframe. This method is faster than trickle, but may pose greater risks. Systems experience periods of downtime as the data is ingested by the new repository.
  3. Build the migration solution.Take adequate time to get your implementation process perfect, down to the last detail. It is crucial to get it right the first time if you are to avoid losses. One suggested tactic it to break the datasets into subcategories, testing each one after they are built.
  4. Conduct a live test.Test, test, test. Initially, the code should be tested after the build phase is complete, but an organization cannot leave it at that. The design should be tested with real data to ensure a working plan, and to uncover any overlooked design flaws or bugs. Running the process before the actual event will enable your team to confirm the efficacy of your plan and achieve the desired results. If you are choosing the trickle method, multiple phases should be tested.
  5. Make the switch.

After successful testing, it’s time to migrate. Upon initiation, physical data structures are frozen, and interfaces are shut down where required. After this happens, quality reports are run and errors are identified, then being fixed in the staging location. Data is then migrated. After landing, reconciliation reports are run. If everything meets acceptance parameters, interfaces are activated on the target platform and become live.

  1. Audit. Once implementation is live, it is necessary to set up a system in which to audit the data in order to ensure a successful transfer. If physical errors occur, they are typically repairable through syntax corrections in the migration scripts. Logical errors may be due to a problem in the data mapping itself.

Data Migration Checklist

  • Assessment phase:- Analyze and classify data- Consider target platform data-mapping requirements- Set clear data migration requirements- Audit data mapping for ensured data comprehension

- Determine best method for data migration

  • Design phase:- Assign roles and responsibilities to qualified staff members- Map data elements from source platform to target- Define acceptance criteria- Choose appropriate methodology: “trickle” or “big bang”
  • Build phase:- Set tactics according to volume and type of data- Source tools, software- Determine time frame- Test tactics to determine efficacy and suitability
  • Test phase:- Test code at conclusion of build phase- Test migration strategy using actual data- Address flaws; bugs; errors
  • Migration phase:- Initiate migration- Run reconciliation reports- Address errors, if any
  • Audit phase:- Create system for auditing data- Repair physical errors with syntax corrections- Repair logical errors with data mapping corrections- Run live new system

Data migration is a process necessitated by an ever-growing dependence on data-driven HR solutions. Through careful strategic planning, and with a comprehensive knowledge of the datasets, any organization may experience a successful and painless data migration.

Is your organization considering migrating your data into a data lake? Check out our other article, The Value of Data Lake Management Tools, to get started on building your system.

The SwoopTalent platform automatically connects all of your talent systems and data to power everything you need to do across the full talent lifecycle. Whether it's integrations, automation, data refresh, analytics, migrations, machine learning or any other priority, we free you up to do the strategic things. To find out how SwoopTalent can help your organization, request a demo here.

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