Good Clinical Practice: A Question and Answer Guide2024/2025 Edition - 265

Clinical Data Management
11.4 Q. What key aspects should be considered when planning a Data Migration strategy?
A. Each strategy will vary in the specifics, based on the organization's needs and goals, but generally, a data
migration plan should follow a common, recognizable pattern:
1. Explore and Assess the Source: Before migrating data, you must know (and understand) what you're
migrating, as well as how it fits within the target system. Understand how much data is pulling over and what
that data looks like.
There may be data with lots of fields, some of which won't need to be mapped to the target system. There
may also be missing data fields within a source that will need to pull from another location to fill a gap. Ask
yourself what needs to migrate over, what can be left behind, and what might be missing. Beyond meeting
the requirements for data fields to be transferred, run an audit on the actual data contained within. If there are
poorly populated fields, a lot of incomplete data pieces, inaccuracies, or other problems, you may reconsider
whether you really need to go through the process of migrating that data in the first place.
Before migrating data, you must know (and understand) what you're migrating, as well as how it fits
within the target system. Beyond meeting the requirements for data fields to be transferred, run an audit on the
actual data contained within. 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, the organization could run into
a critical flaw in the data mapping that halts any progress in its tracks.
2. Define and Design the Migration: The design phase is where organizations define the type of migration to
take on - big bang or trickle. This also involves drawing out the technical architecture of the solution and
detailing the migration processes. Considering the design, the data to be pulled over, and the target system,
you can begin to define timelines and any project concerns. By the end of this step, the whole project should
be documented. During planning, it's important to consider security plans for the data. Any data that needs
to be protected should have protection threaded throughout the plan.
3. Build the Migration Solution: A common tactic is to break the data into subsets and build out one category
at a time, followed by a test. If an organization is working on a particularly large migration, it might make
sense to build and test in parallel.
4. Conduct a Live Test: The testing process isn't over after testing the code during the build phase. It's
important to test the data migration design with real data to ensure the accuracy of the implementation and
completeness of the application.
5. Flipping the Switch: After final testing, implementation can proceed, using the style defined in the plan.
6. Audit: Once the implementation has gone live, set up a system to audit the data in order to ensure the
accuracy of the migration.
11.5 Q. What would be the best practices recommended with regards to Data Migration strategy?
A. Regardless of which implementation method you follow, here are some best practices to consider:
1. Back up the data before executing: In case something goes wrong during the implementation, you can't
afford to lose data. Make sure there are backup resources and that they've been tested before you proceed.
2. Stick to the Strategy: Too many data managers make a plan and then abandon it when the process goes " too "
smoothly or when things get out of hand. The migration process can be complicated and even frustrating at
times, so prepare for that reality and then stick to the plan.
3. Test, Test, Test: During the planning and design phases, and throughout implementation and maintenance,
test the data migration to make sure you will eventually achieve the desired outcome.
11.6 Q. We are familiar with migrating data to other software systems, but there is an increased utilization of
" cloud " services. What do we need to consider if we move our data to the " cloud " ?
A. Data migration to the cloud refers to the process of transferring data from on-premises or existing cloud
environments to a cloud-based infrastructure. This is a common practice as organizations increasingly adopt
cloud computing for its scalability, flexibility, and cost-efficiency. Many of the considerations below are similar to
those when considering data migration from one system to another. There are key steps and considerations for a
successful data migration to the cloud.
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Good Clinical Practice: A Question and Answer Guide2024/2025 Edition

Table of Contents for the Digital Edition of Good Clinical Practice: A Question and Answer Guide2024/2025 Edition