ERP Data Migration Mistakes
ERP data
migration is a high-stakes process where technical or strategic errors can lead
to project failure, massive budget overruns, or operational paralysis.
According to Gartner, roughly 83% of data migration projects fail or
exceed their original budgets and schedules.
1.
Treating Migration as an Afterthought
One of the
deadliest mistakes is waiting until the end of the ERP implementation to start
the migration.
- The Risk: Rushed migrations often
skip critical data reconciliations.
- The Solution: Kick off the data
workstream as early as possible—ideally during the initial planning
phase.
2.
"Garbage In, Garbage Out" (Lack of Cleansing)
Attempting a
"lift-and-shift" (moving all data exactly as it is) into a new system
replicates existing inefficiencies.
- The Risk: Migrating duplicate
records, outdated product codes, or invalid customer files leads to
reporting errors and broken workflows.
- The Solution: Perform a thorough data audit and cleanse data before extraction.
3.
Underestimating Complexity and Resources
Many
organizations assume data will "just move" and fail to allocate
specialized talent.
- The Risk: In-house IT teams may lack
the specific expertise for complex mapping between old and new database
structures.
- The Solution: Dedicate a
cross-functional team including IT, finance, and operations leads.
4. Poor
Data Mapping and Transformation
ERP systems
have unique business rules and logic; data must be reshaped to fit.
- The Risk: Incorrectly mapped fields
(e.g., a single "Name" field in the old system vs.
"First" and "Last" in the new) cause corrupted or
truncated data.
- The Solution: Use ETL (Extract, Transform, Load) tools and conduct
a pilot migration with a subset of data first.
5.
Excessive Historical Data Migration
Trying to
move every transaction from the last 10–20 years is rarely necessary.
- The Risk: Excessive history bloats
the new database and slows system performance.
- The Solution: Migrate only what is
essential for legal compliance or critical reporting; archive the rest in
a separate, searchable database.