"Our current sales management system is outdated and nearing end of support. We want to move to a modern cloud service, but we're terrified of whether ten years of transaction logs and client records will transfer cleanly. If the numbers don't match after cutover, the fallout would be catastrophic." A wholesale business owner recently shared this concern. While eager to upgrade, horror stories of failed data migrations made them hesitate.
When trouble arises during a system modernization, it rarely stems from bugs in the new system's features. Rather, accidents concentrate on the single point where data is moved from the legacy system to the new one. Compounding the problem, this phase is largely opaque to the client and often bundled into an estimate as nothing more than "Data Migration: 1 Set." In this article, from our perspective providing custom development and hands-on support, we outline key considerations clients must understand beforehand to prevent migration disasters. Because deciding whether to rebuild a system entirely is covered in our legacy migration article, here we focus exclusively on migrating data.
Why data does not transfer seamlessly
People often assume migration is just exporting and importing, but reality is far messier. The legacy and new systems store and structure data differently.
Take client information as an example. The old system may store company name and contact person in a single field, while the new system splits them into separate fields. Date formats differ. The same business partner might be registered twice due to naming discrepancies ("Company Inc." versus "Company, Inc."). Rounding conventions and tax calculations may not align. If you load data without resolving these master data discrepancies, the new system will throw errors—or worse, ingest the data silently with incorrect values.
Furthermore, operational data does not exist in isolation. Order records link to client master and product master tables. If these relational links break during migration, you end up in a situation where orders exist but the customer field is completely blank. The true difficulty in data migration lies not in copying individual records, but in migrating data while preserving relationships intact. The inherent challenges of architecting business data transfers are explored further in our business data handoff article.
Accidents erupt on cutover day
The tricky part is that these issues remain hidden until cutover day. During testing, things appear fine because only a small sample of test data is verified. However, the moment full production data is loaded and real monthly closings and billings are processed, inconsistencies erupt all at once.
This is not unique to small businesses. A well-known enterprise incident occurred in 2024 when a major food manufacturer overhauled its core ERP onto a new SAP platform, suffering severe disruptions that halted shipments across numerous product lines and required several months for full recovery. Even with substantial budgets and dedicated staffing, migrations fail if data verification is inadequate. Conversely, the core principles for preventing migration failures remain identical regardless of organizational scale.
3 essential principles to prevent migration failures
As a client, ensure that the following three elements are explicitly included in your proposal and timeline. Any proposal omitting them carries a high probability of cutover failure.
1. Migration rehearsals. Practice the migration end-to-end beforehand using the exact cutover procedures and full production volume. Merely confirming that it worked on a small sample is insufficient. Only by running production-scale data can you accurately gauge processing time and uncover errors unique to high-volume datasets. Rehearsals should not be a one-off event; resolve identified discrepancies and rerun the process repeatedly until it executes cleanly.
2. Acceptance testing against real production data. Cross-check figures between the legacy and new systems using real migrated data rather than artificial test datasets. Establish clear verification criteria with your development partner in advance, checking items you can inspect yourself, such as "Does the total count of business partners match?" and "Does this month's revenue total match the legacy system?"
3. A parallel operation period. Avoid discarding the legacy system immediately upon cutover; run both systems concurrently for a defined period and compare results. Pay close attention to duration. Instances have occurred where shortening parallel operation to one week for cost-saving purposes resulted in discrepancies surfacing during month-end or quarterly closings, forcing an emergency rollback to the legacy system. Business operations follow cycles—monthly closings, inventory audits, and financial reporting—and you must secure enough time to validate a complete cycle (typically at least one full month).
| Key principle | What to verify | If omitted |
|---|---|---|
| Migration rehearsal | End-to-end dry runs conducted with production-scale data volume | Timeouts and unforeseen errors on cutover day |
| Acceptance testing with real data | Defined criteria for reconciling figures between old and new systems | Discovering corrupt data after the fact |
| Parallel operation | Sufficient duration to validate a full operational cycle | A flood of discrepancies during monthly closing |
Case study: A company that migrated smoothly by maintaining parallel operation for one month
Here is a concrete example. In a project for a wholesale company of about forty employees (kept anonymous), they needed to migrate an order and inventory system used for over ten years to a cloud service. When they initially reached out, their primary concern was that inventory counts and accounts receivable balances would mismatch after cutover.
Rather than rushing into an immediate cutover, we started by auditing and cleaning up naming variations and duplicate records in their client and product master files. We then conducted two migration rehearsals using production-scale data, eliminating lingering inconsistencies on the second run. After cutover, we kept the legacy system running for a month, executed the monthly close in both systems to confirm all figures matched, and only then decommissioned the old system. Nothing elaborate was done. What proved effective was cleaning master data before migration, validating with real data, and keeping the old system running until a full business cycle completed. Consequently, the company never experienced operational confusion or mismatched numbers.
Verify this single line in your estimate before signing
Whether data migration succeeds is largely decided at the pre-contract estimate stage. When reviewing proposals or quotes, check whether migration rehearsals, acceptance testing with production data, and a parallel operation period are explicitly detailed. If they are compressed into a single line item like "Data Migration: 1 Set," always ask for specifics on what will be done and to what extent. Verifying this alone provides an accurate assessment of whether a proposal risks failing on cutover day. The fundamentals of commissioning projects, including requirements definition and contract types, are compiled in our guide to commissioning system development.
If you want to migrate from a legacy system but fear data loss, need to evaluate the validity of a migration plan, or are struggling with mismatched numbers post-migration, feel free to contact GleamHub via our custom development, AI, and automation consultation. From master data audits and rehearsal design to acceptance criteria and parallel run planning, we will guide your team to prevent any cutover mishaps.
Sources
- 5 Failed Core ERP Migration and Replacement Case Studies & 3 Golden Rules to Avoid Failure | C3 Index
- What Is a System Migration Plan? Procedures, Cautions, and 8 Keys to Success | PROACTIVE | SCSK
- Why Do Data Migrations Fail!? Risks and 4 Key Points for Success | GrandPaz
- Common Failures and Countermeasures When Replacing Core ERP Systems | Canon IT Solutions
