Every ERP implementation requires moving data from your existing systems into the new one. And almost every ERP project team, once they are in the middle of it, agrees that data migration was harder than they expected.

It is not that the process is mysterious. It is that it exposes problems with your data that have been accumulating for years, and dealing with those problems takes more time and effort than anyone budgets for in advance.

This guide explains why data migration is so challenging, what decisions you need to make before you start, and how to approach the process in a way that does not derail your go-live.

Why Data Migration Is Where Projects Go Wrong

Data migration failures do not usually look like a catastrophic crash. They look like subtle, persistent problems that surface weeks or months after go-live: purchase orders that do not match their corresponding invoices, inventory counts that do not match physical stock, customer balances that are off by small amounts, historical records that cannot be found.

These problems are costly because they erode trust in the new system. When users see that the data in the ERP does not match reality, they start keeping their own shadow records — spreadsheets, paper logs, workarounds. At that point, the ERP loses its value as a source of truth.

Most data migration problems trace back to one of three root causes:

1. Data quality issues in the source system that were tolerable in the old system’s context but cause failures in the new system’s tighter validation rules
2. Incomplete mapping between legacy data structures and the new system’s schema, leaving fields blank or incorrectly populated
3. Insufficient testing before the final migration, so problems are only discovered in production

Understanding these failure modes helps you design a migration process that avoids them.

What Data to Migrate vs. What to Archive

Before any technical work begins, your team needs to make a deliberate decision about what data to migrate and what to leave behind.

Migrating everything feels safe. In practice, migrating years of low-quality, rarely-accessed historical data adds cost, extends the migration timeline, and brings problems from the old system into the new one. It is not automatically the right choice.

Data Category	Typical Decision
Open transactions (orders, invoices, POs)	Always migrate — live business depends on this
Active master data (customers, suppliers, items)	Always migrate — necessary for operations
Recent transaction history (current fiscal year)	Migrate for reporting continuity
Prior fiscal year history	Migrate selectively, often summary balances only
Multi-year historical transactions	Often archive, not migrate
Inactive or closed records	Archive in legacy system, not migrate
Duplicate or clearly incorrect records	Clean or delete before migration

The right cutoff for historical data depends on your reporting requirements and any regulatory obligations. If you have a legal requirement to produce records from seven years ago, you need either to migrate them or to maintain read-only access to the legacy system for a defined period.

Data Quality: The Work Nobody Wants to Do

Data quality assessment is the unglamorous but unavoidable foundation of a successful migration. Before you can map data from your old system to the new one, you need to understand what quality problems exist in your data.

Common Data Quality Problems

Duplicate records. Customer, supplier, and item master data in legacy systems often accumulates duplicates over years. Merging duplicates before migration prevents incorrect aggregation of balances and transaction history in the new system.

Incomplete mandatory fields. Your new ERP may require fields that were optional or non-existent in your legacy system. Customer records missing credit terms, item records without cost methods, or address records without postal codes will fail validation during import.

Inconsistent formats. Date formats, currency codes, units of measure, and address formats vary across systems. A migration that does not standardize these values produces data that looks correct but fails processing logic in the new system.

Stale or incorrect data. Customer addresses that are years out of date, items that are no longer produced, or supplier records for companies that no longer exist. These records add noise to the new system and may cause errors in transactions.

Unreconciled balances. Open accounts receivable and accounts payable balances that do not match bank statements or third-party confirmations. These need to be resolved before migration, not after.

Running a structured data quality assessment early — before partner work begins — identifies the scope of the cleanup effort and prevents it from becoming a schedule-delaying crisis during migration execution.

Understanding Legacy System Structure

One of the most technical challenges in data migration is mapping your legacy system’s data structure to the new ERP’s structure.

Every ERP system organizes data differently. Fields that exist as a single value in your legacy system may need to split across multiple fields in the new system. Relationships between records — how customers link to transactions, how items link to bills of materials — may be structured completely differently.

Building a Migration Mapping Document

A migration mapping document is the central artifact of the technical migration effort. For each data object being migrated (customers, items, open orders, etc.), the mapping document specifies:

• The source field or table in the legacy system
• The target field in the new ERP
• The transformation rule — how the value is converted from source format to target format
• Validation rules that must pass before the record is accepted
• What to do when the source field is blank or contains an invalid value

This document is built collaboratively between your implementation partner (who knows the new system’s data structure) and your team (who knows the legacy system and the business rules). It is not a document that can be completed in a single meeting — it requires iteration as edge cases and exceptions surface.

Migration Testing: The Cycle That Cannot Be Skipped

Data migration should be tested multiple times before the final production migration. Each test cycle follows the same process:

1. Extract source data from the legacy system
2. Apply transformation rules
3. Load data into a non-production instance of the new ERP
4. Run validation checks and review error reports
5. Investigate errors, fix mapping rules or source data, and repeat

Most well-run migration projects complete three to five test cycles before the final migration. The first cycle typically surfaces many errors. Each subsequent cycle surfaces fewer as the mapping rules are refined and source data is cleaned.

What Validation Checks Should Cover

• Record counts: the number of records loaded matches the number extracted
• Key field population: mandatory fields are populated for all records
• Balance reconciliation: open financial balances match source system totals
• Reference integrity: records correctly link to their related entities (orders to customers, lines to orders)
• Business logic: sample transactions execute correctly using the migrated master data

Involving business users in migration testing — not just technical staff — is essential. Technical validation catches structural problems. Business validation catches meaning problems: records that are technically valid but represent incorrect business data.

The Cutover Migration

The final migration — the one that loads data into the production system immediately before go-live — requires additional planning beyond the test cycles.

You need to define:

• Cutover timing. When does the legacy system go read-only? When does the migration extract begin? How long does the migration process take?
• Reconciliation timing. When do you complete final balance reconciliation against the legacy system?
• Rollback criteria. If the migration discovers critical errors, at what point is it feasible to roll back and delay go-live?
• Parallel period. Will you run both systems simultaneously for any period, and if so, how do you handle transactions that happen during the overlap?

The cutover migration is almost always done over a weekend or during a period of low business activity to minimize the window during which transactions are held between systems.

Frequently Asked Questions

How long does data migration typically take in an ERP project? Data migration work runs throughout the implementation project, not as a discrete phase at the end. The mapping and analysis phase typically starts in the first quarter of the project. Test cycles run through the middle portion. The cutover migration happens in the final days before go-live. Total elapsed time is usually several months, with active effort concentrated in test cycles and cutover.

Should we migrate all historical data or just open transactions? The decision depends on your reporting requirements and regulatory obligations. Many companies find that migrating open transactions and current-period history, then archiving older history in a read-only legacy environment, gives them what they need operationally without the cost and risk of migrating years of historical records.

Who should own the data migration effort on the internal team? Data migration requires a combination of technical and business knowledge. The technical mapping work is usually led by your implementation partner or an internal IT resource. The data quality cleanup and business validation requires active participation from the operational staff who know the data best — finance, operations, procurement. Without clear internal ownership, migrations stall or produce poor quality results.

What happens if we discover data quality problems late in the project? Late discovery of serious data quality problems is one of the most common causes of ERP go-live delays. The mitigation is early assessment — run a data quality check in the first month of the project, not the last. If problems are discovered late, the options are to clean the data as quickly as possible (which requires dedicated internal resources), to scope down the initial migration (migrating only the cleanest data and dealing with the rest in a second phase), or to delay go-live.