6 Data Migration Challenges During System Transitions and How to Overcome Them
Data migration during system transitions can be a complex and daunting task for organizations. This article explores the key challenges faced during data migration and provides practical solutions to overcome them. Drawing on insights from industry experts, it offers valuable strategies for planning, executing, and managing successful data migrations across various aspects of the process.
- Plan Gradual Migration with Backup Safeguards
- Audit Data Before Migration to Reduce Complexity
- Use Tools to Streamline Large-Scale Transfers
- Align Teams on Data Definitions Pre-Migration
- Prioritize Creator Trust During Financial Integration
- Execute Phased Migration with Data Cleanup
Plan Gradual Migration with Backup Safeguards
When we handled a major data migration for a client moving to a new system, the first step was building a clear plan. We defined the scope, set a timeline, and aligned the team on objectives early. The migration approach we chose was a gradual trickle method. Moving the data in smaller batches reduced the chance of disruption and allowed us to catch errors before they spread. We also backed up everything so the client had a safety net if something went wrong.
The unexpected challenge came when we found serious data quality issues in the legacy system. There were duplicates, missing fields, and inconsistencies that hadn't shown up in the initial review. It would have been a huge mistake to push that data directly into the new platform. To prevent corruption, we set up a staging area where the problematic data could be isolated and reviewed without putting the new system at risk.
The key to overcoming the problem was collaboration and steady testing. We worked with business users who knew the data best and could explain discrepancies. Automated checks were added to stop bad data from slipping through. Each batch went through testing before being approved. Everything was documented, which later helped the client strengthen their data governance practices. My advice: never underestimate the value of backups, incremental testing, and keeping domain experts involved at every stage.
Audit Data Before Migration to Reduce Complexity
A successful way to manage data migration during a system transition is to start with a detailed audit and cleanup of existing data before moving anything. Migrating only what's accurate and relevant reduces complexity and prevents carrying over legacy issues.
One unexpected challenge that often arises is data mapping mismatches—fields in the old system may not align neatly with the new one. This can be overcome by creating transformation rules and running small pilot migrations first to catch inconsistencies early. Doing phased rollouts with validation checkpoints ensures errors are caught before they scale across the full dataset.
Use Tools to Streamline Large-Scale Transfers
We ran a migration project from SharePoint 2013 to SharePoint Online where we needed to move about 10 GB of data. We used a data migration tool called ShareGate to help us move all the files to the new environment. ShareGate also allowed us to migrate user-level file permissions, helping us ensure the security of our data.
The volume of our data presented a challenge, and initially, migration was taking a long time. We worked around it by splitting our file transfer into multiple smaller streams.
Align Teams on Data Definitions Pre-Migration
When we transitioned Zapiy to a new CRM system, I thought the biggest challenge would be the technical side—mapping fields, cleaning records, and ensuring everything transferred correctly. But the unexpected challenge turned out to be less about the data itself and more about the people using it.
Midway through the migration, we discovered that different teams had been entering information in their own "language." Sales reps had one way of categorizing leads, customer support had another, and marketing added their own shorthand. On paper, it was all "data," but in reality, it was fragmented context. If we had simply moved everything over as-is, the new system would have been technically complete but practically unusable.
The solution was to pause and involve the end users. We set up working sessions where each department explained how they used the data in their daily workflows. Those conversations were eye-opening. For instance, one team's "inactive" meant a lead hadn't responded in 30 days, while another team used the same label for contacts dormant for over a year. By surfacing these differences, we created shared definitions and standardized fields before completing the migration.
It slowed us down initially, but in hindsight, it saved us months of frustration after the system went live. The new CRM didn't just hold cleaner data—it became a tool everyone could actually use with confidence because they had input in shaping it.
What I learned is that successful data migration isn't just about transferring information accurately; it's about translating that information into a common language your whole organization understands. The technology part can always be solved, but alignment across people and processes is where the real success lies.
Prioritize Creator Trust During Financial Integration
Managing data migration is never just about moving information; it's about protecting trust. When we transitioned Ranked to integrate Stripe for transparent creator payments, the migration involved sensitive financial data. The biggest priority was ensuring every creator felt confident that their history, payouts, and bank details would be safe and accurate on day one.
The unexpected challenge came with creators using different email addresses for Stripe and Ranked. That mismatch threatened delays in payouts. Instead of pushing ahead and risking errors, we paused to build a matching protocol and a simple guide that encouraged creators to sync their emails before the migration. We even offered real-time support to walk them through the process.
The result was a smooth transition where creators got paid without disruption. The lesson was clear: migration is less about the code and more about communication. When you put people at the center of the process, the data takes care of itself.
Execute Phased Migration with Data Cleanup
While executing a data migration in the context of a system change, my focus was on preparation and testing in phases. Rather than migrating everything at the same time, I performed the data migration in smaller data chunks, after confirming that the data was accurate each time. This step reduced the chance of issues arising and instilled confidence in the team that the new system was functioning correctly before we committed completely.
One unanticipated challenge was that prior to migration, we discovered inconsistencies in the way data had been captured over the years — duplicates of data items, missing fields, or conflicting formats. If we simply migrated everything from the old system to the new system, those errors would also be migrated and would create bigger challenges. To mitigate this, I built a cleanup step into the migration plan — getting things standardized after consolidating results before the actual import of captured data into the new system.
Not only did we accomplish a smoother transition for data migration, but we also achieved a cleaner and more reliable dataset, allowing for better reports in the future. The process confirmed that data migration is more than a technical process; it is an opportunity to improve the quality of data you rely on.
